Digital Gems

Imagine having a thinking partner that’s not just smart but also deeply attuned to your specific needs. This is the promise of building a custom GPT model: an AI companion tailored to you, which can help you ideate, learn, and grow. The power of a custom GPT lies in its personalization. Instead of the one-size-fits all model you use through the default ChatGPT interface, you can train a model based on your unique personal, professional, and cultural context. As you interact with it, you can continuously provide your thinking partner with feedback regarding your preferred communication style, latest interests, and even frameworks you’d like it to use. This means the insights it offers become increasingly relevant over time. Whether it’s a tricky problem at work or a personal dilemma, your thinking partner won’t offer  generic advice. It will provide potential solutions that consider your specific situation and aspirations. Here are some of the ways you can use a custom GPT as a thinking partner: Analyze your journal entries to identify emotional patterns and spot recurring behaviors Identify blind spots and cognitive biases that might cloud your judgment Assist you in making more informed and rational decisions Guide you through priority-setting exercises to sort through your task list Provide metacognitive prompts to encourage you to think about your thinking Prepare you for difficult conversations and defuse conflict Brainstorm for a new project with fresh perspectives and creative ideas Help you understand and manage your feelings more effectively By fostering deeper insights, a GPT thinking partner can support your personal growth, keeping step with you in every twist and turn. And the good news is that it’s very easy to build. Just follow this simple step-by-step tutorial, which includes pre-written prompts you can copy and paste. 1. Setting up your custom GPT First, go to this link and make sure you are in the “Create” tab. You will see two panels: one on the left side where you’ll be building your GPT, and a preview panel on the right side where you’ll be testing its functionality. Next, let’s provide your GPT with instructions as to how it should behave. We’re going to start with a short description. Copy-paste this into the messaging box and hit enter: “Make a thinking partner who helps me find patterns in my personal reflections, asks me metacognitive questions, and offers suggestions to organize my thoughts.” GPT Builder will then suggest a name and a profile picture for your custom GPT. Feel free to accept these default settings by typing “looks good” or customize it to your taste. I decided to call my thinking partner Nessie, and I liked the default image suggested by GPT Builder so I kept it as is. In the next step, you will provide your GPT with more details to tailor its responses to your needs. 2. Configuring your custom GPT This is the fun part! You will now train your GPT to become the perfect thinking partner. You can do this through trial and error, but the template below will save you lots of time: INPUT: What specific goals do you want to achieve with this thinking partner? Type here… Are there particular areas of personal reflection or topics you want this thinking partner to focus on? Type here… Could you give examples of metacognitive questions you find valuable? Type here… Are there specific frameworks you would like your thinking partner to use? Type here… OUTPUT: How do you envision receiving feedback from your thinking partner? Type here… How do you envision the interaction style with your thinking partner? Type here… Would you like your thinking partner to adapt its approach based on your responses or maintain a consistent method? Type here… SPECIAL CONSIDERATIONS: How should the GPT handle emotional responses? Type here… Are there any ethical considerations the GPT should be aware of? Type here… Do you have preferences regarding the use of external data or sources for suggestions? Type here… Answer all the questions or, if you’re in a rush or just want to quickly go through this tutorial, you can copy my answers from here. Paste your answers in the messaging box, and hit enter. The GPT Builder will save your instructions. To see how these have been interpreted, click on the “Configure” tab, where you’ll see the name, description, and instructions for your thinking partner. Feel free to tweak anything in there. You can also upload files to your custom GPT’s knowledge base. This could include a PDF list of mental models you find helpful, cognitive biases you tend to be prone to, or your preferred decision-making frameworks. Next, you’ll finally get to play with your custom GPT and teach it to become the perfect thinking partner. 3. Testing your custom GPT It’s time to refine the behavior of your thinking partner. Let’s try a prompt: “Can you help me figure out what to focus on next year to build my business?” As you can see, the answer is quite long, rambly, and not as practical as we’d like it to be. To fix it, go to the left-side panel, and tell the GPT builder how you would like to improve its behavior. The GPT builder will then acknowledge the changes, like so: Now, let’s try the exact same prompt again to see how your thinking partner responds: Much better! As you can see, it’s now using a conversational tone with short paragraphs, and suggesting one first step to explore instead of a long bullet-point list. However, when I keep on chatting with my thinking partner, it goes back to bullet-points, which can feel overwhelming. Instead of listing all the potential directions my mind could be going, I want it to provide simple metacognitive prompts that guide my thinking. Let’s give that feedback via GPT Builder in the left-side panel. Now, when we try the same prompt again and keep on chatting with our custom GPT, it maintains the focused approach and conversational tone we want: Congratulations, you are now done with the basics of building your very own custom thinking partner with ChatGPT! You can use it in all sorts of ways. For instance, you could paste journaling entries to help you brainstorm solutions to problems you face in your personal and professional life. Let’s give it a try. If you have your journal handy and digitalized already, you can use one of your own journal entries. If not, feel free to use the mock entry here. Here’s what you get back, just by pasting the entry in the messagig box without any further prompting: As you can see, your thinking partner suggests some potential courses of action, asking you what your thoughts are and encouraging you to explore other aspects to better understand the situation. Do you feel overwhelmed with work? You can also use your thinking partner to list all of your projects and ask it to help you prioritize them: Because you’re using a custom GPT instead of the generic version of ChatGPT, the feedback you get is based on the personalized training you provided earlier in this tutorial, incorporating your favorite mental models, thinking frameworks, and productivity methods. Whenever you feel like the answer is falling short, use GPT Builder in the left-side panel to give feedback to your thinking partner. This will be incorporated in its knowledge base so it keeps on improving. 4. Saving and sharing your GPT Although you could spend endless hours making tweaks and providing more training data, but for now click on “Save” in the top right corner and decide who can access your GTP. The default is “anyone with the link” but you can alternatively keep it private or make it fully public. If you want to try the custom GPT I built for this tutorial, you can access it here. And in the future, you can find all the custom GPT models you built by clicking on your profile picture, then “My GPTs” (direct link). That’s it! Please tag @ness_labs on social media if you use this tutorial and create your first custom GPT, we’d love to see them. The post Think with AI: How to Use a Custom GPT as a Thinking Partner appeared first on Ness Labs.

While waiting for the Zoom call to start, I took a few deep breaths. In a few minutes, I would get to interview one of my role models; someone who had changed millions of lives through his writing, had only one employee, and, like me, began his journey with a weekly newsletter. The guest was James Clear, the author of Atomic Habits and the 3-2-1 newsletter. The interview had special significance because the publisher of Atomic Habits had agreed to publish my very first book. However, nobody knew. I’m half Algerian and, despite my scientific training, years of exposure to superstitious beliefs from my mother and grandmother meant that I couldn’t share the news until the contract was signed. This was January 4th, the year had barely started, and I already felt nervously excited, but also slightly off balance. Writing a book in English while completing a PhD and running Ness Labs… Wasn’t I trying to bite more than I could chew? Let’s rewind a little bit to understand how I ended up there. This is my 2023 annual review, but now that the risk of attracting the evil eye should be gone — I’m not 100% sure how it work s— I can finally share the events that led me to writing a book. Gradually, then all at once The newsletter was born in 2019 out of a public pact to write a hundred articles in a hundred weekdays. “I started this experiment to get back into writing and to find my voice. I’m not a native speaker, so writing in English has always felt intimidating. I believe there’s no better way to learn than to actively practice,” I explained at the time. I had no idea this experiment would change my life. At first, the change was gradual; the kind of subtle life improvements you didn’t know you needed. The newsletter helped me create a daily reading and writing practice. It became a forcing mechanism to crystalize my thoughts. It connected me to interesting minds from all around the world, some I now have the privilege to call friends. These changes alone would have made it worthwhile to write online, which is why I’ve always been a strong proponent of learning in public. But a bigger change was around the corner. All of a sudden, in the spring of 2022, publishers started reaching out. I received three separate inquiries from editors asking if I’d be interested in writing a book. I was over the moon; this had been a lifelong dream! But not so fast. Coming from a world where you could get up, choose an idea to write about, and hit publish by noon, working with traditional publishers required a radically different sense of timescales. First, you need to write a book proposal. Then, find an agent to send it to publishers before going through several rounds of negotiation. All in all, this process took more than six months. And that’s before you even start writing the actual book! Through it all, I felt a distinct kind of tension which took me a while to recognize — just like you have to swirl your tongue around an unfamiliar flavor. It was a tension between doing and being, between drive and presence, between ambition and aliveness. Part of me wanted to let life unfold; the other part craved to be in the pilot’s seat and go all-in on this creative project. For the first months of 2023, I fought hard to resolve that tension. And my body fought back. I had trouble sleeping, craved sugar all the time, and ended up caving in and turning to alcohol once again as a quick fix to numb my overworking mind. Then, through what felt like digging myself out of a pit — journaling, plant medicine, breathwork, more journaling — I came to a realization: I didn’t have to resolve that tension. The desire to explore, learn, and grow is not at odds with fully connecting with the present. “I’m ambitious,” I wrote in my journal. “My ambitions are to live a full life, contribute interesting ideas, and nurture healthy relationships.” All of the sudden, the painful tension turned into a gentle oscillation, which has since then guided much of the creative process for the book — a book about being driven and being present; a book about doing things that motivate you and inspire you; a book for people who believe that ambition and aliveness are two sides of the same coin. Yesterday, I printed a first draft of the entire book, which I will bring with me to Paris over the holiday to read and annotate. I can hardly believe the weight of it in my hands — it’s real! And I’m so excited for you all to read it when it comes out. The birth of a neuroscientist With the exception of a small percentage who write full-time, the vast majority of authors, including myself, have other projects going on. Beside the book, I have largely focused on my doctoral studies in psychology and neuroscience at King’s College London. I’m fortunate that I have two amazing supervisors who are fully aware of my other projects and let me organize my time as I see fit. Their trust and flexibility has been crucial this year. (Ellie, Vincent, thank you!) So, how does one become a neuroscientist? Everything you’ve heard is true: it takes a lot of energy and patience. This year, I learned how to use new machines in the lab and look at data in different ways. I saw a brain being cut open for the first time. I guided participants through the experiments and designed new methods. I published my first papers and presented my own research at an academic conference. I taught my first class to undergraduate students. You know the meme with the dog saying, “I have no idea what I’m doing”? That’s me. Every week brings a new challenge, whether it’s something I’ve never done before or something that didn’t work as expected. And, most weeks, figuring these things out gives me immense satisfaction. (except when it’s figuring out how to fill a grant application, that’s never fun) There’s still so much I want to learn. Some of my projects for next year include helping curate a special issue about neuroeducation and host an exhibition at the intersection of art and neuroscience. And, if all goes well, I’ll write my doctoral thesis and defend it. I also have lots of ideas for after the PhD. I’d like to make neuroscience a lot more accessible, and for the lab to be a place where people with all sorts of backgrounds and experiences can come and contribute, regardless of their academic credentials. Stay tuned. No one can whistle a symphony I was told that writing a book would be lonely — and sometimes it was. You do sit alone in front of a screen for long periods of time. Still, I’ve felt supported by so many people this year. The newsletter had almost 7,000 subscribers at the end of 2019. As I write these words four years later, more than 95,000 curious minds receive my essays in their inbox. When I have questions, members of the Ness Labs community always answer with insightful comments and suggestions. Some of them are currently beta reading the first draft of my book. We also had a lovely picnic meetup in London this summer and a breakfast meetup in Austin this winter. I’m obviously biased, but I think this is one of the smartest and kindest corners of the Internet and I’m grateful I get to access this little mastermind. Both my editor and agent have been amazing sounding boards. Authors who have been where I’m now have generously shared their hard-earned wisdom. The folks at Ultraspeaking helped me overcome my fear of public speaking. Last but not least, none of the work I do would be possible without the wonderful team at Ness Labs. I’ve somehow managed to spend some time with my loved ones, even if it’s never quite enough. I went a few times to Singapore to visit my partner. I spent a few weekends with friends in Lisbon, Marseille, Alicante, and Brittany. We celebrated my dad’s 70th birthday by taking a family vacation in Vietnam for two weeks. Meeting people in person has become much more important to me lately. Outside of a few meetups, I attended three bigger live events — hosted respectively by Anna Gát + Dan Shipper, Rand Fishkin, and Nick Gray — and left each time feeling re-energized, my mind brimming with new ideas. I also did lots of things by myself: beautiful hikes, harmonica classes, drawing, dancing. Not everything went smoothly this year. I had planned to launch a TikTok account back in January but haven’t started yet. I wanted to re-record my Collector to Creator course as a series of high-quality self-paced videos, which didn’t happen. My mental health was rocky at times. But I can trace back almost every struggle to a stubborn desire to figure things out on my own. Next year, I want to become better at reaching out for help when I need it. I want to spend more time connecting with people both online and in person. I want to keep on surrounding myself with curious minds. I’m still feeling slightly off balance at times. It’s a continuous dance between focusing on the present and exploring my dreams, a delicate undulation between the inner and the outer, between stillness and momentum. There’s joy in this dance. And there’s no greater joy than dancing with others. The post 2023 Year in Review: Of Ambition and Aliveness appeared first on Ness Labs.

As December unfolds, Ness Labs brings you a handpicked collection of this month’s best books. These cover a range of topics, from artificial intelligence to the science of attention. These books are chosen for their ability to open new doors of understanding and offer fresh perspectives on both personal and technological frontiers. Enjoy our selection, and have a great end of the year! The World I See This memoir offers an intimate and inspiring look into the life and work of Dr. Fei-Fei Li, a pioneering figure in the field of AI. It traces her remarkable journey to becoming one of the key architects of modern AI, skillfully intertwining her personal and professional life, and provides a compelling narrative about the evolution of AI itself. Through her life story, the book demystifies AI, explaining its complexities and implications in a clear and engaging way. Her experiences bring to light the extraordinary possibilities of AI, while also acknowledging its potential dangers and ethical dilemmas. Emotionally raw and intellectually rigorous, The Worlds I See is more than just a memoir; it’s a celebration of persistence, curiosity, and the transformative power of technology, as well as a testament to how personal experiences can fuel scientific breakthroughs. Feel Good Productivity Departing from the traditional notion that success is a product of relentless hard work and grind, Ali Abdaal offers a new perspective: the key to productivity is actually feeling good. His journey from a stressed-out doctor to a successful entrepreneur makes this perspective both relatable and inspiring. Feel Good Productivity is a well-researched blend of psychology and practical advice, presented through a simple three-part framework: Energize, Unblock, and Sustain. It provides actionable advice which will make the book a great resource for anyone looking to enhance their productivity while also feeling good, making your projects feel so enjoyable that productivity takes care of itself. Conscious Consciousness is a topic that has long intrigued philosophers, scientists, and thinkers alike. In her book Conscious, Annika Harris embarks on a journey to demystify this elusive concept, guiding the reader through the latest theories, scientific discoveries, and philosophical debates surrounding consciousness. She challenges readers to think beyond the superficial and to consider the profound implications of consciousness in our lives and in the broader universe. The discussion ranges from the possibility of consciousness being an illusion to its potential as a universal property inherent in all matter. The book’s strength lies in its accessibility; Harris presents complex ideas in a manner that is engaging and understandable for a general audience. Seek Scott Shigeoka offers a practical guide for cultivating deep curiosity in a world increasingly marked by division and misunderstanding. Seek is not just about fostering curiosity; it’s about using curiosity as a tool for connection, growth, and healing in our personal and collective lives. challenges readers to look beyond their comfort zones and conventional thinking. The book emphasizes the importance of seeing the dignity in every person, including oneself, which is a crucial step towards understanding and empathy, and presents a fresh perspective on how challenges can deepen our connections with others and ourselves. It promises to shift perspectives, aid in understanding differences, and lead to a more curious and fulfilling life. A compelling case for the power of curiosity to transform our lives, this will be an invaluable resource for anyone looking to heal relationships and connect across divides. Attention Span In this book, psychologist Gloria Mark explores the modern challenges of maintaining focus in a technology-driven world. Recognized as a leading expert on distraction, she delves into the intricacies of how technology affects our attention spans, offering surprising insights backed by her extensive research and debunking common misconceptions about multitasking and productivity. She also explores the role of social media and modern entertainment in reducing our already short attention spans and discusses what drains our mental resources and how we can effectively replenish them. Attention Span is a practical guide to regaining control over our attention in a world filled with distractions, with practical strategies for finding success in our careers and achieving wellness in our daily lives. Do you have any books to recommend for the Ness Labs Best Books series? Please let us know via the contact form. We welcome self-recommendations. The post Ness Labs Best Books of December 2023 appeared first on Ness Labs.

This article will be updated as the state of the academic literature evolves; you can read the latest version here. You can listen to this post above, or via most podcast apps here. Reminder: If you have finished the first year of a PhD in economics or a related field, you can apply for the (free) ten-week online Economics of Ideas, Science, and Innovation short course. Deadline to apply is January 9 2024. More details here. Here’s a striking fact: through 2022, one in two Nobel prize winners in physics, chemistry, and medicine also had a Nobel prize winner as their academic advisor.1 What accounts for this extraordinary transmission rate of scientific excellence? There’s two main possibilities. Maybe great students and great teachers seek each other out and tend to work together. Or maybe great teachers give their students resources that make them better scientists: teaching, access to networks, support, etc. Both are probably important to one degree or another. But in this post I’ll focus on an aspect of the second channel: what do we know about how innovative teachers influence their students, and their students’ subsequent innovative career? I’ll focus on two strands of literatures: roughly speaking, how teachers influence what their students are interested in and the impact of their work. Subscribe now Interesting Correlations To start, we’ll establish some correlations between the interests of students and their teachers. Borowiecki (2022) focuses on teacher to student transmission of interests among musical composers from 1450-1975; Koschnick (2023) among undergraduates and faculty at Oxford and Cambridge over 1600-1800; Azoulay, Liu, and Stuart (2017) on modern post-docs and their advisors in the life sciences. In the next section, we’ll try to go further and show that these correlations are likely to be in large part about the teacher’s influence on student interests, rather than students sorting themselves to work with teachers who share their interests. All three papers involve heroic data construction efforts. Borowiecki’s core analysis relies on data about 341 composers, where they lived, what music they wrote, and how impactful their music is (measured by either modern Spotify follows, length of their biographies in a major musical dictionary, or rankings by Charles Murray). Borowiecki also identifies 221 student-teacher connections among this group, when the one taught the other at a music conservatory. Lastly, because Borowiecki has detailed information on the musical themes of his composers, he can algorithmically assess how similar are the musical themes of any two composers. Borowiecki’s main analysis shows that composers write music with themes that are more similar to the themes of their teachers, than to other composers. This effect holds when you restrict the comparisons to other composers living in the same country and alive at the same time as the teacher. He finds this similarity persists for around 20 years, and even across generations: composers write music more similar to the teacher of their teacher than to other composers who mighthave taught their teacher but didn’t. Let’s turn to interests in science, which are studied by Koschnick (2023). Koschnick’s analysis builds on a dataset that matches students and faculty at Cambridge and Oxford (over 1600-1800) to a database of publications in England, based on names and birth and death dates (where available). He wants to use these matched publications to infer student and faculty’s interest in different areas of science (or other topics): for example, students/faculty with more publications about astronomy are probably more interested in astronomy. To do so, Koschnick trains a large language model to classify publications into topics - he’s helped here by the era’s propensity to write very long and descriptive titles of their works.2 Finally, he wants to match students to teachers, to see if being around teachers more interested in a specific area of science makes the student more likely to work on that area. For that, he relies on the college system employed by these universities. Students at these universities belong to one of dozens of colleges, where they live with their college peers and are primarily taught by faculty from their college. Since Koschnick knows which college each faculty belongs to, he knows with a high degree of certainty which faculty are teaching which students. Koschnick documents that after they graduate, students tend to publish more on scientific topics which were more common among the publications of the faculty at the college they attended. If the share of faculty publications at your college in one scientific field doubles, then the share of publications in that field written by its students rises by 1-3%. That doesn’t sound like much, but note the average college share of science in any field is tiny - only 0.6%. So doubling the share is quite easy. In fact, the variation across colleges can vary by much more than double. One standard deviation in this data is more like a 6x increase over the average. Finally, Azoulay, Liu, and Stuart (2017) build a dataset on 489 elite life scientist post-doctoral students and their 333 advisors. These post-docs are Pew or Searle Scholars, which is useful because the Pew Scholar Oral History and Archives Project provides extensive documentation on the biography of Pew scholars, which Azoulay, Liu, and Stuart will draw on in the analysis discussed in the next section. For now, suffice it to say Azoulay and coauthors show that post-docs who work with advisors that have previously held patents are more likely to seek patents of their own in the future. Birds of a Feather? These three papers establish that students appear to share interests with their teachers, whether that interest be a particular style of music, a field of science, or commercializing research. But we haven’t done anything to establish this correlation is down to teacher influence. It might just as easily be that young composers seek out teachers whose music they like, that students go to colleges strong in the subject area they are interested in, and that budding entrepreneurial scientists seek out mentors with experience commercializing their research. All three papers present evidence that these kinds of explanations are probably not the main story. To begin with, both Borowiecki and Koschnick’s papers involve students making decisions at a relatively young age, before we might imagine they have deeply developed personal preferences. In Borowiecki (2022), 75% of students begin their training at a music conservatory, with their advisor, before the age of 22. Koschnick’s paper focuses on undergraduates. Both papers also primarily take place in eras that predate the information technology revolutions, when information about potential teachers was less readily available. Borowiecki’s paper goes on to argue that, instead, undergraduates to Oxford often selected their college based on geographical affinities. For example, in his data, students from Devon and Cornwall are more likely to go to Exeter college and students from Pembroke more likely to go to Jesus college. In one analytical exercise, he shows that students are more likely to write about a given scientific topic if the faculty of the college people in their region usually go to happen to be stronger in that field, during the years the student is at uni. In that particular exercise, he doesn’t even need to know where students actually ended up going to school, just where they would be predicted to go based on where they live. For Azoulay, Liu, and Stuart’s study of postdocs and their advisors, they have access to an unusually rich source of information about the decision-making process of their subjects: the oral histories of Pew scholars. The authors read a sample of 62 such histories (each is long; 100-400 pages) to see what kinds of factors Pew scholars self report as being important in their decision of which postdoc mentor to work with. The overwhelmingly most important factor cited was the scientific topic being investigated, followed by geography (where the lab was), the advisor’s prestige in the field, and interpersonal rapport. None mentioned the commercial orientation of the advisor, or their interest in patenting. And this wasn’t simply because they were shy to talk about non-academic goals; when asked about their own patents, interviewees were apparently quite candid. Azoulay, Liu, and Stuart use this qualitative analysis to form the basis of some additional quantitative exercises. They come up with measures of scientific similarity, geographical proximity, and prestige, which they use to derive statistical models of the matching process between postdocs and mentors. They can then see if matches that are poorly explained by these stated factors seem to be unusually correlated with the decision to patent, which would be evidence that people left their true motivations - a desire to work with a scientist who patents - unstated. But they don’t really find any evidence of this. The statistics back up what the scholars say: recent graduates don’t really think about patenting when deciding who to work with for their postdocs. But if they “accidentally” end up working with an advisor with a history of patenting, they’re more likely to patent themselves, later in their career. Both Borowiecki and Koschnick also perform an exercise based on teacher composition at conservatories and colleges. In one exercise, Borowiecki looks at how similar are the musical styles of a student and teacher, as compared to teachers at the same conservatory who either left shortly before the student joined or arrived shortly after the student left. The idea here is that if students had started at conservatory at a slightly different time they might well have ended up working with this alternative teacher. Koschnick’s study exploits an even...

Welcome to this edition of our Tools for Thought series, where we interview founders on a mission to help us think better and work smarter. Oliver Sauter is the founder of Memex, a tool designed to help you better read and research online, by yourself and with the collective knowledge of your most trusted writers, researchers, friends and team members.  In this interview, we talked about the history of hyperlinked knowledge, the chicken-and-egg problem in online collaborative reading, how to process large amounts of information in meaningful ways, and much more. Enjoy the read, and make sure to check out the exclusive discount all the way down the page! Hi Oliver, thanks so much for agreeing to this interview. We can’t start without asking you about the name you chose for Memex, which has a long history. Memex is a dream by the scientist Vannevar Bush, also known as a key figure in the Manhattan Project, to have a device that allows to search, annotate, connect and share his knowledge more effectively with peers. Vannevar laid out his ideas in a 1945 article As we may think in the Atlantic Magazine that got it somewhat of a cult status among people interested in knowledge management and collective intelligence. It is often cited as a key inspiration for early Hypertext eventually leading up to the creation of the world wide web. Bush envisioned Memex to work on microfilm which is an obvious technical reason why this never really came to fruition back then. So naturally once this name made it to the shores of my mind, it seemed like a very fitting for what (our version of a) Memex allows users to do: save, annotate and share what you read and research online, by yourself or with your team. Let’s start with your vision. You are on a mission to help people build on the research of their most trusted writers, researchers and peers. Why tackle this specific challenge? From the start our north star for Memex was to contribute ways for people to overcome the impact of misinformation and polarization. Otherwise as a society we won’t be able to solve the most difficult challenges of our time. Not because there is no way forward but because we won’t agree on what it is. In the last decade we could watch in real time how the internet morphed from being a place of knowledge exchange to one where it’s easy to get distracted, misled and into ideological trench warfare. Even as a skilled researcher, it’s becoming hard to know who and what to trust, and where to find good information sources for more targeted research questions. AI generated content will make it even more difficult. Unfortunately the lowest barrier to get other opinions are social media comment sections, but there we have to endure scrolling through feeds and get distracted and monetised — and it’s simply too much serendipity. Especially when you have more targeted questions. And on the other hand there are so many writers and researchers out there that I trust but it’s hard to keep up with all their work or know when they write about something I need an answer for. Memex aims to bridge that knowledge gap and make it possible to read and research with the collective knowledge of your most trusted writers, researchers and peers – and easily compare their perspectives. My key insight came from being involved in fact checking communities in around 2015-2017 and saw how that work is not really scalable, nor do people tend to believe fact-checked information that comes from a source they don’t already trust. So all our work’s impact seemed very limited. What I realized is that people want to make their own mind up – they want to come up with the answer with free will, otherwise they won’t believe it. The most impactful approach to change people’s mind seemed to be to provide people with low friction ways of comparing different perspectives to understand more nuance, even if that nuance is not 100% correct. I believe that this would create many more iterative improvement of mindset shifts where more people understand some grey areas and transitive knowledge. That then leads to more groups sharing similar understandings or “intersubjective truths”, that again are compared and understood, leading to the next iteration cycle. However, the key challenge here is how to make it easy and interesting for people to share their views. Collaborative efforts such as Wikipedia are very rare blimps of social technology that still require a lot of work by a few dedicated volunteers and are a model not scalable for the dynamic real-time nature of the web. In order for people to share their research with Memex, it had to first solve a personal/group productivity problem at the core, so people use it just for their own benefit at first, but making it increasingly easy to share their knowledge with others. And that’s when I started working on it to solve my own itch: Not being able to easily capture the most important things I read, and make notes while doing so without breaking my flow by copy pasting stuff around. Memex is collaborative too. You can annotate and discuss websites, PDFs and videos with your peers. We use it as a shared reading list for our team. We also just released our new Rabbit Hole feature which I think is the closest to our mission for enabling people to build on each other’s work we’ve been so far. Now you can follow your favorite writers and researchers and use AI to search and answer questions with those sources in mind, and get related content recommendations from them while browsing around. It almost feels like downloading someone else’s brain. This sounds like many other lines of work would benefit from having better reading and research tools, and having access to their peer’s knowledge? We’ve seen people using many different reasons, like personal reading and research organization, sharing their research and collaborative annotation with friends or colleagues. Other more specific use cases we’re seeing often are founders and product/sales teams collecting customer insights from the web and on social platforms; academic and corporate research teams with the need to collaboratively discuss the latest research papers; teachers and coaches use Memex’ ability to share lists of pages and annotations with their clients and students. I can’t wait to see how people use our new Rabbit Hole feature. With it you can ask our AI questions across the pages you saved and all the RSS feeds you follow. Why do you think nobody has solved the problem of online reading collaboration so far? As a community of knowledge toolmakers we are just beginning to solve the problem of collaborative writing and reading with still lots of work ahead of us. Part of it is that the market of digital note taking is just starting to ramp up. Until the pandemic many people didn’t work that much digitally. Covid combined with the rise of new note-taking tools like Roam, Obsidian and Notion were really a perfect storm for making digital note-taking and reading much more a common workflow for people. We also consider the problem of collaboration to be downstream of personal note taking, so we’re expecting a lot more people entering the market in the next few years. But there is another problem: It does not work without people. I think our new Rabbit Hole feature could change a lot here because it removes one of the biggest frictions for online collaboration in reading and research: every collaboration tool requires other people to sign up and share information for it to be useful to you. Classic chicken-and-egg problem. Rabbit Hole changes that because it enables people to use AI to ask questions across their favorite blogs, personal bookmarks and soon also shared team documents. That’s all information already being shared but just way too much to process right now. AI has changed a lot of things to make the problem approachable because you can process much larger amounts of information, and even if people don’t share in a new network, you can now do things like Rabbit Hole where you can process already existing information more effectively. Just think through the amount of work it would take for literature review when you have to sift through hundreds of articles just to find an answer – which is now possible to get within a few seconds. This is a knotty problem. Tell us how you’re approaching it—the product philosophy and guiding principles behind Memex. One design objective was to bring as much power as we can into the browser tab so that people have the least amount of context switches due to needing to copy paste around links and notes.  We’ve also added a bunch of AI powered features in the past year and think they are well placed in areas where they can save people a lot of time, without overwhelming the core of the product. Moving forward with the Rabbit Hole feature, AI will become a bit more prominent in its goal to help people synthesize large amounts of their own and followed knowledge. As a company we’ve built our tool without taking classic venture capital investments that have uncapped return expectations. We think they’ll ultimately lead to profit maximization incentives that disproportionately affect users through extractive business practices – often not in the beginning but in the long run as the company reaches its natural growth limits. Often you feel its effect in the forms of more addictive algorithms or more lock-ins.  Instead we use a model called “Steward Ownership” which limits investor and team returns and prevents the company from being sold to external investors. For those interested, here is a talk I gave about it. Specifically, how does it work? Memex is a really great allrounder to help you with your online reading and research. If you are an avid reader of websites/papers/videos or have to do literature reviews with friends or your team I am pretty sure you’ll find something in Memex that can support you with 2-3 high v...

In this special edition of What’s New Under the Sun, we have a big bundle of the titles, abstracts, and links to innovation-related PhD job market papers from 2023 that I either found or were sent to me in response to last week’s solicitation (thank you!). This is not an exhaustive list - I am sure I have missed many great papers. If you have a paper that you think belongs on this list, please send it my way following the instructions here, and I’ll add it. I enjoyed reading all these abstracts, and am excited to dig into the papers. Back to our regular programming next week! Thanks for reading What's New Under the Sun! Subscribe for free to receive new posts. Titles Index Titles are presented in random order. Do Standard Error Corrections Exacerbate Publication Bias? by Patrick Vu Machines and Superstars: Technological Change and Top Labor Incomes by Donghyun Suh I, Google: Estimating the Impact of Corporate Involvement on AI Research by Daniel Yue Returnee Inventors and Home Country Innovation by Sherry Xue Executive contracts for sustainable innovation: incentivising gains in wealth and health by Slavek Roller Measuring Knowledge Capital Risk by Pedro H. Braz Vallocci Multinational Production and Innovation in Tandem by Jin Liu Staggered Rollout for Innovation Adoption by Ricardo Fonseca Spillovers and the Direction of Innovation: An Application to the Clean Energy Transition by Eric Donald Technology Adoption, Learning by Doing, and Reallocation by T. Jake Smith The Effect of Funding Delays on the Research Workforce: Evidence From Tax Records by Wei Yang Tham, with Joseph Staudt, Elisabeth Ruth Perlman, Stephanie Cheng Batman Forever? The role of trademarks for reuse in the US comics industry by Franziska Kaiser, with Alexander Cuntz, and Christian Peukert Race and Science by Gaia Dossi When are Patents Traded and Why: A Dynamic Structural Model of Drug Development and Patent Trading by Jie Fang The Effect of Robot Assistance on Skills by Sungwoo Cho Worker Mobility, Knowledge Diffusion, and Non-Compete Contracts by Jingnan Liu Equilibrium IPR Protections, Innovation and Imitation in A Globalized World by Leo C.H. Lam Public R&D Spillovers and Productivity Growth by Arnaud Dyèvre Optimal Skill Mixing Under Technological Advancements by Elmer Zongyang Li STEMming the Gender Gap in the Applied Fields: Where are the Leaks in the Pipeline? by Shasha Wang Reluctant to Grow: The Unintended Effects of R&D Tax Credits Targeting Small Firms by Alexandre Lehoux Technological Change and Unions: An Intergenerational Conflict with Aggregate Impact by Leon Huetsch Innovation-Facilitating Networks Create Inequality by Cody Moser, with Paul Smaldino Embracing the Future or Building on the Past? Growth with New and Old Technologies by Bernardo Ribeiro Intangible Assets, Knowledge Spillover, and Markup by Yusuf Ozkara Money, Time, and Grant Design by Wei Yang Tham, with Kyle Myers The Effects of the Affordable Care Act on Pharmaceutical Prices, Demand and Innovation by Zhemin Yuan Multidimensional Skills in Inventor Teams by Hanxiao Cui Return Innovation: The Knowledge Spillovers of the British Migration to the United States by Davide M. Coluccia, with Gaia Dossi Information provision and network externalities: the impact of genomic testing on the dairy industry by Victor Funes-Leal, with Jared Hutchins Reveal or Conceal? Employer Learning in the Labor Market for Computer Scientists by Alice H. Wu Innovation and Technological Mismatch: Experimental Evidence from Improved Crop Seeds by Sergio Puerto Relying on Intermittency: Clean Energy, Storage, and Innovation in a Macro Climate Model by Claudia Gentile Intellectual Mobility Frictions by Jordan Bisset, with Dennis Verhoeven Consequences of Indian Import Penetration in the US Pharmaceutical Market by Jinhyeon Han Markups, Firm Scale, and Distorted Economic Growth by Jean-Felix Brouillette, with Mohamad Adhami, Emma Rockall Teacher-directed scientific change: The case of the English Scientific Revolution by Julius Koschnick Strategic Network Decisions and Knowledge Spillovers: Evidence from R&D Collaborations of U.S. Firms by Kippeum Lee The Market Effects of Algorithms by Lindsey Raymond Decline in Entrepreneurship: A Tale of Two Types of Entrepreneurs by Angelica Sanchez-Diaz Scale-Biased Technical Change and Inequality by Hugo Reichardt The Effect of Inventor Mobility on Network Productivity by Brit Sharoni Titles and Abstracts Do Standard Error Corrections Exacerbate Publication Bias? Patrick Vu Over the past several decades, econometrics research has devoted substantial efforts to improving the credibility of standard errors. This paper studies how such improvements interact with the selective publication process to affect the ultimate credibility of published studies. I show that adopting improved but enlarged standard errors for individual studies can lead to higher bias in the studies selected for publication. Intuitively, this is because increasing standard errors raises the bar on statistical significance, which exacerbates publication bias. Despite the possibility of higher bias, I show that the coverage of published confidence intervals unambiguously increases. I illustrate these phenomena using a newly constructed dataset on the adoption of clustered standard errors in the difference-in-differences literature between 2000 and 2009. Clustering is associated with a near doubling in the magnitude of published effect sizes. I estimate a model of the publication process and find that clustering led to large improvements in coverage but also sizable increases in bias. To examine the overall impact on evidence-based policy, I develop a model of a policymaker who uses in- formation from published studies to inform policy decisions and overestimates the precision of estimates when standard errors are unclustered. I find that clustering lowers minimax regret when policymakers exhibit sufficiently high loss aversion for mistakenly implementing an ineffective or harmful policy. Link Machines and Superstars: Technological Change and Top Labor Incomes Donghyun Suh I construct a model of production hierarchies in which agents and machines differ in skill levels. The skill level of an agent determines the difficulty of work tasks she can perform. Relatively low-skill agents become workers and high-skill agents become managers who help workers perform difficult tasks. Machines can either augment or substitute workers. Two main findings emerge: First, whether machines augment or substitute workers depends on the highest skill level of machines. If machines can perform sufficiently difficult tasks, then machines substitute workers and augment managers. However, if machines can only perform relatively easy tasks, then machines augment workers. Second, for sufficiently advanced machines, technological change increases income concentration at the top. This occurs as gains from technological change are greater for those with higher skills, thus benefiting the most skilled managers the most. By contrast, if machines augment workers, technological change has the opposite effect on top income shares. The paper further examines the implications of Artificial Intelligence (AI) for managerial functions. If machines can perform more difficult tasks than any worker, they substitute managers. I find that management by machines most significantly raises the wages of the least skilled workers. On the other hand, managers' wages fall, with the decline most pronounced among the least skilled managers. Therefore, while less inequality between workers and managers leads to lower top income shares, the inequality among managers increases. Link I, Google: Estimating the Impact of Corporate Involvement on AI Research Daniel Yue While corporate involvement in modern scientific research is an indisputable fact, the impact of corporate involvement on scientific progress is controversial. Corporate interests can lead to constraints that redirect research activities into applied problems in a way that benefits the company but reduces scientific impact. However, corporations also provide resources such as funding, data sets, collaborators, engineers, and technical problems that researchers may otherwise be unable to access or know about, spurring knowledge creation. This paper empirically assesses the impact of corporate involvement on scientific research by focusing on dual-affiliated artificial intelligence researchers located at the intersection of academia and industry. After controlling for the researcher's quality and topic preferences, I find that corporate involvement leads to up to a 44% increase in field-weighted citations received by a paper. I document evidence that this effect arises because the average benefit of a firm's scientific resources exceeds the cost of that firm's scientific constraints. Specifically, I show that corporate involvement significantly increases the likelihood of a breakthrough paper and that these effects are magnified by the involvement of firms with greater resources. However, corporate involvement also alters the direction of the dual-affiliate author's research to be more aligned with the firm's commercial interests. This is the first large-scale quantitative study of any field of science to demonstrate a direct positive effect of corporate involvement on science or to describe the underlying mechanism. Link Returnee Inventors and Home Country Innovation Sherry Xue I analyze the innovations produced by Chinese companies and research organizations (”receivers”) after hiring returnee inventors – Chinese inventors who returned from abroad. Following their return, receivers significantly increase patenting and the number of involved inventors in technological fields where the returnee has experience. However, the new patents receive fewer citations, especially from abroad. Additionally, there is a decrea...

FEATURED TOOL Welcome to this edition of our Tools for Thought series, where we interview founders on a mission to help us make the most of our mind. Alan Chan is the co-founder of Heptabase,  a visual note-taking tool that helps you learn complex topics. In this interview, we talked about the inherent dilemma of intelligent product design, how to create a co-evolution system to address this dilemma, the five parts of the knowledge lifecycle, how to solve interoperability across use cases with meta-apps, how to support both individual and collective knowledge creation, and much more. Enjoy the read! Hi Alan, thanks so much for agreeing to this interview. Let’s start with your vision. You want to build a truly universal Open Hyperdocument System. What does that mean? Our vision at Heptabase is to create a world where anyone can effectively establish a deep understanding of anything. And this universal Open Hyperdocument System (OHS) is a means to that vision. It’s hard to explain what OHS is without context, so I’d like to first share the story of how I encountered this concept. So, one of the most enjoyable things in my life is learning things that are interesting to me. In middle school, that thing was math. In high school, it’s physics. That’s why, when I went to college, I double majored in Physics and Mathematics. One thing that I really appreciate in these disciplines is how one concept in Mathematics can be applied to so many different areas of Physics, and one theory of Physics can explain so many phenomena in the world. There were many moments when I started to understand how all these things are interconnected and how things are simple yet complex at the same time. That’s what I mean by “deep understanding.” In my sophomore year, I wanted to spend more time exploring other disciplines such as history, psychology, computer science, business, and many others. I had this fundamental desire to make sense of everything in the world, and I wanted to try my best to read as much as I could and see how far I could get. So, I dropped out of college and bought a lot of books to read and started building my own knowledge system to manage all the reading notes I wrote. I used Evernote for a while and switched to Notion a bit later, and I immediately noticed that Notion has many interesting and powerful capabilities. I looked into Notion’s website and found these big names in HCI: Douglas Engelbart, Alan Kay, Ted Nelson, Bret Victor, etc. I read and watched everything on Bret Victor’s website. I read most of the things on Doug Engelbart Institute’s website, including the Augment Human Intellect report. I read a lot of Alan Kay’s essays, Ted Nelson’s Literary Machines, and Seymour Papert’s Mindstorms. And then I read this mind-blowing book called The Dream Machine, which I still think is the best book about computer history. After reading all this stuff, one thing that inspired me the most was how these computer pioneers and thinkers think about how humans and computers can work together to solve complex problems. For example, Engelbart has this approach of using computers to improve the collective IQ of a group of people. What he’s suggesting was building a new kind of tool called Dynamic Knowledge Repositories (DKRs) that can integrate and update the latest knowledge from a group of people, and all the DKRs in the world will be powered by the same Open Hyperdocument System (OHS). That caught my attention, so I looked into the specs of OHS, and then I noticed that its capabilities look so much like Notion. Then I realized what Notion did was they implemented many of OHS’s specs and wrapped it with a modern UI and sold it as a team collaboration product. But fundamentally, it’s very similar to Engelbart’s OHS specs. So, the next thing that came to my mind was: why implement an old spec from the late 20th century? How will Engelbart design OHS if he lives in the 21st century and is familiar with all the computer technologies we have now? Engelbart’s intention was to augment human collective intelligence, and I think modern digital collaborative workspaces are still far from that. The original OHS system doesn’t seem to address much about the process of how humans discover and distill knowledge and foster deep understanding on different topics, and which part of the process happens independently and which part happens collectively. Thinking about this was intellectually stimulating, and this question had been on my mind for a few years. And that was the time the idea of Heptabase started to take shape. I wanted to design and create a new Open Hyperdocument System in the 21st century that serves as the foundation of all modern knowledge repositories. Everything built on top of this system should have the inherent capabilities to empower people to effectively create a deep understanding of anything they’re learning and researching. What a journey. Why do you think this vision is so challenging to bring to life? I think the most challenging part is that you’ll face this dilemma between building a system that is general enough to become the foundation for many things, and building a product that is useful enough for end-users to solve their problems. One thing I’ve seen many companies do is to begin by considering what a perfect system would look like and what capabilities it should have. They write clear specs for that system and then build everything based on these specs. The biggest challenge of this approach is that you end up with a Swiss knife that has a wide range of capabilities, and while it might be able to do many things, it can be intimidating for end-users. Most people just want to find a solution that solves their problem out of the box. Most people don’t care about all the concepts and capabilities you introduced in your system. And no matter how good your technology is, if not many people use it, you’ll end up going nowhere. So, these companies usually have to spend a lot of time working on improving usability, simplifying their product, and understanding their users’ needs. Personally, I prefer the approach of fostering a “co-evolution” process between the design of the system and the users’ jobs to be done. This is another important concept from Engelbart, in which he used to describe the back-and-forth process of how humans evolve with the tools they use and how tools evolve with the humans using them. One great example of such a process is Bret Victor’s project called Dynamicland, which I think of as an environment where people can explore and understand systems and have data-driven conversations through authoring dynamic visual representations of data. The system includes a lot of paper cards that contain programs, and a protocol that enables people to make claims and wishes on these cards to facilitate communication across programs. What fascinates me is how they built the system—they invited many people with different backgrounds to come to Dynamicland and observe how these people interact with Dynamicland, and then use such learning to evolve the design and the protocol of the entire system. So, the biggest difference between these two approaches is how much you believe you know and how much you believe you don’t know. If you think you know everything, then you design the entire system from the beginning, and the risk is that you might be wrong about many things. If you think you know just a few things, then you start by designing a system that handles these few things well, put it out and see how people use it, gain more knowledge on how people work, and use this knowledge to evolve your system to accommodate more capabilities while taking care of usability. The most challenging part of this approach is to resist the urge to try to design a perfect system from the start and admit that there are still things you don’t know. Once you admit that, you’ll start thinking about how you can acquire those insights from your users. So how is Heptabase approaching this? When building Heptabase, there are some mental models and guiding principles that I have been using since the very beginning. The first mental model is called “The Knowledge Lifecycle”, which consists of five parts: exploring, collecting, thinking, creating, and sharing. We want to ensure that knowledge can be seamlessly passed from one part to another, and we want to ensure that for each part of the lifecycle so we can design and build a great solution that addresses the problems people face really well. In the end, it’s all about whether we can create this synergy across all five parts of the cycle. We have been working on the “thinking” part since 2021, and then the “collecting” and “creating” parts since 2023, and will work on the “sharing” and “exploring” parts in 2024. The second mental model is system layering. The way I abstract the system we’re building is that there will be multiple independent layers, each focusing on one unique job. For example, a contextual layer for preserving thinking context, a descriptive layer for managing categories and adding properties, an annotation layer for annotating static files, an integration layer for creating aliases for third-party data, a communication layer for enabling a group of people to construct a deep understanding of complex topics, and an application layer for users to build card-based software on top of our system in the future, and so on. So when many users request a feature, the first thing I think about is which part of the knowledge lifecycle it belongs to and how we can design and integrate this feature with our existing solution in this part of the lifecycle. The second thing I think about is which abstraction layer this feature belongs to, so I can have a clear picture of how the system design is evolving. On the other hand, sometimes we reach a point where we believe we have done great work in building one abstraction layer and want to shift...

Dear readers, Regular programming will be back next week or so; today’s post is two quick announcements that may be of interest to readers working on or completing PhDs. #1. The Economics of Ideas, Science, and Innovation PhD Short Course This spring, the Institute for Progress is once again organizing a free online PhD short course on the Economics of Ideas, Science, and Innovation. We did this last year and it went really well. This year we have expanded our roster of fantastic lecturers: Pierre Azoulay, Janet Freilich, Ina Ganguli, Ben Jones, Chad Jones, Kyle Myers, John Van Reenen, Caleb Watney, Heidi Williams, and me. The course consists of weekly assigned reading groups, a group slack, 10 two-hour zoom lectures, and an option to attend small group meetings with an instructor. The live zoom lectures will be held from 1:30-3:30pm ET on Tuesdays starting end of January. The course is meant to cover the same kind of material that would be covered in a second-year economics PhD field course, and so our target audience for this course is students who have completed at least one year of a PhD in a related field.1 While the course is free, you’ll need to do a little bit of work to apply and signal interest. The application deadline in January 9. Learn more and apply here! #2. Share Your Innovation Job Market Paper If you are a PhD (or recent PhD) who is going on the job market this year, I would like to invite you to send me the title, abstract, and link to your job market paper, if it is related to the social science of innovation and science.2 Basically the kind of thing that would be interesting to readers of New Things Under the Sun. Next week, I’ll bundle all the responses together and send out a special post with all of the new innovation job market papers. NewThings has more than 14,000 readers now, so it’s a good way to get your work in front of a lot of readers interested in these topics! If you would like to participate, please email matt.clancy@openphilanthropy.org with the subject line “JMP post:” and then the title of your paper. In the body of the email, please include your paper title, your name (+ the names of any coauthors), an abstract, and a link to where people can read the paper. If you want to be in the JMP post, please email me your details by end of day on December 5. Lastly, please share this invitation widely to anyone you think might be interested. There is no need to be a subscriber to New Things Under the Sun to submit. Cheers, Matt Subscribe now P.S. As always, if you want to chat about this or innovation in general, let’s grab a virtual coffee. Send me an email at matt.clancy@openphilanthropy.org and we’ll put something in the calendar. 1 If this isn’t you, note that we will make slides publicly available. However, because we want the zoom meetings to be interactive discussion with students, we don’t plan on releasing recordings of them. But we are exploring ways to make this material available in other ways. 2 If you are going on the market and have innovation-related work you want to share, but it isn’t your job market paper, feel free to send it anyway.

At this point, most people are aware of the benefits of physical exercise. Like with many things we know are good for us, it doesn’t mean we actually act on it: it’s estimated there is between  $400 million and $1.3 billion spent on unused gym memberships in the U.S. only. But at least we do know physical activity is good for us. Now, what about mental exercise? Shouldn’t we train our brains, too? Doing mental push ups Going to the gym builds muscles over time. This occurs due to muscle fibers tearing during exercise, then repairing and growing bigger and stronger. In a similar way, when we learn new skills or have new experiences, our brains create new neural connections. The more we stretch our minds, the more connections between neurons our brains can build to adapt to these new challenges. Building mental strength is not too different from building physical strength. It’s all about consistency. The same way you would take a few minutes to do a few push ups, you can incorporate mental pushups in your daily routine. And you don’t need any expensive “brain training” games — which don’t even work. There are simple, quick mental activities you can do to use your brain in new and creative ways. In fact, all of the following tools are completely free. How to build a mental gym Your mental gym workout should consist of practicing activities that challenge cognitive and emotional skills. Ideally, you need to balance those activities across four pillars: curiosity, creativity, mindfulness, and rest. Consistency matters more than duration, so experiment with different practices until you find the ones that are the easiest to stick to. Doing mental push ups is something you should be looking forward to. Practice #1: Be curious There are many benefits to learning something new. In fact, research suggests that it’s one of the best ways to keep your brain sharp. It may also help you cope with stress. So how can you go about learning something new? Read a book Listen to a podcast Take an online course Have a friendly debate Learn a new language Watch a TED video Learn a new skill Teach someone The last one is extremely powerful. Multiple research studies show the positive impact teaching someone else has on the comprehension and recall of any material. Speaking of recall, a good mental push up is to actually test your memory. The process of retrieving information from your mind will not only make it more accessible in the future, but will also make you a better learner, studies show. This can be as simple as recalling something you heard in a podcast to tell a friend about it, or writing about a topic you recently learned about from memory before checking your notes. Practice #2: Get creative There is evidence that practicing activities such as music, drawing, arts and crafts stimulate our brain in a way that enhances our health and well-being. These activities also have a positive impact on our emotional resilience. And the good news is that you don’t need to work with an art therapist to get therapeutic benefits from creative activities. It’s become so much easier to find a craft club or artistic activities to do in your neighborhood. Or, you could just buy some supplies and give it a go with the help of online tutorials. Again, the type of creative practice itself doesn’t matter as much as your ability to stick with it consistently. So choose an activity you enjoy, and don’t be afraid to switch it up. Practice #3: Be mindful It’s very easy to go about our busy lives without ever taking the time to reflect on our thoughts and emotions. Between your social and professional obligations, you could in fact wake up and go to bed without a moment for yourself. Making space to connect with your inner world is crucial to take care of your mind. And being mindful doesn’t have to take a lot of your time. You can start with a one-minute mindfulness practice. Notice the posture that you’re in. Take a deep breath. Focus on what’s going on around you. It’s just one minute, but it’s a minute where you can be fully present in the moment. Another great way to be more mindful of your experiences is writing. You don’t need to feel like you’re good at writing to benefit from the practice. Research has found that writing has positive effects on both our psychological and mental health. In particular, if you need a bit of guidance to tap into your emotions, expressive writing has been extensively studied and is very simple to apply. James W. Pennebaker, the psychologist who devised the method, suggests to do the following exercise for 20 minutes each day for four consecutive days: Choose a topic. It should be personal, emotional, and important to you. Write for yourself. Do not imagine your writing being read by other people. Let go. Don’t worry about style, spelling, punctuation, or grammar. That’s it. After four days, you can put it away, and come back to it later once you feel ready to reflect on it, but it’s not mandatory. The benefits lie in the exercise itself. If you feel empty or sad after a session, that’s completely normal, and it’s actually good for your brain to experience these emotions—it means you are actively processing them. Practice #4: Get some rest All athletes need to rest. Similarly, there’s no need to spend all of your time in your mental gym. Not only is there evidence that taking short breaks can help us better acquire new skills, but longer periods of rest — and in particular sleep —support healthy brain function and the maintenance of your overall health. According to the National Sleep Foundation, these are the optimal amount of sleep you should get based on your age: Teenagers (14-17): 8-10 hours sleep Adults (18-64): 7-9 hours sleep Older adults (65+): 7-8 hours sleep A great way to combine short breaks and sleep are, of course… Naps! If you’re fortunate enough to work in an environment where taking a nap during the day is possible, do take that opportunity. Bonus if you’re a student: research shows that taking naps works better for long-term retention compared to cramming. In short, building a mental gym consists of keeping our brain stimulated with activities that improve our creativity, productivity, and well-being, while giving it space to rest and recharge. Building your own mental gym takes time to figure out your perfect regimen. Consider this an exercise in deliberate experimentation. Just like an anthropologist, take some field notes to see what works, what doesn’t, and what you could tweak. The post Building your mental gym appeared first on Ness Labs.

As the year gradually draws to a close, at Ness Labs we continue our commitment to uncovering books that not only inform but also transform. This November, our selection is an invitation to explore your mind. In this month’s collection, we delve deep into the marvels of cognitive neuroscience, the often-misunderstood functioning of memory, how to understand and manage anxiety, the transformative potential of redefining failure, and more. As we approach the year’s end, a time for reflection and forward planning, these topics are more relevant than ever. They offer a chance to pause, ponder, and prepare for what lies ahead. Whether you’re seeking personal growth, professional development, or simply a deeper understanding of the human mind, we hope our November picks inspire you! Seeing the Mind Stanislas Dehaene’s latest book marries the complexity of cognitive neuroscience with the accessibility and visual appeal of an art book. Dehaene, a renowned neuroscientist, takes readers on an extraordinary journey into the intricate world of our brain, asserting a profound yet simple idea: we are, at our core, neuronal machines. Seeing the Mind presents one hundred topics, each accompanied by a striking full-page color image. The book serves as a modern cabinet of curiosities, revealing the astonishing biological processes occurring within our brains and bringing readers face-to-face with the tangible reality of their thoughts and consciousness.  More than just an educational resource, Seeing the Mind is a stunning journey into the self. This book is a must-read for anyone fascinated by the workings of the human brain and the neuronal underpinnings of our identity. Building a Non-Anxious Life Dr. John Delony presents a compelling roadmap for navigating the turbulent waters of anxiety that increasingly dominate modern life. Recognizing the heightened levels of stress in today’s society, he draws from his two decades of research and personal experience to outline a practical and accessible approach to cultivating mental well-being. The book is centered around six daily choices that Delony identifies as crucial in building a life less burdened by anxiety: choosing reality, connection, freedom, health and healing, mindfulness, and belief. Each choice is thoroughly explored, providing readers with practical steps for implementation. The writing is straightforward and relatable, and will resonate with those who have experienced anxiety and are seeking tangible ways to address it. If you are looking for an easy-to-use resource to better navigate anxiety and build a healthier, more grounded life, Building a Non-Anxious Life is for you. The Daily Pressfield Known for his influential works such as The War of Art and Turning Pro, Steven Pressfield now offers a year-long companion to guide, motivate, and inspire you through your projects, whether it’s writing a book, starting a business, or maintaining a fitness regime. This is a daily source of empowerment for anyone embarking on a creative or personal endeavor, with beautiful visuals crafted by Victor Juhasz, an award-winning illustrator. Pressfield’s style is direct and unapologetic, delivering the hard truths about creativity, and his insights provide the necessary ‘kicks-in-the-butt’ and ‘pats-on-the-back’ to keep on progressing on any challenging project. This book is not just about motivation; it’s about sustaining momentum and pushing through the inevitable challenges and doubts that come with any meaningful pursuit. For fans of Pressfield’s previous works and newcomers alike, The Daily Pressfield will be an invaluable tool in the journey towards realizing your creative potential. Remember As a Harvard-trained neuroscientist and acclaimed author of Still Alice, Lisa Genova is in a unique position to offer a compelling exploration of how and why we remember, why we forget, and how we can nurture our memory. In Remember: The Science of Memory and the Art of Forgetting, Genova addresses common fears and misconceptions about memory loss, particularly among those over forty who worry about Alzheimer’s and dementia. This is a masterful blend of scientific expertise and engaging storytelling that delves into the complex world of human memory, which strength lies in its ability to translate complex neuroscience into relatable concepts. She also examines the roles played by emotion, sleep, stress, and context in shaping our memory processes. Genova doesn’t just offer a description of how memory works; she provides practical advice on how to improve memory functions and establish a healthier relationship with our memory system. By understanding the ‘language of memory’ you will gain confidence in your mental faculties and reduce your anxiety around the idea of forgetting. Right Kind of Wrong An award-winning Harvard Business School professor, Amy Edmondson presents in this book a transformative perspective on failure, challenging conventional views and offering a new framework to understand and leverage it effectively. She elegantly addresses the dichotomy in our modern culture — where on one hand we demonize failure and on another we overly romanticize the ‘fail fast, fail often’ mantra, arguing that both approaches lack nuance and don’t distinguish between the different types of failure, thereby missing critical learning opportunities. Edmonton offers a simple framework that will enable you to identify and minimize unproductive failures while maximizing learning from inevitable missteps. Enriched with vivid anecdotes and examples from business, pop culture, and history, this book is not just insightful but also highly engaging, making it easier to replace shame and blame with a culture of curiosity, vulnerability, and personal growth. In essence, Right Kind of Wrong is essential reading for anyone looking to reframe their relationship with failure and unlock potential in themselves and their organization. Do you have any books to recommend for the Ness Labs Best Books series? Please let us know via the contact form. We welcome self-recommendations. The post Ness Labs Best Books of November 2023 appeared first on Ness Labs.

This post was jointly written by me and Caroline Fry, assistant professor at the University of Hawai’i at Manoa! Learn more about my collaboration policy here. This article will be updated as the state of the academic literature evolves; you can read the latest version here. You can listen to this post above, or via most podcast apps here. According to most conventional measures of scientific output, the majority of global research takes place in a handful of countries. In the figure below, we pulled data on three measures of R&D efforts across every country in the world: number of scientific/technical articles published by researchers in a country, number of researchers engaged in R&D in a country, and R&D spending by country. We then combined that data with information on the population of every country to create the following chart, which shows the share of R&D occurring in countries with some share of the earth’s population. Based on data from Our World in Data - source file here According to this data, countries with about 12% of the world’s people produce half the world’s research. On the other side of the coin, half the world’s population resides in countries that collectively produce about 9% of scientific articles. The ratios are even more skewed if we rely on data on R&D spending or the number of researchers. Put differently, much of the world’s population lives in countries in which little research happens. Is this a problem? According to classical economic models of the “ideas production function,” ideas are universal; ideas developed in one place are applicable everywhere. If this is true, then where research takes place shouldn’t be a problem. Indeed, if research benefits from clustering, we would actually prefer to concentrate our research communities into a small number of places.1 This is probably true enough for some contexts. But there are at least two problems here. First, as has been well established in the literature on technology diffusion, there are significant frictions associated with the diffusion of knowledge over geographic distances.2 Second, and what we plan to discuss in this post, research may be less useful in countries where it did not occur – or, nearly as consequential, people may believe this to be so. In this post we’ll look at four domains - agriculture, health, the behavioral sciences, and program evaluation research - where new discoveries do not seem to have universal application across all geographies.3  Thanks for reading What's New Under the Sun! Subscribe for free to receive new posts and support my work. Different places, different problems In a previous post we discussed some evidence that researchers tend to focus on problems in their local area. To the extent that the prevalence of problems varies around the world, this could mean that the distribution of researchers influences the levels of research to solve problems in some locations (irrespective of diffusion). If the problems of places with few researchers differ from the problems of places with many, then some problems will be under-researched if researchers focus on what’s happening locally. So the first important question is: do problems vary around the world? Of course they do. We can start with Moscona and Sastry (2022), which documents that the prevalence of crops and pests varies around the world. In a first step of their analysis, they use a dataset on international crop pests and pathogens from the Centre for Agriculture and Bioscience International to map the prevalence of crop pests or pathogens around the world, documenting significant variation in where crops and their associated pests and pathogens tend to be found. From Moscona and Sastry (2022) Similarly, it is well documented that diseases also vary around the world, due to variations in animal hosts, local climates, demographics, and socioeconomic conditions (see Wilson 2017 for a review).4 For example, the 13 parasitic and bacterial infections that make up ‘neglected tropical diseases’ primarily occur in low-income countries in sub-Saharan Africa, Asia and Latin America (Hotez et al. 2007). From Hotez et al. 2007 Going beyond differences in the pest and disease burden, a well-known 2010 article by Henrich, Heine, and Norenzayan documents extensive variation in human psychology study results depending on the population under study. In particular, they emphasize that the study populations in behavioral science research are overwhelmingly drawn from Western, Educated, Industrial, Rich, Democracies – so-called “WEIRD” countries (indeed, they point to one study showing nearly 70% of subjects in top psychology journals came from the USA alone!). They show that along many important dimensions, findings that are derived from WEIRD samples do not generalize to the broader human population. To take one example, Henrich and coauthors point to a 1966 cross-cultural study about the Müller-Lyer illusion, presented below. In this study, American undergraduates were more likely to perceive line b to be longer than line a, though the two are actually equal in their length. Others, such as San foragers of the Kalahari, tended to perceive the lines to be of equal length. From Henrich, Heine, and Norenzayan (2010) A 2020 retrospective by Apicella, Norenzayan, and Henrich, which looked back on the decade since the 2010 article, found samples drawn from WEIRD countries continued to dominate major journals, even as (infrequent) studies continue to find variation across countries is important.5 Finally, economics presents another domain where results in one country may not generalize to other. For example, Vivalt (2020) assesses the extent to which results from impact evaluations of economic development interventions generalize to new contexts. To do so, the author compiles a dataset of all results across hundreds of impact evaluations covering 20 types of development programs (as an example, one type of development program is conditional cash transfers). Vivalt summarizes the variation by intervention and by intervention-outcome, using meta-analysis methods, and documents that there exists significant variation for the same intervention-outcome across contexts, and that this variation is greater than variation that exists across other types of interventions, such as medical interventions. Trust of evidence from different places So the problems related to agriculture, disease, human psychology, and economic development are not universal but vary substantially from region to region. If research done in one region is more likely to be related to the problems of that region (and we argued it is, here), then that means the substantial concentration of research means a lot of problems are receiving very little research effort. Decision-makers beliefs also matter. If people believe research done elsewhere isn’t applicable to their context, then that research is less likely to inform their decisions. That’s true even if the research actually is applicable, but people don’t believe it. And some papers indicate this potential concern is a real one. Two recent papers attempt to isolate this mechanism in the context of program evaluation evidence. Vivalt et al (2023)and Nakajima (2021) both investigate how policymakers evaluate potentially relevant research with some experiments where they surveyed policymakers on their views about different hypothetical research papers. In both of these papers, the authors provide policymakers with evidence from sets of hypothetical impact evaluations, and ask them to rank or rate which evaluations they prefer. These hypothetical evaluations vary in their methodologies (RCTs versus observational studies), results, sample size, and, importantly for this post, the location of the study. The two studies find similar results: that policymakers tend to have a preference for studies conducted in similar settings to their own country, preferably their own country (Vivalt et al 2023). Some related evidence from medical research has similar implications. Alsan et al (forthcoming) use a similar approach, a survey experiment, to assess how doctors and patients interpret the results of clinical trial data. In this study the authors provided profiles of hypothetical diabetes drugs, which included the drug’s mechanism of action and supporting clinical trials. In a supplementary experiment the authors asked respondents in the United States how much they trusted clinical trial results conducted in different countries. They found that respondents tended to be less confident about the effectiveness of a drug tested outside of the United States, and several respondents expressed concerns that the drug would not work in the same way due to biological factors, socioeconomic and environmental factors. (As an aside, geography is of course not the only factor affecting which kinds of populations are underserved by research. The primary experiment in Alsan et al. (forthcoming) is actually about whether representation of different racial groups in clinical trials influences the likelihood that physicians would recommend that drug to their patients, and whether patients would adhere to the drug regimen. The study randomized the share of Black trial subjects and average drug efficacy in trials across drug profiles. Physicians were asked to indicate their intent to prescribe the drugs, and in a separate experiment, hypertension patients were asked their interest in novel therapies to treat hypertension that had been tested in trial sites with varying shares of Black participants. They found that physicians were more likely to state an intention to prescribe drugs that had been tested on representative samples, and that this effect was driven by doctors who routinely saw Black patients. As for the patients, Black respondents were more likely to state that a drug would work for them if the trial was representative.) So another rationale fo...

FEATURED TOOL Welcome to this edition of our Tools for Thought series, where we interview founders on a mission to help us think better and work smarter. Maks Kuchur is the founder of xTiles, an all-in-one workspace for notes, tasks and projects which allows you to organize and manage your knowledge as you see fit. In this interview, we talked about the challenge of spending excessive time designing a productive workspace instead of focusing on actual work, how to get back in control of your productivity, why tasks should not be an isolated part of your workflow, the relationship between productivity and creativity, the power of using templates, and much more. Enjoy the read! Hi Maks, so much has happened since our last interview! How would you describe the big shift from the latest version to xTiles 2.0? Since our last conversation, xTiles has experienced remarkable growth and development. First and foremost, being named Product of the Week on Product Hunt in September 2022 was an incredible recognition of our commitment to providing a valuable solution for users. Our user base has expanded substantially, reaching 10,000 active users.  This growth underscores the versatility of xTiles, as we’ve captured the interest and engagement of creative professionals and students. The fact that we’ve resonated with such a diverse audience is a testament to the broad applicability of our tool. One of the most exciting aspects of our journey is the significant progress in product development. In response to user feedback and the evolving needs of our community, xTiles has evolved into a comprehensive all-in-one solution. It now covers a wide range of essential use cases, from planning and task management to knowledge base creation and collaboration. This transformation reflects our unwavering commitment to delivering a tool that genuinely meets the evolving needs of our users. In addition to these advancements, we’ve introduced a gallery featuring pre-made templates. This addition enhances users’ experience by offering ready-made life, work, and education planners. It streamlines their onboarding process and empowers them to harness the full potential of xTiles immediately. Our journey from these milestones to the current version aligns seamlessly with our overarching vision. We aspire to provide individuals and teams with a tool that boosts productivity and fosters creativity while maintaining a user-friendly approach. We’re eagerly anticipating the future and the innovative developments that await us. Creating an all-in-one planner is such an ambitious endeavor. How does this new version of xTiles work? Creating an all-in-one planner like xTiles has been an ambitious journey, and the new version brings a lot to the table. First and foremost, we’ve worked diligently to ensure that xTiles seamlessly functions across various platforms. Whether on a desktop, mobile or even on iPad, you can expect a smooth and consistent experience. This adaptability is crucial as it allows users to access and utilize xTiles wherever and whenever needed. One of the cool things about our new version is that we’ve integrated Google Calendar into our task management system. We’ve developed a robust and user-friendly task management system that takes advantage of this integration, making it easy for users to schedule and handle tasks like a boss. Additionally, we’ve introduced the collections. This functionality is akin to Notion’s database or AirTable’s tables, and it’s a game-changer for those who require a knowledge base use case. It enables users to structure and organize their information to suit their needs, offering a high degree of flexibility. In our commitment to providing a comprehensive solution, we’ve put significant work into capturing information. Our mobile app and web clipper have been fine-tuned to make gathering and storing information seamless. It’s essential for users who want to quickly save and organize ideas and data. Moreover, we’ve added a backlinks feature that enables linked workspaces and smooth navigation. This feature empowers users to create interconnected systems, facilitating the swift movement between related content and enhancing productivity. Finally, we’ve introduced widgets that allow users to create beautiful dashboards. These widgets enhance the visual aspect of xTiles, offering users the ability to customize and visualize their data in a way that’s meaningful to them. To summarize, this new version of xTiles shows how committed we are to making the planning and organization process super efficient and user-friendly. We’ve really listened to what our users want and have made significant improvements to give you a complete, flexible, and feature-packed tool. With these new features, you can create all sorts of planners for different areas of your life and work. They also help you implement frameworks like the Second Brain and more, making xTiles a super helpful tool for keeping yourself personally and professionally organized. Collections sound like an amazing new feature. Can you tell us more? Collections are an exciting addition to xTiles, offering users four beautiful views: card, calendar, kanban, and table views. These views provide versatile ways to organize and interact with your data. What makes Collections truly powerful is the ability to add properties to collection entities. You can utilize popular data types such as memo, date, select, bool, and relations, allowing for detailed customization and organization of your content. One game-changing aspect of Collections is the capability to add blocks and tiles from existing documents to a collection. This feature lets you structure your content exactly when needed, providing clarity and making it easy to manage your information effectively. Collections serve various team use cases, from project management to social media planning, building business strategies, and conducting research. Their flexibility and adaptability empower users to tackle diverse challenges and streamline collaborative efforts. Something people often complain about is how much time they spend designing their productivity workspace versus actually doing the work. How does xTiles address this challenge? I understand the challenge of spending excessive time designing a productive workspace instead of focusing on actual work. That’s precisely one of the issues xTiles aims to address. xTiles is designed to provide users with a unique level of flexibility and customization that caters to their specific needs.  We understand that productivity requirements can vary from person to person and even from one moment to the next. That’s why xTiles allows users to choose the level of complexity in their digital planner. You have the power to select the combination of functionalities that are necessary for you at any given time. Whether it’s simple note-taking, efficient task management, building a knowledge base, or any combination, xTiles can adapt to your requirements. It’s all about giving you the flexibility to tailor your workspace to your current situation, state of mind, and specific goals. In essence, xTiles puts you in control. You can start with the basics and gradually introduce more advanced features as you feel ready.  This approach ensures you can build your productivity step by step without any unnecessary complexity. Ultimately, the choice is yours, and xTiles supports you at every stage of your productivity journey. You made an interesting design choice regarding tasks in xTiles. What does that mean for task management? Indeed, defining tasks as individual blocks in xTiles is a design choice that brings significant advantages to task management. By creating tasks as blocks, we’re enabling users to seamlessly integrate tasks into their content, enhancing clarity and providing a detailed explanation of how to accomplish each task. Tasks in xTiles are not isolated but integral parts of your work content. This approach allows you to place tasks exactly where they make the most sense. You can embed tasks alongside relevant information, instructions, and contextual details. It means that each task can be accompanied by a clear and comprehensive explanation of how it should be executed. We know that effective task management calls for a centralized solution, and that’s precisely what xTiles delivers. With the Task Panel, we’ve created a convenient hub where all your tasks from various pages come together. But that’s not all – we’ve also seamlessly integrated your Google Calendar events into this central location. This streamlined approach means you can easily plan your day by having events and tasks at your fingertips, side by side in the Task Panel. Can you tell us more about creating digital products and templates in xTiles? ​​At xTiles, we’re redefining productivity and creating a platform that empowers you to unleash your creativity and knowledge. Our template creator feature opens up possibilities for individuals from diverse backgrounds. Here’s what you need to know about this exciting opportunity: We’ve established a beautiful template gallery within xTiles, a marketplace where creators like you can showcase and sell your templates or digital products. This gallery isn’t just a showcase; it’s a vibrant marketplace where your expertise and creativity meet the needs of a global audience. It’s a place where you can transform your insights into valuable, monetizable resources. Your templates in xTiles can encompass a wide range of productivity frameworks. These aren’t just run-of-the-mill templates but powerful tools that people can apply in different professional spheres. Whether you’re an expert in business, education, coaching, or any other field, xTiles allows you to create templates that resonate with your domain. Your digital products can be a repository of trusted and valuable information. As a creator, you can share your knowledg...

New Things Under the Sun is a living literature review; as the state of the academic literature evolves, so do we. This post highlights some recent updates. Subscribe now Risk Aversion and Budget Constraints The post Conservatism in Science looked at some evidence on whether science was biased in favor of incremental science. One argument made in that post is that it’s easier to identify really good research proposals if they rely on a knowledge base reviewers are familiar with. If only really good proposals can be funded because the research budget is too tight, then that might mean more unusual ideas that are harder to evaluate don’t make the cut, creating a bias towards conservatism in science. A new paper provides some further evidence on this point. The updated post now includes the following paragraphs: A 2023 working paper by Carson, Graff Zivin, and Shrader provides some further support for the notion that, when budget constraints bite, proposals with a greater degree of uncertainty are the first to be dropped. Carson and coauthors conduct a series of experiments on scientists with experience serving as NIH peer reviewers. In one experiment with 250 participants, they showed reviewers a set of ten grant proposals. The title and abstract of these proposals were drawn from real NIH grants, but in the experiment participants were provided with a set of 30 fictional peer review scores, ranging from 1 (best) to 9 (worst). They were then asked to pick four to (hypothetically) fund. We don’t have a measure of novelty here, but the variance of peer review scores is a potentially informative related measure, as it indicates disagreement among peer reviewers about the merits of a proposal. Carson and coauthors show that, among proposals with the same average score, participants are actually more likely to select proposals with a greater variance in their peer review scores to be funded! But in the next stage of their experiment, they ask participants to imagine their research budget has been cut and now they have to drop one of the four proposals they selected to fund. When asked to tighten their belts, which projects do reviewers in this experiment choose to drop? As we might expect, they cut the ones with the lowest average. But above and beyond that, participants are also more likely to choose to cut the ones with the more variable scores. Read the whole article Measuring the extent of knowledge spillovers A key idea in the economics of innovation is the knowledge spillover: the research work I do tends to benefit people besides myself. This dynamic is an important reason why innovation has unusual properties, relative to other kinds of economic activity. The post Knowledge Spillovers Are a Big Deal looks at some papers to argue that knowledge spillovers matter in practice, as well as in theory. I’ve rearranged this paper a bit to highlight two new additions. First, a new paper by Aslan and coauthors provides descriptive data on the extent of knowledge spillovers in biomedicine. From the article update: Aslan et al. (2023) show pretty similar results in biomedicine. Since 2008, the NIH has classified its research grants into hundreds of different research categories, such as “cerebral palsy”, “vector-borne diseases”, and “lead poisoning” (to pick three examples at random). How often do grants for one category result in research publications in other categories? Quite often it turns out. To see how often this kind of unexpected spillover happens, Aslan and coauthors get data on 90,000 funded NIH grants over 2008-2016, and 1.2mn associated publications. If the NIH and journals used the same classification system, it would then be a simple question of seeing how often a grant and its publications are assigned the same category (minimal spillovers) versus different categories (large spillovers). But there are two challenges. First, unfortunately journals do not classify articles into categories using the same system that the NIH uses to classify its grants. Aslan and coauthors instead use machine learning algorithms to assign journal articles to the NIH’s categories, based on the text of the journal abstracts. Second, the NIH classification system can be too granular for identifying significant knowledge spillovers. For example, there are categories for both “tobacco” and “tobacco smoke and health.” If research dollars are spent on a proposal assigned to the category “tobacco” but then generate a publication tagged as “tobacco smoke and health”, then while it is technically true that the grant generated knowledge applicable to a different category of knowledge than expected, the new category is so similar to the original that it doesn’t really feel like a significant knowledge spillover. To reduce this worry, Aslan and coauthors use a clustering algorithm to cluster categories frequently assigned to the same grants. This results in 32 different clusters of NIH topics. “Tobacco” and “tobacco smoke and health” now fall under the same category, for example, so that a grant assigned to “tobacco” but generating research assigned to “tobacco smoke and health” would no longer be classified as a knowledge spillover, since both categories are part of the same cluster. In the end, 58% of publications are assigned at least one category that is different from the ones assigned to the grant. In other words, more than half of the publications emerging from NIH grants are at least partially about a topic significantly different from the topics that the research grant was originally assumed to be about. The original article also included a discussion of Bloom, Schankerman, and Van Reenen (2013), which showed private sector R&D appears to “spillover” to other firms working on similar technologies, leading to more patents and greater productivity for these peers. The update now (briefly) notes that this paper’s analysis was repeated on a larger dataset in 2019, finding broadly similar results as the earlier paper. Read the whole thing Aging Economists Finally, the post Age and the Impact of Innovation looked at some of the literature on how research impact metrics change over a researcher’s life. The original post looked at Yu et al. (2022) and Kaltenberg, Jaffe, and Lachman (2021) which showed that the average citations received by biomedical scientific research and patents, respectively, decline substantially as scientists and inventors age. We can now add economists to this dataset. A new paper by Kosnik and Hamermesh (2023) finds that as economists get older, the citations to their publications in a set of top journals also decline substantially. As discussed in the post, the story is actually more complicated than it seems though. One complicating wrinkle discussed in the appendix to that post is that Yu and coauthors show life scientists who do not produce as many papers and whose work isn’t as highly cited drop out of research over time. That means older researchers are, on average, as productive as younger ones, but only because the set of older researchers is limited to the most productive and the set of younger ones includes all the people who will eventually drop out. Hamermesh and Kosnik (2023) also show that economists are less likely to retire if they have published more often in top journals in the preceding decade. Read the whole thing Until Next Time Thanks for reading! If you think the updated posts above are interesting, you might also be interested in the following related posts: For more on conservatism and science, see Biases against risky research For more on spillovers, see Adjacent knowledge is useful For more on age and innovation, see Age and the nature of innovation As always, if you want to chat about this post or innovation in generally, let’s grab a virtual coffee. Send me an email at matt.clancy@openphilanthropy.org and we’ll put something in the calendar.

Many people believe that creativity is a natural gift that only a select few are born with and that it cannot be taught or learnt. This could not be further from the truth. Yes, creativity is innate in the sense that we are all born with it. But, as we grow up, most of us slowly unlearn it. The good news is that what is unlearned can be learned again. It’s just a matter of figuring out how. There is a lot of content out there with various tips and tricks that may or may not work. What does the science say? How can we be more creative and effectively brainstorm new ideas? Quantity versus quality We have an implicit conception that good work takes time. This is why prolific authors are often judged as bad; and their work, inconsequential. In an amazing essay for the New York Times titled “Can a novelist be too productive?”, Stephen King — who has published more than 55 novels — argues that while quantity is never a guarantee of quality, being prolific can definitely result in quality work. Agatha Christie wrote 91 books and gave us Hercule Poirot. Picasso painted over 20,000 artworks. James Dyson developed 5,127 prototypes when trying to design a better vacuum cleaner. Thomas Edison still holds the record for the most patents with over a thousand in his name. Were all of these groundbreaking? Probably not, but that’s exactly the point. It may sound counterintuitive, but research suggests that quantity yields quality when it comes to creativity. In the book Art & Fear, David Bayles shares the anecdote of a ceramics teacher who conducted an experiment with his students. He divided the class into two groups. Group A was to be graded based on the quality of the work they produced, whereas group B would be graded on quantity. To get a perfect grade, group A had to produce only one pot — the most perfect ceramic pot possible — while group B would have to create as many as possible. The results are fascinating: when it was time for grading, the best work came out of group B, the “quantity” group. While group A was busy debating and theorizing, group B was dutifully creating pots after pots, and learning from their mistakes in the process. Think you’re out of ideas? According to research, we tend to grossly underestimate how many ideas we can generate. Even more interesting, according to the same research, the more ideas we keep on generating, the more creative they become. Creative routine Of course it’s tempting to spend a lot of time reading and researching your area of interest — and such research also has its place! — but you will not improve your creative thinking without consistent output. Creativity is like a muscle. You need to use it to stay in “creative shape”. This means — however uncomfortable that may sometimes be — forcing yourself to create on a schedule. Whether your goal is to write a book, become a better illustrator, or build an app, don’t leave creativity to random bursts of inspiration. Block some time every day or every week to generate new ideas and new work. I personally use the PARI framework to ensure my daily creative output aligns with my long-term ambitions, but as long as you flex your creative muscle consistently, you will be on your way to do your best creative work. As poet W.H. Auden so beautiful put it: “Decide what you want or ought to do with the day, then always do it at exactly the same moment every day, and passion will give you no trouble.” Having a creative routine allows you to keep your cognitive bandwidth for creative thinking. According to William James, considered by many as the father of modern psychology, such routine allows us to “free our minds to advance to really interesting fields of action.” Basically, the resources you don’t waste trying to decide when or where to do creative work can be used to, you know, to actually do the work. So, how can you go about creating on a schedule? Get up early or stay up late. There is no right or wrong way to go about your routine. Some creative people are early risers, others are night owls. In her diary, Anaïs Nin wrote: “I do my best work in the morning.” In contrast, Jack Kerouac said: “I had a ritual once of lighting a candle and writing by its light and blowing it out when I was done for the night.” Pick whichever works for you. Choose your creative space with intention. If you can, find a secluded spot that is solely dedicated to creating. Especially when working remotely, we tend to just sit wherever, for example at the kitchen table. Pick a spot and make it your creative space. Make it comfortable, and make sure to have all the creative tools you need. Warm up for creative work. Take some time to loosen up and get your creative juices flowing. Write a few sentences without thinking too much, play with your design software for a bit without trying to create something concrete. This process will give your mind time to shift into a more creative state. If you want to read more about the creative routines of famous artists and inventors, I recommend reading Daily Rituals by Mason Currey — full of fascinating stories. Disclaimer: some routines include taking mind-altering substances and other more original approaches — which can have their place in the creative process, but as usual don’t just blindly apply what you read. Pick your creative mode According to psychology research, there are several types of creativity you can leverage to brainstorm ideas more effectively. Combinational creativity. We are often seeking original ideas, when in reality most creative concepts are a combination of old ideas. First, collect as many old ideas as possible. This can be done by reading science fiction or just taking notes every time you hear a commonplace idea in a conversation. Then, let these old ideas incubate for a while. Yes, there’s no second step. Let your brain do the work. “Drop the problem completely and turn to whatever stimulates your imagination and emotions. Listen to music, go to the theatre or movies, read poetry or a detective story,” recommends James Webb Young. The combined idea will most probably come to you when your brain is relaxed, such as in the shower. Exploratory creativity. In academia, exploratory creativity is defined as “the process of searching an area of conceptual space governed by certain rules.” This means that you try to generate new ideas within a given space, taking into account its specific rules. For example, let’s take transportation. Why is it expensive to fly? Why is it so hard to find a taxi? Exploratory creativity is all about exploring existing concepts and ideas you may already have and questioning their validity to come up with new solutions. Transformational creativity. This method takes things even further. Instead of exploring a space and questioning its rules, transformational creativity is about ignoring fundamental rules to come up with potentially impossible but highly creative ideas. Let’s keep on using transportation as an example. Instead of questioning the cost of air transportation, you may ask yourself: Why do cars have to park? Why do we need to travel at all? Transformational creativity has the potential to generate the most radical ideas. In reality, we may very often be using a combination of these three types of creativity when brainstorming, and this is a good thing. By starting with transformational creativity, then moving onto exploratory and combinational creativity, you are not leaving any potential idea of the table, and can go from crazy to actionable creative ideas. What about brainstorming as a team? The process is not too different, and may even be more powerful, since you’re combining the brain power of several people. It does need to be more structured, though. Make sure to have these safeguards in place: Create a safe environment. eEnsure everyone feels comfortable, and that there is no competition. Define acceptance as the default. Psychological safety is paramount for a productive and creative team. There are no bad ideas in a good brainstorming session. Avoid planting a solution. Don’t start with an example so people are not primed and coming up with similar solutions. Don’t shoot for the stars. “If you start a meeting and you say, ‘Okay, we are going to come up with really good ideas,’ that can be a really bad way to start,” says Christian Schunn of the University of Pittsburgh, who published an interesting paper about idea generation. How to brainstorm in five steps How does this all work in practice? Here is a step-by-step guide to effectively brainstorm and generate new ideas. Remember the principles laid out earlier: quantity versus quality, building a creative routine, and using all three creative modes to ensure you don’t leave any ideas off the table. Set your focus. Define the problem or area you will be looking at. It can be as narrow as a specific annoyance you face in your life, and as broad as a whole industry, but you can’t just have a vague brainstorm with no predefined focus. Gather new material. Give yourself — and the team if it’s a group brainstorm — time to familiarise yourself with the area of focus. This means reading articles, watching videos, etc. If it’s a group brainstorm, this step should ideally happen before the session to give time to your brain to incubate these ideas, but if not you can block a bit of time at the beginning of the session. Generate ideas. Remember, quantity over quality. Use the three creative modes presented earlier. Combinational to mix old ideas together, exploratory to investigate new potential ideas within the rules of a given space, transformational to break the rules and come up with radical ideas. Test your ideas. This is where most brainstorming sessions fail to take the one extra but necessary step. Instead of selecting your ideas on the spot, you need to test them in the real world. Select the few most promising candidates, and see how your audience reacts. Fo...

FEATURED TOOL Welcome to this edition of our interview series, where we meet founders on a mission to help us think better and work smarter. Thomas Paul Mann is the co-founder of Raycast, a blazingly fast and extendable launcher that lets you complete tasks, calculate, share common links, and much more. In this interview, we talked about how to make productivity truly personal, how to balance powerful features with ease-of-use, why everyone should customize their productivity tools, the power of app integrations, and much more. Enjoy the read! Hi Thomas, thanks so much for agreeing to this interview. Let’s start from the very beginning. What inspired you to build Raycast? I’ve been a Software Engineer for more than a decade. During this time I’ve seen first hand how the role of an Engineer changed. Over the years, I had to use more tools to get my job done. This felt like a conflict to me: The more time I spent in tools outside of my IDE, the less features I was able to build, and the less I enjoyed my job. Though, those other tasks were important to collaborate with my team. So ideally, I could spend as little time as possible on mundane tasks to maximize my productivity.  While I was working at Facebook (now known as Meta), I met Petr. We were on the same team, shared our passion for productivity tools, and were both frustrated with how clunky existing tools felt. Too many clicks, too little speed. But perhaps the biggest motivator was realizing that we weren’t alone; there were loads more like us! That was our eureka moment. We wanted to create a new way of using our Macs — a way that would be speedier, smoother, and would bring the joy back into our work. And that’s how Raycast was born — out of frustration, love for efficiency, and shared dreams with Petr. What makes Raycast unique as a launcher? What really sets us apart is our commitment to strike the perfect balance between immediacy and customizability. Out of the box, Raycast is intuitive to use. But the real magic begins when users start personalizing it. With over 1,000 extensions available in our store, the possibilities are endless. There are extensions for GitHub, Linear, Notion, and other SaaS tools. And for those who want a truly custom experience, there’s the option to build their own extensions with our easy-to-use API. But it’s not just the functionality we’ve focused on, it’s also the user experience. We’re crafting Raycast to be the tool we’ve always wanted – simple to interact with, lightning-fast to navigate, and an absolute joy to use. Specifically, how does it work? Surprise, it’s very simple: You press your global hotkey, e.g. `⌘ + Space`, to summon Raycast and then search for what you want to do. It’s designed to be a drop-in replacement for Apple’s Spotlight, so you can launch your apps, search for files, or do calculations (ours are more advanced, e.g. `123k USD + 456 GBP in EUR`).  On top of the basics, Raycast comes with a lot of built-in utilities that would be otherwise separate applications. You can type “Schedule” to get an overview of your upcoming meetings and you can join video calls right from there. Our Clipboard History feature acts like a time machine, allowing you to scroll back through everything you’ve copied. Our Window Management extension allows you to position and resize your windows with keyboard shortcuts, making it a blast to work on a bigger monitor. The Screenshot Search helps to find images by text. The list goes on and on… I mentioned earlier that the real magic begins when users start personalizing the tool. You can add Quicklinks to open your frequently visited websites or files from anywhere. Or you can set up Snippets for faster typing. And you can even share those within your team to boost knowledge sharing. Many of our users assign Hotkeys and Aliases to specific commands. This way, they can launch those commands quicker. F.e. I open Notion with `⌥ + N` and don’t need to command-tab to it. I have the same for other things. It’s all about shaving off seconds from your workflow. And if Raycast can’t handle what you want to do natively… …you open our Store. The Store has over 1,000 extensions ranging from SaaS apps like Linear to utilities like switching your Bluetooth devices. All of the extensions are open source and are getting constantly evolved by our community. Here are a few extensions that I use daily: Notion to search my pages and copy a formatted link to share or simply open the page. I open the search with `⌥ + ⇧ + N`. Slack to change my status. We are a remote company and it’s important to broadcast if I’m not available or want to focus on something. Linear to create issues and manage my assigned issues. It’s especially handy to file a bug report quickly. GIF to find a good reaction. You need to have some fun after all! I’m constantly impressed by what our community builds. Some of those extensions are really rich applications by now.  These extensions sound amazing. You also recently launched a pro version of Raycast. Can you tell us more? We had ideas for more advanced features that unlock a new level of productivity that would require a paid plan. On the other hand we also wanted to make sure that the existing features stayed free. So we added an optional priced tier to the product. With a Pro subscription, users can choose a custom theme, use AI-powered features, have an unlimited Clipboard History, synchronize their data and settings between multiple Macs and more. Personally I’m really excited about AI and what it enables for productivity. It already became a main part of how I work everyday and I believe it will only get better over time. Within Raycast, you have three ways to experience AI: 1. Quick AI: Simply type a question in Raycast and hit tab to get an AI-generated answer. Soon it will also show sources for the answer. 2. AI Chat: Our Chat is really the assistant that follows you around. It stays open and enables you to have ChatGPT anywhere on your Mac. 3. AI Commands: Those allow you to automate things in plain english, f.e. select some text and execute the Fix Spelling and Grammar command to improve your writing. This all sounds amazing for personal productivity. Can Raycast also help improve team productivity? Yes, we have an offering for teams that is used by companies like Atlassian, GitHub, or Shopify. Raycast for Teams allows sharing of Extensions, Quicklinks, and Snippets via a private Store. It’s ideal to keep workforces in sync and provides an easy way to share information and align processes. Some examples include release tooling for engineering teams, shared calendars to see when somebody is off, or having an extension to generate gift codes for support teams. The easiest way to start is sharing a handful of links to your current roadmap and other frequently visited internal resources. If you are working in an engineering team, you probably have some local script for some automations. Convert them to extensions and share it within your organization to boost others. What about you, how do you use Raycast? Funny you asked. We’ve recently recorded about my setup. This probably shows best how I use Raycast. This is great. You’re clearly a power user, but how do you recommend someone get started? I recommend the following: Download Raycast from raycast.com Replace Spotlight with it and you will already feel the difference in speed Head to the Store in Raycast and connect your top 3 tools Check out our YouTube channel for content The best way to use Raycast is to experience it yourself and customize it to your needs. That’s where the true power of a great productivity tool comes from: It’s personal! By replacing Spotlight you keep an existing muscle memory and you can add more tools to your toolbelt over time. That’s all super clear. And finally… What’s next for Raycast? We want Raycast to be a better way of using computers — simpler, faster, more delightful. AI offers a great opportunity to make things powerful while maintaining its ease-of-use. It’s a new technology that is fast-moving and we’re just getting started to really understand how we can use it. We feel that there are many opportunities for productivity tools to really unlock the next level of efficiency. Thank you so much for your time, Thomas! Where can people learn more about Raycast? Thanks for having me. The easiest is to check our Twitter to stay up-to-date. The post Put the Pro in Productivity with Thomas Paul Mann, co-founder of Raycast appeared first on Ness Labs.

At Ness Labs, we understand the transformative power of knowledge. In a world inundated with content, finding truly impactful books can be a daunting task. That’s why, every month, we sift through the vast literary landscape to bring you books that stand out—books that have the potential to reshape the way you think about life, work, and personal growth. This October, dive into a curated list of titles that promise to inspire, challenge, and guide you. From the philosophical musings of a cognitive science veteran to the sharp observations of a literary giant, these selections offer a blend of introspection, innovation, and invaluable insights.  Whether you’re looking to enhance your decision-making skills, understand the nuances of human potential, or simply embark on a journey of self-discovery, our picks for this month have got you covered. Here is our pick for October 2023! I’ve Been Thinking In his latest book, Daniel C. Dennett, a stalwart in philosophy and cognitive science, delivers a reflective voyage through his illustrious career, which has persistently grappled with some of the most enigmatic questions about the human mind. The book unfurls Dennett’s personal and intellectual journey, offering readers an intimate view into the life of a thinker. As Dennett revisits the dominant themes that have punctuated the philosophical landscape—ranging from language and evolution to AI and religion—he offers both trenchant insights and candid admissions about his evolving perspectives. I’ve Been Thinking isn’t just an autobiography; it’s an evocative testament to the value of intellectual curiosity and the intertwined dance of doubt and conviction. Dennett’s narrative will resonate deeply with those who cherish the life of the mind while remaining rooted in the tangible world of experiences and emotions. Learn more Burning Questions This collection of over fifty essays by Margaret Atwood is a thought-provoking exploration of some of the most pertinent issues facing our world today. With her characteristic blend of wit and wisdom, Atwood delves into a vast array of topics ranging from storytelling’s universal appeal across cultures to the implications of technological advancements and the urgency of the climate crisis. Written against the backdrop of significant global events including financial meltdowns, political upheavals, and a pandemic, Burning Questions captures Atwood’s keen observations and her ability to connect seemingly disparate themes. Whether she’s musing on the sustainability of our planet or pondering the essence of individuality, her insights are both timely and timeless. This collection underscores her role as a keen observer and commentator of the human experience. In her latest book, readers are invited not just to absorb but to engage, to question, and to reflect on the multifaceted world around them. Learn more Hidden Potential Adam Grant’s latest book challenges popular notions of innate talent and highlights the importance of continuous learning and personal growth. Grant, a renowned author and expert in organizational psychology, offers compelling evidence that individual potential isn’t just about natural talents or the starting point but the journey and the progress made along the way. Using research and real-life examples from various domains, he underscores the significance of character development in achieving success. The book also touches on creating inclusive systems that recognize and uplift those who have been traditionally marginalized. Hidden Potential is a persuasive argument for redefining how we measure success and potential, emphasizing resilience, adaptability, and the pursuit of improvement. It’s a refreshing read offering actionable insights for individuals and organizations aiming to foster growth and unlock untapped potential. Learn more Masterpiece in Progress In Masterpiece in Progress, Sean DeLaney brilliantly draws upon his multifaceted experiences as an executive life coach, podcast host, entrepreneur, and former professional athlete, offering readers a transformative manual to rediscover the suppressed spark within. The book, structured around 365 thought-provoking passages, serves as a daily muse, pushing one to step out of the monotony of everyday life and into a world fueled by passion, authenticity, and dream-driven pursuits. DeLaney’s nuggets of wisdom are not mere platitudes but are rooted in timeless truths that act as helpful guides in the ambiguity of any life’s journey. But what sets this book apart is its actionable strategies—giving readers the inspiration and the tools to awaken and harness their latent potential. This book beckons readers to take the reins of their lives and start crafting their unique masterpieces. Learn more Clear Thinking Shane Parrish’s Clear Thinking is a compelling exploration of the art and science behind effective decision-making. Drawing on diverse examples, from evolutionary psychology to historical incidents, Parrish lays out a pragmatic guide for anyone looking to navigate life’s complexities with greater clarity and precision. What sets the book apart is its emphasis on the day-to-day, seemingly mundane decisions that cumulatively determine the trajectory of our lives. Parrish posits that the path to a clearer, more successful future isn’t just about our big choices but is deeply rooted in our daily decisions. The book dispels the myth that luck or innate talent is the primary driver of success; instead, it’s about the strategic choices one makes consistently. In essence, Clear Thinking is an invaluable guide for anyone seeking to elevate their decision-making skills. With actionable insights and a straightforward framework, Parrish offers readers the tools to approach life’s challenges and opportunities with a renewed sense of clarity. Learn more Do you have any books to recommend for the Ness Labs Best Books series? Please let us know via the contact form. We welcome self-recommendations. The post Ness Labs Best Books of October 2023 appeared first on Ness Labs.

For generations, hard skills have been prioritized over soft skills. Today, engineering and computer science education still places a strong emphasis on building technical expertise through math, science, and programming courses, with little attention paid to fostering interpersonal abilities. Many parents and educators still operate under the assumption that academic achievement and hard skills should be the top priority. But success in today’s world depends just as much on soft skills like creativity, collaboration, empathy, and adaptability. The Collins English Dictionary defines soft skills as the “desirable qualities for certain forms of employment that do not depend on acquired knowledge: they include common sense, the ability to deal with people, and a positive flexible attitude.” Note the part that says soft skills do not depend on acquired knowledge. Indeed, many people think that soft skills cannot be taught or learned. Is that really the case? Nothing soft about soft skills The term “soft skills” was created by the U.S. Army in the late 1960s to refer to any skill that does not employ the use of machinery. Since then, interest in soft skills has greatly increased. Here are the most in-demand soft skills according to a survey conducted by LinkedIn: Creativity Persuasion Collaboration Adaptability Time Management It’s unfortunate that we chose to call such fundamental skills “soft”, making them sound somewhat weaker and less crucial to the job compared to hard skills. This couldn’t be further from the truth. In today’s world, the average lifespan of a technical skill is roughly 18 months. Soft skills, by contrast, will never get obsolete, and can be transferred from role to role and anywhere outside the company. Hard skills are linked to your ability to do a specific task, while soft skills are about the way you do them. As technology continues to evolve at breakneck speed, soft skills may be the only constant in an ever-changing work environment. In the most basic sense, hard skills will get you the job, but soft skills will make you excel at it. There is in fact scientific evidence to this. A review from Rutgers University lists 19 research findings building a case for how emotional intelligence, a commonly used proxy for soft skills, contributes to the bottom line in the workplace. For example, one study found that leaders with higher emotional intelligence delivered greater profits—139% higher in one study—as well as higher customer satisfaction levels. This was confirmed in a famous research study conducted with hundreds of employees by Google, called Project Aristotle. The goal of the study was to answer the question: “What makes a team effective?” The big surprise? Hard skills did not top the list. Psychological safety—basically teammates being nice and caring—was the top factor in team performance, followed by dependability—being able to count on your teammates. So what should we call “soft skills” instead? I vote for “life skills” but other people have suggested “power skills”. Whatever you want to call them, one thing is for sure: they are in demand. So, is there a way to teach or learn them? Measuring soft skills People say that you can’t measure it, you can’t improve it. Fortunately, as I’ve discussed in the past, that’s a fallacy. But it’s still true that soft skills are incredibly hard to measure. There have been many attempts to create tests that would give a score assessing how well developed soft skills are in people, with no clear winner so far. The most common approach is to measure people’s EQ, or Emotional Quotient. Some research found that one of the most important foundations of emotional competence — accurate self-assessment — was associated with superior performance among several hundred managers from twelve different companies. But EQ is only a small subset of soft skills. Some people are trying to develop frameworks that touch on other specific aspects. A few years ago, Brent Hoberman announced a new business school called Founders Academy, where I was a faculty advisor to support the first cohort of students. The school focuses on developing your AQ, or Adaptability Quotient, a measure of how well you are able to thrive in a world of accelerating change. You also have this paper which examines a new creativity test designed to test for CQ, or Creativity Quotient, using verbal tests and eye-tracking to measure engineers’ creative thinking skills. Collaborative Quotient, Persuasion Quotient… You can stick the word “quotient” to practically any soft skill, and you will find that someone has created a test to measure it, and many of them lack solid science to support them. As you can see, measuring soft skills is actually hard work, and you may be better off focusing on developing your soft skills and measuring the impact they have on your work, your relationships, and your life in general, rather than creating a measurement scale for the skills themselves. Life skills There’s an increasingly large body of evidence showing that soft skills can help predict work performance. The consensus is that curiosity, emotional resilience, and general learning ability will make you better at your job. So, how can we go about developing these skills in people and yourself? The main challenge is that you cannot just give people a step-by-step guide on how to be a nice person, or how to be a better listener. Reading about soft skills or watching a lecture is not enough. Soft skills need to be practiced, and the student needs a strong intrinsic motivation to learn them and incorporate them into their lives. More than demonstration, soft skills require participation. Here are some learning approaches that do work when it comes to acquiring or improving soft skills: Coaching and mentoring. Research suggests that coaching significantly enhances motivation, coping skills, and overall emotional wellbeing. One of the most important aspects of coaching is to provide feedback, which helps people identify their key areas of improvement. This is crucial when it comes to soft skills, as people are pretty much unaware of their own soft skills, with only a 10% overlap between the skills people think they have, and the ones they actually have. Interactive training. There is strong evidence of the effectiveness of interactive training when it comes to learning soft skills. For example, many creativity training programmes do produce positive results. Again, the interactive part is essential! Soft skills cannot be taught through a good old traditional lecture. Online interventions. Interestingly, whether soft skills training is delivered online or offline doesn’t seem to matter. Which is pretty exciting if you’re thinking about learning new soft skills or improving existing ones — no need to travel far to attend face-to-face events. One big caveat is that soft skills training works best for people who are motivated to improve these skills — and who therefore may need it the least. But I personally find it extremely exciting to know that whatever your current levels of comfort when it comes to interpersonal and emotional skills, you can always improve them should you wish to invest some effort into developing them. Another example of the magic of lifelong learning! The post Some hard truths about soft skills appeared first on Ness Labs.

Making your bed in the morning, that first cup of coffee, grabbing a croissant on your way to work, listening to your favorite podcast on the train… As much as we wish for each day to be different, repeating some of the same actions is an important part of our lives. Researchers have found that more than 40% of our actions are consciously self-selected. Instead, we perform these actions in an automated way, without conscious awareness. How can you ramp up that percentage and live a more intentional life? The key is to understand the difference between habits, routines, and rituals, and to design a life where your daily actions allow you to play with the entire spectrum of consciousness. Shades of Consciousness Waking up, commuting, walking past a particular store, or starting a meeting at work are all common cues that can trigger actions such as smoking a cigarette, buying a croissant, or drinking coffee. Many books have been written about building better habits and breaking bad ones. The most famous is probably Atomic Habits by James Clear (2018), but other ones such as The Seven Habits of Highly Effective People by Stephen Covey (1989) and The Power of Habits by Charles Duhigg (2012) have also sold millions of copies. It’s fair to say that people are convinced habits matter. And it makes sense. To maintain a healthy lifestyle, it helps to be able to set some behaviors on autopilot so that you don’t have to make a conscious effort every single time. Habits are great for those actions. But most good habits don’t start as habits. They start as routines. The main difference between habits and routines is how aware and intentional you are. A habit usually manifests itself as an automatic urge to do something, often triggered by a particular cue. The stronger the connection between the trigger and the habit, the more ingrained the habit. In contrast, routines require deliberate practice. Making your bed in the morning, going to the gym, going for a hike every Sunday, and meditating are all routines that require you to keep on consciously practicing them or they eventually die out. Your brain will not go into automatic mode and walk you to the gym for your weekly HIIT class. Both habits and routines are regular and repeated actions, but habits happen with little or no conscious thought, whereas routines require a higher degree of intention and effort. With enough time, routines can turn into habits, but you need enough repetitions to create that habit loop: Cue. Choose a trigger to tell your brain to start the routine you want to turn into a habit. Routine. Execute the routine, ideally starting with a small, actionable chunk. Reward. Do something enjoyable to tell your brain that this particular action is worth performing again in the future. But what about the actions where you actually want to make a conscious effort? The ones where you get satisfaction from pushing yourself out of your comfort zone? From Routine to Ritual We tend to associate rituals with very specific types of activities: communal rites of worship, rites of passage, commemorative rites… Yes, these are rituals, but this is only the narrowest definition of the term. More broadly, the difference between a routine and a ritual is the mindset behind the action. While routines can be actions that just need to be done—such as making your bed or taking a shower—rituals are viewed as more meaningful practices that have a real sense of purpose. Rituals do not have to be spiritual or religious. What matters is your level of intentionality. With rituals, you are fully engaged with a focus on the experience of the task, rather than its mere completion. You are investing your highest levels of energy and consciousness. And you can virtually turn any routine into a ritual by becoming more mindful and making mental space for the action. For instance, when you eat, you could practice paying attention to the textures and the way you chew. Research actually shows that mindful eating can indeed improve the flavor of your food, making you feel more satisfied. Showering can become an opportunity to become mindful of your body and its connection to your mind. Focus on the sensation of the water on your skin and the way your thoughts seem to flow more easily. This way, a simple morning routine can become a morning ritual. Even cleaning the house can be used as a way to become more aware of your body movements and sensations in your muscles and joints. Just look at some of your existing routines and see if any could become more intentional. The Intentional Life The power of playing with the spectrum of consciousness when performing daily activities is that you don’t need to carve extra time for a separate mindfulness practice. Yes, there is lots of research showing the benefits of journaling, yoga, and meditation, but sometimes life gets busy. Turning a daily routine into a daily ritual is an easy way to inject more intentionality into your life, even when you don’t have lots of time or energy. And being aware of your consciousness levels can also help you create better habits. Just ask yourself: What routines do I want to turn into habits by lowering my intentionality? What routines do I want to turn into rituals by increasing my intentionality? Those two simple questions, if you ask them regularly, can help you avoid living your life on autopilot. And that’s an idea worth playing with. The post How to live more intentionally with habits, routines, and rituals appeared first on Ness Labs.

This article will be updated as the state of the academic literature evolves; you can read the latest version here. A podcast version will be released next week (traveling this week). Special thanks to Yian Yin for pointing me to Haustein, Costas, and Lariviére (2015) and Fang et al. (2020). We here at New Things Under the Sun are big fans of literature reviews. In a world where perhaps ideas are getting harder to find because of the burden of knowledge, it sure seems like literature reviews, that curate and synthesize a large volume of work, must be an important. But is that true? What do we really know about the effects of literature reviews on science and innovation? Subscribe now Do People Read Literature Reviews? One indicator of the importance of literature reviews is how well they get cited relative to traditional articles. If they tend to be highly cited, that’s one sign that they’re an important part of the knowledge ecosystem (though obviously not decisive on its own). To assess that, we can pull data from Haustein, Costas, and Lariviére (2015), which counts short-run academic citations1 to both traditional and review articles published in 2012. Using the altmetrics database, it also tracks a variety of other indicators; we’ll look at mainstream media mentions, which are part of how research results get communicated to the public at large. Lastly, I’m particularly interested in whether literature reviews are more informative for policy development. To get a handle on that, we can use Fang et al. (2020), which counts citations from 2.7mn policy documents to the academic literature. These policy documents are drawn from around the world, and include government, think tank, NGO, and IGO documents. The following figure compares the average citations received by review articles to the average citations of traditional articles across three audiences: academia, the policy world, and mainstream media. Data on academic and mainstream media cites is from the density entries of Table 2 of Haustein, Costs and Lariviére (2015); data on policy document cites is from figure 3 of Fang et al. (2020) Across the three domains, review articles tend to be more highly cited, on average, than original research. Within academia, review articles are cited by other academic publications at a rate about 2.3x that of traditional articles, at least for this sample of publications from 2012. Reviews are also more highly cited by the policy world, with review articles receiving on average 1.8x as many cites from policy documents per article as traditional articles. Among the mainstream media, the two are cited at the same rate. You get similar results when you look at the probability a particular article type is cited. (One thing the above figure obscures is the vast differences in citation rates across audiences; the policy world cites review and traditional articles at roughly 10-20x the rate the mainstream media does, and the academic world cites them at 30-40x the rate of the policy world!) There are some caveats to the above. How review articles are identified in academic databases is the subject of some controversy. Moreover, normally it is desirable to normalize citation counts by field; it’s easier to get many more citations, for example, in a field that is very large, compared to one that is very small. If fields differ systematically in their size and how much they use reviews, or in how difficult it is to correctly classify reviews, then that could make the aggregate data above misleading. In an appendix to this post, I dig into these issues a bit. I don’t think they change any of the substantive conclusions though, so I omit them from the main text. My bottom line is that review articles are significantly more highly cited than traditional articles, on average, in academia and among the policy world. But citation does not necessarily signify genuine influence.2 Let’s turn to some of the (scant) evidence we have on the genuine influence of reviews. Literature Reviews and Field Formation We’ll begin with academia. McMahan and McFarland (2021) argue that one of the effects of literature reviews is to draw together work scattered across different microcommunities, often via highlighting the role of papers that can act as bridges between multiple niche communities. To illustrate their argument, let’s start with an example (from their paper). In the figure below, we have two networks representing a field of climate science. This figure represents a lot of information. In each of these networks, the nodes represent papers cited by a specific review article (“Integrated Assessment Models of Global Climate Change”, published in the Annual Review of Energy and the Environment in 1997). The bigger the node, the more citations the paper has received during a particular time period. In the figure, links between nodes represent how often these papers are cited together by other articles. This is an indication that they are about a topic that is somehow related. Finally, the figure covers two time periods. At left, we have the linkages between articles in climate science, during the seven years preceding publication of the review article that references all these publications. At right, the seven years after publication. From McMahan and McFarland (2021) We can see how a field changes by studying the changes between these two networks. Let’s start with the network on the left. Prior to the publication of the review article, we can see a few different clusters of papers: one cluster (in blue) for integrated assessment models of regional policy; one cluster (in green) for integrated assessment models related to uncertainty; and one in yellow for climate modeling. That is, in the seven years preceding publication of this literature review, there were, roughly speaking, a few different sub communities that worked on different niche topics in climate modeling. We see this through the frequent co-citation of articles within each cluster and infrequent co-citations between clusters. If I’m writing about modeling uncertainty, I am likely to cite more than one of the papers in the uncertainty cluster, but less likely to cite any papers in the climate modeling cluster. After the review is published, we no longer see these three distinct clusters. Instead, we have moved towards one denser cluster with more of a hub and spoke structure. Papers from the various original clusters are now frequently co-cited with papers in formerly separate clusters, and especially with a few major papers, which previously bridged different clusters. This is most clear for paper 1, which in the left figure is not highly cited, but is co-cited with papers in two different clusters and has now become highly cited. After the review, it’s now the central hub of a dense network of papers. McMahon and McFarland show this kind of pattern isn’t an anomaly specific to climate science, but a pattern that broadly follows the publication of a review article. They build a dataset based on all the Annual Review articles published between 1990 and 2016, as well as all the articles published in a set of more than 1,000 major journals. The set of articles published in Annual Review journals forms their set of literature reviews, since this journal series specializes in review articles. They then use some statistical analyses to establish some reliable statistical associations. After an Annual Review article is published: The network of cited articles is divided into fewer distinct clusters The number of steps in a chain of citation between two different papers shrinks (for example, because most papers are now co-cited with at least one major hub paper) Most papers start to receive fewer citations, but a small number start to receive more Those three traits largely match the consolidation dynamics in the illustration: less separation into distinct clusters, and a few papers emerging as central hubs (with the rest perhaps a bit left behind). That doesn’t necessarily prove that is was the Annual Review article that caused these changes though. It’s quite plausible that these dynamics are merely the natural evolution of fields. Maybe Annual Review articles merely act as records of processes that are underway with or without them, much in the way that newspapers record the great events of the day without causing them. Ideally, we would want to run an experiment, where we get Annual Reviews to commission a bunch of literature reviews, but then randomly publish only some of them. We could then compare the evolution of the network structure of cited references in the published and unpublished articles. McMahan and McFarland can’t do that; but they try the next best thing, which is to at least identify sets of cited articles that look like they could be the target of an annual review article, but which do not in fact get one (maybe for random reasons). Let’s call these the reviewed and unreviewed networks. If both reviewed and unreviewed networks look the same before Annual Review articles are published, and different afterwards, then that’s some evidence the Annual Review publication induced the change. To identify a set of unreviewed networks that closely resemble reviewed networks (prior to publication), they look at the citation networks of traditional articles. Specifically, they identify a subset of articles whose co-citation networks resemble the co-citation networks of the cited references in an Annual Review article, in terms of the number of clusters and length of citation paths between papers, and where the cited documents also are of a similar “age” and receive similar numbers of citations as in the reviewed set. McMahan and McFarland then look at how the reviewed and unreviewed co-ciation networks evolve in the wake of an Annual Review article being published (for the reviewed networks) or a traditional article (for the unreviewed). They find t...

By most standards, I work a lot. Between running a company, pursuing a Ph.D., speaking at events, and writing a book, my days are filled with work. My friends sometimes comment that I work too much. But it doesn’t feel this way. I do work a lot, but not too much. I know because I have experienced what it’s like to work too much. About ten years ago, when I was offered a full-time job at Google, I could not believe it was true. Surely, they had made a mistake. Someone will realize that I’m not nearly as smart and talented as everyone around, and I will get fired. The classic imposter syndrome. As a result, I decided to work hard. Really hard. It didn’t help that my manager and I both joined Google on the exact same day. We were both eager to prove ourselves. We said yes to everything and offered to help on every project. I was getting very little sleep. Some days, I could barely keep my eyes open. The wake-up call happened on a work trip to San Francisco. Because of all of my commitments, I had decided to work double-shift, both UK and US times (I know, I know). I had a call in the middle of the night with a colleague based in London, where I presented the results of some research I conducted. When he questioned the methodology, I felt tears filling my eyes and pretended that the connection was too bad to continue the call. What the heck was happening to me? Burning out I didn’t know it then, but, of course, it was burnout. I did manage to power through — after all, I really had to make sure no one would notice the imposter in the room — but it felt miserable. The interesting part is that it was not so much the quantity of work that made it hard to cope. It wasn’t the long hours that were making me mentally, emotionally, and physically exhausted. It was the lack of control. I had no control over the goals, no control over the timelines. As a result, I was emotionally exhausted and made poor self-care choices (and I certainly didn’t have any mindful productivity tools at the time!), which only aggravated the situation. This is in line with the most recent research, which shows that burnout is multifaceted and rarely has to do with the volume of work you have to do. Burnout is emotional in nature. The research literature uses many different terms to describe the main types of burnout, but here are the ones I use for the sake of simplicity: burnout can be linked to weariness, withdrawal, or worry — and often to a mix of those three dimensions. Weariness. You feel emotionally exhausted by the efforts required at work, and you lack sufficient emotional energy to cope with work tasks. This dimension of burnout is associated with a frenetic approach to work, desperately trying to manage the overload and working intensely until exhaustion. Withdrawal. You feel like you have lost your idealism towards work, which manifests into feelings of detachment and indifference. This dimension of burnout is typical in monotonous and unstimulating professions, but it may happen in any work where you feel under-challenged. Worry. You feel doubt about your ability to perform the job effectively, and you progressively develop a tendency to evaluate your work negatively. This dimension of burnout is often linked to a lack of confidence in the results of your work and an unmet need for recognition of the efforts you invested. As you can see, the absolute amount of work itself has little to do with burnout, except that burnout can be triggered by a lack of control over how much work you have. Burnout also doesn’t always feel like a frenzied experience. It can be lots of little flares of anxiety or a very slow burn where we increasingly detach from work and become cynical. The latter is colloquially called a “boreout” where people struggle with the daily sameness of their jobs. Psychologist Steve Savels explains: “You become irritated, cynical, and you feel worthless. Although you don’t have enough to do, or what you have to do is not stimulating you enough, you get extremely stressed. (…) With a boreout, you get stuck in your ‘comfort zone’ for too long, until your personal development comes to a halt.” Burnout is when you are overstimulated, and boreout is when you are understimulated. Both leave you exhausted, feeling empty, and unable to cope with the demands of work and life. So, how can you restore your energy and your enthusiasm? Steering the ship Many people suffering from burnout tend to be high-achievers, for whom a sense of control is particularly important. However, trying to regain complete control can be counterproductive and frustrating. Instead, take a gentle, methodical approach: Align. Ask yourself why you started working at this company or on this project in the first place. Is your work still aligned with your values? Do the people you work with give you energy, or drain your energy? Are you still learning and growing? If not, time to consider changing projects or changing jobs. However, don’t do anything drastic while feeling burned out. Just make some space for research and reflection. Brainstorm. Sometimes it can be hard to come up with solutions on your own. Does the lack of meaning come from the projects, the work style, the team? Sit down with a friend or trusted colleague to come up with ideas for moving forward. Just tell them you’re experiencing signs of burnout and would like to “debug” the situation with them. Most people would be happy to help. Experiment: Instead of trying to find the perfect solution that solves all of your issues at work, put your scientist hat on, and try various small changes. Burnout can arise from forcing yourself into a linear path instead of embracing a circular model of growth. You could design a different project management strategy with your team, talk to a therapist or a coach, spend part of your work time on more creative projects… Just make sure to learn from each growth loop so you can design work that works for you. There is no silver bullet, but it helps to understand that burnout is first and foremost emotional, and then ask yourself whether it comes from a lack of alignment and if you could brainstorm and experiment with different ways to better align your work with your emotional needs. P.S. As with all things mental health, it’s also much better to proactively listen to the signals your brain and your body are sending you. Feel free to try some of my interoceptive journaling prompts so you can notice any early symptoms of burnout before it becomes more difficult to handle. The post Is it burnout or boreout? appeared first on Ness Labs.

What’s uniquely human that can improve your decision-making, creativity, and productivity and is completely free? The answer is: Writing. To date, we haven’t found any other animal on Earth that has developed any form of writing, whether carved symbols or inked patterns, as a tool for transcribing ideas. We humans are the only ones with access to this powerful tool. Unfortunately, many of us make use of this tool in only the most basic ways: to send emails, write down a to-do list, or text other people. When, in fact, writing is a superpower that can unlock parts of your mind that are harder to access otherwise. The Writing Ikigai Your mind on writing If metacognition is your compass, then writing is your map. By putting down your thoughts on paper, you can navigate them more easily. As such, and especially in our age of information overload, writing is not just a means of expression. It’s a tool for clarity, comprehension, and connection. 1. Writing is a cognitive filter. Instead of consuming a lot of random content, writing about what you read, watch, or listen to will force you to do some preliminary research to select high-quality sources and become more intentional with your information diet. In this way, writing becomes a filter for what information enters your mind — for the seeds you plant in your mind garden. 2. Writing is the greatest explainer. “Ce qui se conçoit bien, s’énonce clairement” (“What is clearly thought out is clearly expressed”) once said Boileau, a French writer. This is the principle behind the Feynman Technique, named after the Nobel prize winning physicist who has been dubbed The Great Explainer. “Without using the new word which you have just learned, try to rephrase what you have just learned in your own language.” — Richard Feynman, Physicist. When you struggle to write something in your own words, it often means you haven’t completely grasped the idea. Writing is a sometimes painful way to highlight those gaps: there is no hiding behind moving your hands in circles and using your most authoritative voice. If you can write it, you can truly explain it. 3. Writing is a memory enhancer. The generation effect is the phenomenon where information is better remembered if it is actively created from your own mind rather than read in a passive way. Instead of passively taking notes, making notes ensures you are in active learning mode and form connections between new and pre-existing knowledge, which will make it easier to retrieve information later on. And when your memory inevitably fails you, you will always be able to go back to your notes to refresh them. Bonus tip: You may even edit your existing notes to rephrase them in a more memorable way. 4. Writing sparks creativity. Creativity relies on your ability to connect existing ideas together. To be able to form such connections, you need a way to retrieve and explore ideas that you encounter or that pop into your mind. Writing is a great way to create such a searchable database of ideas, so you can connect them together and generate your own incremental ideas. In addition, while many people have similar ideas, the pathway to these ideas often differs from mind to mind. Writing your thoughts down will help you track the life of your thoughts and provide unique material to produce creative content. 5. Writing is a connector. Sharing your work multiplies the power of writing. By “working with the garage door open,” as Robin Sloan said—you create a feedback loop allowing you to improve your thought processes, learn something new, discover a different way to tackle a problem, or even make friends with like-minded people. Don’t wait until you have a perfect draft of an article. Share to learn, not to shine. Writing is more than a practical tool—it’s a way to think better, both on an individual and collective level. To make the most of it, write more, write often, and share some of your writing with the world. Creating your writing practice What to write about? How often should you write? In which format? You could spend hours and days overthinking every aspect and not writing a single line. Instead, you can find your “Writing Ikigai” by answering these three powerful questions: Why do I write? This question probes your deeper motivations for writing. Is it to express yourself, inform others, entertain, or perhaps to heal? Your ‘why’ will help you get started even if the format and frequency are uncertain. With a clear underlying motive, you can confidently experiment and refine the execution. What do I love learning about? The writing process is much more enjoyable and the words flow more easily when you follow your curiosity. Your ‘what’ could be external (new knowledge, skills, topic of expertise) or internal (your emotions, past experiences, hopes for the future). Who am I writing for? Are you writing for yourself, for a specific group of people, or for the world at large? Understanding your ‘who’ will help you tailor your tone and content to the reader — which could simply be your future self. Just write down these questions — meta, I know — and answer them as truthfully as possible. Once you’re done, just get started! Maybe it’s a daily journal you keep to yourself, maybe it’s a quarterly update to your friends and former colleagues, or maybe it’s, like I did, a weekly newsletter. And you don’t have to stick to just one way of writing. Mix and match it, play, change it up… In short, have fun! Because writing is thinking, and thinking should be fun. The post Writing is a thinking tool appeared first on Ness Labs.

At Ness Labs, we believe in the power of ideas and the profound impact of continuously feeding our minds with thoughtful content. Each month, we meticulously curate a selection of books that truly stand out in an ocean of books that can be overwhelming. This series aims to highlight the work that can serve as a compass to navigate life and work, so we can collectively learn, evolve, and thrive. This is your guide to discovering the most insightful, inspiring, and transformative books on mindful productivity, creative growth, holistic ambition, and developing a healthier relationship with work. Here is our pick for September! Master of Change This new book by Brad Stulberg is a timely guide in our rapidly evolving world, presenting change not as an adversary but as a cyclical and transformative force. With a blend of contemporary research and timeless wisdom, Stulberg introduces the mindset of “rugged flexibility”, encouraging adaptability amidst life’s inevitable shifts. The book balances profound insights with actionable strategies, offering readers both a compass and a toolkit for navigating personal and global disruptions. This is an essential read for anyone aiming to thrive in an age where change is the only constant. Learn more The Learning Game In The Learning Game, Ana Lorena Fábrega, affectionately known as Ms. Fab, challenges conventional education’s foundational structures and principles. As a teacher turned edupreneur, Fábrega brings to the fore the glaring inconsistencies in our age-old schooling system, prompting readers to question whether the classroom confines truly serve our children’s best interests. The book delves into the restrictive nature of standardized learning and juxtaposes it against the boundless potential of self-directed, passionate pursuits. Drawing from her own experiences and pioneering educational insights, Ms. Fab offers many actionable strategies to foster genuine curiosity and independent thinking in children. This book is more than just a critique—it’s a call for every educator, parent, and stakeholder to re-envision education, steering it away from rote memorization and towards true, lifelong learning. Learn more Scarcity Brain With his newest book, Michael Easter delivers an incisive exploration into our insatiable quest for more—whether it’s food, material possessions, information, or power. Drawing on his expertise in behavior change, Easter paints a vivid picture of our evolutionary predispositions. He suggests that our ancient survival instincts, rooted in a scarcity mindset, have become maladaptive in our modern world of plenty. Scarcity Brain reveals the manipulative scarcity cues propagated by various modern systems, which, often unbeknownst to us, steer us toward detrimental habits. This book offers not only a reflection on human behavior—from understanding the mechanics of slot machines in Las Vegas to the solitude practiced by Benedictine monks—but also a manual to navigate our world of excess with wisdom and intentionality, with practical methods to cultivate an abundance mindset. Learn more The Art of Explanation Demystifying the process behind his viral explainer videos, BBC journalist and presenter Ros Atkins dives deep into the art of articulate communication. With a cacophony of information bombarding us daily, Atkins highlights the need for crisp, clear, and compelling explanations. The objective is that your voice isn’t just heard but genuinely understood. Drawing from his vast experience in the pressure-cooker environment of newsrooms, he breaks down the mechanics of effective communication into ten crucial elements and seven actionable steps. These insights, presented with relatable examples in The Art of Explanation, serve as a practical toolkit for anyone seeking to elevate their explanatory prowess—whether it is for essays, presentations, or day-to-day emails. This book is an excellent guide on effective communication and will convince you even more of the value of clarity amidst informational chaos. Learn more The Perfection Trap In an age where burnout and depression are rampant, fueled by social media comparisons, aggressive workplace dynamics, elite credentialing, and overzealous parenting, Thomas Curran sheds light on the grave consequences of our obsession with perfection. Drawing on extensive research and evidence, Curran challenges the profoundly ingrained cultural narratives around success and achievement. In The Perfection Trap, he meticulously dissects the detrimental effects of perfectionism, from hampering genuine performance to exacerbating social and financial disparities. Curran also provides actionable guidance for individuals to resist the crushing pressures of perfection, urging readers to redefine success. If you’re overwhelmed by today’s unrealistic standards, this book can provide a path to a more realistic approach to life and work. Learn more Do you have any books to recommend for the Ness Labs Best Books series? Please let us know via the contact form. We welcome self-recommendations. The post Ness Labs Best Books of September 2023 appeared first on Ness Labs.

FEATURED TOOL Welcome to this edition of our Tools for Thought series, where we interview founders on a mission to help us think better and work smarter. Alec Nguyen is the co-founder of Afforai, an AI tool to perform research and interact with documents. He is now on a mission to make AI tools more accessible to everyone, whatever their background. In this interview, we talked about privacy and security in the AI industry, why multilingual support is paramount for inclusive AI, the importance of reliability for knowledge workers, and much more. Enjoy the read! Hi Alec, thanks so much for agreeing to this interview! Afforai started as a college project. Can you tell us about the early discovery journey? My cofounder, Hung, and I have known each other since our first year of college. We both have backgrounds in economics, data science and software engineering, which means we read a lot of research papers. But it started feeling overwhelming—so much to read and summarize. That’s when we thought about a faster way to read through papers, get the main points, and still do our work accurately. And this is how we came up with Afforai. During our final year at university, we developed Afforai for a startup competition. Our success as finalists, along with acceptance into two amazing startup accelerators, 1871 and gener8tor, marked significant milestones that led to us pursuing Afforai full-time. We are building Afforai to be the solution we wish heavy researchers like us had. Your ambition is to create Google for knowledge—what does that mean, exactly? With so much knowledge about anything and everything around the world, it’s really easy to get drowned from the sea of information. No one person can master anything anymore. There’s simply too much to know.  Google managed to index websites and data from the entire internet, but it doesn’t understand the information it indexed. We’re working to build a platform that index knowledge, making infinite knowledge instantly accessible. Specifically, how does it work? Afforai helps you input all the information on any topic, from any discipline, in any language, and summarize the key findings relevant to your goals. Providing you an invaluable tool to research damn near anything. It can search information, summarize reports, and translate between languages, answering and explaining in a different language than the original text. You can upload hundreds of documents and files like pdf, docx, text, and even websites and Afforai will be able to comprehend the entire body of knowledge you provided to give you the answer you’re looking for. Can you also tell us about the different modes you have created for the assistant? Afforai has three different modes, Fast, Powerful, and Google Modes, when it comes to the way Afforai gathers and comprehends the knowledge to give you the answer you’re looking for. The “fast mode” (default) is designed for tasks like information look-up and creating website chatbots. This mode uses Regular Retrieval Augmented Generation AI (RAG) to embed a collection of text into a vector database. This technology struggles with answering questions that are not explicitly given in the database and has length limits for generating responses. The “powerful mode” is designed to address the above limitations. This mode identifies information that may not be explicitly stated or requires additional reasoning. It also combines information from different answers and filters out redundant or irrelevant information. The resulting output is an information-dense answer with a reading coverage of 100,000 words, which is 10 times that of Fast mode. Powerful model is recommended for tasks like document comprehension, reasoning tasks, writing reports, and research.  The “Google Mode” is pretty self-explanatory. Turning this mode on will allow Afforai to access the internet to supplement the answer when our AI determines that the provided documents don’t have enough information. This also adds an extra layer to guarantee up-to-date information for your answer. AI + Internet = Magical Answers. Something people often worry about with AI tools for research is the accuracy and reliability of the output. How does Afforai address this challenge? Of course, my goal is to build an AI tool that people can trust: accurate, fast, and reliable. You can upload hundreds of files like pdf, docx, and websites to Afforai. Afforai will use our Powerful Mode algorithm combined with Azure OpenAI model to understand, extrapolate information that is both explicitly and implicitly stated from the sources. To be even more helpful with your knowledge research work, you can view the files, documents side-by-side with our document viewer feature as you ask questions to Afforai. So you don’t have to switch tabs back and forth as you work. Expanding on the document viewer, you get accuracy and reliability for every answer given by the AI with data citation feature. With every answer given by Afforai, you will get clickable citation links that would highlight on the document viewer where Afforai got the answer from. From the page of the document and down to the paragraph, every single time. You can also connect Afforai chatbot with Google, giving you the ability to do real-time research with up-to-date information. Building on top of an overpowered data comprehension ability, Afforai also accesses the internet and fills in any information gaps to provide you with the most accurate answer. You’ve also worked hard to address the challenge of multilingual content. I came to the US as an international student, so English is not my first language. This gives me the understanding that knowledge exists not only in just English. Supporting multilingual content was incredibly important to me personally and I believe this gives my users in the US and internationally around the world equal access to Afforai and equal access to knowledge, anywhere and everywhere, regardless of cultural and language barriers. With Afforai, you can upload files and research papers in foreign languages and get a response in English and vice versa. Giving an example, you can upload a pasta recipe on an Italian website and ask in Japanese about the recipe, Afforai will give you an answer back in Japanese about a pasta recipe in Italian. This is possible to do with over 100 languages. What about privacy and security? I’ve been a user for so many apps and services, so I understand firsthand how I’d like my data to be protected. So, having an opportunity to build my own startup, I always think how I would feel if I’m an user of Afforai. With that said, I do not ever use your data or sell your data to any other companies. I don’t store your conversation with the AI, and the files you uploaded on the system are stored and encrypted on the cloud using Microsoft Azure and MongoDB with their standard security. LLM calls are made using Azure OpenAI with their security measures. What about you, how do you use Afforai? There are many ways I’ve personally used Afforai in my own work. For instance, I use Afforai to scan through partnership agreements for my startup. I upload an entire 600-page macroeconomics book and use “Powerful Mode” to learn, summarize chapters, and create knowledge check questions. I have also used Afforai to create an internal training chatbot for my team members. How do you recommend someone get started? To get started with Afforai, I recommend following these steps. First, you can sign up for Afforai for free and get 50 credits to try the platform. You will go through a quick welcome guide that will help you get started with using Afforai. Then, you will be able to upload many types of documents such as books, papers, reports, and even Ness Labs articles. You can upload multiple documents at once. And this is the aha moment you will experience: You can immediately start asking questions to Afforai just like you would ask a know-it-all friend. For example, you can ask for a summary of the Ness Labs blogs, extract common themes from the documents, or even draft an email based on a team report. Tasks that would have taken hours or even days to complete can now be done in just minutes with the help of Afforai. The reliability of the information provided by Afforai satisfies me as a knowledge worker. The AI provides detailed citations, giving me the confidence to use the knowledge it provides. Many users, including myself, have saved a lot of mental power, significant amounts of time and boosted productivity with Afforai. Students use Afforai to summarize their readings for their school work. Sales teams have relied on Afforai to conduct customer research faster and get the most relevant info to send effective outreach emails. That sounds fantastic, and very easy to get started. And finally… What’s next for Afforai? In terms of development, I want to stay true to my vision, making Afforai your second brain, helping you make infinite knowledge instantly accessible and making you the smartest person in your field. I have sales teams, researchers, and knowledge workers starting to use Afforai in their work and I’m very happy to see the value I bring to these amazing people.  We’re also expanding our team, having new brilliant minds joining Afforai to help make it even better. This enables my team and I to polish up the platform, develop new features that would give our users a better experience. Because at the end of the day, the users are the people that bring value to Afforai. Thank you so much for your time, Alec! Where can people learn more about Afforai? If you read all the way here, I encourage you to sign up and try it here. You can message me directly on my LinkedIn or email me. I personally check this inbox every day. So these two methods are the fastest to get a direct hold of me. The post Supercharge your research with Alec Nguyen, co-founder of Afforai appeared first on Ness Labs.

When was the last time you stopped to truly observe your own life? Turning an anthropological lens on yourself might feel strange, but it can lead to invaluable insights, allowing you to uncover patterns, gain self-knowledge, and imagine new possibilities. Anthropologists ask fundamental questions such as: What does it mean to live in our world as a human being? How can the study of humanity reveal new ways of being human and help us imagine our collective future? It’s a game of curiosity and patience, an exercise in humility and receptiveness. And it’s a game you can play to learn more about yourself and where you stand in the world. You simply need to turn into an anthropologist where the topic of study is your own life. In search of answers, anthropologists conduct fieldwork: they go into the field and write field notes. These notes could be written accounts of observations, or they may take the form of visual maps to chart relationships and uncover intriguing paths. In the same way that anthropologists take field notes to understand humanity, you can use this practice to learn more about who you are and how to improve your life. Keeping a personal field journal will allow you to create a trail of breadcrumbs to deconstruct patterns and imagine new directions. Let’s see how it works. The power of personal field notes I was first introduced to the idea of adding timestamps to my notes by Tony Stubblebine, the CEO of Medium, who notes the time and writes a few sentences in a journal every time he switches work projects. Because he journals in the interstice between projects, Stubblebine dubbed this practice interstitial journaling. Then, I started seeing such timestamped notes everywhere. Timestamped notes are ubiquitous in professions where important decisions must be made based on rapidly changing information. Doctors write patient charts, pilots keep flight logs, scientists track their research in lab notebooks, system engineers record events to the syslog, journalists have interview transcripts, and project managers often maintain work logs. Inspired by all these forms of timestamped notes, personal field notes offer a hybrid of journaling and note-taking specifically designed to audit your daily experiences. The basic idea is to write a few lines every time you take a break and track the exact time you take these notes. Unlike logs that focus on events at work or interstitial journaling, which is confined to workday transitions, personal field notes can be captured anytime and anywhere – whether at the office, home, commuting, or even mid-conversation when something piques your interest. (My friends sometimes make fun of me when I grab my phone saying “Wait, I need to write that down!” while we chat – it usually means it’s a good chat). Field notes are powerful for several reasons. By encouraging you to capture your thoughts while listening to podcasts, reading articles, or even during conversations, they help you become a more active observer of your own life. They take very little time; a few seconds whenever you observe something interesting. And because they are timestamped, they help make it easier to identify under what conditions you work, learn, and feel best. By taking notes in the present moment instead of waiting until a dedicated time to reflect, you are less likely to forget some important experiences; this includes fleeting moments of inspiration and ideas that often get lost in the bustle of the day. And when you collect lots of small data points, you create a “breadcrumbs trail” and are more likely to notice overarching trends than if you only focus on the most salient experiences. By recording your activities, thoughts, and emotions, these notes will serve as a rich source of observations you can then turn into insights to guide your next growth loop.  How to practice self-anthropology Practicing self-anthropology with field notes only takes three steps. This exercise in self-exploration requires no special skills but the willingness to slow down and take notes throughout the day. You will, however, need to approach this practice with the same receptive and inquisitive attitude of an anthropologist studying an unfamiliar culture. With a little curiosity and patience, your own fieldwork will reveal inspiration to create positive change. Let’s go over the steps to turning an anthropological lens on yourself: Step 1: Set up your field journal First, you need a simple, low-friction way to take notes. Where do you take quick notes when you’re in a rush? This is where your field notes should go. It could be in your phone or a notebook – wherever it feels most comfortable. Seriously, don’t overthink it: you can use Asana, Evernote, or any other notetaking app. Apple Notes or Google Keep is fine! Create a note on your phone or start a new page in your notebook. This will be your field journal (mine is synced between my phone and my laptop, so I have access to it on the go and I can open it in a tab when working). Step 2: Capture your field notes You need enough data to start noticing patterns, so aim to capture field notes for at least 24 hours. When feeling particularly lost, I do intense personal fieldwork for three to five days. Choose a day in the next week when you will start this exercise. Ideally, it should be a typical day with a mix of professional and personal activities. Don’t do it on your best friend’s wedding day or when management is due to announce the latest round of promotions. Keep your field journal with you (that’s why a note on your phone works great). Write the time and a couple of sentences whenever you take a break, switch tasks, or notice something interesting. That “something interesting” could be external such as an event, or internal such as a feeling – maybe uneasiness or excitement. If something made you stop for a second to wonder whether you should write it down, then it’s interesting enough. Embrace non-linearity: You have complete freedom to write in a stream-of-consciousness style to capture and connect your observations as they arise. Interactions, emotions, moments of curiosity, emerging interests… Did someone compliment you? Were you excited by a particular announcement? Were you faced with a surprising challenge? Did you find a piece of work particularly draining or stimulating? There are no limitations as to what you can include in your field notes, but here are some ideas to inspire you: Insights: Your moments of curiosity, random thoughts, new ideas, and questions that spark your interest. Encounters: Your social interactions or new connections made and any insights or feelings that arose from them. Mood: Your emotions during or after an experience, whether a meeting, a workout, a podcast, etc. Energy: Your shifts in energy levels throughout the day, as well as what gives you energy or drains your energy. Other: Anything else that doesn’t fit in the previous categories. It may seem like a lot, but remember that this is only for a few days at most. You are doing deep field work and want to ensure you don’t miss anything. Use your curiosity as a compass to decide what to write down. Step 3: Analyze your data After 24 hours (or a bit longer), you will have a treasure trove of field notes. It’s time to review them. If your field notes are on paper, you may want to grab some colored pens. If you captured them digitally, it can be easier to copy and paste them into a document to highlight and move text around. Spend time reading your notes and reflecting on the experiences you’ve documented. Look for recurring themes, interesting details, and general feelings that come up repeatedly. This is a very fluid process. You may want to create a category for “things that give me joy” and “things that drain me”, or “what I want more of” and “what I want less of”, or create big categories for important aspects of your life like learning, relationships, and health. Simply by grouping your breadcrumbs into larger piles, you will start to see some patterns emerge. Identify an observation that stands out to you. This could be a recurring theme, a persistent challenge, or a point of curiosity. For instance, you could notice that you have the “morning blues” every day when it’s time to go to work, that working on a specific type of task drains your energy, or that your moods tend to be higher when you work on group projects. You can then turn your observation into an actionable question. For example, if your observation is that you’re feeling energized when discussing certain topics, you might ask: “How can I incorporate more of this into my daily life?” This can be the seed of a little life experiment – something new you want to try to see if it improves your creativity, productivity, and wellbeing. And if you enjoyed the few days of taking field notes, you don’t have to stop! I personally take them all the time, albeit in a less intense way than I do when I’m feeling lost and need to recalibrate. Practicing self-anthropology opens up new possibilities. Taking field notes is like planting the seeds of insights that will eventually grow into greater self-knowledge. Equipped with a fresh perspective, you can rethink habits, relationships, and priorities. So create a new note, grab your journal, and explore the uncharted territory of your own life with an open mind; you never know what you could find. The post Self-Anthropology: Become your own anthropologist with personal field notes appeared first on Ness Labs.

New Things Under the Sun is a living literature review; as the state of the academic literature evolves, so do we. This post highlights some recent updates. One theme of this update is responding to feedback, which is always welcome. Thanks! Thanks for reading What's New Under the Sun! Subscribe for free to receive new posts and support my work. Peer Review The article “What does peer review know?” surveyed some studies that compare peer review scores to long-run outcomes, both for grant proposals and journal submissions. It argues peer review scores do predict long-run outcomes, but only with a lot of noise. Misha Teplitskiy pointed me to some additional papers on this topic, which reinforced this point. The updated article now includes the following section. Gallo et al. (2014) obtain pretty similar results as above for the peer review scores of the American Institutes of Biological Sciences, an organization that provides expert peer review services for clients. In the figure below, on the horizontal axis we see the peer review scores for 227 projects reviewed by American Institutes of Biological Sciences peer reviewers that were ultimately funded. These range from 1 (the best) to 5 (the worst) (note the figure stops at 4; no projects receiving a score worse than that were funded). On the vertical axis we have a normalized count of all the citations to publications that emerged from the grant. As with the NIH data, we again observe a noisy but pretty consistent relationship: the better the peer review score, the more citations eventually earned.1 From Gallo et al. (2014) Clavería et al. (2000) obtains similar results in a review of 2,744 proposals funded by the Spanish Health Research Fund over 1988-1994. In this case, the peer review data available is pretty coarse: Claveria and coauthors just know if reviewers classified projects as “excellent/good”, “acceptable”, or “questionable/rejected.” However, a distinguishing feature of this study is that in 1996 the authors arranged for each of these proposals to be reviewed retrospectively by new reviewers. These reviewers looked at the original proposals, the annual and final reports, and published papers originating from the project, and assigned each of the now-completed proposals a score of 1-10 (higher is better) for its actual scientific performance. So, if we are concerned that quantitative indicators like citations or publication counts are inappropriate ways to evaluate science, this study gives us a more holistic/subjective assessment of research quality. The study again finds that peer review scores are noisily correlated with measures of quality. Spanish Health Research Fund proposals were reviewed by two commissions, one comprised of experts with topical expertise, and one with experts from related fields. After controlling for research level, duration, budget, and year of project onset, projects that received an “excellent/good” review at the proposal stage from the related field commission were rated 0.3 points higher when the completed projects were reviewed (recall, on a ten point scale). An “excellent/good” review from the commission with more direct topical expertise was associated with a 0.7 higher rating. (If you do not adjust for research level and others, the association is a bit stronger). Again - better peer review scores seem to be associated with better outcomes, but the association isn’t super strong (for context, the average rating for completed projects was 5.0/10). The rest of the article turns to similar evidence from peer review reports to journal submissions. Read the whole article Screening for Statistical Significance? Turning to the effects of peer review and editor discretion on publication bias, the article “Publication bias without editors? The case of preprint servers” looks at the causes of publication bias. It could be that publication bias arises at the journal submission stage; maybe editors and peer reviewers screen out papers that find non-significant results? The article looks at preprint servers to see if that’s so, and argues such a process is not the main driver of publication bias. It is not merely the case that reviewers bounce all the papers that are submitted but obtain results that are not statistically significant. Instead, such papers do not seem to even be written up and submitted. A new paper by Brodeur et al. provides quite clear evidence of this dynamic by following submissions and publications at the Journal of Human Resources. I’ve incorporated discussion of that paper into a discussion of another (Broderick, Cook, and Heyes 2020), already covered in the original version of the article. We pick up after describing how you can identify the statistical fingerprints of p-hacking by looking for a suspicious pileup of test-statistics that are just barely statistically significant (and hence, perceived to be publishable). Brodeur, Cook, and Heyes (2020) and Brodeur et al. (2023) look for [a] suspicious pileup right above the conventional thresholds for statistical significance. The set of four figures below plot the distribution of two kinds of test statistics found in various samples of economics papers. The top row, from Brodeur, Cook, and Heyes (2020) plot the distribution of something called a z-statistic, which divides a normalized version of the effect size by an estimate of precision. A big z-statistic is associated with a precisely estimated effect that is large - those are places where we can be most confident the true effect is not actually zero. A small z-statistic is a small and very imprecisely estimated effect size; those are places where we worry a lot that the true effect is actually zero and we’re just observing noise. The bottom row, from Brodeur et al. (2023) plots a closely related statistic, a p-value, which is (colloquially) the probability a given set of data would arise simply by chance, if there is no genuine effect out there. Top row from Brodeur, Cook, and Heyes (2020), bottom row from Brodeur et al. (2023) There are two interesting things we can read off this figure. First, we look to see if there is a suspicious pileup right above (for z-statistics, so top row) or below (for p-values, so bottom row) important thresholds. Those thresholds are indicated by vertical lines and each distribution shows spikes of test statistics just barely in the statistically significant range. In other words, lots of papers just happen to be finding with results that are barely statistically significant by conventional standards. The second interesting thing relates to the similarity of these patterns across the four figures. In the top-right, we have the distribution of test-statistics from papers published in top 25 economics journals in 2015 and 2018. In the top-left, Brodeur and coauthors go back and identify published pre-print versions of these papers and do the same analysis. For the purposes of the current discussion, the main point is that this anomalous distribution of test statistic results is already there in the working paper stage. If we interpret this as evidence of p-hacking, it’s telling us that researchers don’t do it when reviewers complain - they do it before they even submit to reviewers. A limitation of the top row is that we don’t actually see how peer review affects what gets published. We started with the set of published papers, and then looked back to see what those papers looked like when they were just working papers. But we don’t know if the stuff that wasn’t published was better or worse, in terms of evidence for p-hacking. That’s where the second row comes in. Although it’s a more limited sample, in the bottom left we now have a large sample of papers that were submitted to one particular journal. In the bottom right, we have the papers that ended up being published. Again, there’s not a large difference between the two. It’s not really the case that economists submit papers without much evidence of p-hacking but then peer reviewers only publish the stuff that exhibits signs of p-hacking. If it’s there, it’s there from the start. (Aside - Brodeur et al. 2023 actually finds some evidence that editors are a bit more likely to desk reject papers with results that are just barely statistically significant, while peer reviewers display the opposite tendency. The two effects seem to mostly wash out. For more on the relative merits of accountable individual decision-makers, such as editors, relative to peer review, see Can taste beat peer review?) Read the whole article Variation in Publication Bias The preceding argued that publication bias ultimately stems from researchers anticipating a better reception for papers that obtain statistically significant results. But as highlighted in “Why is publication bias worse in some disciplines than others?” an additional puzzle is why this problem seems to be worse in some fields. I’ve updated this article to incorporate discussion of Bartoš et al. (2022), which uses more sophisticated methods to assess the extent of publication bias across different fields. After discussing how different forms of publication bias can lead to unusual distributions of statistical test statistics, and how Bayesian model averaging can leverage those distortions to assess the likelihood of different forms of bias, the post continues: Bartoš et al. (2022) identify about 1,000 meta analyses across environmental sciences, psychology, and economics, covering more than one hundred thousand individual studies (the lion’s share in economics), and another 67,000 meta-analyses in medicine that cover nearly 600,000 individual studies in medicine. For each field, they see how likely it is that different sets of assumptions would generate data displaying these patterns, and then how likely it is that each of these models is “correct.” Lastly, once they have the probability all these different models are correct, they can “turn off” publ...

“I’ve decided to take it easy at work this year and focus on myself.” I’ve recently been hearing variations of this sentence over and over again. Magazines are publishing stories about “the end of ambition” and how more people are taking extended sabbaticals. It seems like we need to make a constant choice between our personal and professional and personal growth. If you want to achieve your entrepreneurial dreams or build a successful career, then your personal development will take a backseat. Or, if you want to get to know yourself better and expand your consciousness, you should disconnect from work. But is it truly a zero-sum game? The finite mental energy fallacy The American social psychologist Roy Baumeister and his colleagues proposed a model that compared self-control to a muscle that can become fatigued. The researchers hypothesized that the of your willpower “muscle” would leave you exhausted and unable to muster the same level of effort in subsequent tasks—a phenomenon called ego depletion. According to this view, the brain is like a battery with a finite amount of mental energy per day. We believe that every challenge we navigate at work will drain this battery. And as we pour our energy into work, there’s an underlying worry that we’re using up this precious, limited energy that could have been allocated to personal pursuits and self-improvement. This perspective has significantly influenced the discourse around work-life balance. Just have a quick look online, and you’ll find a deluge of articles and courses aimed at helping people strike the right balance between their personal and professional lives. The premise of these resources is often the same: Since our mental energy is limited, we must find ways to ration it wisely. Strategies revolve around optimizing work productivity to ensure enough energy is left for personal pursuits. The narrative is clear: personal sacrifice is necessary to achieve professional success, and vice versa. But researchers are starting to challenge this idea. Recent studies suggest that after an initial burst of effort, people’s motivation shifts from control to reward. This indicates that we don’t necessarily experience a depletion of mental energy, but a change in focus. Let’s say you have a bucket of water and you’re using it to water the plants in a garden. The traditional view of ego depletion suggests that every time you use mental energy, it’s like drawing from the bucket to water the plants. Over time, the bucket will eventually be empty. The newest research offers a different perspective. Instead of a bucket, imagine that you have a hose. After using some water for watering the plants, you may use the hose for something more immediately gratifying, like filling a kiddie pool. The water source hasn’t run out; it’s just being channeled in a different direction based on changing priorities. It’s not about a loss of mental energy, but a decision—which can be conscious or subconscious— to redirect your efforts. So, what if our mental energy isn’t as limited as we’ve been told? Then, the strategies we’ve been employing to balance our professional and personal lives might need a complete overhaul. It opens the door to a paradigm shift where personal and professional growth aren’t at odds, but can actually complement and fuel each other. Nurturing your mental energy Instead of seeing your mental energy as a limited resource you need to ration, breaking free from this scarcity mindset can help you create a virtuous circle where your day jobs and side projects both fuel your productivity and creativity, where your personal relationships provide inspiration to solve professional challenges, and where learning and growth permeate all area of your life. Learning how to use a new tool at work could inspire you to start a new digital project when you get home. Researching your local archives to create historically accurate characters in a novel you are writing could provide insights into building a more engaging community at work. A conversation with a colleague can offer the exact perspective you needed to approach a thorny conversation with a friend. The key is not to treat what you learn in your professional life as separate from what you learn in your personal life. It’s to see them as porous, equally important parts of your life, full of opportunities to gain energy from. Here are three simple ways you can apply to start breaking free from the ego depletion paradigm and nurture your mental energy: 1. To manage your energy, manage your focus. Challenge the belief that you’re “out” of energy after a day of hard work. Instead, you can expand your energy by directing your focus toward activities that feed your curiosity and creativity. 2. Reflect on how your energy flows between various areas of your life. We often struggle to manage our energy levels when we feel stretched between unrelated commitments. Take some time to look at all your personal and professional projects, and ask yourself: Where can I create synergies? For instance, is there a topic you’re personally curious about that could benefit your colleagues? Or, is there something you have to learn for work that could be useful for a personal project? 3. Surround yourself with energy expanders. Connect with people who also believe in nurturing and expanding their mental energy by seeking growth in both professional and personal parts of their lives. Not only will they inspire you to not place false limitations on yourself, but they can provide advice to create new synergies across all your areas of potential growth. Of course, we can be physically and psychologically exhausted for many reasons—lack of sleep, emotional upheavals, or even nutritional imbalances—but it doesn’t mean that our mental energy is inherently finite. By challenging this belief, creating growth loops across different areas of your life, and surrounding yourself with like-minded people, you can significantly expand your mental energy to achieve more without sacrificing your mental health. The post You Don’t Need to Choose appeared first on Ness Labs.

Interoceptive journaling is a mindfulness practice that involves recording and reflecting upon one’s own bodily sensations. It’s an intentional way of tuning into the often subtle signals our bodies send us, ranging from hunger pangs and heartbeats to flutters of anxiety in the stomach or warm waves of contentment. By deliberately writing about these internal signals, you can improve your interoceptive awareness and strengthen the bond between mind and body. By helping you recognize and understand your bodily signals, this method can help you enhance your emotional regulation, self-awareness, and overall well-being. Interoceptive journaling can be practiced through free-flow writing. If you’d prefer to follow some guided prompts, I’ve read many articles about interoception and developed the following eight questions to get you started. How does my body feel right now, in this moment? Describe your bodily sensations in as much detail as possible. Are there any tingling, warmth, or cool sensations anywhere in your body? Where in my body do I feel the most tension or discomfort? Can I associate this feeling with a particular event or emotion from today? Do I feel any sensations of hunger or fullness? If so, where do I feel it, and how intense is it? How is my breath? Is it shallow or deep, fast or slow? Can I feel it more prominently in my chest, throat, or abdomen? Can I detect my heartbeat without touching my chest or wrist? If so, what does it feel like? Does my body feel heavy or light? Can I connect this feeling to something I’ve ingested or a particular activity? How did physical activity (or lack thereof) impact my bodily sensations today? Are there parts of my body that feel sore? Have I noticed any recurring bodily sensations throughout the day or week? If so, can I identify any patterns or triggers? Like many other mindfulness practices, interoceptive journaling is most effective when done regularly. When you do this practice every day, you get more out of it and can better understand and connect with your inner states.  Interoceptive journaling can be easily included as part of any mindfulness practice, whether you’re just starting out or have been practicing for some time. Just a few minutes a day focusing on your sixth sense can strengthen your awareness of and improve your response to your body’s signals, leading to greater overall well-being. This list of prompts is licensed under a Creative Commons Attribution ShareAlike 4.0 International License, which means you can feel free to copy and distribute this list of questions as long as you attribute it and link back to this page. The post Interoceptive Journaling appeared first on Ness Labs.

This post is a collaboration between me and Arnaud Dyèvre (@ArnaudDyevre), a PhD student at the London School of Economics working on growth and the economic returns to publicly funded R&D. Learn more about my collaboration policy here. This article will be updated as the state of the academic literature evolves; you can read the latest version here. You can listen to this post above, or via most podcast apps here. In a previous post, we documented a puzzle: larger firms conduct R&D at the same rate as smaller firms, despite getting fewer (and more incremental) innovations per R&D dollar. Why wouldn’t firms decelerate their research spending as the return on R&D apparently declines? In this follow-up post, we look at one explanation: firms of different sizes face different incentives when it comes to innovation. In a later post, we’ll review another explanation, that large firms have different inventive and commercialization capabilities.1 Subscribe now Cost spreading and invisible innovations To start, let’s revisit our claim that the return to R&D seems to fall as firms get larger. Is this accurate? We can think of the returns to R&D as the “results” a firm gets out of R&D, divided by that firm’s R&D “effort.” Typically we measure those “results” by new patents, products, or streams of profit. It turns out some of these measures might understate innovation by large firms, because larger firms are more likely to generate process rather than productinnovations. Process innovations are concerned with better ways of delivering a service or manufacturing a product, not creating a new business line. Process innovations will not show up directly in product based measures of innovation.2 For example, some earlier posts have looked at the introduction of new consumer products or the attributes of car models as measures of the output of innovation. And while process innovations can be patented, they are probably less likely to be patented than new products. For example, a 1994 survey (Cohen, Nelson and Walsh 2000) asked 1500 R&D labs in the manufacturing sector to rank five different ways of capturing the value of new inventions. Among the 33 different sectors to which the firms belonged, just 1/33 thought patents the most effective way to protect process inventions compared to 7/33 who thought them the most effective way to protect a new product invention. In contrast, 16/33 sectors think patents the worstway to protect new process inventions, compared to 10/33 think patents the worst way to protect product inventions. Another way to summarize the survey is to note that only 23% of respondents reported that patents were effective means to appropriate process innovations while 35% considered them effective to appropriate product innovations. If process innovations are less likely to find their way into the catalogues of new products or the patent portfolio of firms, then they are less likely to be picked up by conventional measures of innovation. If larger firms are disproportionately likely to engage in process innovation, that will make it seem as if larger firms get fewer results from their R&D. And we do have some evidence large firms are more process innovation oriented. Liu, Sojli, and Tham (2022) use natural language processing to try and classify patents as protecting process or product innovations. The main approach breaks the title of patents and their claims into multiple components, and then looks to see if these strings of words contain words like “process”, “method” or “use” (which indicate a process), or words like “product”, “apparatus” or “tool” (which indicates a product). When they ask patent examiners and an IP management firm to classify a random sample of hundreds of patents classified by their algorithm, they come up with the same answer around 90% of the time. They show that, over 1976-2020, US public firms that have more active process patents than active product patents to be larger. We also have some non-patent evidence, though it’s based on pretty old surveys at this point. Akcigit & Kerr (2018)match Census data on U.S. firms to a comprehensive survey of R&D activities by the NSF (covering 1979-1989) and find a positive correlation between firm size (defined here as log employment) and the share of R&D dedicated to process innovation. So both the patent and survey-based evidence suggests larger firms do more process innovation than product innovation. And we also have pretty good theoretical reasons to expect this should be the case. As Matt has written elsewhere, when a particular kind of technology gets more profitable to invent, firms do more R&D on that kind of technology. To the extent the profitability of different kinds of R&D differ as firms scale, it’s not surprising that their R&D choices should differ. For example, larger firms typically have a wider portfolio of products and sell more products in each line, so it therefore makes sense for them to find more efficient ways to produce and deliver these products because they can spread the costs of their process innovation over more products and product lines. If you expect to sell ten thousand cars, it’s worth $10,000 to invent a process that reduces the cost of manufacturing by $1 per car. If you expect to sell a million, you’ll pay $1 million to invent the same technology. This explanation has been referred to as the cost spreading advantage of larger firms in conducting R&D: the bigger the firm, the greater the level of output over which it can apply its process R&D. Cost spreading pushes bigger firms toward process innovation. So one reason we may observe fewer innovations per dollar among large firms is that their size incentivizes them to focus on harder-to-observe process improvements. More speculatively, it might be that a similar dynamic also affects our measurement of the inputs to R&D that further biases our measures of the R&D productivity of firms. It has long3 been suggested smaller firms might underreport R&D expenditures, which would tend to inflate their measured R&D productivity (because they would seem to get more from less). One reason for that might be that, if firms can receive tax breaks for R&D expenditure, larger firms may invest more in sophisticated ways of claiming these breaks, either via more careful documentation or by pushing the boundary of what can be claimed as an expense. It’s kind of a cousin to cost-spreading; if there is a fixed cost of aggressively reporting R&D spending (for example, because you have to hire more tax lawyers), that cost might be more worth enduring for larger firms with more plausible R&D expenses. Boeing & Peters (2021), for example, provide evidence that R&D subsidies are often used for non-research purposes in China. And this isn’t the only possible reason small firms might under-report R&D. Roper (1999) suggests it could also be because it’s harder to measure R&D spending in smaller firms that don’t have full time research staff or dedicated research labs (and so it’s harder to tell what’s R&D and what’s not). That said, while it seems plausible, I’m not aware of evidence that documents biased R&D reporting. Indeed, in Boeing and Peters (2021), they actually do not find any statistically significant correlation between the size of firms and their tendency to mis-report R&D. The Replacement Effect The cost spreading incentive pushes firms toward process innovation, which might be harder to observe but should still be considered a form of genuine innovation. Another incentive pushes them away from product innovation though: the replacement effect. If a better version of a product is invented, most people will buy the improved version rather than the older one. If you are an incumbent firm that was previously selling that older version, that’s a reason to be less excited about a new product: if you invent a new product, you are partially competing against yourself. If you’re an entrant though, you won’t care. Since incumbents will tend to be larger firms, this dynamic might also explain differences in how firms innovate as they grow larger. This is an old argument in economics, dating back to Kenneth Arrow (1962), which was later named the ‘replacement effect.’4 Incumbent firms’ reluctance to do R&D in domains that could threaten their core business is closely related to what is sometimes called the innovator’s dilemma in the business literature and is a core tenet of some endogenous growth models.5 The recent development of chatbots powered by large language models offers a possible illustration of this dynamic. Google seems to have underinvested in the type of AI technology powering OpenAI’s ChatGPT because it would be a direct siphon of the ads revenues generated by its own search engine. As a result, Google is finding itself having to make up for lost ground in the AI race it once dominated. Documenting the extent of the replacement effect at large is a bit tricky because you are looking for R&D that doesn’t happen. One way we could do this is if we came up with a bunch of good ideas for R&D projects and randomly gave the ideas to large and small firms. We could then see which firms ran with the ideas and which ones left them alone. The trouble is, it’s hard enough for firms to come up with good ideas for themselves, let alone innovation researchers to come up with good ideas for them. But there are two studies that are related to this thought experiment. Cunningham, Ederer, and Ma (2021), while not about innovation and the size of firms specifically, provides some excellent documentation of replacement effect style dynamics. Their context is the pharmaceutical sector, where it is quite common for large incumbent firms to source new R&D projects from small startups. The sector is also one where there is high quality data available on the different research projects (here, new drug compounds) that firms are working on. Cunningham, Ederer, and Ma...

“See, hear, smell, taste, touch… With our five senses, we can learn so much!” You’ve probably heard some variation of this nursery rhyme. Most languages have their own version, walking kids through each of their senses. But those songs paint an incomplete picture of our sensory system, for they only include our outward-facing senses, which scientists call exteroception (literally, “external perception”). We also have an internal sensory system that allows us to perceive and interpret signals originating from within the body — such as your heart, stomach, or lungs. For instance, you may feel hungry, sense your heartbeat increasing, or notice the air in your lungs. For instance, you may feel hungry, sense your heartbeat increasing, or notice the air in your lungs. This process through which your nervous system maps your body’s internal landscape is called interoception. Interoception is how we understand our body’s inner sensations. It’s our brain’s ability to sense what’s happening inside the body and adjust accordingly. And recent research suggests that this sixth sense may play a key role in our well-being and even our sense of self. Making Sense of The Sixth Sense People usually think of the brain as an organ designed to respond to external stimuli. Let’s say you’re in the kitchen, heating a pan of oil to fry some food. When you drop a piece of food into the pan, the heated oil splatters. You feel a few hot droplets hitting your skin and reflexively pull your hand away to avoid further splashes. Now, imagine reading this in a cookbook: “When adding food to hot oil, especially those with high moisture content like fresh fish or certain vegetables, always do so cautiously to prevent dangerous splattering.” A weird thing may happen: simply reading this cautionary advice might make you experience the burning feeling of the hot oil droplets! You’ve probably experienced something similar when a friend tells you a story and you get goosebumps, or when you wince when someone recounts an accident, or when watching a movie where someone is on the edge of a tall building and your palms get sweaty or your stomach is churning. That’s because our brains didn’t evolve to merely react to the world around us, but rather to try to predict what will happen to us next based on both external and internal signals. This predictive process is how your brain makes sense of the world and guides your actions. In addition to your five other senses, interoception is crucial to this predictive process. Interoception is how your brain integrates information about the body’s internal state. It helps the brain keep your body in homeostasis — continuously adjusting many variables such as your temperature and blood pressure to maintain the equilibrium that’s best for your survival. The Five Fundamentals of Interoception Interoception is an emerging topic of research that fascinates neuroscientists, including myself. Here are five things you need to know about how this sixth sense works: 1. Interoception can be conscious or subconscious. Interoception includes the processing of signals such as the rate of your heartbeat, your breathing, and whether you’re full or hungry, among many others. We perceive many of these sensations unconsciously, but some make their way into our conscious awareness. This conscious processing of our internal signals is known as interoceptive awareness. And this seems to be a useful skill, as the ability to regulate our emotions has been found to be associated with interoceptive awareness. 2. Many factors shape our interoceptive abilities. Traumatic experiences can affect interoceptive awareness, either dulling or heightening your sensitivity to your internal experience. Our day-to-day environment, which includes factors like stress, dietary habits, and overall health, also has a significant impact on our capacity for interoceptive awareness. For instance, researchers explain that “we are currently exposed to an excess of exteroceptive stimuli for food consumption, marked by the high availability of a wide variety of ultra-processed and hyperpalatable foods, in addition to increasingly larger food portions that end up intensifying the reward responses and circumventing the homeostatic balance mechanisms.” As a result, it can markedly vary across the lifespan. 3. Interoception deeply influences both mental and physical health. Research suggests that a higher degree of interoceptive awareness has been linked to enhanced mental health, while lower interoceptive awareness is associated with several mental disorders. For instance, people suffering from depression may have a reduced ability to perceive bodily signals, which may contribute to emotional numbness. People with anxiety may be hypersensitive to cues from their own bodies, leading to exaggerated responses. This disconnection between what the body feels and how those signals are acted upon has also been found to be central to eating disorders like anorexia or bulimia. 4. Interoception can go awry. Being aware of our body’s internal signals is helpful, but we shouldn’t always use them to guide our decisions. For instance, in a study of decision-making and interoception, participants’ heart rates were monitored while they engaged in a gambling task. They were asked to identify profitable card decks. Interestingly, those with more accurate interoception aligned their choices with cardiac activity. But choosing a deck in response to an increased heart rate was a double-edged sword. If their heart rates surged when they picked a bad deck, these people fared worse than those with lower interoceptive awareness. 5. Interoception can be trained. Interventions focusing on enhancing interoceptive awareness are still in their early stages but show promise. One recent study looked at autistic adults, a demographic known to be at increased risk for anxiety. Participants were trained using heartbeat detection tasks, receiving feedback on their performance. The results were striking: those trained in interoceptive awareness exhibited a significant reduction in anxiety rates compared to the control group. Simply put, being able to tune into their inner states helped them de-catastrophize them more effectively. Now that you know the five fundamentals — and you are hopefully convinced of the usefulness of mastering your sixth sense — let’s move on to some more practical insights. How to Practice Conscious Interoception We have established that better interoceptive awareness is linked to better mental and physical health. But how the heck are you supposed to improve your interoceptive awareness? There are countless articles that have been published on the topic, but I’ll distill some of the most immediately applicable strategies you can start using right now. First, you want to know where you stand in terms of interoceptive awareness. One simple exercise is to count your heartbeat in your head for over a minute and then compare it with the actual reading. You may want to use a Heart Rate Variability (HRV) tracker — most smartwatches have one — or you could do it the old-school way by asking someone to count your heartbeats by placing their index and middle fingers on your wrist, at the base of your thumb. The second method is not as accurate but can be a good way to get started. You can also fill out the Body Perception Questionnaire, which has been translated into many languages. You can download a version in English here. This is not necessary, but knowing your baseline score will allow you to track your progress over time. Next, let’s have a look at three simple exercises that can help you improve your interoceptive awareness: Body scanning. This involves mentally scanning your body from head to toe. Just sit down in a quiet space, and spend a few minutes noting sensations, tensions, or discomforts in each part of your body. Is your throat itchy? Does your chest feel tight? Over time, this will help you become better at recognizing your bodily signals. Interoceptive journaling. Taking a few minutes daily to jot down internal sensations and emotions can help you create a habit of tuning in to the body’s signals. You can even incorporate this into your existing journaling practice. Here is a list of questions you can use for interoceptive journaling. Interoceptive exposure. This one consists of intentionally placing yourself in situations that elicit stronger physiological responses and practicing noticing and labeling the corresponding internal sensations. You can start with simple ones, like brief cold exposure or safe cardiovascular exercise, and ramp it up to more challenging situations, like public speaking. In addition to these three simple exercises, any mindfulness practice would probably help increase your interoceptive awareness (though of course not all have been thoroughly researched yet). That includes meditation, breathwork, and yoga. That’s it, folks! As with most tools that can help you live better, it takes time and dedication to unlock some of the most impactful benefits of better interoceptive awareness. But even if you only do one of those exercises for a little while, you’ll find that it helps you become more aware of and able to regulate your emotions. 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At Ness Labs, we believe in the power of ideas and the profound impact of continuously feeding our minds with thoughtful content. Each month, we meticulously curate a selection of books that truly stand out in an ocean of books that can be overwhelming. This series aims to highlight the work that can serve as a compass to navigate life and work, so we can collectively learn, evolve, and thrive. This is your guide to discovering the most insightful, inspiring, and transformative books on mindful productivity, creative growth, holistic ambition, and developing a healthier relationship with work. The World Behind the World Dr. Erik Hoel offers a captivating exploration of the frontier of consciousness science. The World Behind the World deftly unpacks the historical dichotomy between extrinsic perspectives based on the principles of physics and mechanisms and intrinsic perspectives which revolve around feelings, ideas, and thoughts. In his book, Dr. Hoel chronicles the quest to reconcile these perspectives under the banner of consciousness science, where metaphysical concepts clash and often yield paradoxical outcomes. The book delves into fascinating topics such as physics and morality, the unexpected similarities between black holes and our brains, and AI consciousness. It’s an invitation to ponder the profound questions that emerge from the study of consciousness, including the implications for brain death, free will, and mathematics. This book is a must-read for anyone intrigued by what we can learn at the intersection of consciousness, neuroscience, and technology and the transformative impact of this field on the fabric of our society. Learn more The PARA Method Tiago Forte’s practical guide delves into the intricacies of managing the influx of information that defines modern life. Building upon the foundations of his best-selling book Building a Second Brain, Forte offers readers a pragmatic, four-step system to efficiently categorize and manage information, fostering productivity and helping individuals achieve their goals. With a straightforward approach to sorting the barrage of information in our lives, Forte introduces four categories: Projects, Areas, Resources, and Archives. Projects are short-term tasks with specific goals, such as completing a website; Areas refer to broader, ongoing areas of responsibility like health or finances; Resources include various content to support projects and areas; and Archives store inactive information for future reference. This system can be effortlessly implemented in mere seconds, but its impact on one’s work and life is immeasurable. By unlocking the power of digital organization, The PARA Method will help you transform information overload into creative possibility. Whether you are struggling to stay organized or looking to enhance your focus, this book will be a valuable companion in your journey toward a more organized digital life. Learn more Excellent Advice for Living This little book by Kevin Kelly is a treasure trove of wisdom garnered over a lifetime of experiences. Born from the desire to pass down knowledge to his children, the co-founder of Wired has crafted a compelling collection of insights that span a wide array of life’s facets. Excellent Advice for Living encompasses themes ranging from setting audacious goals to practicing generosity and fostering compassion, with advice on topics as varied as careers, relationships, parenting, finances, and even practical travel and troubleshooting tips. While the book is primarily intended for younger audiences, its universal truths will resonate with readers of all ages. The words shine with the authenticity of a well-lived life. As Seth Godin remarks, part of the book’s uniqueness lies in its nonlinear approach, which is “part of its magic.” With a timeless quality that sets it apart from the ephemeral, Kelly has distilled the essence of a life lived with curiosity, creativity, and generosity into a book that will serve as a trusted companion for readers seeking to traverse life with grace. This book is to be savored, revisited and shared with others — a testament to the profound impact of a well-lived life. Learn more Stolen Focus Johann Hari provides an eye-opening exploration of our declining ability to pay attention in the modern world. The bestselling author of Chasing the Scream and Lost Connections delves into the reasons behind this alarming trend and offers practical solutions for reclaiming our attention. In Stolen Focus, Hari shares his struggle with maintaining focus in a world filled with devices and distractions. Driven to uncover the true causes of our attention deficit, he embarked on a journey to interview leading experts on human attention. Through this work, he identified twelve deep-rooted causes of this crisis, ranging from the decline of mind-wandering to the rise of pollution, and provides actionable steps for individuals and society to reclaim their attention. Stolen Focus is a timely and vital book that challenges our understanding of attention and provides a roadmap for navigating an increasingly distracted world. This book not only sheds light on the causes of our attention crisis but also offers evidence-based solutions to regain control of our focus. In short, a book that’s worth our attention! Learn more Thinking in Pictures Michael Blastland offers a refreshingly candid take on smart thinking in an age where words often fall short. Rather than focusing solely on written explanations, Blastland uses an extensive range of illustrations to help bring ideas to life, providing a more vivid way to explore complex concepts. Deep and broad, insightful and wise, the book is equal parts guide and gallery. Blastland takes readers on a journey beyond the limitations of typical smart-thinking books, encouraging them to explore multiple perspectives, embrace uncertainty, and accept that there might not always be a clear answer. Thinking in Pictures is a breath of fresh air, a welcome departure from the conventional approach to problem-solving and decision-making, advocating for a more comprehensive and humble perspective on the world. This is a must-read for anyone seeking a more nuanced and thoughtful approach to understanding the world — a testament to the power of visual thinking and a celebration of the richness and complexity of the human experience. Learn more Do you have any books to recommend for the Ness Labs Best Books series? Please let us know via the contact form. We welcome self-recommendations. The post Ness Labs Best Books of August 2023 appeared first on Ness Labs.