Public Health & Medicine

The lack of mental health professionals in insurer networks can make it difficult for Americans to access counseling or other behavioral care.

With the end of enhanced premium tax credits on the horizon, millions are at risk of losing health insurance, especially those living in states that haven’t expanded Medicaid eligibility.

It’s easy to stay calm when things are under control. But, sometimes, life happens. A deadline is moved up. Your train is canceled. Your luggage gets lost. Then, your heartbeat goes faster. You can feel the tension in your muscles. Your breathing accelerates. That’s it: You’re stressed. Psychologists often say that our freedom lies in the gap between stimulus and response (this quote is often misattributed to Viktor Frankl). Although you cannot control many of the external events that impact your life, that liminal space offers an opportunity to choose how you react to negative triggers and to regulate your emotions. 1. Identify the emotion When we’re stressed because of external factors, this can translate into anger, disappointment, or resentment. The first step is to pinpoint exactly what emotional state you’re in and to remind yourself that this is just a state, which means that you have the power to alter it. Neuroscientist Antonio Damasio explains how the relationship between the body and the mind shapes our conscious experience. More specifically, what we identify as emotions are just patterns in our body, such as our rate of breathing, our blood flow, or constriction in our gut. By becoming aware of these patterns, a skill called interoceptive awareness, we can gain back control over the corresponding emotions. It’s easier than it sounds to get started. Here are the five main categories of negative emotions: Anxiety: worry, fear, nervosity, panic, etc. Anger: irritation, frustration, rage, etc. Guilt: culpability, remorse, etc. Sadness: despair, hopelessness, etc. Shame: embarrassment, humiliation, etc. Try and find the closest word possible to describe how you feel. This is not about judging the emotion. You are just putting a label on it to illuminate the relationship between your body and your mind. Once you found the right word, move onto the next step. 2. Analyze the emotional trigger While the trigger may seem obvious at first glance. Maybe someone was rude to you or canceled on you last minute. Still, it is crucial to take the time to go deeper and understand the underlying mechanics that connect the trigger to the negative emotion. You need to approach this step from a place of genuine curiosity. Be honest with yourself. What are the things you feel you are not getting? What is the need that isn’t met? Organizational psychologist Dr. Marcia Reynolds created a list of the most common unmet needs, which can be a great place to start. Do you need… Acceptance Attention Autonomy Balance Being in control Being liked Being needed Being right Being treated fairly Being understood Being valued Comfort Consistency Feel included Freedom Fun Independence Love New challenges Order Peacefulness Predictability Respect Safety Dr. Reynolds recommends choosing three unmet needs from this list. These unmet needs, when combined together, form an equation which results in your negative emotional trigger. For instance, let’s say that your client suddenly moved up a deadline without consulting with you. Your equation could look something like this: trigger = lack of consistency + respect + feeling included Or, if you’ve been suddenly asked to deliver a big presentation without much time to prepare, it could look like this: trigger = lack of predictability + peacefulness + being in control Think about a recent stressful experience, and see if you can come up with your equation for this specific event. 3. Shift your emotional state Now that you’ve recognised your emotional state and analyzed the trigger, you can actively shift your emotional state. Remember the gap between stimulus and response? This step is about choosing to feel something different. Depending on where you are, you can use one of the following exercises to effectively change your mood: Walking meditation. Find a quiet place with enough room to walk around — a place as small as 10 by 10 feet / 3 by 3 meters is enough — and start to walk slowly while letting go of the emotions attached to the negative trigger. Focus on the sensations of standing and the movements that keep your balance. Conscious breathing. Breath to relax your body and clear your mind, developing a soft awareness of your breath as it moves in and out of your body. To replace the negative emotional trigger with something positive, it can be helpful to focus on one word which represents how you want to feel, and to keep breathing in and out while visualizing the word. Gratitude practice: Gratitude has many benefits, including a positive impact on relationships, work performance, and even physical health. Think about three things you’re grateful for and write them down in your notes or your journal. As always, there’s no quick hack that applies to everyone. Just like a scientist, try different techniques and see which ones work best for you. It may sound cheesy, but it does boil down to one belief: that even though you’re not in control of these stressful external events, you can regulate your emotions. Even if you don’t apply the exact techniques outlined above, this belief is in itself powerful enough to help you manage those negative triggers. Note: A previous version of this article suggested power posing as a technique to manage negative emotional triggers, but a later study found that power posing doesn’t work. This is a good reminder that strategies based on the latest science can be later debunked. The post How to deal with negative emotional triggers appeared first on Ness Labs.

As we step into the new year, Ness Labs proudly presents a curated selection of January’s most thought-provoking books. This collection, spanning diverse themes from emotions to imagination and embodied living, have been carefully chosen to spark new insights and foster personal growth. They are hopefully gateways to understanding the subtle complexities of your inner lives and the world around you. Enjoy our January selection! Awe In this groundbreaking exploration into the elusive emotion of awe, Dacher Keltner offers a novel perspective on how this profound feeling shapes our lives and societies. An expert in the science of emotions and consultant to Pixar’s Inside Out, Keltner delves deep into the history and science of awe, blending his extensive research with personal anecdotes. The book masterfully illustrates how awe transcends our basic understanding, transforming our brains, bodies, and communities. The narrative is engaging and enlightening, presenting awe not just as an emotion, but as a fundamental human experience that fosters cooperation, cultural development, and a collective identity. This book will resonate with anyone seeking to comprehend the complexities of human emotions, and is a compelling read for those interested in psychology, sociology, and the innate human capacity for wonder, offering practical insights into harnessing the power of awe to enrich our lives and societies. Practices for Embodied Living Hillary McBride’s latest book is a concise yet profound guide that offers an array of practical exercises for people aiming to deepen their connection with their bodies and emotions. This book is a great companion to her previous work, The Wisdom of Your Body, with a wealth of prompts, activities, and reflections, making it an invaluable resource for those grappling with body image issues, trauma, or seeking deeper self-awareness. Its accessibility could extend its application beyond personal use to professionals in therapeutic fields, providing a versatile tool for healing and growth. The book’s layout, with its beautiful illustrations and quotes, further elevates the reading experience, making it not just informative but visually engaging. Overall, Practices for Embodied Living is a transformative guide for anyone on a journey towards more embodied living. Transient and Strange An extraordinary debut from NPR science correspondent Nell Greenfieldboyce, Transient and Strange is a captivating blend of scientific and personal narrative. In this collection of essays, Greenfieldboyce masterfully intertwines her experiences in motherhood and marriage with a deep reverence for the natural world, exploring the intimate ways in which science intersects with our daily lives. Her stories range from the big adventures, like reporting from a space shuttle, to the small touching moments, such as using the science of tornadoes to address her children’s anxieties. Greenfieldboyce’s writing is both emotionally raw and intellectually stimulating, grappling with life’s dualities—birth and death, love and aging, memory and doubt—while maintaining a sense of wonder about the universe, reflecting on the natural world’s marvels from micrometeorites to black holes. This is more than just a science book; it’s a poignant exploration of the human condition through the lens of science, beautifully capturing the ache and awe of ordinary life. The 5 Resets Dr. Aditi Nerurkar’s book presents a transformative approach to managing stress and burnout, challenging the common perception of stress as inherently negative. Her perspective, backed by extensive clinical experience and research, suggests that stress, in its healthy form, is vital for tackling life’s challenges but becomes detrimental when misaligned with our life’s priorities and natural rhythms. The book introduces five powerful mindset shifts to recalibrate stress levels: getting clear on priorities, finding quietness, synchronizing brain and body, taking breaks, and bringing your best self forward. These resets are complemented by fifteen practical techniques aimed at introducing sustainable changes in small, manageable steps. Dr. Nerurkar’s insights debunk myths such as the effectiveness of multitasking and propose time-efficient, cost-free solutions suitable for anyone’s lifestyle. Beying managing stress, the book offers to fundamentally change our relationship with it, offering a path to a more balanced and calmer life. The Imagination Matrix This book by Professor Stephen Aizenstat is an inspiring guide that offers to unlock the vast potential of human imagination to address the complex challenges of today’s world. The Imagination Matrix describes a step-by-step process designed to tap into your creativity with a blend of guided exercises and meditative practices aimed at dismantling mental barriers and habitual patterns that constrain our creative and problem-solving abilities. By helping you go below the superficial layers of everyday life to tap into your inner experiences, the method is particularly helpful for those who feel overwhelmed, allowing them to reconnect with their creative self so they can harness their creativity and in turn meaningfully contribute to our collective intelligence. 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 January 2024 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 people make the most of their minds. Jakob Storjohann is the CEO and founder of Ideamap, which is part of Ideanote. Ideamap is a visual workspace for brainstorming where teams collaborate on ideas and use AI to boost their creativity. In this interview, we talked about the relationship between AI and creativity, how to foster collaborative creativity, the integration of AI with human ingenuity in brainstorming, the role of AI in organizing ideas, the importance of high-quality feedback in the creative process, and much more. Enjoy the read! Hi Jakob, thanks for agreeing to this interview! What inspired you to help people brainstorm better? Honestly, it all started because seeing ideas lost in the slog of old, inefficient systems was painful. Our co-founders, the Mehlsen twins, experienced too many brilliant thoughts getting stuck in bureaucracy. So, it started from the perspective of wanting to make ideas matter. And Excel sheets were not the answer. That really led us to try to make working with ideas so simple that we could democratize both the tools and the knowledge of how to innovate. We’ve now built Ideanote, an ultra-efficient idea management platform for businesses of all sizes. We enjoy working with ideas – and everyone we meet likes ideas – but only if we make working with them simple and time-saving enough. Only then can more of them matter across the board. For that one big brand, government agencies, and even your team. We invested early in making AI a part of Ideanote, but at some point, I just had to call it off and say, listen, I don’t know where the AI will lead us. We need a place for experimentation. So we took our time, and what became our AI Brainstorming experiment is now called Ideamap.  This is exciting. How do you think AI can support creativity? It’s one of the most surprising twists isn’t it? If you try to travel back five years and talk about AI, most people said: Sure AI can automation can take menial tasks, but they can’t be creative. Turns out that’s literally what they’re best at. ChatGPT can’t be sure of its facts, but with the right instructions, it can draw on inspiration from, well, anywhere from Napoleon to Platypus to Non-Newtonian fluids. And what is really setting fire to creativity if not inspiration and diverse thought? Sure, AI has a bias, just as our knowledge has, but the sheer breadth and depth of knowledge can inspire anything from a brainstorming session about the next location for your local soccer tournament to new ways of curing diseases. We see AI as a friendly buddy, a sidekick that can help you be a brainstorming partner, and that’s also how we’ve designed Ideamap. How does Ideamap help ideas and AI come together? Ideamap is a cutting-edge tool where AI and human creativity kind of reinvent brainstorming. It stands out by providing a visually engaging and structured space that prompts creative collaboration for teams and even if you are alone, trying to come up with ideas. Here, AI doesn’t just mimic humans, you could say it elevates it: with instant idea generation, automatic idea categorization, AI Actions and interactive AI Copilot. It’s AI from the ground up. It’s very much designed to be a massive time-saver across the board. From ice-breakers at the beginning to not having to sift through ideas by your own to sort them to visualizing them or summarizing them en masse. On top of that it is focused on brainstorming sessions. So, facilitating advanced idea rating sessions like a dot matrix or elo rating is one click. Summarizing the brainstorming session is one click. Templates like SCAMPER or the Disney Method are also just one click away for all of your ideas. You can think of it as an endless canvas where your team’s creativity is amplified by AI co-pilots, who not only generate fresh concepts but also organize and refine them. It’s a brainstorming sidekick for your team. You also have developed some inbuilt AI tools to help people brainstorm faster. Yes! The platform comes with a ton of super useful templates and AI Actions and copilots but it gives you all the power to create your own ways of working with ideas. So, if you’re a Content Marketer you can create a brainstorming workflow with 3 custom AI Actions that let’s you generate 5 Pillar Articles, then 3 Sub-Articles and 5 Headlines per Article in one visual canvas. Or, when working with AI Copilots you can make your buyer personas into copilots and let them give feedback on your ideas organically. Another important part of brainstorming is high-quality feedback. Absolutely. I think sometimes we’re all faced with the fact that we are a little afraid of feedback. We’ve built something nice or we’re thinking in one direction and we’re wondering, does this hold up? With only our own perspective it can sometimes be hard to brainstorm. It’s just not natural to keep being your own devils’ advocate or hype man or fact checker. We can’t have that many hats on. But AI can. So if you have a mindmap of ideas laid out in Ideamap you can invite Grumpy Gus, our grumpiest copilot and really see things from a different perspective without having to do 4D chess with yourself when brainstorming.  How does Ideamap facilitate collaborative creativity? Ideamap is all about boosting creative collaboration, especially for remote teams. We understand that distance can sometimes dampen the brainstorming spark, so we’ve crafted a real-time, infinite canvas where teams can come together no matter where they’re located. Teams can now work side-by-side on the same digital whiteboard, adding, refining, and developing ideas as they flow. Real-time collaboration means no lag, no lost ideas, just seamless exchange and building on each other’s creativity. For structured sessions, Ideamap’s facilitation features help you turn ideation sessions more actionable. When it’s time to make decisions, the voting system simplifies prioritizing ideas, making it faster to identify the gems. Plus, automatic summaries make ideation sessions more actionable, faster. In short, Ideamap brings the energy of in-person ideation sessions to remote teams, ensuring that distance doesn’t hinder creativity but rather opens up a world of collaborative potential. The problem with many brainstorming tools is that you can find yourself locked into the tool. Ideamap is designed with the philosophy that your ideas should never be captive to a single platform. We understand that collaboration and implementation often span across different tools and environments. You can easily share your brainstorming sessions with anyone using shareable links or embed them directly onto websites. For those who work within other productivity suites, Ideamap offers smooth integrations, like with Microsoft Teams, so that you can incorporate brainstorming into your existing workflow without missing a beat. Importing from and exporting to other applications is a snap. You can pull in existing data with a simple CSV import to jumpstart your sessions with pre-existing insights. Conversely, when you’re ready to take your ideas to the next stage, exporting is just as effortless—be it as a CSV for data crunching, an XLSX for further analysis, or into presentation formats to share your vision with stakeholders. By ensuring Ideamap fits intuitively into your broader toolkit, ideas are free to move and evolve beyond the brainstorming phase, fully realizing their potential wherever they’re needed. What kind of people use Ideamap? It’s really useful for freelancers, consultants, students and solo entrepreneurs that might not have a team to rely on for playful diversity of thought. A sounding board if you will. But it is equally lifting up the productivity of brainstorming sessions for your typical product team that wants maximum creativity during product strategy planning. Every role and every team really brainstorms. Ideamap just comes with inbuilt AI templates and facilitation tools that speed the whole process up.  What about you, how do you use Ideamap? We’ve even realized ourselves that we have changed the way we brainstorm as we were developing Ideamap: turns out it’s much faster to let AI generate some topics and ideas just to get everything kickstarted. Way less awkward and way faster. So, boom, generate ice-breakers, generate topics, generate three ideas per topic in 10 seconds, and suddenly, we have a more neutral ground on which we can start talking about opinions, feelings, and ideas. It kickstarts the whole process and lets the ideas flow. The challenge generator that helps us create “how-might-we” type questions has also been a source of inspiration in terms of what challenges we could address. How do you recommend someone get started? Getting started with Ideamap is straightforward and rewarding from the get-go. First, sign up for a free account on our website. You’ll instantly gain access to your own workspace. From there, AI will already generate some brainstorming “rooms” with topics that match your business or interests. A quick way to experience the magic is to step into one of those rooms and just generate about 20 ideas and then hit “AI Group into Topics.” You’ll experience those aha moments as you see ideas being generated and automatically categorized into topics before your eyes. Clicking on the idea to let AI visualize it is another one of those experiences. Next up is inviting your team via a shareable link. Just click share and post that link to your work chat and let people join anonymously in a second so they and the Copilot can all build at the same time. Real-time. Then, try out the comparative voting. Click the facilitation button, then choose from the voting options. We’ve put some work into making it nearly a game-like experience. Ideamap is very visual and clean. For now, people descr...

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. Vladimir Oane is the co-founder of Deepstash, an app for building your personal knowledge library, helping you become more inspired and productive through bite-sized ideas curated by a community of smart people like you. In our previous interview, we discussed the nature of ideas and how curation is an act of creation. In this interview, we further explored these topics, and also talked about multiplayer knowledge management, how to combine organization and discovery, the power of ideas to spark conversations, and much more. Enjoy the read! Hi Vladimir, thanks for agreeing to a follow-up interview! Deepstash has grown a lot since we last spoke. Before we get into all the exciting changes, can you remind our readers of Deepstash’s core mission? Delighted to be back. Deepstash is like a sneaky ninja of knowledge. It looks like a regular social media app, but instead of boring news and fake influencer posts, you’ll find amazing insights from books, articles, and podcasts. It’s like a fresh breath of air, keeping you away from the endless scroll of doom and instead filling your mind with stuff that will actually help you grow. For most, Deepstash is like a healthier alternative to social media. For the pro knowledge junkies, it’s like a secret weapon that lets you capture and share the best stuff you’ve learned, turning you into a personal knowledge hub. Is there such a thing as having too many ideas? Yes. I’m one of those unlucky folks who’s constantly plagued by a never-ending stream of ideas. It manifests by never-ending curiosity and involuntary propensity to boil the ocean by debating any little thing… just for the fun of it. On a regular day you may get bored by all the small talk and non-sense that you are exposed to, but you get labeled as an insensitive know-it-all when you open your mouth. Thank God for platforms like ours where I can share our thoughts with like-minded people. Kidding aside, having too many ideas can sometimes lead to procrastination, especially when you feel like you need more information before taking action. But hey, compared to the anxiety-inducing vortex of social media dopamine hits, I’d say it’s a pretty awesome problem to have. The multiplayer aspect of Deepstash is quite unique in the personal management space. What are some of its challenges and opportunities? Let’s take most note-taking tools (and there are so many cool ones out there). They are so appealing with their promise to act like this second brain for all your precious insights. They get me all excited with their graphs, ontologies, and bi-linking features. So cool. But the reality is that taking good notes and keeping them organized is a whole other beast. Months later, your knowledge base turns into a jumbled mess of poorly written, incomplete notes, and that’s the norm for most of us. Left alone, we’re not the best at this knowledge-gathering game. But what if we start playing it together? That’s where Deepstash comes in. Our secret sauce is our social DNA. Ideas are public by default, easily discoverable, and ready to be shared with like-minded folks. Your published ideas get seen by others who are interested in those topics, and you can even grab ideas from others and add them to your private stash. This social multiplayer element not only makes the whole thing more fun but also acts as the ultimate quality check. Knowing that others will read your ideas is a powerful motivator to write things that are clear, concise, and insightful. That’s awesome for both you as the curator and for the ones who save your work. Specifically, how do you balance keeping ideas organized while still making them discoverable? There’s no need to choose between organization and discovery. When you open Deepstash, you’re automatically in discovery mode, and we’ll show you the ideas we think you’ll love, based on the topics and users you follow. We’re taking the amazing recommendation algorithms from those social media apps that we love to hate for ruining our lives and using them for a good cause. As a bonus, all that metadata we gather for recommendations also makes organization a breeze. You don’t need to create a complex tagging system in Deepstash. You can make your own folders, which we call stashes, but all your saved ideas come with topics and other metadata already attached. If you want to see everything you’ve collected about a specific topic, just tap on it, and voilà! It’s all right there. Deepstash features more than 200,000+ ideas from books, podcasts, and articles. What are some of the most popular types of ideas? Productivity and personal growth ideas are the most popular, but they’re just a tiny fraction of the hundreds of diverse topics we cover, like health, investments, and relationships. The beauty of sharing ideas in this bite-sized form is that they can come from anywhere. We’ve seen a surge of ideas from anime TV shows lately! Who knew there were so many life lessons to be learned from this medium? We were so impressed that we’ve added it to our list of featured categories. Each user can check their own stats and explore their individual preferences with some new visuals we just added to their profiles. A lot of apps keep ideas in a silo which makes it hard to actually use those ideas. To re-use one of my favorite quotes: “Ideas are not true unless shared.” It’s something that smarter people like Jefferson or Feynman have highlighted as well. I think it’s important to spread and discuss knowledge with others. That’s why Deepstash doesn’t keep any content behind a paywall like some summarization or reading apps. We believe that ideas should be free and accessible to everyone. For those interested in taking their experience to the next level, we offer a Pro plan that comes with additional goodies on top of the ideas themselves. You’ll get offline storage, more stashes, and audio playback. And if you’re part of the Nesslabs community, we have a pretty good deal for you to try it out. How do users typically use those ideas? Deepstash isn’t your average “content consumption” experience. Beyond life-changing impact, Deepstashers use the platform to: Spark conversations and spread new ideas: “Hey, did you hear about this mind-blowing concept?” Communicate with clarity and precision: “This groundbreaking idea has three key steps.” Reflect on what they’ve learned and absorbed: “What was the most profound takeaway from this book?” It’s about diving into ideas, sharing them with others, and transforming your life. Our community is buzzing with fresh perspectives, and our members are raving about the transformative power of Deepstash. We have over 100,000 glowing testimonials for proof. What about you, how do you use Deepstash nowadays? As an active Deepstash contributor, I devour books, podcasts, and anything else that sparks my curiosity. I love stashing these gems for future reference and sharing them with the world. I am quite grateful for the newly released AI companions. They help me polish my writing or suggest images that would make them easier to remember and stand out from the pile. My obsession with specific topics often leads me to deep dives into particular fields and I use Deepstash to document and share my findings. I recently wrapped up a phase of intense fascination with René Girard’s ideas and because of Deepstash I can have a thought trail of everything that I consumed on the topic, alongside my favorite insights. I’ve got a few stashes that overflow with practical ideas I constantly refer to. Like my treasure trove of interview questions that come in handy during recruitment drives. Or that ever-evolving list of gamification methods we pull from in our product design meetings. How do you recommend someone get started? Grab our app from the App Store or Google Play and embark on a journey of intellectual growth. Tell us about your passions, the books and podcasts that ignite your curiosity, and the authors who inspire you. We’ll curate a personalized collection of ideas to kickstart your learning adventure. With daily recommendations, we’ll keep your reading streak alive, celebrate your achievements, and ensure you’re always on track to become a smarter, more informed individual. And finally… What’s next for Deepstash? This must be the least original answer so far, but we do have some amazing new AI features, helping you polish your writing, suggest relevant images, and ensure your content stands out from the crowd. With more to come. Outside of that you will see Deepstash becoming more fun. Ideas are to be discussed, trashed, debated. As a “great” man once said: “Why so serious?” Thank you so much for your time, Vladimir! Where can people learn more about Deepstash? Get our app for Apple or Android, visit our website or follow us on: X, LinkedIn or Instagram.  The post The Art of Sharing Ideas with Vladimir Oane, Founder of Deepstash appeared first on Ness Labs.

Imagine two aspiring writers, both with the idea of establishing an online writing presence. The first writer, Alex, sets a fixed goal: they aim to grow their newsletter to 1000 subscribers by the end of the month. Alex has heard that consistency is key, so they choose a topic, decide on a format, block time in his calendar, and get started. To maintain the necessary self-discipline, they use a time tracker and a rigid but reassuring productivity framework. The second writer, Jordan, also recognizes the value of consistency but approaches the challenge in an experimental way. They commit to publishing one article every week, but they explore different formats (listicles versus long-form essays), different times to write (morning versus evening), even different places to work from (at home versus a coffee shop). Along the way, Jordan takes notes about how they feel and how the audience responds. Alex is laser-focused on sticking to their plan. In contrast, Emily focuses on discovering the best approach—the ‘creative ikigai’ that fulfills them while benefiting their audience. Before you keep on reading, take a second to ask yourself: Who do you think will succeed? Who will stick to their project for the longer? And, crucially, who will have the most fun? Our two aspiring writers demonstrate how a simple shift in mindset can radically transform the way you approach your most ambitious aspirations. Leveraging your Curiosity In some rare cases, we know exactly what we ought to do next, and it’s just a matter of doing it. But, most times in life, the path forward is unclear—even when we have the illusion of clarity. For instance, let’s say you want to lose weight. You may think that how to do it is clear enough: reduce your calorie intake and increase your physical activity. But you’re actually missing lots of information that you won’t find packaged in a neat framework. When do you tend to crave sugar the most? What are your emotional triggers that make you turn to food as a coping mechanism? What are the best healthy recipes that you find tasty? The only way to find the answers to these questions is through personal experimentation. And yet, most diet programs advocate rigid meal plans and strict exercise routines. You can find this pattern in all areas of our lives. At school, we choose a field of study and follow a strict curriculum. At work, we set KPIs at the beginning of the year and follow a strategic plan. Even in romantic relationships, we often adhere to traditional dating norms and expectations. We optimize our life for certainty. What if instead, we approached everything and everyone from a place of curiosity? Curiosity is humanity’s superpower. It’s the driving force behind our greatest discoveries. It fuels our imagination and enables us to challenge the status quo. In fact, I’m convinced the secret to happiness is curiosity. You can’t stay anxious or lonely for long when you approach everything and everyone from a place of curiosity. And one of the best ways to inject more curiosity into your life is to turn rigid goals into personal experiments. How to run a personal experiment Just like a scientist who observes the world, formulates hypotheses and tests them, you can run personal experiments to gain profound insights into your own life. By systematically exploring different approaches, challenging your assumptions and biases, and embracing failure as a valuable data point, you can make deliberate decisions and unlock your potential without sticking to a predefined path. It’s like building a laboratory for your personal growth, and it only requires four simple steps inspired by the scientific method. 1. Assess the current situation First, you need to understand where you currently stand. A quick way to do this is to capture field notes for a day or two. Just link an anthropologist, you want to keep a log of your experiences. Whenever you take a break or switch between tasks, write down the time and anything you noticed. This could be your reaction after a conversation, moments of procrastination, or ideas that gave you energy. 2. Formulate a research question Go through your notes and look for patterns. What’s working? What are your stressors and sources of joy? What could be better? Then, just like a scientist, turn these observations into a research question. Observation Question I tend to get more done when I wake up early. Would I be more productive if I woke up earlier? I often procrastinate on logistical tasks. Would I find it helpful to work with a virtual assistant? I always get pumped after an interesting conversation. Would I enjoy hosting a podcast? 3. Test your hypothesis Experiments follow a simple format: one action repeated enough times to collect sufficient data. In contrast with a habit where you’d ideally repeat the action forever, an experiment has a predefined number of trials. For instance, “write four articles in two weeks” or “wake up at 6am everyday for one month” or “review progress with an accountability buddy every Monday morning until the end of March.” A simple experiment will help turn your research question into a testable hypothesis. Then, make a pact with yourself to commit to this experiment. 4. Analyze the results After two weeks, one month, one quarter, or whatever the duration of your experiment, review the outcome. How did it feel? Did you manage to stick to your pact? If not, what got in the way? Reflect on the results without any judgment. Remember that the aim of the experiment is not a fixed notion of success, but instead intentional progress. Running personal experiments will allow you to transform linear goals into growth loops. By breaking free from rigid targets, you will unlock many benefits: Increased adaptability: Personal experiments allow for flexibility, enabling you to adjust your approach depending on changing circumstances and preferences. Systematic innovation: Personal experiments often lead to novel insights that can inspire unexpected solutions. Reduced pressure: Personal experiments reduce the pressure of achieving a predefined outcome, fostering a sense of relaxed adventure. Enhanced decision-making: By trying out different approaches, you can make more informed decisions and avoid pitfalls that rigid goals might overlook. The great thing about personal experiments is that you cannot fail. Any outcome is a source of data. Any result is fuel for self-discovery. Your only ambition is to learn more. So, what is your next experiment? The post The Power of Personal Experiments 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 (short) post highlights two recent updates. Subscribe now But before getting into that, a reminder: if you have completed the first year of a PhD in economics or a related field and are interested in innovation, the deadline to apply for the Institute for Progress’ (free) Economics of Ideas, Science, and Innovation Online PhD Short Course is January 9, 2024. More info here. Where Research Happens Counts In our post When research over there isn’t helpful here, Caroline Fry and I looked at a series of examples where the applicability of research findings is geographically localized. In this update, we add discussion of a Job Market Paper by Sergio Puerto (see a list of more innovation job market papers here). After discussing some papers that show policy-makers, doctors, and patients have more trust in research conducted in their home countries, the updated post continues… Lastly, Puerto et al. (2023) documents similar results for agriculture, but looking within one specific country - Costa Rica - rather than across them. Puerto is interested in plant breeding in developing countries, where in many years only a very small number of new and improved plant varieties are released. As implied by our opening to this post, in these countries domestic and international private sector R&D on new plant varieties tends to be low or absent, so that resource-constrained public sector breeding programs are often the primary source of new plant varieties. Breeding of these new varieties happens at experimental research stations. Puerto shows the location of these research stations affects the ultimate levels of adoption of new seeds by Costa Rican farmers. His main experiment randomizes 800 farmers across 118 villages into different experimental conditions. Some groups are given the opportunity to buy a recommended new bean seed variety, recently bred by the experimental research station. Puerto argues this matches a typical year, where the resource-constrained public breeders recommend a single new seed variety for a large group of farmers. But another group in Puerto’s experiment is given that same opportunity, but also asked to plant three different bean varieties on test plots on their farm, and then given the opportunity to buy whichever of those three test varieties they most prefer. The idea here is to approximate the value of much more local information on seed varieties - soil, topography, and climate vary across farmers and can mean different seeds are suited to different conditions. Puerto finds the farther a farm is away from the research station where breeding takes place, the more likely it is that farmers will diverge from the research station recommendation and choose an alternative seed. Specifically, the 25% of farms closest to the research station adopted the recommended seed at essentially the same rate as farmers who did not have an opportunity to test other seeds, while the one quarter farthest away were 26% less likely to pick the recommended seed (the average distance was about 150km). Read the whole post Home Bias in Management Research In our post Geography and what gets researched, Caroline Fry and I looked at evidence that where researchers reside affects their choice of study topic. We’ve now added a discussion of Nagaraj and Yao (2023), which presents new data on management research. The updated post now includes these new paragraphs: Nagaraj and Yao (2023) provide additional evidence on the focus of management research. Their study focuses on the complete set of articles published in six top management science journals and, among other things, they’re interested in seeing how where the authors work affects what they choose to study. They can infer where the authors work from the location of their employer; to estimate the place(s) under study, they look for the city, state, country, and nationality words in the title and abstract. By this method, about 15-25% of articles have some kind of regional focus. That lets us ask - do researchers tend to study where they are? Yes and no. In the figure below, Nagaraj and Yao (2023) focus on the 13 countries that are either among the top ten for researcher locations or research focus. On the vertical axis we have the researcher’s location; on the horizontal axis, the country under study (note articles can have more than one researcher and research topic). For each cell, they take all the authors from a given country and compute the share of their regions mentioned in their articles that go to a particular country. The darker the shading, the larger the share. If the diagonal line is darkest, that would tell us researchers are most likely to study their own countries. From Niagara and Yao (2023) We do see a dark diagonal line, consistent with researchers disproportionately studying their home country. But the most striking pattern on this chart is probably the dark vertical line on the right: everyone studies the United States! But setting aside the USA, Nagaraj and Yao’s work does find management researchers tend to be more likely to study their own countries. Read the whole post Thanks for reading! 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.

Whether it’s quitting a job, breaking up with someone, or leaving behind a place, we all have at one point or another to learn the difficult art of letting go. Unfortunately, learning to let go is much harder than holding on. Human beings have a tendency to define themselves through what they own, and so we cling onto past sorrows, bad relationships, and even meaningless goals. We hold grudges, dwell on past mistakes, and attach sentimental meaning to inert objects. This is a lot to carry around — a weight that can impede your ability to explore, create, and reinvent yourself. Fortunately, learning to let go is a science as much as an art. Here are five simple exercises you can practice to let go of some of this weight and start living a lighter, freer life. 1. Letting go of things Let’s start with the most practical level of letting go. Mari Kondo built a huge empire around helping people to declutter their homes by letting go of their old stuff. Why do people find it so hard to let go of these physical things? Sometimes, it’s because they have sentimental value. This sentimental value can stem from past experiences or future expectations. Objects with past sentimental value may include a souvenir from a holiday, a bracelet you were wearing on your first date, a cup your grandmother gave you. Objects with future sentimental value may be a box of craft supplies for when you’ll finally start learning how to paint, a collection of books about architecture you will definitely study at some point, or a set of kettlebells for when you will exercise at home — someday. Letting go of these feels like letting go of a dream. Other times, it’s because we are scared of being wasteful. Either we think we’ll need that thing again, or we feel guilty about the money we spent on it in the first place. This is the sunk cost fallacy rearing its ugly head. A great way to get rid of stuff is to do it progressively. Start with the easy stuff—items such as gadgets you haven’t used in years, old papers without much sentimental value, or, the easiest of all, stuff you don’t even remember what they are or what they’re supposed to be used for. Most of us have tons of these in our houses. Gradually move up your decluttering work towards more sentimental items, asking yourself: “Why do I care about this item?” Often, you will realize that the need behind your sentimental attachment to a particular item can be fulfilled in other ways, such as a gratitude practice around the experience tied to the object, or writing about the memory in your journal. This should not be an extremely painful process. If you do deeply care a lot about some items, keep them. Who cares how tidy your home is? As long as it feels like home, it really is all that matters. 2. Letting go of goals Old goals are an invisible weight we carry around without noticing it. Although they may no longer align with our current aspirations, they persist in our subconscious, influencing our decisions and actions. We often cling to old goals out of an artificial sense of obligation to our younger self. For instance, you may be compelled to stick to a career path that was set due to family expectations. Financial milestones — such as achieving a certain level of wealth or property ownership by a specific age — can put too much pressure on you. The weight of outdated goals may even manifest in areas normally considered as intrinsically good, such as wanting to run a marathon when your health might not allow it or pursuing an advanced degree when your life circumstances make it impractical right now. In many cases, specific goals can limit your learning opportunities. Some of the most interesting discoveries we make are serendipitous. Tinkering, playing, experimenting, trying new things are all intrinsically rewarding activities which don’t need an end goal to bring you enjoyment and intellectual stimulation. Learning to let go of your goals is about keeping space in your life for doing things just for the sake of it, not because it neatly fits into a grander scheme. Simply write down your current goals, and ask yourself: “Why do I care about this goal?” Most goals can be transformed into sustainable systems based on enjoying the process rather than obsessing over the end result. If you want to learn, you can do it without aiming for a specific outcome. If you want recognition, you can get it by consistently showing up rather than racing to get to a finish line. If you want to feel helpful, you can do that by meaningfully connecting with others instead of ticking off checkboxes based on artificial milestones. 3. Letting go of control Everyone can see when a manager micromanages their team, when a colleague always takes the lead in conversations, or when a family member sticks to rigid traditions. It’s a bit harder to notice our own attempts at exerting control over our environment. We might plan every detail of a vacation without leaving room for spontaneity or input from others. In our homes, we might plan our meals in advance and adhere to a strict cleaning schedule. Or we may be so committed to our fitness routine that we neglect to take breaks when we’re unwell. Recognizing these patterns in ourselves is challenging, as they often stem from a deep-seated desire for stability and predictability, making them seem more like necessary habits than controlling behaviors. Most parents will also go through an acute experience of having to let go of control: the terrifying moment where they will need to let their kid explore the world on their own. It starts with their first step and only gets harder with time, until they leave for university or get their first job and move out of the house. Learning to let go of your control can be extremely difficult in this case. To break free of the illusion of control, we need to give control to get control. In practice, it means giving people (your kid, your employees, yourself) the flexibility to play with the rules. It starts by asking yourself: “Why do I care about this rule?” Then, instead of a set of rigid rules akin to a psychological prison, define a playground with key principles that are flexible enough to allow for uncertainty and creativity. For instance: “You must finish all your homework before playing” (rigid rule) can become “Focus on finishing your tasks but remember to make time for fun too” (flexible principle) “Exercise every day for exactly one hour” (rigid rule) can become “Exercise three times a week, adjusting the intensity and duration to match your daily health and energy levels” (flexible principle) “No sweets or junk food ever” (rigid rule) can become “Focus on a balanced diet while allowing yourself the flexibility to enjoy treats in moderation” (flexible principle) Ultimately, letting go of control will allow you to embrace adaptability so you can thrive even when things don’t go to plan. 4. Letting go of people Let’s dial it up a bit. It may sound harsh, but it is sometimes better to let go of certain relationships. Some relationships may not reflect who you are today or people may have evolved in a way that has changed the relationship. Learning to let go of a “legacy relationship” doesn’t have to be negative; it can be an opportunity for personal growth. Ask yourself: “Why do I care about this relationship?” A helpful exercise is writing a letter to the person but not sending it to them. Thank them for everything you learned from the relationship. Allow yourself to experience the whole spectrum of emotions that this exercise might elicit, both positive and negative. Most relationships we care about are complex. Embrace that complexity. Maintain an attitude of forgiveness throughout this exercise. Research suggests that forgiving and wishing someone well is connected to better health. It may be that by going through this process you actually realize the relationship is worth keeping. And if that’s not the case, gently let go, be grateful for the lessons you learned about the world and yourself, and keep the good memories as tokens of a relationship that helped you grow. 5. Letting go of the past We also tend to cling onto memories, especially hurtful ones. That’s because our brains are wired to remember painful experiences more vividly as a survival mechanism and these memories often have strong emotional ties that keep them active in our thoughts. This is supposed to help us avoid similar situations in the future. However, this mechanism can sometimes backfire, leading us to relive the negative emotions associated with these memories repeatedly, which can hinder our emotional well-being and prevent us from moving forward. Letting go of the past is hard because, in some way, it requires letting go of a piece of ourselves. Our experiences form the basis for who we are, and breaking free from the weight of past memories also means reinventing our identity. To start this difficult but rewarding process, ask yourself:  “Why do I care about this memory?” You might believe that remembering the pain will prevent future hurt You might consider this memory to be a key part of your personal story You might have a strong emotional connection to the people in this memory You might be concerned that forgetting may lead you to repeat the same mistakes Answering this question will help you replace the static memory with more generative thoughts. For instance, you could journal about the key lessons to be learned from this memory and how you might avoid making a similar mistake in the future, or you might look for more positive experiences in your life that provide healthier alternatives to reinforce your identity. In summary, here are the five questions you can use to practice the art and science of letting go: Letting go of things: “Why do I care about this item?” Letting go of goals: “Why do I care about this goal?” Letting go of control: “Why do I care about this rule?” Letting...

Despite its small size compared to the rest of the body, the human brain consumes a lot of energy, consuming about 20% of the body’s resources. This energy is vital for maintaining the brain’s complex functions such as thinking, memory, and controlling the body. Neuroenergetics is a specialized area of research that aims to understand how the brain manages this energy. The three pillars of neuroenergetics Neuroenergetics connects brain function with its energy demands. It’s the study of how the brain produces, distributes, and uses energy. You can think of neuroenergetics as studying the brain’s budgeting system, similar to how one might manage a financial budget, prioritizing certain activities and functions. This approach helps us understand how the brain uses its limited energy for maximum benefit. And, just like in financial budgeting, sometimes this energy allocation can go awry, leading to deficits or inefficiencies that might contribute to various neurological or mental health disorders. Neuroenergetics aims to understand both the efficient management and potential mismanagement of the brain’s energy resources. Specifically, researchers investigate: 1. Energy Production: This involves how the brain cells, or neurons, produce energy. Neurons are highly active cells that require a constant supply of energy. This energy is primarily produced through oxidative phosphorylation, a process similar to what happens in other body cells, where glucose and oxygen are used to produce ATP (adenosine triphosphate), the main energy molecule. 2. Energy Distribution: The brain has a unique way of distributing energy. Unlike other cells in the body, neurons have long extensions (axons and dendrites), and they need to transport energy efficiently to these distant parts. Understanding how energy is transported and distributed in neurons is a key aspect of neuroenergetics. 3. Energy Usage: Neurons use energy for various functions, like transmitting signals, maintaining the balance of ions across their membranes, and synthesizing neurotransmitters and other molecules. Neuroenergetics examines how energy is utilized for these critical tasks. Researchers in neuroenergetics also look at how changes in brain energy metabolism can affect health. For example, in conditions like Alzheimer’s disease, the brain’s ability to use glucose is impaired, which affects its function. The ultimate understanding of the brain Neuroenergetics is based on the idea that the deepest understanding of brain activity is achieved when it’s broken down into electrochemical processes. As biophysicist Robert Shulman explains: “We have reached the ultimate in understanding brain activity when it is expressed in its different components of action potentials and energy consumption which are describable in measurable chemical terms of the phenomenon.” Specifically, neuroenergetics is based on key assumptions that drive research questions: The laws of physical science can be applied to biological phenomena, including brain function. The brain’s functions are intrinsically tied to its energy consumption and metabolism. Understanding brain activities involves dissecting these into elementary electrical, chemical, and structural processes. Those assumptions serve as the foundation for questions such as: How does the brain produce and distribute energy, particularly how it metabolizes glucose and oxygen? How are different brain states related to energy consumption? How do changes in energy metabolism affect brain health and function, especially in neurological disorders? What are the electrochemical bases of brain activities traditionally described in psychological terms, such as consciousness and memory? For instance, neuroenergetics approaches consciousness by linking it to observable brain energy levels. It suggests that high global brain energy levels are necessary to maintain a state of consciousness, and that changes in energy levels, such as during anesthesia, sleep, or coma, correlate with changes in consciousness states. This approach provides a more concrete, measurable basis for studying consciousness, focusing on electrochemical processes  in the brain rather than abstract concepts. Another promising area of research is the impact of alcohol consumption on brain energy metabolism. By using a new method for combining measures of brain activity and glucose consumptions, scientists have found that heavy drinking shifts the brain toward less efficient energetic states. Finally, neuroenergetics also studies how brain energy relates to mental health issues. Harvard psychiatrist Dr. Chris Palmer discusses this in his book Brain Energy, where he argues that problems with how the brain uses energy could be linked to many disorders including anxiety, depression, ADHD, alcoholism, eating disorders, bipolar disorder, autism, and schizophrenia. Neuroenergetics is a promising field of research. By focusing on how the brain uses energy, it can provide deeper insights into the workings of the brain and opens up new possibilities for treating mental health disorders and other brain-related conditions. The post Neuroenergetics: The Brain’s Energy Budgeting System appeared first on Ness Labs.

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...

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...

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.

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.

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.

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...