In complexity science, ‘emergence’ describes the way that interactions between individual components in a complex system can give rise to new behavior, patterns, or qualities. For example, the quality of ‘wetness’ cannot be found in a single water molecule, but instead arises from the interaction of many water molecules together. In living systems, emergence is at the core of adaptive evolution.

Design for emergence prioritizes open-ended combinatorial possibilities such that the design object can be composed and adapted to a wide variety of contextual and idiosyncratic niches by its end-user. LEGO offers an example — a simple set of blocks with a shared protocol for connecting to one another from which a nearly infinite array of forms can emerge. Yet as we will see, design for emergence can generate value well beyond children’s toys.

In contrast to high modern design, user-centered design takes a more modest position; the designer does not inherently know everything, and therefore she must meticulously study the needs and behaviors of users in order to produce a good design. User-centered design remains the dominant design paradigm today, employed by environmental designers, tech companies, and design agencies around the world.

In this paradigm, design is about gaining knowledge from the user, identifying desirable outcomes, and controlling as much of the process as possible to achieve those outcomes. ‘Design’ remains synonymous with maximizing control.

But consider even the ‘desire path’ example pictured above. The modal user may be well supported by paving the desire path indicated by their behavior, but what good is a paved path leading to stairs for a wheelchair user? In practice, user-centered design tends to privilege the modal user at the expense of the long-tail user whose needs may be just as great.

User-centered design has a better track record than high modern design, but it still exerts a homogenizing effect. The needs of the modal user are accommodated and scaled through software or industrial manufacturing, while power users and those with edge cases can do nothing but actively petition the designer for attention. In most cases, diverse users with a wide variety of niche use cases are forced to conform to the behavior of the modal user.

In design for emergence, the designer assumes that the end-user holds relevant knowledge and gives them extensive control over the design. Rather than designing the end result, we design the user’s experience of designing their own end result. In this way we can think of design for emergence as a form of ‘meta-design.’

In other words, to address the long-tail problem, the tool must be flexible enough that it can be adapted to unexpected and idiosyncratic problem spaces—especially those unanticipated by the tool’s designer.

In contrast to user-centered design, design for emergence invites the user into the design process not only as a subject of study, but as a collaborator with agency and control.

What all these tools have in common is support for open-ended adaptation to highly contextual problems without the need for technical knowledge. Rather than building a static, purpose-built solution to a single common problem with lots of users (and lots of competitors), they’ve won robust user bases by supporting a broad swath of long-tail user needs.

Design for emergence is composable. It provides a limited ‘alphabet’ and a generative grammar that’s easy to learn and employ, yet can be extended to create powerful, complex applications. As Seymour Papert once remarked, “English is a language for children,” but this fact, “does not preclude its being also a language for poets, scientists, and philosophers.”

It creates more generic-looking interfaces that may perform well in numbers but fall short of appealing to our senses.

It’s easy to make data-driven design decisions, but relying on data alone ignores that some goals are difficult to measure. Data is very useful for incremental, tactical changes, but only if it’s checked and balanced by our instincts and common sense.

It became clear to the team in that moment that we cared about more than just clicks. We had other goals for this design: It needed to set expectations about what happens next, it needed to communicate quality, and we wanted it to build familiarity and trust in our brand.We could have easily measured how many customers clicked one button versus another, and used that data to pick an optimal button. But that approach would have ignored the big picture and other important goals.

Not everything that can be counted counts. Not everything that counts can be counted.Data is good at measuring things that are easy to measure. Some goals are less tangible, but that doesn’t make them less important.While you’re chasing a 2% increase in conversion rate you may be suffering a 10% decrease in brand trustworthiness. You’ve optimized for something that’s objectively measured, at the cost of goals that aren’t so easily codified.

Design instinct is a lot more than innate creative ability and cultural guesswork. It’s your wealth of experience. It’s familiarity with industry standards and best practices.

Overreliance on data to drive design decisions can be just as harmful as ignoring it. Data only tells one kind of story. But your project goals are often more complex than that. Goals can’t always be objectively measured.