Why Simplicity Matters

Since the very beginning of my own quest to find a way for people to learn together as well as we are able to learn individually, I’ve quietly held one rule (hypothesis) in mind that I seldom talk about. As the field of Emergent Learning evolves, I feel the need to make my thinking visible.

Emergent Learning is meant to be generative—to unleash our agency and creativity in the face of endlessly different and changing contexts. Each time we draw on the principles and practices of EL, something emerges; something that makes sense to us, given where we have been and where we are going. It’s the gift that keeps on giving.

What makes that possible? My own hypothesis about this is simplicity. The principles and practices of Emergent Learning are not complicated. They are designed to be shared. They naturally make sense to people and can be applied to many different contexts. And yet, I can say from a lot of experience, they continue to unfold in endlessly interesting ways. The art, as we always say, is in how and when and why you use them, and what you learn along the path.

I was reminded of this recently as I was reading one of a now endless number of articles about generative AI. There are some interesting parallels between Emergent Learning and generative AI. The idea of ‘rubbing stories together’ in EL is about expanding our data set as we develop hypotheses. And learning in fast iterations by being deliberate about identifying opportunities to experiment and noticing our results speeds the rate at which we learn and adapt. The notable difference between EL and AI (aside from massive computing power) is the layers of meaning we can bring to this generative process with our human history, vision and moral compass.

There is much more to say about all of that, but for now the common thread I want to focus on—simplicity—is described beautifully in an article I read in the Boston Globe about how the design of AI has evolved. One product that was created using earlier, less generative, AI models was ITA Software, which pioneered technology that lets people compare airline flights and prices online. The founder of that company, Jeremy Wertheimer, described in an interview the difference between how he built ITA and how generative AI is being built these days: 

“The initial AI program behind ITA was about 2 million lines of code. But Wertheimer said that if he were starting the company today, the entire program to sift and compare thousands of flights per day could run in just 100 lines of code — or maybe even 10.

“‘Back in the day, we called it good old-fashioned AI,’ Wertheimer, who is now a visiting scientist at the Broad Institute, said in an interview. ‘But the future is to forget all that clever coding. You want to have an incredibly simple program with enough data and enough computing power.’1

One of the most fundamental ideas of emergence is how simple rules generate complex, contextually adaptive patterns. Think about the power of language. In English, we use 26 letters, a handful of punctuation points, and a few grammatical and semantic rules to create an infinite number of sentences that are instantly meaningful to other English speakers, even if they have never encountered that sentence before. Because we learned this set of rules as children, they become an unconscious competence that allows us to be endlessly generative in our communication with each other.

Yet the English language has evolved over time. People make language work for them in their sub-cultures. But what makes that possible is that it continues to draw on the basic letters and rules of grammar and syntax – even if they sometimes deliberately bend or subvert them. So too with Emergent Learning. In fact, while it may not look like it to newcomers, how Emergent Learning gets expressed has been evolving all along, informed by rubbing our stories of practice and experimentation together. The more we rub our diverse stories together—something we do regularly in our community calls—the more EL and how we talk about it evolves. A beautiful example of this is our just published Guide to the Principles of Emergent Learning, which includes many stories from across the EL Community. 

But if, in the process of evolving, Emergent Learning becomes complicated and stops making natural sense, it would make it harder to understand, harder to share, and harder to apply in a fit-for-purpose and just-in-time way without external assistance. We could, for example, decide there should be, say, 10 questions in an After Action Review—just enough to have to decide if we want to invest the time to do an AAR or to feel that we need someone to help facilitate it. If changes like that happen, the conditions for generativity that EL creates will begin to erode.

Early in his seminal book, The Fifth Discipline, Peter Senge wrote: “This, then, is the basic meaning of a ‘learning organization’—an organization that is continually expanding its capacity to create its future.”2 He used the rest of the book to introduce and describe the five disciplines and a number of related practices—systems thinking, personal mastery, mental models, shared vision, and team learning. From an EL perspective, that book represented Peter Senge’s hypothesis about what it takes to create a learning organization. 

In the years that followed, I observed many organizations measuring their progress towards being a learning organization in a technical way—by their mastery of the practices related to those five disciplines, often at significant cost and with the assistance of many consultants or researchers. It felt unnecessarily complicated to me. I have always argued and still believe that we would be further along if the disciples of organizational learning measured themselves against that original, simple, intuitive definition: In what ways are we expanding our capacity to create the future to which we aspire? What else will it take? And then measure the usefulness of the practices they employ against their ability to help achieve that more fundamental intention.

Likewise, Emergent Learning is not about the practices—Before and After Action Reviews (BAR/AAR), EL Tables, Learning Logs—as much as it is about what they make possible. Though the practices can be used in technical ways, EL is not intended for that purpose. The intent of Emergent Learning is to learn as well together around the things we care about as we are able to learn as individuals. What will it take to do that?

If we always come back to that intent and test the ideas we are experimenting with against it, we should feel free to improvise. We don’t have to measure our success—nor should we—by our ability to facilitate EL Table conversations. There are lots of ways to create powerful hypotheses; to establish line of sight. There is a lot of great work being done to bring diverse voices to the table and to treat all expertise, including lived experience, with the respect it deserves.

How can we make our practice of EL as simple as possible, so that it continues to make intuitive sense and to create the conditions for generative ideas to arise, so that we can achieve the goals to which we collectively aspire? 

We’d love to hear what other members of the EL community think about what it takes to make (and keep) Emergent Learning emergent as it evolves.

  1. Pressman, A. (2023, August 10). This tech founder built one of Boston’s biggest AI companies. But he says the West Coast approach is the future. Boston Globe. https://www.bostonglobe.com/2023/08/10/business/this-tech-founder-built-one-bostons-biggest-ai-companies-he-says-west-coast-approach-is-future/?et_rid=1582233. ↩︎
  2. Senge, Peter. (1990) The Fifth Discipline: The art and practice of the learning organization. New York: Doubleday/Currency, P. 14. ↩︎