Slow Hunches, Curiosity, and Innovative Ideas

To be following a slow hunch is to be at the fuzzy edge of our current knowledge. It is to be actively putting vague intuitions into words and building a conceptual map at the edges of understanding. These slow hunches help explain the experience of being at the edge of scientific discovery, as explained by the writer Steven Johnson in his book Where Good Ideas Come From. Johnson compares one-off ideas with innovative ideas that incubate and develop over a longer period of time. As he states:

“Most hunches that turn into important innovations unfold over much longer time frames. They start with a vague, hard-to-describe sense that there’s an interesting solution to a problem that hasn’t yet been proposed, and they linger in the shadows of the mind, sometimes for decades, assembling new connections and gaining strength….But that long incubation period is also their strength, because true insights require you to think something that no one has thought before in quite the same way” (Johnson, 2010 p. 77).

Scientific discovery, by and large, illustrates how a “hunch” (or more formally conceived as a “hypothesis”) guides the pursuit of understanding. Over time, hunches are tested and either gather support or are discarded. In this way there is a certain trial and error to our thinking, where the strongest ideas survive, both in our own minds and in conversation with others (see Weick, 1989).

In the field of economics, the process of following “slow hunches” is what Richard Thaler describes as he and colleagues would eventually revolutionize the field of economics to create the field of behavioral economics. Behavioral economics brings insights from psychology into standard economic questions such as savings and investment decisions. It is work that eventually won Richard Thaler the Nobel prize in economics, but it all started with noticing “anomalies” and following slow hunches. As Thaler describes:

“A slow hunch is not one of those ‘aha’ insights when everything becomes clear. Instead, it is more of a vague impression that there is something interesting going on, and an intuition that there could be something important lurking not far away. The problem with a slow hunch is you have no way to know whether it will lead to a dead end. I felt like I had arrived on the shores of a new world with no map, no idea where I should be looking, and no idea whether I would find anything of value” (Thaler, 2015, p. 40).

Thaler was noticing cases where we fall short of the rational model of behavior. The rational model of behavior often describes our behavior, but not always. We sometimes exhibit self-control, but in many situations we do not. We often plan for the future, but often fall short. The rational model of behavior sets a standard of what we should do, and this model was largely at the heart of economics. Thaler and colleagues, however, began adding descriptive realism into how we behave. For example, one of the signature programs developed from behavioral economics is Save More Tomorrow. Save More Tomorrow starts with the insight that as our income increases in the future, rather than save it, we’ll likely spend it. But, if you ask anyone, they will tell you they should save more in the future. Save More Tomorrow is an automatic plan to increase the percentage of savings from your paycheck when you receive a pay increase in the future. It makes sense that we will fall short of our intentions for savings, but this kind of descriptive realism was largely absent from economics.

This doesn’t mean, of course, that any slow hunch will lead us to revolutionize our respective fields, but we’d never develop innovative ideas if we always ignored a vague notion that our current understanding is incomplete—if we never followed our curiosity.

But how do “slow hunches” and incubation work exactly? The process of incubation seems to happen in three ways (Sio & Ormrod, 2009): First, incubation can help us access greater knowledge networks (both externally and within our own minds) through spreading activation. In this way, over time, new knowledge networks are activated and provide new avenues to approach a problem. Second, incubation can help with selective forgetting of a dominant approach. We can get anchored on initial solutions and be unable to flexibly see the problem in different ways. Incubation can foster selective forgetting which allows a more appropriate solution to surface. Finally, incubation can lead to a restructuring of the problem in more productive ways. Restructuring can be a fundamental shift in how you represent the problem, allowing you to see it in a new light (e.g. the nine-dot problem). In a meta-analysis of 117 studies that examined incubation, support was found for all three aspects of incubation: spreading activation, selective forgetting, and restructuring (Sio & Ormerod, 2009). The positive effects for incubation were found to be strongest for creative problem solving tasks, especially when there are multiple solutions options.

But how do we distinguish a productive slow hunch from a “dead end?” As Thaler mentions, “The problem with a slow hunch is you have no way to know whether it will lead to a dead end.” I often see a preoccupation with wanting to avoid “dead ends” or “rabbit holes.” But, dead ends and “rabbit holes” are only revealed in retrospect, after we’ve spent some time pursuing a line of inquiry. If we are overly concerned with predicting, in advance, if a line of inquiry will lead to a “dead end” we run the risk of not following productive hunches that can lead to innovative ideas. Of course, we face a never-ending dilemma of opportunity cost with our time, but if we’re aiming for innovative ideas, then we need to be more willing to follow slow hunches.

So, if you are aiming for innovative ideas in your work, be more willing to follow slow hunches. An innovative solution may emerge slowly over time and will be well worth your effort.

References:

Johnson, S. (2010). Where good ideas come from: The natural history of innovation. New York: Riverhead.

Thaler, R. H. (2015). Misbehaving: The history of behavioral economics. New York: Norton.

Sio, U. N., Ormerod, T. C. (2009). Does incubation enhance problem solving? A meta-analytic review. Psychological Bulletin, 135, 94–120.

Weick, K. E. (1989). Theory construction as disciplined imagination. Academy of Management Review, 14(4), 516-531.

Photo Source: TeroVesalainen/Pixabay

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