The Maths Of Getting Into The Flow

Getting into a state of flow has taken on almost mythical status after Mihaly Csikszentmihalyi’s work uncovered the huge power it brings to those lucky enough to enter it. Such a state is strongly linked with things such as creativity and productivity, and Yale researchers believe they have uncovered the ingredients that provoke such a state.

“These principles underlying flow may be unconscious but they are not random—and work within a biological system that can be described in mathematical terms,” the researchers explain.

In the flow

The process behind the computational theory of flow is fairly straightforward. It calculates the information between the desired, state, and our means of obtaining them. The researchers use exercise as an example to illustrate their approach.

They explain that when we exercise, we usually have a desired end state we want to reach, whether it’s getting fitter or losing a certain amount of weight. We also have a clear means of trying to attain that end state, such as via an exercise program. The chances of us achieving our desired end state are thus dependent upon whether we successfully perform our means of doing so.

“Our theory says that the more informative a means is, the more flow someone will experience while performing it,” the researchers explain. “The formula is a way of mathematically quantifying exactly how informative a particular means happens to be.”

Immersion

The paper explains that modern exercise companies, such as Peloton and Zwift, are increasingly adept at creating highly immersive experiences in large part because they make the means highly informative. For instance, leaderboards are provided to rank each user according to the amount of exercise they perform, which gives users far more information than they would ordinarily receive.

“There are thousands of positions on the leader board where a rider could finish—thousands of possible end states—and the rider’s performance reveals which of these end states will occur,” the authors say. “That is a lot of information, far more than you’d normally get from a workout. When is the last time exercising allowed you to rule out literally thousands of possible end states?”

The optimization of I(M;E) is also something of great interest to AI developers who are trying to develop machines that almost behave in a state of flow. The authors believe that the formula can be deployed to improve performance for almost any task, so should also be of great interest to HR departments.

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