Towards two-marshmallow government

There’s a well-known psychological experiment, where children are offered a marshmallow, and told that if they could wait a few minutes before eating it, they’d get two. The kids who could handle the delayed gratification were more successful in later life.

I visited a city on the east coast of the US recently, and was surprised to see deteriorated infrastructure—cracking sidewalks and broken walls—right next to brand-new construction. On the plane back, my seatmate—a long-term resident—expressed her frustration with city planners. “They seem to have a bit of tunnel vision,” she explained, going on to say that the lack of a light rail and a downtown sports center were also symptoms of short-term thinking. Fearful of the construction impact, local residents voted these initiatives down.

Like the one-marshmallow kids, these residents weren’t able to envision a future in which a short-term cost led to a much greater long-term benefit.

What’s less widely understood about the marshmallow study is that delayed gratification can be taught. Says this article, “‘Impulsivity,’ [the authors] concluded, ‘is not a purely maladaptive trait, but one whose consequences hinge on the structure of the decision-making environment.’
Delayed gratification can be learned, through better systems modeling. Click To Tweet
Not only can we teach delayed gratification, but we can also learn much more, with huge consequences, in government and more. As my co-founder Mark and his colleagues Neil Thomason and Geoff Cumming demonstrated in the StatPlay project, a visual computer game-like environment can substantially improve our intuitions about statistics. But this is just the beginning.

Together, my seatmate and I rattled off other American cities who are flourishing because they seem to understand how a investment leads to benefits: San Diego, Denver (where there will soon be Light Rail all the way to the airport, and, down the road, a heated track into the mountains), New York, and San Francisco all came to mind, not to mention Paris, London, and more.

In microcosm, this situation represents a much broader global situation, where a bit of community/environmental support can loop back and provide substantial benefits.  Maybe a bit of an investment in sidewalks would drive much greater return on that investment in the new apartment block.

To make these decisions, the solution is to visualize the cause-and-effect pathways that make up the path to prosperity versus a cycle of poverty.

It’s not too late. Let’s start using government data, build some interactive models, and start a collaborative interactive graphics-based discussion. Who wants to help?

(Please see more decision model examples under the Resources link in the menu at the top of this page)

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