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.’
[bctt tweet=”Delayed gratification can be learned, through better systems modeling.”]
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)
