Reward Limits

Behavioral scientists divide tasks--job or school--into two categories. An algorithmic task has a set of established behaviors for success. There is a sequence, or algorithm, for it. A heuristic task has no algorithm, no prescribed pattern of behavior. Often described as "trial and error," the heuristic task must be worked through, experimented with, and solved. Algorithmic work follows successful pathways, heuristic work requires innovation. Something new.

 

This academic sounding separation is becoming more important and practical. Most jobs in the United States up until the last decades have been algorithmic. Production lines, clerking, warehousing--most jobs had predictable assignments and definable paths. But the consulting firm McKinsey & Co. estimates that in the United States, only 30 percent of job growth now comes from algorithmic work, while 70 percent comes from heuristic work. It may be that the nature of modern work has changed but an easier explanation is that  routine work can be out sourced or automated while artistic, empathic, non-routine work generally cannot.

 

Which brings us to Sam Glucksberg. His work--from the 1960's--is summarized in Daniel Pink's book "Drive: Surprising Truth About What Motivates Us." Glucksberg, a psychologist now at Princeton University, took two groups and asked them to solve a puzzle. The first group was told they were being timed to establish a norm for how long the puzzle solving should take. The second group was offered incentives. The fastest 25% would get five dollars, the fastest puzzle solver would get twenty.

 

The the incentivized group took nearly three and a half minutes longer than the control group to solve the puzzle. Longer.

 

There are a lot of explanations for this but one possibility is significant: The creative forces necessary to solve the puzzle were blunted and narrowed by the demand of time created by the incentive. Rewards are helpful when the path is straight but limiting when creativity is demanded. Focus eliminates breadth of thinking, expansiveness.

 

Now, just for fun, how would you classify the American education process, algorithmic or heuristic?