Using an algorithmic approach to shape human decision-making through attraction to patterns.

Evidence suggests that people are attracted to patterns and regularity. We hypothesized that decision-makers, intending to maximize profit, may be lured by the existence of regularity, even when it does not confer any additional value. An algorithm based on this premise outperformed all other contenders in an international challenge to bias individuals' preferences. To create the bias, the algorithm allocates rewards in an evolving, yet easily trackable, pattern to one option but not the other. This leads decision-makers to prefer the regular option over the other 2:1, even though this preference proves to be relatively disadvantageous. The results support the idea that humans assign value to regularity and more generally, for the utility of qualitative approaches to human decision-making. They also suggest that models of decision making that are based solely on reward learning may be incomplete.