Behavior engineering using quantitative reinforcement learning models

Effectively shaping human and animal behavior is of great practical and theoretical importance. Here we ask whether quantitative models of choice can be used to achieve this goal more effectively than qualitative psychological principles. We term this approach, which is motivated by the effectiveness of engineering in the natural sciences, 'choice engineering'. To address this question, we launched an academic competition, in which teams of academic competitors used either quantitative models or qualitative principles to design reward schedules that would maximally bias the choices of experimental participants in a repeated, two-alternative task. We found that a choice engineering approach is the most successful method for shaping behavior in our task. This is a proof of concept that quantitative models are ripe to be used in order to engineer behavior. Finally, we show that choice engineering can be effectively used to compare models in the cognitive sciences, thus providing an alternative to the standard statistical methods of model comparison that are based on likelihood or explained variance.