Computational mechanisms for context-based behavioral interventions: A large-scale analysis

Choice context influences decision processes, and is one of the primary determinants of what people choose. This insight has been used by academics and practitioners to study decision biases and design behavioral interventions to influence and improve choices. In this paper we analyze the effects of context-based behavioral interventions on the computational mechanisms underlying decision making. We collect data from two very large laboratory studies involving nineteen prominent behavioral interventions, and model the influence of each intervention using a leading computational model of choice in psychology and neuroscience. This allows us to parametrize the biases induced by each intervention, interpret these biases in terms of underlying decision mechanisms and their properties, quantify similarities between interventions, and predict how different interventions alter key choice outcomes. In doing so, we offer researchers and practitioners a theoretically principled approach to understanding and manipulating choice context in decision making.