Analyzing Human Search Behavior When Subjective Returns are Unobservable

Abstract

AbstractThe exploration versus exploitation dilemma is a critical issue in human information acquisition and sequential belief formation, and the multi-armed bandit problem has been widely used to address it. Because of its high descriptive accuracy, the SGU model, which combines SoftMax type probabilistic selection, Gaussian process regression type belief updating, and upper confidence interval type evaluation, has attracted much attention. However, this model assumes that the analyst has access to the returns from people’s choices, but in many realistic tasks, this assumption cannot be made because only choices are observable. Moreover, many of the returns are subjective. The authors introduce a new model-fitting method that overcomes this barrier and evaluates its performance using data sets derived from agent-based simulations and real consumer data. This approach has the potential to significantly broaden the range of issues to which the SGU model can be applied.