Bayesian Modeling of Human Sequential Decision-Making on the Multi-Armed Bandit Problem
- Authors: Daniel Acuña, Paul Schrater
- Year: 2008
- Abstract: In this paper we investigate human exploration/exploitation behavior in sequential-decision making tasks. Previous studies have suggested
that people are suboptimal at scheduling exploration, and heuristic
decision strategies are better predictors of human choices than the
optimal model. By incorporating more realistic assumptions about subject's
knowledge and limitations into models of belief updating, we show
that Bayesian models of human behavior for the Multi-Armed Bandit
Problem (MAB) on experimental data perform better than previous accounts.
- Conference proceedings: In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Washington, DC: Cognitive Science Society.
- Files: Paper [PDF] Poster [PDF]