Bayesian and Approximate Bayesian Modeling of Human Sequential Decision-Making on the Multi-Armed Bandit Problem
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چکیده
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 optimal Bayesian and approximate Bayesian models of human behavior for the Multi-Armed Bandit Problem (MAB) outperform the best heuristic methods on experimental data for 2-arm, 3-arm, and 4-arm bandit problems. Moreover, we show that Bayesian modeling is more consistent to the exploratory and exploitative human behavior by disaggregating the fitting performance of decision sequences into several phases.
منابع مشابه
Bayesian and Approximate Bayesian Modeling of Human Sequential Decision-Making on the Multi-Armed Bandit Problem
In this paper we investigate human exploration/exploitation behavior in a sequential-decision making task. 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...
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