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Bayesian modeling of human sequential decision-making on the multi-armed bandit problem

In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 100--200 (2008)

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  1. Ambiguity Aversion in Multi-Armed Bandit Problems.Christopher M. Anderson - 2012 - Theory and Decision 72 (1):15-33.
    In multi-armed bandit problems, information acquired from experimentation is valuable because it tells the agent whether to select a particular option again in the future. This article tests whether people undervalue this information because they are ambiguity averse, or have a distaste for uncertainty about the average quality of each alternative. It is shown that ambiguity averse agents have lower than optimal Gittins indexes, appearing to undervalue information from experimentation, but are willing to pay more than ambiguity neutral agents to (...)
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