Learning prospect theory value function and reference point of a sequential decision maker

Abstract

Given a decision problem, the reference point of a person determines whether the outcomes are perceived as gain or loss and influences the decision. In this paper, we assume that a person is given the same decision problem repeatedly, and the person chooses an action to maximize her value function while her reference point could possibly change over time. We estimate the value function and the reference point of the person from the observed actions by constructing a hidden Markov model and using the expectation-maximization algorithm. Then we test the suggested algorithm on the data set of New York City taxi drivers.

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