Towards the Design of Prospect-Theory Based Human Decision Rules for Hypothesis Testing
Detection rules have traditionally been designed for rational agents that minimize the Bayes risk (average decision cost). With the advent of crowd-sensing systems, there is a need to redesign binary hypothesis testing rules for behavioral agents, whose cognitive behavior is not captured by traditional utility functions such as Bayes risk. In this paper, we adopt prospect theory based models for decision makers. We consider special agent models namely optimists and pessimists in this paper, and derive optimal detection rules under different scenarios. Using an illustrative example, we also show how the decision rule of a human agent deviates from the Bayesian decision rule under various behavioral models, considered in this paper.
V. S. Nadendla et al., "Towards the Design of Prospect-Theory Based Human Decision Rules for Hypothesis Testing," Proceedings of the 54th Annual Allerton Conference on Communication, Control, and Computing (2016, Monticello, IL), pp. 766 - 773, Institute of Electrical and Electronics Engineers (IEEE), Sep 2017.
The definitive version is available at https://doi.org/10.1109/ALLERTON.2016.7852310
54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016 (2016: Sep. 27-30, Monticello, IL)
Keywords and Phrases
Binary Hypothesis Testing; Optimists; Pessimists; Prospect Theory
International Standard Book Number (ISBN)
Article - Conference proceedings
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01 Sep 2017