Title

Towards the Design of Prospect-Theory Based Human Decision Rules for Hypothesis Testing

Abstract

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.

Meeting Name

54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016 (2016: Sep. 27-30, Monticello, IL)

Department(s)

Computer Science

Keywords and Phrases

Binary Hypothesis Testing; Optimists; Pessimists; Prospect Theory

International Standard Book Number (ISBN)

978-1-5090-4550-1

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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