Review of Behavioral Economics > Vol 9 > Issue 3

Psychology in Neural Networks – In Honor of Professor Tracy Mott

Harpreet Singh Bedi, Assistant Professor Mathematics/Computer Science, Department of Mathematics, Alfred University, USA, bedi@alfred.edu
 
Suggested Citation
Harpreet Singh Bedi (2022), "Psychology in Neural Networks – In Honor of Professor Tracy Mott", Review of Behavioral Economics: Vol. 9: No. 3, pp 251-262. http://dx.doi.org/10.1561/105.00000158

Publication Date: 26 Sep 2022
© 2022 H. S. Bedi
 
Subjects
Behavioral economics,  Psychology,  Neuroeconomics,  Computational
 
Keywords
JEL Codes: D91, G40
Behavioral economicsneural networksneuroeconomicsprobability weighting functions
 

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In this article:
1. Introduction 
2. Prospect Theory 
3. Probability Weighting Functions 
4. Applications 
5. Conclusion 
References 

Abstract

This paper introduces psychology into neural networks by building a correspondence between the theory of behavioral economics and the theory of artificial neural networks. The connection between these two disparate branches of knowledge is concretely constructed by designing a dictionary between prospect theory and artificial neural networks. More specifically, the activation functions in neural networks can be converted to a probability weighting functions in prospect theory and vice versa. This approach leads to infinitely many activation functions and allows for their psychological interpretation in terms of risk seeking and risk averse behavior.

DOI:10.1561/105.00000158