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,
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.

Publication Date: 26 Sep 2022
© 2022 H. S. Bedi
Behavioral economics,  Psychology,  Neuroeconomics,  Computational
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 


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.