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Psychology in Neural Networks – In Honor of Professor Tracy Mott

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  • Harpreet Singh Bedi

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.

Suggested Citation

  • Harpreet Singh Bedi, 2022. "Psychology in Neural Networks – In Honor of Professor Tracy Mott," Review of Behavioral Economics, now publishers, vol. 9(3), pages 251-262, September.
    • Handle: RePEc:now:jnlrbe:105.00000158
    DOI: 10.1561/105.00000158
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