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From local utility to neural networks

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  • Ke, Shaowei
  • Zhao, Chen

Abstract

We introduce and analyze two preference-based notions of local linearity in the spirit of Machina (1982). We show how the weaker among the two extends Machina’s local utility analysis, and that the stronger among the two characterizes continuous finite piecewise linear (CFPL) utility functions. We introduce a representation of the decision maker’s preference called the neural-network utility representation that is equivalent to the CFPL representation, in which the decision maker evaluates an alternative through a neural network.

Suggested Citation

  • Ke, Shaowei & Zhao, Chen, 2024. "From local utility to neural networks," Journal of Mathematical Economics, Elsevier, vol. 113(C).
    • Handle: RePEc:eee:mateco:v:113:y:2024:i:c:s030440682400065x
    DOI: 10.1016/j.jmateco.2024.103003
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    Cited by:

    1. Ke, Shaowei & Wu, Brian & Zhao, Chen, 2024. "Learning from a black box," Journal of Economic Theory, Elsevier, vol. 221(C).
    2. Hsieh, Sung-Lin & Ke, Shaowei & Wang, Zhaoran & Zhao, Chen, 2025. "Logit neural-network utility," Journal of Economic Behavior & Organization, Elsevier, vol. 236(C).

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