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An Economy of Neural Networks:Learning from Heterogeneous Experiences

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  • Artem Kuriksha

    (University of Pennsylvania)

Abstract

This paper proposes a new way to model behavioral agents in dynamic macro-?nancial environments. Agents are described as neural networks and learn policies from id-iosyncratic past experiences. I investigate the feedback between irrationality and past outcomes in an economy with heterogeneous shocks similar to Aiyagari (1994). In the model, the rational expectations assumption is seriously violated because learning of a decision rule for savings is unstable. Agents who fall into learning traps save either excessively or save nothing, which provides a candidate explanation for several empir-ical puzzles about wealth distribution. Neural network agents have a higher average MPC and exhibit excess sensitivity of consumption. Learning can negatively a?ect intergenerational mobility.

Suggested Citation

  • Artem Kuriksha, 2021. "An Economy of Neural Networks:Learning from Heterogeneous Experiences," PIER Working Paper Archive 21-027, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:21-027
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    File URL: https://economics.sas.upenn.edu/sites/default/files/filevault/21-027.pdf
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    Cited by:

    1. Simone Brusatin & Tommaso Padoan & Andrea Coletta & Domenico Delli Gatti & Aldo Glielmo, 2024. "Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling," Papers 2405.02161, arXiv.org, revised Oct 2024.

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