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On the Equivalence of Neural and Production Networks

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Listed:
  • Roy Gernhardt
  • Bjorn Persson

Abstract

This paper identifies the mathematical equivalence between economic networks of Cobb-Douglas agents and Artificial Neural Networks. It explores two implications of this equivalence under general conditions. First, a burgeoning literature has established that network propagation can transform microeconomic perturbations into large aggregate shocks. Neural network equivalence amplifies the magnitude and complexity of this phenomenon. Second, if economic agents adjust their production and utility functions in optimal response to local conditions, market pricing is a sufficient and robust channel for information feedback leading to macro learning.

Suggested Citation

  • Roy Gernhardt & Bjorn Persson, 2020. "On the Equivalence of Neural and Production Networks," Papers 2005.00510, arXiv.org, revised Mar 2021.
    • Handle: RePEc:arx:papers:2005.00510
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    References listed on IDEAS

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    1. Daniel Vaughan-Whitehead, 2016. "Introduction," Economia & lavoro, Carocci editore, issue 2, pages 7-12.
    2. Vasco M. Carvalho, 2014. "From Micro to Macro via Production Networks," Journal of Economic Perspectives, American Economic Association, vol. 28(4), pages 23-48, Fall.
    3. Daron Acemoglu & Vasco M. Carvalho & Asuman Ozdaglar & Alireza Tahbaz‐Salehi, 2012. "The Network Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 80(5), pages 1977-2016, September.
    4. Daron Acemoglu & Asuman Ozdaglar & Alireza Tahbaz-Salehi, 2015. "Networks, Shocks, and Systemic Risk," NBER Working Papers 20931, National Bureau of Economic Research, Inc.
    5. Vasco M. Carvalho, 2015. "From Micro to Macro via Production Networks," Working Papers 793, Barcelona School of Economics.
    6. Sanjeev Goyal, 2015. "Networks in Economics: A Perspective on the Literature," Cambridge Working Papers in Economics 1548, Faculty of Economics, University of Cambridge.
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