Spooky Boundaries at a Distance: Inductive Bias, Dynamic Models, and Behavioral Macro

Mahdi Ebrahimi Kahou, Jesus Fernandez-Villaverde, Sebastian Gomez-Cardona (), Jesse Perla and Jan Rosa
Additional contact information
Mahdi Ebrahimi Kahou: Bowdoin College
Jesse Perla: University of British Columbia
Jan Rosa: University of British Columbia

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: In the long run, we are all dead. Nonetheless, when studying the short-run dynamics of economic models, it is crucial to consider boundary conditions that govern long-run, forwardlooking behavior, such as transversality conditions. We demonstrate that machine learning (ML) can automatically satisfy these conditions due to its inherent inductive bias toward finding flat solutions to functional equations. This characteristic enables ML algorithms to solve for transition dynamics, ensuring that long-run boundary conditions are approximately met. ML can even select the correct equilibria in cases of steady-state multiplicity. Additionally, the inductive bias provides a foundation for modeling forward-looking behavioral agents with self-consistent expectations.

Keywords: Machine learning; inductive bias; rational expectations; transitional dynamics; transversality; behavioral macroeconomics (search for similar items in EconPapers)
JEL-codes: C1 E1 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2024-08-12
New Economics Papers: this item is included in nep-big and nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Spooky Boundaries at a Distance: Inductive Bias, Dynamic Models, and Behavioral Macro (2024)
Working Paper: Spooky Boundaries at a Distance: Inductive Bias, Dynamic Models, and Behavioral Macro (2024)
Working Paper: Spooky Boundaries at a Distance: Inductive Bias, Dynamic Models, and Behavioral Macro (2024)
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