Machine learning for dynamic incentive problems

Philipp Renner and Simon Scheidegger

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We present a flexible and scalable computational framework integrating machine learning and optimization theory to solve dynamic adverse selection models with persistent private information and many types. Our approach reformulates the model into a numerically tractable structure that bypasses set-valued dynamic programming; we formally prove that, under verifiable conditions, this relaxation yields the solution to the original problem. The recast problem is solved via a parallelized value function iteration algorithm, where high-dimensional, nonlinear functions are approximated using Gaussian process regression combined with Bayesian active learning. We apply our framework to two previously intractable models: one with persistent hidden information involving up to ten types and another incorporating multiple persistent types and overreporting. Validation against known solutions and rigorous credibility measures confirms accuracy. Allowing overreporting significantly alters long-run contract outcomes, concentrating consumption away from extremes and smoothing utility promises over time.

Keywords: dynamic contracts; dynamic programming; gaussian processes; heterogeneous agents; machine learning; principal-agent model (search for similar items in EconPapers)
JEL-codes: C61 C73 D82 D86 E61 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2026-09-30
References: Add references at CitEc
Citations:

Published in Review of Economic Studies, 30, September, 2026. ISSN: 0034-6527

Downloads: (external link)
https://researchonline.lse.ac.uk/id/eprint/141438/ Open access version. (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:141438

Access Statistics for this paper

More papers in LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library LSE Library Portugal Street London, WC2A 2HD, U.K.. Contact information at EDIRC.
Bibliographic data for series maintained by LSERO Manager ().