JOURNAL ARTICLE

Theory of Claim Resolution.

  • Published In: Journal of Law, Economics & Organization, 2023, v. 39, n. 1. P. 77 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Baker, Scott; Kornhauser, Lewis A 3 of 3

Abstract

This article develops a game-theoretic model of claim resolution involving a principal and an agent who observe different pieces of evidence: a global fact observable by both and a local fact observable only by the agent. The model captures disagreements between principal and agent over the "method" of weighting evidence—formalism (overweighting global facts) versus anti-formalism (overweighting local facts)—and over the burden of proof. It shows that despite lacking commitment power, the principal delegates decision-making authority over an interval of global facts, often affirming the agent’s decisions even when contrary to her own preferences, to leverage the agent’s private information. Importantly, the principal prefers anti-formalist agents, who overweight local facts and face credible reversal threats on both valid and invalid decisions, because such agents are easier to motivate and yield better welfare outcomes than formalist agents or those who disagree only on the burden of proof. The findings have implications for judicial hierarchies, loan officer supervision, and bureaucratic delegation, highlighting how the nature of disagreement affects delegation, control, and welfare in hierarchical decision-making.

Additional Information

  • Source:Journal of Law, Economics & Organization. 2023/03, Vol. 39, Issue 1, p77
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2023
  • ISSN:8756-6222
  • DOI:10.1093/jleo/ewab017
  • Accession Number:162026182
  • Copyright Statement:Copyright of Journal of Law, Economics & Organization is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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