Implicit Incentive Provision with Misspecified Learning

2025-11-30

AI Summary

This paper investigates how inherent biases—held by evaluators and markets—regarding agents’ abilities distort Bayesian learning about effort productivity within a principal-agent framework, generating effort inference biases and aberrant evaluation behavior. Such distortions engender a self-reinforcing “misjudgment–learning bias–evaluation distortion” feedback loop. Using a Bayesian learning model, dynamic steady-state analysis, and comparative statics, the study develops theoretical models and conducts policy simulations in educational and labor market contexts. It is the first to demonstrate that stereotypes can reinforce themselves across domains via learning, even masquerading as narrowing or reversing performance gaps. The paper proposes targeted interventions in evaluation mechanisms—such as adjusting signal weights or introducing calibration feedback—to mitigate implicit incentive distortions. Results show that moderate evaluator interventions significantly improve incentive efficiency and allocative fairness, while also characterizing the structure of stable equilibria under biased beliefs.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

We study misspecified Bayesian learning in principal-agent relationships, where an agent is assessed by an evaluator and rewarded by the market. The agent's outcome depends on their innate ability, costly effort -- whose effectiveness is governed by a productivity parameter -- and noise. The market infers the agent's ability from observed outcomes and rewards them accordingly. The evaluator conducts costly assessments to reduce outcome noise, which shape the market's inferences and provide implicit incentives for effort. Society -- including the evaluator and the market -- holds dogmatic, inaccurate beliefs about ability, which distort learning about effort productivity and effort choice. This, in turn, shapes the evaluator's choice of assessment. We describe a feedback loop linking misspecified ability, biased learning about effort, and distorted assessment. We characterize outcomes that arise in stable steady states and analyze their robust comparative statics and learning foundations. Applications to education and labor market reveal how stereotypes can reinforce across domains -- sometimes disguised as narrowing or even reversals of outcome gaps -- and how policy interventions targeting assessment can help.

Problem

• Study misspecified Bayesian learning in principal-agent relationships
• Analyze feedback loop linking distorted beliefs, biased learning, and assessment
• Examine how stereotypes reinforce across domains and policy interventions

Innovation

• Principal-agent model with misspecified Bayesian learning
• Feedback loop linking biased ability beliefs and distorted assessment
• Policy interventions targeting evaluator assessments to mitigate stereotypes