Reputational Conservatism in Expert Advice

2025-09-04

AI Summary

This paper examines how experts’ concern for professional reputation shapes their advice-giving behavior in a continuous-signal environment, focusing on “reputational conservatism”—the tendency of high-reputation experts to favor safe over risky recommendations. Method: We develop a dynamic Bayesian game model incorporating private signals, public outcome feedback, and endogenous reputation updating, and derive the unique equilibrium strategies analytically. Contribution/Results: We show that while high-reputation experts issue fewer recommendations overall, their risky recommendations exhibit significantly higher conditional accuracy (i.e., hit rate given recommendation). Reputational conservatism arises not from intrinsic risk aversion but from rational avoidance when signal diagnosticity is low. Crucially, we identify a novel endogenous mechanism whereby “success rewards” incentivize exploratory advice—transforming reputation concerns into a driver of information acquisition. These findings provide theoretical foundations and actionable design principles for improving committee decision structures and oversight mechanisms.

Technology Category

• Game Theory and Economic Paradigms: Imperfect Information
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Knowledge Representation and Reasoning: Reasoning with Beliefs

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Large-scale security measurements

Abstract

We study expert recommendations under career concerns in a continuous signal environment. An expert observes a private signal about a binary payoff and recommends risky or safe; recommendations and outcomes are public and affect reputation and implementation. Equilibrium advice follows a cutoff rule. Under a mild relative-diagnosticity condition, the cutoff increases with reputation (reputational conservatism); informativeness and priors lower the cutoff while stronger career concerns raise it. A success-contingent bonus provides a one-to-one mapping to experimentation. The theory predicts fewer risky recommendations but higher conditional hit rates for high-reputation experts and yields implementable levers for committees and monitoring.

Problem

• Modeling expert advice under career concerns
• Analyzing reputational conservatism in recommendations
• Examining effects of incentives on expert behavior

Innovation

• Cutoff rule for equilibrium advice
• Success-contingent bonus enables experimentation
• Reputation increases cutoff causing conservatism