Dynamic Delegation with Reputation Feedback

TL;DR

This paper develops a model of dynamic delegation influenced by reputation feedback, showing how reputation affects advice cutoff strategies and learning dynamics, with implications for optimal incentives and decision-making.

Contribution

It introduces a recursive, belief-based equilibrium framework for reputation-dependent advice, including a diagnosticity condition and a success-contingent bonus mechanism.

Findings

Advice cutoff depends on reputation and signal diagnosticity.

Reputation dynamics differ under competent and less competent types.

A success bonus can implement targeted experimentation rates.

Abstract

We study dynamic delegation with reputation feedback: a long-lived expert advises a sequence of implementers whose effort responds to current reputation, altering outcome informativeness and belief updates. We solve for a recursive, belief-based equilibrium and show that advice is a reputation-dependent cutoff in the expert's signal. A diagnosticity condition - failures at least as informative as successes - implies reputational conservatism: the cutoff (weakly) rises with reputation. Comparative statics are transparent: greater private precision or a higher good-state prior lowers the cutoff, whereas patience (value curvature) raises it. Reputation is a submartingale under competent types and a supermartingale under less competent types; we separate boundary hitting into learning (news generated infinitely often) versus no-news absorption. A success-contingent bonus implements any target experimentation rate with a plug-in calibration in a Gaussian benchmark. The framework yields testable predictions and a measurement map for surgery (operate vs. conservative care).