Résumé
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).
Mots-clés
Dynamic delegation; expert advice; moral hazard; experimentation; reputational conservatism.;
Codes JEL
- D82: Asymmetric and Private Information • Mechanism Design
- D83: Search • Learning • Information and Knowledge • Communication • Belief
- C73: Stochastic and Dynamic Games • Evolutionary Games • Repeated Games
Référence
Georgy Lukyanov et Anna Vlasova, « Dynamic Delegation with Reputation Feedback », TSE Working Paper, n° 25-1677, octobre 2025.
Voir aussi
Publié dans
TSE Working Paper, n° 25-1677, octobre 2025