Bibliographic record
Abstract
Classic models of reputation consider an agent taking costly actions to affect a single, homogeneous audience’s beliefs about his ability, preferences or other characteristic. However, in many economic settings, agents must maintain a reputation with multiple parties with diverse interests. In this paper we study reputation incentives for an agent who faces two audiences with opposed preferences. We ask if the existence of multiple audiences per se changes reputation incentives. Further, should the agent deal with the different audiences commonly or separately? Our analysis yields some new qualitative insights. Specifically, the presences of heterogeneous audiences is more likely to lead the agent towards “pooling” equilibria in which he takes an intermediate compromise action. Instead, dealing with only one audience leads the agent to cater towards that audience’s preferences, giving rise to a “separating” outcome or pooling on some extreme action. We analyze the welfare implications, and show that the agent most prefers that both audiences commonly observe all the actions that he takes. In our setting, reputation acts as an informal contract that enforces desirable behavior through future continuation payoffs. Our analysis highlights that the presence of multiple heterogeneous audiences can, naturally, lead these rewards to be non-monotonic in an agent’s reputation. We show different ways that this non-monotonicity arises. In an infinite horizon setting, it can emerge through endogenous interactions between the audiences, through equilibrium expectations of the agent’s choice of action. It can also arise, perhaps more trivially, through direct payoff interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".