How Often Should Reputation Mechanisms Update a Trader's Reputation Profile?
Bibliographic record
Abstract
Reputation mechanisms have become an important component of electronic markets, helping to build trust and elicit cooperation among loosely connected and geographically dispersed eco-nomic agents. Understanding the impact of different reputation mechanism design parameters on the resulting market efficiency has, thus, emerged as a question of theoretical and practical interest. Along these lines, this paper studies the impact of the frequency of reputation profile updates on cooperation and efficiency. The principal finding is that, in trading settings with pure moral hazard and noisy ratings, if the per-period profit margin of cooperating sellers is sufficiently high, a mechanism that does not publish every single rating it receives but rather, only updates a trader’s public reputation profile every k transactions with a summary statistic of a trader’s most recent k ratings, can induce higher average levels of cooperation and market efficiency than a mechanism that publishes all ratings as soon as they are posted. The paper derives expressions for calculating the optimal profile updating interval k, discusses the implications of this finding for existing systems, such as eBay, and proposes alternative reputation mechanism architectures that attain higher maximum efficiency than the, currently popular, reputation mechanisms that publish summaries of a trader’s recent ratings. 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".