Image Versus Information: Changing Societal Norms and Optimal Privacy
Bibliographic record
Abstract
We analyze the costs and benefits of using social image to foster virtuous behavior.A Principal seeks to motivate reputation-conscious agents to supply a public good.Each agent chooses how much to contribute based on his own mix of public-spiritedness, private signal about the value of the public good, and reputational concern for appearing prosocial.By making individual behavior more visible to the community the Principal can amplify reputational payoffs, thereby reducing free-riding at low cost.Because societal preferences constantly evolve, however, she knows only imperfectly both the social value of the public good (which matters for choosing her own investment, matching rate or legal policy) and the importance attached by agents to social esteem and sanctions.Increasing publicity makes it harder for the Principal to learn from what agents do (the "descriptive norm") what they really value (the "prescriptive norm"), thus presenting her with a tradeoff between incentives and information aggregation.We derive the optimal degree of privacy/publicity and matching rate, then analyze how they depend on the economy's stochastic and informational structure.We show in particular that in a fast-changing society (greater variability in the fundamental or the image-motivated component of average preferences), privacy should generally be greater than in a more static one.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".