Club good mechanisms: from free-riders to citizen-shareholders, from impossibility to characterization
Bibliographic record
Abstract
Consider a community that shares a technology for producing a club good (Buchanan, 1965): any group of agents can “win” for an associated monetary cost. Who should win, and how should production be funded? To address this question, we seek rules (that is, direct mechanisms) where each agent participates voluntarily and is incentivized to report his valuation honestly, and where these reports are used to select winners efficiently without running a deficit. We find that whether or not there are such rules depends on the production technology. If costs are even “somewhat concave,” then there are no such rules: the free-rider problem (Wicksell, 1896; Samuelson, 1954; Green and Laffont, 1979) persists even when agents who do not contribute can be excluded. If costs are symmetric and convex, however, then there are such rules that moreover satisfy no-envy-in-trades (Kolm, 1971; Schmeidler and Vind, 1972). We characterize this class, whose Pareto-worst member is the familiar minimum-price Walrasian rule (Vickrey, 1961; Clarke, 1971; Groves, 1973; Demange, 1982; Leonard, 1983); the other rules do better by treating the agents as equal shareholders in the technology and offering social dividends (Lange, 1936).
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".