TRUST AS A TRADABLE COMMODITY: A FOUNDATION FOR SAFE ELECTRONIC MARKETPLACES
Bibliographic record
Abstract
In large electronic marketplaces populated by buying and selling agents, it is difficult to judge trustworthiness. A variety of systems have been proposed to help traders to find trustworthy partners by learning to discount or disregard disreputable parties. In this article, we present a novel model for providing safe electronic marketplaces: Commodity Trunits, a system that considers trust as a tradable commodity. In this system, sellers require units of trust (trunits) to participate in transactions, and risk losing trunits if they act dishonestly. Sellers can purchase trunits when needed, and sell excess quantities. We demonstrate that under Commodity Trunits, rational sellers will choose to be honest, since this is the profit maximizing strategy. We also show that Commodity Trunits provides protection from a number of vulnerabilities common in existing trust and reputation systems, e.g., the important exit problem, where sellers can cheat without fear of repercussions if they intend to leave the market. We then present a simulation that validates the system by demonstrating that a market operator can manage the trunit marketplace to ensure sustainability. We conclude with a discussion of the value of Commodity Trunits as a method for promoting trust in electronic marketplaces.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".