Improving User Satisfaction in Agent-Based Electronic Marketplaces by Reputation Modelling and Adjustable Product Quality
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a market model and learning algorithms for buying and selling agents in electronic marketplaces. We take into account the fact that multiple selling agents may offer the same good with different qualities, and that selling agents may alter the quality of their goods. We also consider the possible existence of dishonest selling agents in the market. In our approach, buying agents learn to maximize their expected value of goods using reinforcement learning. In addition, they model and exploit the reputation of selling agents to avoid interaction with the disreputable ones, and therefore to reduce the risk of purchasing low value goods. Our selling agents learn to maximize their expected profits by using reinforcement learning to adjust product prices, and also by altering product quality to provide more customized value to their goods. This paper focuses on presenting results from experiments investigating the behaviours of buying and selling agents in large-sized electronic marketplaces. Our results confirm that buying and selling agents following the proposed algorithms obtain greater satisfaction than buying and selling agents who only use reinforcement learning, with the buying agents not modelling sellersý reputation and the selling agents not adjusting product quality.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it