Trust-oriented Utility-based Community Structure in Multiagent Systems
Bibliographic record
Abstract
The problem we address in this chapter is how to design the community structure of a multiagent system in such a way that agents join the communities that will maximize their utility and communities accept the agents that will maximize their utility, towards a stable and productive multiagent system. In order to accomplish this goal, we propose allowing communities to exchange information about the reputability of agents. In particular, it agent a 1 exists in community c 1 and would now like to join c 2, c 2 will ask c 1 for the reputation rating of a 1 and then decide whether to allow the agent to join. Allowing for the sharing of reputation ratings then requires i) a method for determining the truthfulness of the reputation reports ii) an incentive mechanism to encourage the sharing of information iii) some consideration of privacy of information within the system. In order for agents to make effective selection of communities in which to participate, it is also ideal community enjoy and about the tendency for the community to be truthful, when it reports reputation ratings of agents. We present a reputation sharing system that promotes effective community structure, along with examples to demonstrate the benefit of this particular approach.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".