Towards con-resistant trust models for distributed agent systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Artificial societies – distributed systems of au-tonomous agents – are becoming increasingly im-portant in e-commerce. Agents base their decisions on trust and reputation in ways analogous to hu-man societies. Many different definitions for trust and reputation have been proposed that incorporate many sources of information; however, system de-signs have tended to focus much of their attention on direct interactions. Furthermore, trust updat-ing schemes for direct interactions have tended to uncouple updates for positive and negative feed-back. Consequently, behaviour in which cycles of positive feedback followed by a single negative feedback results in untrustworthy agents remain-ing undetected. This con-man style of behaviour is formally described and desirable characteristics of con-resistant trust schemes proposed. A con-resistant scheme is proposed and compared with
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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 | 0.000 | 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 | 0.000 |
| 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