Towards a formalization of value-centric trust in agent societies
Bibliographic record
Abstract
This work focuses on the design and implementation of a new model of trust based on the formalizations of reputation, self-esteem, and similarity within an agent. In this work we universalize reputation through the use of values found within all virtual and agent societies. The following values are manifested within a society of agents: responsibility, honesty, independence, obedience, ambition, helpfulness, capability, knowledgability, and cost-efficiency. Manifestations of these values lead to a more universalized approach to formalizing reputation. This new model of trust is examined within the context of an e-commerce framework. The e-commerce based multiagent system is comprised of buyers and sellers that wish to conduct business. Sellers can engage in untrustworthy business behavior at the buyer's expense. It is the job of the model to decide whether a selling agent is trustworthy enough to engage in business. The trust model is analyzed with respect to stability, scalability, accuracy in attaining e-commerce objectives, and general effectiveness in discouraging untrustworthy behavior. Based on the experiments, the model is scalable and stable dependent upon the agent population of buyers and sellers. It achieves its primary objective of discouraging untrustworthy behavior as measured through the acceleration of Gross Domestic Product growth over time. Within the simulator, a high degree of random outcomes is possible. Stability is used to examine the predictability of the model (on average) given a fixed set of given data about the simulations.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".