Multi-layer cognitive filtering by behavioral modeling
Bibliographic record
Abstract
In the absence of legal enforcement procedures for the participants of an open e-marketplace, trust and reputation systems are central for resisting against threats from malicious agents. Such systems provide mechanisms for identifying the participants who disseminate unfair ratings. However, it is possible that some of the honest participants are also victimized as a consequence of the poor judgement of these systems. In this paper, we propose a two-layer filtering algorithm that cognitively elicits the behavioral characteristics of the participating agents in an e-marketplace. We argue that the notion of unfairness does not exclusively refer to deception but can also imply differences in dispositions. The proposed filtering approach aims to go beyond the inflexible judgements on the quality of participants and instead allows the human dispositions that we call optimism, pessimism and realism to be incorporated into our trustworthiness evaluations. Our proposed filtering algorithm consists of two layers. In the first layer, a consumer agent measures the competency of its neighbors for being a potentially helpful adviser. Thus, it automatically disqualifies the deceptive agents and/or the newcomers that lack the required experience. Afterwards, the second layer measures the credibility of the surviving agents of the previous layer on the basis of their behavioral models. This tangible view of trustworthiness evaluation boosts the confidence of human users in using a web-based agent-oriented e-commerce application.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".