A Probabilistic-Based Trust Evaluation Model Using Hidden Markov Models and Bonus Malus Systems
Bibliographic record
Abstract
In the paper, the uncertainty of trust is transformed into a probability vector denoting the probability distribution over possible trust states that are hidden from observation but determined by an entity's expected performance. We suggest the use of Hidden Markov Models (HMMs) for estimating the unknown probability distributions in peer-to-peer interactions. HMMs allow us to explicitly consider an entity's unobserved trustworthiness that influences it's occurrences of behavioral patterns. The proposed hidden Markov processes are associated with a specified Bonus-Malus System (BMS) that is interpreted as a Markov chain with constant transition matrix and is used to simplify the structure of model and to reduce the computational complexity of parameter estimations in HMMs. The maximum likelihood estimators of the unknown HMM parameters are obtained using EM algorithm. An application of the model in the scenario of detection of probabilistic packet-drop attack has been investigated. The simulations demonstrate that the approach is capable of accurately estimating the (hidden) trust states probability distribution as well as the expected performance for the entities that have different observed behavioral patterns.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".