Utility-Driven Minimum Cost Consensus Model With Preference Learning and Fairness Concerns
Bibliographic record
Abstract
Nowadays the emergence of digital technology and artificial intelligence brings a lot of extra information and convenience for group decision activities, such as rich historical decision data, mobile decision platform, artificial interactive device and others. However, it also results in high complexity and uncertainty of the decision environments, modeling and analysis, and the preference of decision maker is crucial to the design of group decision and consensus mechanism. Meanwhile, the information transparency results in more attention being paid to fairness concerns in group decision making. Based on the historical group decision data, this study proposes a novel minimum cost consensus model with preference learning and fairness concerns (MCCM-PLFC), which characterizes the modification motivation of decision makers in a utility-driven way. A preference learning framework is first proposed to determine the parameters of utility functions of decision makers, where fairness concerns are modeled based on the return bias among the decision makers. Then, in a Stackelberg game consensus framework, the MCCM-PLFC is constructed to minimize the consensus cost of the moderator with consideration of the utility-driven behavior of decision makers. In addition, a best-response update algorithm is designed to solve the equilibrium solution among the decision makers and an adaptive differential evolution algorithm is used to solve the MCCM-PLFC. Finally, a throughout of experimental study is performed to quantify the validity of the proposed consensus model.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".