Analysis of the Influence of Decision Makers’ Fuzzy Behavioral Patterns Under Power Asymmetry Conflict
Bibliographic record
Abstract
Asymmetric power conflicts arise from resource imbalances among stakeholders, where dominant parties often control situations through rule-setting, while weaker parties face suppression and manipulation. Decision makers (DMs) in such conflicts exhibit bounded rationality and diverse risk attitudes, significantly influencing conflict outcomes. Traditional conflict resolution frameworks, like the Graph Model for Conflict Resolution (GMCR), inadequately address power asymmetry and risk attitudes, leading to unrealistic equilibria. This study aims to bridge this gap by integrating risk attitude analysis into the GMCR framework, enhancing its capability to resolve asymmetric power conflicts. Specifically, we introduce a novel approach called Triangular Fuzzy Optimal Discrete Fitting (TFN-ODF) to assess the risk attitude of DMs amidst asymmetric power conflicts. Additionally, we enhance the principles for categorizing DMs' risk attitude types, surpassing the original Optimal Discrete Fitting (ODF) method's limitations. Moreover, we define the behavioral pattern stability concepts for the leader and the follower in the GMCR framework during power asymmetry conflicts. Applied to a carbon emission reduction conflict case, we find that as a general risk seeker, although the follower will not choose the options that damage the leader's benefit, it will counter the leader's sanctions by several risky measures for its own benefit. Our methodology and algorithm not only demonstrate practical application but also assist DMs in identifying conflict resolution strategies across varied 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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".