First-Level Hypergame for Investigating Misperception in Conflicts
Bibliographic record
Abstract
A new technique is introduced to model misperception by participating decision makers (DMs) in a conflict having two or more DMs within the framework of the graph model for conflict resolution. This comprehensive approach enables one to model a conflict situation involving misperception: held by and about the focal DM and its opponents. To achieve this, DMs' options in a conflict situation are classified based on different kinds of misperception that can alter the choices of the focal DM and/or the other DMs. Furthermore, the combination of DMs' options can generate the universal set of options for the entire conflict, which can then be used to construct the universal set of states. This novel design can differentiate between the states that are recognized by all DMs and those that are recognized individually. Furthermore, eight sets of equilibria are formally defined within the construction of the first-level hypergame in graph form to provide strategic insights into the conflict and reflect the effect of DMs' misperceptions on the equilibria of the dispute.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".