First-Level Hypergame for Investigating Misperception in Conflicts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it