Nonparametric Identification of Incomplete Information Discrete Games with Non-equilibrium Behaviors
Bibliographic record
Abstract
In the literature that estimates discrete games with incomplete information, researchers usually impose two assumptions. First, either the payoff function or the distribution of private information or both are restricted to follow some parametric functional forms. Second, players’ behaviors are assumed to be consistent with the Bayesian Nash equilibrium. This paper jointly relaxes both assumptions. The framework non-parametrically specifies both the payoff function and the distribution of private information. In addition, each player’s belief about other players’ behaviors is also modeled as a nonparametric function. I allow this belief function to be any probability distribution over other players’ action sets. This specification nests the equilibrium assumption when each player’s belief corresponds to other players’ actual choice probabilities. It also allows non-equilibrium behaviors when some players’ beliefs are biased or incorrect. Under the above framework, this paper first derives a testable implication of the equilibrium condition. It then obtains the identification results for the payoff function, the belief function and the distribution of private information.
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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 | 0.010 | 0.054 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".