Cognitive Modeling Versus Game Theory: Why cognition matters.
Bibliographic record
Abstract
We call into question game theory, as a account of how people play two player zero-sum games. Evidence from a modified version of the game Paper, Rock, Scissors suggests that people do not play randomly, and not according to certain play probabilities. We investigated the relationship between game theory predictions and a cognitive model of game playing based on the detection of sequential dependencies. Previous research has shown that the sequential dependency model can account for a number of empirical findings that game theory cannot. The sequential dependency model has been implemented using both simple neural networks and ACT-R. In this paper we used simple neural networks (a description of how our findings relate to the ACT-R model is included in the Conclusion section). For simple games, such as Paper, Rock, Scissors, game theory has been able to correctly predict aggregate move probabilities. In this paper we show that this is an artifact of the symmetry of the payoffs, and that for asymmetrical payoffs the game theory solution does not predict human behavior. Furthermore, we show that the model of game playing that underlies game theory cannot be used to predict the results no matter what move probabilities are used. Finally, we show that the results can be accounted for by augmenting the network sequential dependency model so that the reward system is related to the game payoffs.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".