Order of Play, Forward Induction, and Presentation Effects in Two-Person Games
Bibliographic record
Abstract
Abstract We investigate the effects of order-of-play (simultaneous, unobserved sequential and fully observed sequential play) and form of presentation (extensive vs. normal) in three simple two person games: battle-of-the-sexes with and without outside option and a three strategy game which differentiates between virtual observability (VO) and iterated elimination of dominated strategies as principles of equilibrium selection. VO predicts that knowledge of the order of play alone will affect the distribution of strategies chosen. We contrast this with the predictions of iterated elimination of dominated strategies. We report results from 1800 one-shot games conducted in 6 sessions with 120 subjects and analysed as panel data. The form of presentation strongly affects the distribution of outcomes and strategies. Information about order of play shifts the distribution of strategies away from the distribution in simultaneous play and towards the distribution in fully observed play, especially in the less complicated games presented in normal order. Order-of-play effects are less evident as complexity of the game increases. Extensive form presentation appears to induce sequential thinking even in simultaneously played games.
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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 | 0.013 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".