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Record W2103448277 · doi:10.1287/mnsc.1100.1271

Cooperation in Games with Forgetfulness

2010· article· en· W2103448277 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueManagement Science · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicGame Theory and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRecallGame theoryComputer scienceMinimaxMicroeconomicsEconomicsPsychologyCognitive psychology

Abstract

fetched live from OpenAlex

Companies and managers are apt to forget information, yet classic game theory analysis assumes that all players have perfect recall. This paper expands the literature by examining how introducing forgetfulness into a multiplayer game-theoretic framework can help or hinder cooperative behavior. We find that forgetfulness impacts the ability of firms to cooperate in countervailing directions. On one hand, forgetfulness can diminish the ability to punish deviators, making cooperation more difficult. On the other hand, under some conditions forgetfulness can make meting out severe punishments—even below-(stage) minimax punishments—credible and decrease the ability for players to effectively deviate, facilitating cooperation even in circumstances where cooperation cannot be sustained under perfect recall. We apply our model to a number of strategic games that commonly appear in the literature. This paper was accepted by Preyas Desai, marketing.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.362
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it