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Record W3124440090

Cooperation through Coordination in Two Stages

2017· preprint· en· W3124440090 on OpenAlexaff
Todd R. Kaplan, Bradley J. Ruffle, Ze’ev Shtudiner

Bibliographic record

VenueRePEc: Research Papers in Economics · 2017
Typepreprint
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsInefficiencyOutcome (game theory)Action (physics)Stage (stratigraphy)Coordination gameRepeated gameMicroeconomicsComputer scienceEconomicsMathematical economicsOperations managementGame theory
DOInot available

Abstract

fetched live from OpenAlex

Efficient cooperation often requires coordination, such that exactly one of two players takes an available action. If the decisions whether to pursue the action are made simultaneously, then neither or both may acquiesce leading to an inefficient outcome. However, inefficiency may be reduced if players move sequentially. We test this experimentally by introducing repeated two-stage versions of such a game where the action is individually profitable. In one version, players may wait in the first stage to see what their partner did and then coordinate in the second stage. In another version, sequential decision-making is imposed by assigning one player to move in stage one and the other in stage two. Although there are fewer cooperative decisions in the two-stage treatments, we show that overall subjects coordinate better on efficient cooperation and on avoiding both acquiescing. Yet, only some pairs actually achieve higher profits, while the least cooperative pairs do worse in the two-stage games than their single-stage counterparts. For these, rather than facilitating coordination, the additional stage invites unsuccessful attempts to disguise uncooperative play, which are met with punishment.

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.

How this classification was reachedexpand

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.099
GPT teacher head0.457
Teacher spread0.358 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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