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Record W4313149334 · doi:10.4324/9780203023594-2

Experimental economic games

2005· book-chapter· en· W4313149334 on OpenAlexaboutno aff
Robert W. Dimand

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicComplex Systems and Time Series Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEconomics

Abstract

fetched live from OpenAlex

This essay, an offshoot of research for Volume II of A History of Game Theory , will examine the encounters of game theory and experimentation in the two decades following the Second World War, and the adoption of experimental games as a characteristic feature of game theory in contrast to the much more limited use of experimentation by economists uninfluenced by game theory. The theory of games of strategy has followed a distinctive path in attempting to confront its abstract theory of conflict and cooperation with evidence about economic and social behavior. In the first twenty years after von Neumann and Morgenstern’s Theory of Games and Economic Behavior (1944) , game theorists began, at first tentatively and then on a large scale, to conduct strategic games as controlled experiments. In contrast, another new sub-discipline, macroeconomics, devoted its efforts to regression analysis of large, simultaneous equations models during the same years: Cowles monographs edited or co-edited by Tjalling Koopmans developed econometric theory for the estimation and identification of simultaneous equations models, while Lawrence Klein and his associates constructed models growing beyond the Brookings and Wharton models to CANDIDE (the Canadian Interdepartmental Econometric Model) with more than two thousand equations and finally Project LINK, which connects such models of 79 countries or groups of countries. Demand studies in microeconomics also focussed on econometrics applied to non-experimental data. Just as survey data has been so little used in economics that the advocacy by Blinder (1990) of “Learning by Asking Those Who Are Doing” struck its audience as quite daring, experiments played little role in economics apart from game theory. This was in sharp contrast to psychology: it is noteworthy that just as the use of survey data in economics was sustained in the 1950s by George Katona, who was cross-appointed in psychology and economics at the University of Michigan, the psychologist Sidney Siegel played a crucial role in promoting experimental games in economics. Some attempts had been made by psychologists (notably L.L. Thurstone 1931 ; cf. Mosteller and Nogee 1951) to measure preferences experimentally, but, in an influential paper, W. Allen Wallis and Milton Friedman (1942) denied that such experiments had any value for economics. Thirty experiments in strategic interaction were published by the time of the first survey of experimental gaming by Anatol Rapoport and Carol Orwant (1962) . The subsequent growth of the literature was signaled by the introduction of an experimental gaming section edited by Rapoport in the Journal of Conflict Resolution in 1965, and Andrew Colman (1982 , 12) reports estimates of over a thousand experimental gaming studies published by 1972, and some 1500 by the early 1980s. Kagel and Roth’s Handbook of Experimental Economics needed more than 700 pages to survey its subject in 1995 (including experiments in economics not limited to studies of strategic interaction). A specialized journal, Experimental Economics , began publication in June 1998; a majority of the articles in its inaugural issue were game-theoretic. A four-paper Economic Journal symposium on experimental economics ( Loomes 1999) was the subject of an article in The Economist (May 8,1999).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0040.006
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0300.002

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 head0.031
GPT teacher head0.209
Teacher spread0.178 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2005
Admission routes1
Has abstractyes

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