An initial implementation of the Turing tournament to learning in repeated two-person games

Arifovic, Jasmina; McKelvey, Richard D.; Pevnitskaya, Svetlana

doi:10.1016/j.geb.2006.03.013

Cited by 29 publications

(28 citation statements)

References 22 publications

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“…The second is the organization of an open choice prediction competition that facilitates the evaluation of a wide class of models. Specifically, Erev et al organized a simplified version of the competition run by Arifovic, McKelvey, and Pevnitskaya [10]. They ran two large experiments examining different problems drawn randomly from the same space, and challenged other researchers to predict the results of the second study based on evaluation of the results of the first study.…”

Section: Introductionmentioning

confidence: 99%

A Choice Prediction Competition for Market Entry Games: An Introduction

Erev¹,

Ert

Roth

2010

Games

107

169

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Abstract:A choice prediction competition is organized that focuses on decisions from experience in market entry games (http://sites.google.com/site/gpredcomp/ and http://www.mdpi.com/si/games/predict-behavior/). The competition is based on two experiments: An estimation experiment, and a competition experiment. The two experiments use the same methods and subject pool, and examine games randomly selected from the same distribution. The current introductory paper presents the results of the estimation experiment, and clarifies the descriptive value of several baseline models. The experimental results reveal the robustness of eight behavioral tendencies that were documented in previous studies of market entry games and individual decisions from experience. The best baseline model (I-SAW) assumes reliance on small samples of experiences, and strong inertia when the recent results are not surprising. The competition experiment will be run in May 2010 (after the completion of this introduction), but they will not be revealed until September. To participate in the competition, researchers are asked to E-mail the organizers models (implemented in computer programs) that read the incentive structure as input, and derive the predicted behavior as an output. The submitted models will be ranked based on their prediction error. The winners of the competition will be invited to publish a paper that describes their model. OPEN ACCESS

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Section: Introductionmentioning

confidence: 99%

A Choice Prediction Competition for Market Entry Games: An Introduction

Erev¹,

Ert

Roth

2010

Games

107

169

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“…But there is a second sampling equilibrium at (0.20, 0.28, 0.52) in which both strictly dominated actions are played with positive probability. It turns out that only the latter equilibrium is stable under the dynamics (2). In fact, the basin of attraction of this equilibrium is the entire unit simplex, excluding the single point (0, 0, 1).…”

Section: Sampling Equilibriummentioning

confidence: 99%

Commons Without Tragedy: Sampling Dynamics and Cooperative Resource Extraction

Mantilla¹,

Cárdenas

Sethi

2013

SSRN Journal

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This paper reconsiders evidence from experimental common pool resource games from the perspective of a dynamic model of sampling. Despite being parameter-free, the model is able to replicate some striking features of the data: monotonic frequency distributions, the persistent use of strictly dominated actions, and stable heterogeneity in choices. We argue that these patterns cannot be fully accounted for by existing theories based on other-regarding preferences and norms, and that the dynamics of sampling provide a useful complementary explanation for behavior in social dilemmas.

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“…Rapoport and Chammah (1965) and Rapoport, Guyer, and Gordon (1976)). Yet standard learning algorithms have limited success in capturing the degree of cooperation in the Prisoner's Dilemma game and the alternation between the two pure-strategy Nash equilibria in the Battle of the Sexes game (Arifovic, McKelvey, and Pevnitskaya (2006)). Along similar lines, Erev and Haruvy (2013) point out that "human agents exhibit higher social intelligence and/or sensitivity than assumed by the basic learning models" (p. 61).…”

Section: The Gamesmentioning

confidence: 99%

A generalized approach to belief learning in repeated games

Ioannou

Romero

2014

Games and Economic Behavior

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We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric 2 × 2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the rules of behavior predicted.JEL Classification: C51, C92, C72, D03

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An initial implementation of the Turing tournament to learning in repeated two-person games

Cited by 29 publications

References 22 publications

A Choice Prediction Competition for Market Entry Games: An Introduction

A Choice Prediction Competition for Market Entry Games: An Introduction

Commons Without Tragedy: Sampling Dynamics and Cooperative Resource Extraction

A generalized approach to belief learning in repeated games

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