Vertigo: Comparing structural models of imperfect behavior in experimental games

El‐Gamal, Mahmoud A.; Palfrey, Thomas R.

doi:10.1016/s0899-8256(05)80004-1

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…5 A number of other authors have also proposed methods for analysis of behavioral heterogeneity. These include McKelvey and Palfrey (1992), Stahl and Wilson (1995), El-Gamal and Palfrey (1995), and Camerer and Ho (1999). Like El-Gamal and Grether (1995), these approaches have in common that the set of possible subject behaviors must be pre-specified by the researcher.…”

Section: Introductionmentioning

confidence: 99%

Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm

Houser¹,

Keane²,

McCabe³

2004

Econometrica

154

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Different people may use different strategies, or decision rules, when solving complex decision problems. We provide a new Bayesian procedure for drawing inferences about the nature and number of decision rules present in a population, and use it to analyze the behaviors of laboratory subjects confronted with a difficult dynamic stochastic decision problem. Subjects practiced before playing for money. Based on money round decisions, our procedure classifies subjects into three types, which we label "Near Rational," "Fatalist," and "Confused." There is clear evidence of continuity in subjects' behaviors between the practice and money rounds: types who performed best in practice also tended to perform best when playing for money. However, the agreement between practice and money play is far from perfect. The divergences appear to be well explained by a combination of type switching (due to learning and/or increased effort in money play) and errors in our probabilistic type assignments.

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Section: Introductionmentioning

confidence: 99%

Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm

Houser¹,

Keane²,

McCabe³

2004

Econometrica

154

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“…We therefore must adapt all of the models so that they assign positive probability (likelihood) to all possible data points. In our recent work (e.g., Boylan and El-Gamal, 1993;McKelvey and Palfrey, 1992;ElGamal, McKelvey, and Palfrey, 1993a;El-Gamal and Palfrey, 1993), we have avoided the zero-likelihood problem by introducing an error structure which posits that subjects occasionally make errors The existence of such a value function uses the fact that the operator T, defined by satisfies monotonicity and discounting, and hence is a contraction mapping (Blackwell, 1965 where is the value to the nth player of using strategy ~' when all other players are using strategy p. It is straightforward (see McKelvey, 1990) If all players were perfectly rational, then all four stage games would end in the red players taking on the first round. If perfect rationality is not assumed, the problem becomes much more complicated (e.g., see Binmore, 1989;Reny, 1993;Rosenthal, 1982;Aumann, 1988;Kreps et al, 1982 corresponds to e jo = F-0/20), and then making our priors uniform over those supports.…”

Section: Introductionmentioning

confidence: 93%

Computational Issues in the Statistical Design and Analysis of Experimental Games

Ei-Gamal

McKelvey

Palfrey

1993

The International Journal of Supercomputing Applications

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“…We have characterized this problem in previous work (e.g. Boylan and El-Gamal (1993), , El-Gamal and Palfrey ( 1994)) and proposed a number of methods of introducing errors in actions to make every observable data set a positive-likelihood set under all models under consideration. In this paper, we follow the same approach-by letting every-agent at every .. deci.sion node-have a probability c of trembling and making their choice by flipping a fair coin (i.e.…”

Section: Perturbing the Game: Errors In Actions And Errors In Be Liefsmentioning

confidence: 99%

Economical experiments: Bayesian efficient experimental design

El‐Gamal

Palfrey

1996

Int J Game Theory

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We propose and implement a Bayesian optimal design procedure. Our procedure takes as its primitives a class of models, a class of experimental designs, and priors on the nuisance parameters of those models. We select the experimental design that maximizes the information (in the sense of Kullback-Liebler) from the experiment. We sequentially sample with the given design and models until all but one of the models has viable posterior odds. A model which has low posterior odds in a small collection of models will have an even lower posterior odds when compared to a larger class, and hence we can dismiss it. The procedure can be used sequentially by introducing new models and comparing them to the models that survived earlier rounds of experiments. The emphasis is not on running as many experiments as possible, but rather on choosing experimental designs to distinguish between models in the shortest possible time period. The first stage of optimal design is illustrated with a simple experimental game with one-sided incomplete information.JEL classification numbers: 026,211,215

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Vertigo: Comparing structural models of imperfect behavior in experimental games

Cited by 7 publications

References 10 publications

Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm

Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm

Computational Issues in the Statistical Design and Analysis of Experimental Games

Economical experiments: Bayesian efficient experimental design

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