Generating Ambiguity in the Laboratory
This article develops a method for drawing samples from a distribution with no finite quantiles or moments. The method provides researchers with a way to give subjects the experience of ambiguity. In any experiment, learning the distribution from experience is impossible for the subjects, essentiall...
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| Veröffentlicht in: | Management science Jg. 57; H. 4; S. 705 - 712 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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Hanover, MD
INFORMS
01.04.2011
Institute for Operations Research and the Management Sciences |
| Schriftenreihe: | Management Science |
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| ISSN: | 0025-1909, 1526-5501 |
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| Abstract | This article develops a method for drawing samples from a distribution with no finite quantiles or moments. The method provides researchers with a way to give subjects the experience of ambiguity. In any experiment, learning the distribution from experience is impossible for the subjects, essentially because it is impossible for the experimenter. We characterize our method, illustrate it in simulations, and then test it in a laboratory experiment. Our method does not withhold sampling information, does not assume that the subject is incapable of making statistical inferences, is replicable across experiments, and requires no special apparatus. We compare our method to the techniques used in related experiments that attempt to produce an ambiguous experience for the subjects.
This paper was accepted by Peter Wakker, decision analysis. |
|---|---|
| AbstractList | This article develops a method for drawing samples from a distribution with no finite quantiles or moments. The method provides researchers with a way to give subjects the experience of ambiguity. In any experiment, learning the distribution from experience is impossible for the subjects, essentially because it is impossible for the experimenter. We characterize our method, illustrate it in simulations, and then test it in a laboratory experiment. Our method does not withhold sampling information, does not assume that the subject is incapable of making statistical inferences, is replicable across experiments, and requires no special apparatus. We compare our method to the techniques used in related experiments that attempt to produce an ambiguous experience for the subjects. This article develops a method for drawing samples from a distribution with no finite quantiles or moments. The method provides researchers with a way to give subjects the experience of ambiguity. In any experiment, learning the distribution from experience is impossible for the subjects, essentially because it is impossible for the experimenter. We characterize our method, illustrate it in simulations, and then test it in a laboratory experiment. Our method does not withhold sampling information, does not assume that the subject is incapable of making statistical inferences, is replicable across experiments, and requires no special apparatus. We compare our method to the techniques used in related experiments that attempt to produce an ambiguous experience for the subjects. [PUBLICATION ABSTRACT] This article develops a method for drawing samples from a distribution with no finite quantiles or moments. The method provides researchers with a way to give subjects the experience of ambiguity. In any experiment, learning the distribution from experience is impossible for the subjects, essentially because it is impossible for the experimenter. We characterize our method, illustrate it in simulations, and then test it in a laboratory experiment. Our method does not withhold sampling information, does not assume that the subject is incapable of making statistical inferences, is replicable across experiments, and requires no special apparatus. We compare our method to the techniques used in related experiments that attempt to produce an ambiguous experience for the subjects. This paper was accepted by Peter Wakker, decision analysis. This article develops a method for drawing samples from a distribution with no finite quantiles or moments. The method provides researchers with a way to give subjects the experience of ambiguity. In any experiment, learning the distribution from experience is impossible for the subjects, essentially because it is impossible for the experimenter. We characterize our method, illustrate it in simulations, and then test it in a laboratory experiment. Our method does not withhold sampling information, does not assume that the subject is incapable of making statistical inferences, is replicable across experiments, and requires no special apparatus. We compare our method to the techniques used in related experiments that attempt to produce an ambiguous experience for the subjects. Key words: ambiguity; Ellsberg; Knightian uncertainty; laboratory experiments; decision analysis; theory History: Received December 20, 2009; accepted November 28, 2010, by Peter Wakker, decision analysis. This article develops a method for drawing samples from a distribution with no finite quantiles or moments. The method provides researchers with a way to give subjects the experience of ambiguity. In any experiment, learning the distribution from experience is impossible for the subjects, essentially because it is impossible for the experimenter. We characterize our method, illustrate it in simulations, and then test it in a laboratory experiment. Our method does not withhold sampling information, does not assume that the subject is incapable of making statistical inferences, is replicable across experiments, and requires no special apparatus. We compare our method to the techniques used in related experiments that attempt to produce an ambiguous experience for the subjects. Reprinted by permission of the Institute for Operations Research and Management Science (INFORMS) This article develops a method for drawing samples from a distribution with no finite quantiles or moments. The method provides researchers with a way to give subjects the experience of ambiguity. In any experiment, learning the distribution from experience is impossible for the subjects, essentially because it is impossible for the experimenter. We characterize our method, illustrate it in simulations, and then test it in a laboratory experiment. Our method does not withhold sampling information, does not assume that the subject is incapable of making statistical inferences, is replicable across experiments, and requires no special apparatus. We compare our method to the techniques used in related experiments that attempt to produce an ambiguous experience for the subjects. This paper was accepted by Peter Wakker, decision analysis. |
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| Author | Dickhaut, John Shields, Timothy Stecher, Jack |
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| Keywords | Uncertain system Knightian uncertainty laboratory experiments Ellsberg Inference Probability learning Decision analysis Sampling Quantile theory Ambiguity |
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| SubjectTerms | Ambiguity Applied sciences Bid prices Computer software Decision analysis Decision making Decision theory. Utility theory Distribution Ellsberg Empirical research Entscheidung unter Unsicherheit Entscheidungstheorie Erwartungsbildung Exact sciences and technology Experience Experiments Inference from stochastic processes; time series analysis Knightian uncertainty Laboratories laboratory experiments Learning experiences Lotteries Management Management science Mathematical moments Mathematics Operational research and scientific management Operational research. Management science Pricing Probability and statistics Random variables Researcher subject relations Samples Sampling Sciences and techniques of general use Selling price Simulation Snakes and ladders Statistics Statistische Verteilung Studies Test theory Uncertainty |
| Title | Generating Ambiguity in the Laboratory |
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