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
Hauptverfasser: Stecher, Jack, Shields, Timothy, Dickhaut, John
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hanover, MD INFORMS 01.04.2011
Institute for Operations Research and the Management Sciences
Schriftenreihe:Management Science
Schlagworte:
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
United States
Uncertain system
Knightian uncertainty
laboratory experiments
Ellsberg
Inference
Probability learning
Decision analysis
Sampling
Quantile
theory
Ambiguity
ISSN:0025-1909, 1526-5501
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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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ISSN:0025-1909
1526-5501
DOI:10.1287/mnsc.1100.1307