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The Ultimate Sampling Dilemma in Experience-Based Decision Making

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  • Fiedler, Klaus

    (Sonderforschungsbereich 504/ Universität Heidelberg)

Abstract

Computer simulations and two experiments are reported to delineate the ultimate sampling dilemma, which constitutes a serious obstacle to inductive inferences in a probabilistic world. Participants were asked to take the role of a manager who is to make purchasing decisions based on positive versus negative feedback about three providers in two different product domains. When information sampling (from a computerized data base) was over, they had to make inferences about actual differences in the data base from which the sample was drawn (e.g., about the actual superiority of different providers, or about the most likely origins of negatively valenced products). The ultimate sampling dilemma consists in a forced choice between two search strategies that both have their advantages and their drawbacks: natural sampling and deliberate sampling of information relevant to the inference task. Both strategies leave the sample unbiased for specific inferences but create errors or biases for other inferences.

Suggested Citation

  • Fiedler, Klaus, 2007. "The Ultimate Sampling Dilemma in Experience-Based Decision Making," Sonderforschungsbereich 504 Publications 07-51, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
  • Handle: RePEc:xrs:sfbmaa:07-51
    Note: The present research was supported by various grants from the Deutsche Forschungsgemeinschaft. Thanks to Ralph Hertwig, Ulrich Hoffrage, Tobias Vogel, Peter Freytag, and Yaakov Kareev for their helpful and constructive comments on a draft of this article. Correspondence should be addressed to Klaus Fiedler, Psychologisches Institut, Universität Heidelberg, Hauptstrasse 47-51, 69117 Heidelberg, Germany.
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    File URL: http://www.sfb504.uni-mannheim.de/publications/dp07-51.pdf
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