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How Do People Take into Account Weight, Strength and Quality of Segregated vs. Aggregated Data? Experimental Evidence

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  • Carlo Kraemer
  • Martin Weber

Abstract

In this experimental study we investigated how people aggregate two sets of signals about the state of the world to reach a single probability judgment. The signal sets may differ in the way signals are presented, in their number as well as their quality. By varying the presentation mode of the signals we investigated how people deal with segregated and aggregated evidence. We investigated whether subjects sufficiently take into account weight (number of signals), strength (composition) and quality of the information provided. The results indicate that consideration of the weight and strength of signals strongly depends on the type of their presentation. Particular patterns can be identified which determine if weight and/or strength are either under- or overweighted.

Suggested Citation

  • Carlo Kraemer & Martin Weber, 2004. "How Do People Take into Account Weight, Strength and Quality of Segregated vs. Aggregated Data? Experimental Evidence," Journal of Risk and Uncertainty, Springer, vol. 29(2), pages 113-142, September.
    • Handle: RePEc:kap:jrisku:v:29:y:2004:i:2:p:113-142
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    Citations

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    Cited by:

    1. Konstantinos Georgalos, 2016. "Dynamic decision making under ambiguity," Working Papers 112111041, Lancaster University Management School, Economics Department.
    2. Daniel J. Benjamin & Matthew Rabin & Collin Raymond, 2016. "A Model of Nonbelief in the Law of Large Numbers," Journal of the European Economic Association, European Economic Association, vol. 14(2), pages 515-544.
    3. Grebe, Tim & Schmid, Julia & Stiehler, Andreas, 2008. "Do individuals recognize cascade behavior of others? - An experimental study," Journal of Economic Psychology, Elsevier, vol. 29(2), pages 197-209, April.
    4. Daniel J. Benjamin, 2018. "Errors in Probabilistic Reasoning and Judgment Biases," NBER Working Papers 25200, National Bureau of Economic Research, Inc.
    5. Antoniou, Constantinos & Harrison, Glenn W. & Lau, Morten I. & Read, Daniel, 2017. "Information Characteristics and Errors in Expectations: Experimental Evidence," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(2), pages 737-750, April.
    6. Ambuehl, Sandro & Li, Shengwu, 2018. "Belief updating and the demand for information," Games and Economic Behavior, Elsevier, vol. 109(C), pages 21-39.
    7. He, Xue Dong & Xiao, Di, 2017. "Processing consistency in non-Bayesian inference," Journal of Mathematical Economics, Elsevier, vol. 70(C), pages 90-104.
    8. Georgalos, Konstantinos, 2021. "Dynamic decision making under ambiguity: An experimental investigation," Games and Economic Behavior, Elsevier, vol. 127(C), pages 28-46.
    9. Daniel F. Stone, 2013. "Testing Bayesian Updating With The Associated Press Top 25," Economic Inquiry, Western Economic Association International, vol. 51(2), pages 1457-1474, April.
    10. Xiao, Wei, 2022. "Understanding probabilistic expectations – a behavioral approach," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).

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