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Choices Between Simple and Compound Lotteries: Experimental Evidence and Neural Network Modelling

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  • Daniel John Zizzo

Abstract

An experiment on choices between single and compound lotteries is presented, and results are calibrated with neural network models. Many subjects tend to average out probabilities, though behaviour becomes more rational with more exposure to compound lotteries in the practice stage. The Prior Knowledge Model hypothesizes that subjects categorize stimuli according to the prior knowledge acquired in their long-run learning history; practice stage cues help them referring to the relevant learning history. The trained networks predict the behaviour of about 3/4 of the subjects with transitive preferences; the model can explain where we would expect the trained networks to fail.

Suggested Citation

  • Daniel John Zizzo, 2001. "Choices Between Simple and Compound Lotteries: Experimental Evidence and Neural Network Modelling," Economics Series Working Papers 57, University of Oxford, Department of Economics.
    • Handle: RePEc:oxf:wpaper:57
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    File URL: https://ora.ox.ac.uk/objects/uuid:5ea0e2a6-b2eb-4d13-a282-d8cef12aab56
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    Cited by:

    1. Daniel Zizzo, 2003. "Verbal and Behavioral Learning in a Probability Compounding Task," Theory and Decision, Springer, vol. 54(4), pages 287-314, June.

    More about this item

    Keywords

    conjunction fallacy; neural networks; heuristics; probability compounding;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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