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Testing for Fictive Learning in Decision-Making Under Uncertainty


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  • Oliver Bunn
  • Caterina Calsamiglia
  • Donald Brown


We conduct two experiments where subjects make a sequence of binary choices between risky and ambiguous binary lotteries. Risky lotteries are defined as lotteries where the relative frequencies of outcomes are known. Ambiguous lotteries are lotteries where the relative frequencies of outcomes are not known or may not exist. The trials in each experiment are divided into three phases: pre-treatment, treatment and post-treatment. The trials in the pre-treatment and post-treatment phases are the same. As such, the trials before and after the treatment phase are dependent, clustered matched-pairs, that we analyze with the alternating logistic regression (ALR) package in SAS. In both experiments, we reveal to each subject the outcomes of her actual and counterfactual choices in the treatment phase. The treatments differ in the complexity of the random process used to generate the relative frequencies of the payoffs of the ambiguous lotteries. In the first experiment, the probabilities can be inferred from the converging sample averages of the observed actual and counterfactual outcomes of the ambiguous lotteries. In the second experiment the sample averages do not converge. If we define fictive learning in an experiment as statistically significant changes in the responses of subjects before and after the treatment phase of an experiment, then we expect fictive learning in the first experiment, but no fictive learning in the second experiment. The surprising finding in this paper is the presence of fictive learning in the second experiment. We attribute this counterintuitive result to apophenia: "seeing meaningful patterns in meaningless or random data." A refinement of this result is the inference from a subsequent Chi-squared test, that the effects of fictive learning in the first experiment are significantly different from the effects of fictive learning in the second experiment.

(This abstract was borrowed from another version of this item.)

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Paper provided by David K. Levine in its series Levine's Working Paper Archive with number 786969000000000660.

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Date of creation: 11 Apr 2013
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Handle: RePEc:cla:levarc:786969000000000660

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  1. Donald J. Brown & Oliver Bunn & Caterina Calsamiglia & Donald J. Brown, 2013. "Fictive Learning in Choice under Uncertainty: A Logistic Regression Model," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University 1890R, Cowles Foundation for Research in Economics, Yale University, revised Mar 2014.


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