Testing for Fictive Learning in Decision-Making under Uncertainty

Oliver Bunn, Caterina Calsamiglia and Donald J. Brown ()
Additional contact information
Oliver Bunn: Dept. of Economics, Yale University
Donald J. Brown: Dept. of Economics, Yale University, https://economics.yale.edu/people/emeritus/donald-j-brown

No 1890, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: 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.

Keywords: Uncertainty; Counterfactual outcomes; Apophenia (search for similar items in EconPapers)
JEL-codes: C23 C35 C91 D03 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2013-03
New Economics Papers: this item is included in nep-cbe, nep-cwa, nep-evo, nep-exp and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Testing for Fictive Learning in Decision-Making Under Uncertainty (2013)
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