Decision-making under uncertainty: biases and Bayesians

Animals (including humans) often face circumstances in which the best choice of action is not certain. Environmental cues may be ambiguous, and choices may be risky. This paper reviews the theoretical side of decision-making under uncertainty, particularly with regard to unknown risk (ambiguity). We...

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Bibliographic Details
Published inAnimal cognition Vol. 14; no. 4; pp. 465 - 476
Main Authors Trimmer, Pete C., Houston, Alasdair I., Marshall, James A. R., Mendl, Mike T., Paul, Elizabeth S., McNamara, John M.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer-Verlag 01.07.2011
Springer Nature B.V
Subjects
Animal behavior
Animal cognition
Bayes Theorem
Bayesian analysis
Behavioral Sciences
Bias
Biomedical and Life Sciences
Cues
Decision Making
Humans
Judgment
Life Sciences
Models, Psychological
Probability
Psychology Research
Review
Risk
Uncertainty
Zoology
Risk
Uncertainty
Animal decisions
Ellsberg Paradox
Ambiguity
Cognitive bias
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Summary:Animals (including humans) often face circumstances in which the best choice of action is not certain. Environmental cues may be ambiguous, and choices may be risky. This paper reviews the theoretical side of decision-making under uncertainty, particularly with regard to unknown risk (ambiguity). We use simple models to show that, irrespective of pay-offs, whether it is optimal to bias probability estimates depends upon how those estimates have been generated. In particular, if estimates have been calculated in a Bayesian framework with a sensible prior, it is best to use unbiased estimates. We review the extent of evidence for and against viewing animals (including humans) as Bayesian decision-makers. We pay particular attention to the Ellsberg Paradox, a classic result from experimental economics, in which human subjects appear to deviate from optimal decision-making by demonstrating an apparent aversion to ambiguity in a choice between two options with equal expected rewards. The paradox initially seems to be an example where decision-making estimates are biased relative to the Bayesian optimum. We discuss the extent to which the Bayesian paradigm might be applied to the evolution of decision-makers and how the Ellsberg Paradox may, with a deeper understanding, be resolved.
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ISSN:1435-9448
1435-9456
1435-9456
DOI:10.1007/s10071-011-0387-4