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Adaptive learning and risk taking
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DOI：10.1037/0033-295X.114.1.177.png)
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摘要
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Humans and animals learn from experience by reducing the probability of sampling alternatives with poor past outcomes. Using simulations, J. G. March (1996) illustrated how such adaptive sampling could lead to risk-averse as well as risk-seeking behavior. In this article, the author develops a formal theory of how adaptive sampling influences risk taking. He shows that a risk-neutral decision maker may learn to prefer a sure thing to an uncertain alternative with identical expected value and a symmetric distribution, even if the decision maker follows an optimal policy of learning. If the distribution of the uncertain alternative is negatively skewed, risk-seeking behavior can emerge. Consistent with recent experiments, the model implies that information about foregone payoffs increases risk taking.
Keyword:
decisions from experience
risk taking
decisions under uncertainty
adaptive behavior
learning from outcome feedback
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