
Contents
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What Is the Nature of Adaptive Decision Making? What Is the Nature of Adaptive Decision Making?
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Robust Models of Uncertain Environments Robust Models of Uncertain Environments
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Out-of-Sample Robustness Out-of-Sample Robustness
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Out-of-Population Robustness Out-of-Population Robustness
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Novelty Robustness and the Problem of Extended Uncertainty Novelty Robustness and the Problem of Extended Uncertainty
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The Robustness of Learning Algorithms The Robustness of Learning Algorithms
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Uncertainty and the Bias–Variance Dilemma Uncertainty and the Bias–Variance Dilemma
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Take-the-Best: A Case Study in Ecological Rationality Take-the-Best: A Case Study in Ecological Rationality
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Being Robust by Being Simple Being Robust by Being Simple
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The Importance of the Bias–Variance Dilemma in Cognition The Importance of the Bias–Variance Dilemma in Cognition
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Achieving Robustness Through Simplicity Achieving Robustness Through Simplicity
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2 How Heuristics Handle Uncertainty
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Published:March 2012
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Abstract
Traditionally, it is assumed that a trade-off exists between effort and accuracy: The more effort we put in, the more accurate our inferences. This chapter shows how this trade-off does not hold in general, or even typically, by explaining how simple heuristics that ignore information can outperform more sophisticated inference strategies. Explanations of such “less-is-more” effects are given using statistical learning theory applied to situations where organisms face what is referred to as the bias–variance dilemma. Under conditions of uncertainty, it is argued, heuristics can both be more accurate and consume fewer resources than typical “rational” models of cognitive processing.
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