2013
NIPS
NeurIPS 2013
Linear decision rule as aspiration for simple decision heuristics
Abstract
Many attempts to understand the success of simple decision heuristics have examined heuristics as an approximation to a linear decision rule. This research has identified three environmental structures that aid heuristics: dominance, cumulative dominance, and noncompensatoriness. Here, we further develop these ideas and examine their empirical relevance in 51 natural environments. We find that all three structures are prevalent, making it possible for some simple rules to reach the accuracy levels of the linear decision rule using less information.
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Interdisciplinary Bridge
— Interdisciplinary and Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— linear decision rule
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Cross-Pollinator
— Interdisciplinary, Machine Learning, Mathematics & Optimization
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Trend Setter
— Evaluation
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Optimization & Theory > Theory
Mathematics & Optimization > Optimization > Continuous Optimization
Interdisciplinary > Cognitive Science > Cognitive Modeling
Artificial Intelligence > Core AI > Reasoning
Machine Learning > Learning Types > Classification
Machine Learning > Learning Types > Evaluation
Machine Learning > Learning Types > Decision Making