2013
NeurIPS
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
- Artificial Intelligence and Interdisciplinary and Mathematics & Optimization
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 > Evaluation
Artificial Intelligence > Core AI > Decision Making
Machine Learning > Learning Types > Decision Making