Foundations of decision analysis along the way
Management Science
Neural networks and the bias/variance dilemma
Neural Computation
A comparison of logistic regression to decision-tree induction in a medical domain
Computers and Biomedical Research
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Introduction to Operations Research and Revised CD-ROM 8
Introduction to Operations Research and Revised CD-ROM 8
Regions of Rationality: Maps for Bounded Agents
Decision Analysis
Ecological Rationality: Intelligence in the World
Ecological Rationality: Intelligence in the World
New tools for decision analysts
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Laypeople as well as professionals such as business managers and medical doctors often use psychological heuristics. Psychological heuristics are models for making inferences that (1) rely heavily on core human capacities (such as recognition, recall, or imitation); (2) do not necessarily use all available information and process the information they use by simple computations (such as lexicographic rules or aspiration levels); and (3) are easy to understand, apply, and explain. Psychological heuristics are a simple alternative to optimization models (where the optimum of a mathematical function that incorporates all available information is computed). I review studies in business, medicine, and psychology where computer simulations and mathematical analyses reveal conditions under which heuristics make better inferences than optimization and vice versa. The conditions involve concepts that refer to (i) the structure of the problem, (ii) the resources of the decision maker, or (iii) the properties of the models. I discuss open problems in the theoretical study of the concepts. Finally, I organize the current results tentatively in a tree for helping decision analysts decide whether to suggest heuristics or optimization to decision makers. I conclude by arguing for a multimethod, multidisciplinary approach to the theory and practice of inference and decision making.