Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
An introduction to model selection
Journal of Mathematical Psychology
Key concepts in model selection: performance and generalizability
Journal of Mathematical Psychology
The lack of a priori distinctions between learning algorithms
Neural Computation
Simple Inference Heuristics versus Complex Decision Machines
Minds and Machines
Information-oriented design and game AI
Proceedings of the second Australasian conference on Interactive entertainment
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The theory of fast and frugal heuristics, developed in a new book called Simple Heuristics that make Us Smart (Gigerenzer, Todd, and the ABC Research Group, in press), includes two requirements for rational decision making. One is that decision rules are bounded in their rationality –- that rules are frugal in what they take into account, and therefore fast in their operation. The second is that the rules are ecologically adapted to the environment, which means that they `fit to reality.' The main purpose of this article is to apply these ideas to learning rules–-methods for constructing, selecting, or evaluating competing hypotheses in science, and to the methodology of machine learning, of which connectionist learning is a special case. The bad news is that ecological validity is particularly difficult to implement and difficult to understand. The good news is that it builds an important bridge from normative psychology and machine learning to recent work in the philosophy of science, which considers predictive accuracy to be a primary goal of science.