David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Minds and Machines 9 (4):543-564 (1999)
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.
|Keywords||Bayesianism complexity decision theory fast and frugal heuristics machine learning philosophy of science predictive accuracy simplicity|
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Armin W. Schulz (2011). Gigerenzer's Evolutionary Arguments Against Rational Choice Theory: An Assessment. Philosophy of Science 78 (5):1272-1282.
Nathan Berg & Ulrich Hoffrage (2010). Compressed Environments: Unbounded Optimizers Should Sometimes Ignore Information. [REVIEW] Minds and Machines 20 (2):259-275.
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