How Do Simple Rules "Fit to Reality" in a Complex World?

This page was last edited on 05/19/02 by Malcolm R Forster

  PDF

Note: You need Adobe Acrobat Reader 3.0, or later, to read and print this article.  It is free.

Published Version (22 print pages). Updated 6/6/01.

Publication Data

(1999) “How Do Simple Rules ‘Fit to Reality’ in a Complex World?”  Minds and Machines  9: 543-564.

 

Main Reference

Gigerenzer, Gerd, Peter Todd, and the ABC Group (1999). Simple Heuristics that Make Us Smart, New York, Oxford University Press.

Abstract

 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 in all cases. 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.

Bertrand.gif (5437 bytes)

Figure 1: In Bertrand’s example (see section 6, Bayesian Decision Making), there are three different ways of applying Laplace’s principle of indifference to answer the question: What is the chance that the straw intersects the equilateral triangle (as shown)? The first distribution gives equal weights to all intersection points on the line OA, the second distribution gives equal probability the angle of intersection of the straw on the circumference (angle q ), and the third gives equal probability to each possible area of the inner circle. Each leads to a different answer to the original question (1/2, 1/3, and 1/4, respectively).

Contents

  1. Introduction
  2. Fast and Frugal Learning
  3. Dennett’s Robot
  4. Automated Scientific Discovery
  5. No-Free-Lunch Theorems
  6. Bayesian Decision Making
  7. The Ecological Validity of Science
  8. Main Conclusion