Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- 1 Selected concepts from probability
- 2 Probability, random variables, and selectivity
- 3 Functional equations
- 4 Network analysis
- 5 Knowledge spaces and learning spaces
- 6 Evolutionary game theory
- 7 Choice, preference, and utility: probabilistic and deterministic representations
- 8 Discrete state models of cognition
- 9 Bayesian hierarchical models of cognition
- 10 Model evaluation and selection
- Index
6 - Evolutionary game theory
Published online by Cambridge University Press: 01 December 2016
- Frontmatter
- Contents
- List of contributors
- Preface
- 1 Selected concepts from probability
- 2 Probability, random variables, and selectivity
- 3 Functional equations
- 4 Network analysis
- 5 Knowledge spaces and learning spaces
- 6 Evolutionary game theory
- 7 Choice, preference, and utility: probabilistic and deterministic representations
- 8 Discrete state models of cognition
- 9 Bayesian hierarchical models of cognition
- 10 Model evaluation and selection
- Index
Summary
Introduction
Evolutionary game theory made its way into the social sciences through a curiously circuitous path. Its origin lay in the realization that the mathematical theory of games developed by von Neumann and Morgenstern (1944) could be used to analyze problems of population biology. Because the fitness of an organism (or a trait) in a population often depends on the relative frequency of other organisms (or traits) present, natural selection can take on a strategic character even when none of the organisms are “rational” in any standard sense.
Although modern evolutionary game theory is typically considered to have begun in the work of Maynard Smith and Price (1973), important precursors exist in the work of R. A. Fisher as early as 1930. In The Genetical Theory of Natural Selection, Fisher sought to explain the approximate equality of the sex ratio in mammals using arguments which can be readily understood in game-theoretic terms. The watershed moment for evolutionary game theory, though, was the publication of Maynard Smith's seminal work Evolution and the Theory of Games in 1982. In that text, Maynard Smith drew together a number of results, both from his own work and that of a number of mathematical biologists, presenting them in a clear, coherent format with minimal mathematical prerequisites.
Over time, economists and other social scientists became interested in evolutionary game theory for a variety of reasons, some stemming from long-standing issues in the traditional theory of games. Of these, perhaps the two most important were as follows: first, as von Neumann and Morgenstern clearly recognized, their theory lacked any underlying dynamics. Early on in Theory of Games and Economic Behaviour, they wrote:
We repeat most emphatically that our theory is thoroughly static. A dynamic theory would unquestionably be more complete and therefore preferable. But there is ample evidence from other branches of science that it is futile to try to build one as long as the static side is not thoroughly understood. (§4.8.2)
Second, the traditional solution concept – that of a Nash equilibrium (which shall be defined below) – suffered from the problem that it was not necessarily unique: games could, and frequently did, have multiple solutions, each of which had some claim to be the “rational” solution.
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- Information
- New Handbook of Mathematical Psychology , pp. 322 - 373Publisher: Cambridge University PressPrint publication year: 2016