Book contents
- Frontmatter
- Contents
- Preface
- Introduction for game theorists
- Introduction for biologists
- About this book
- Part one Dynamical Systems and Lotka–Volterra Equations
- Part two Game Dynamics and Replicator Equations
- 6 Evolutionarily stable strategies
- 7 Replicator dynamics
- 8 Other game dynamics
- 9 Adaptive dynamics
- 10 Asymmetric games
- 11 More on bimatrix games
- Part three Permanence and Stability
- Part four Population Genetics and Game Dynamics
- References
- Index
8 - Other game dynamics
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Preface
- Introduction for game theorists
- Introduction for biologists
- About this book
- Part one Dynamical Systems and Lotka–Volterra Equations
- Part two Game Dynamics and Replicator Equations
- 6 Evolutionarily stable strategies
- 7 Replicator dynamics
- 8 Other game dynamics
- 9 Adaptive dynamics
- 10 Asymmetric games
- 11 More on bimatrix games
- Part three Permanence and Stability
- Part four Population Genetics and Game Dynamics
- References
- Index
Summary
Many of the properties of the replicator equation are valid for other game dynamics which may serve, for instance, to model imitation processes. We describe a large class of game dynamics that eliminate pure strategies which are iteratively strictly dominated, and discuss some instances of the best-reply dynamics motivated by fictitious play. We show that no reasonable dynamics can converge to equilibrium for all games.
Imitation dynamics
The replicator dynamics mimics the effect of natural selection (although it blissfully disregards the complexities of sexual reproduction). In the context of games played in human societies, however, the spreading of successful strategies is more likely to occur through imitation than through inheritance. How should we model this imitation processes?
Let us start with symmetric games defined by an n × n payoff matrix A, and assume that the pure strategies R1 to Rn are adopted by a (large) population of players with frequencies xi(t) at time t, so that the state is given, at any instant, by a point x ∈ Sn. Strategy Ri then earns (Ax)i = ∑ aijxj a s expected payoff, and the average payoff in the population is given by x · Ax. We shall suppose that occasionally a player is picked out of the population and afforded the opportunity to change his strategy. He samples another player at random and adopts his strategy with a certain probability.
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- Evolutionary Games and Population Dynamics , pp. 86 - 100Publisher: Cambridge University PressPrint publication year: 1998
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