lecture9 - Lecture IX: Evolution Markus M. M¨obius March...

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Lecture IX: Evolution Markus M. M¨obius March 10, 2004 Learning and evolution are the second set of topics which are not discussed in the two main texts. I tried to make the lecture notes self-contained. • Fudenberg and Levine (1998), The Theory of Learning in Games, Chap- ter 1 and 2 1 Introduction For models of learning we typically assume a fixed number of players who find out about each other’s intentions over time. In evolutionary models the process of learning is not explicitly modeled. Instead, we assume that strate- gies which do better on average are played more often in the population over time. The biological explanation for this is that individuals are genetically programmed to play one strategy and their reproduction rate depends on their fitness, i.e. the average payoff they obtain in the game. The economic explanation is that there is social learning going on in the background - people find out gradually which strategies do better and adjust accordingly. However, that adjustment process is slower than the rate at which agents play the game. We will focus initially on models with random matching: there are N agents who are randomly matched against each other over time to play a certain game. Frequently, we assume that N as infinite. We have discussed in the last lecture that random matching gives rise to myopic play because there are no repeated game concerns (I’m unlikely to ever encounter my current opponent again). We will focus on symmetric n by n games for the purpose of this course. In a symmetric game each player has the same strategy set and the payoff 1 matrix satisfies u i ( s i ,s j ) = u j ( s j ,s i ) for each player i and j and strategies s i ,s j ∈ S i = S j = { s 1 ,..,s n } . Many games we encountered so far in the course are symmetric such as the Prisoner’s Dilemma, Battle of the Sexes, Chicken and all coordination games. In symmetric games both players face exactly the same problem and their optimal strategies do not depend on whether they play the role of player 1 or player 2. An important assumption in evolutionary models is that each agent plays a fixed pure strategy until she dies, or has an opportunity to learn and about her belief. The game is fully specified if we know the fraction of agents who play strategy s 1 ,s 2 ,..,s n which we denote with x 1 ,x 2 ,..,x n such that ∑ n i =1 x i = 1. 2 Mutations and Selection Every model of evolution relies on two key concepts - a mutation mechanism and a selection mechanism . We have already discussed selection - strategies spread if they give above average payoffs. This captures social learning in a reduced form. Mutations are important to add ’noise’ to the system (i.e. ensure that x i > 0 at all times) and prevent it from getting ’stuck’. For example, in a world where players are randomly matched to play a Prisoner’s Dilemma mutations make sure that it will never be the case that all agents cooperate or all agents defect because there will be random mutations pushing the system...
View Full Document

This note was uploaded on 05/19/2010 for the course 412 002 taught by Professor Dingli during the Spring '10 term at École Normale Supérieure.

Page1 / 9

lecture9 - Lecture IX: Evolution Markus M. M¨obius March...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online