Replicator Dynamics
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Replicator Dynamics. Nash makes sense ( arguably ) if… - Uber - rational - Calculating. Such as Auctions…. Or Oligopolies…. But why would game theory matter for our puzzles ?. Norms/rights/morality are not chosen ; rather… We believe we have rights!

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Replicator Dynamics

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Replicator Dynamics


Nash makes sense (arguably) if…

-Uber-rational

-Calculating


Such as Auctions…


Or Oligopolies…


But why would game theory matter for our puzzles?


Norms/rights/morality are not chosen;rather…

We believewe have rights!

We feel angry when uses service but doesn’t pay


But…

From where do these feelings/beliefs come?


In this lecture, we will introduce replicator dynamics

The replicator dynamic is a simple model of evolution and prestige-biased learning in games

Today, we will show that replicator leads to Nash


We consider a large population, ,of players

Each period, a player is randomly matched with another player and they play a two-player game


Each player is assigned a strategy. Players cannot choose their strategies


We can think of this in a few ways, e.g.:

  • Players are “born with” their mother’s strategy (ignore sexual reproduction)

  • Players “imitate” others’ strategies


Note:

Rationality and consciousness don’t enter the picture.


Suppose there are two strategies, and .

We start with:

Some number, , of players assigned strategy

and some number, , of players assigned strategy


We denote the proportion of the population playing strategy as , so:


The state of the population is given by

where , , and


Since players interact with another randomly chosen player in the population, a player’s EXPECTEDpayoff is determined by the payoff matrix and the proportion of each strategy in the population.


For example, consider

the coordination game:

And the following starting frequencies:


Payoff for player who is playing is

Since depends on and we write


We interpret payoffs as rate of reproduction (fitness).


The average fitness, , of a population is the

weighted average of the two fitness values.


How fast do and grow?

Recall

First, we need to know how fast grows

Let

Each individual reproduces at a rate , and there are of them. So:

Next we need to know how fast grows. By the same logic:

By the quotient rule, and with a little simplification…


This is the replicator equation:

Current frequency of strategy

Own fitness relative

to the average


Growth rate of A

Current frequency of strategy

Because that’s how many As can reproduce

Own fitness relative

to the average

This is our key property.

More successful strategies grow faster


If:

: The proportion of As is non-zero

: The fitness of A is above average

Then:

: A will be increasing in the population


We are interested in states where

since these are places where the proportions of A and B are stationary.

We will call such points steady states.

0


 The steady states are

such that


A

B

Recall the payoffs of our (coordination) game:

b, c

a, a

A

B

c, b

d, d

a > c

b < d


= “asymptotically stable” steady states

i.e., steady states s.t.the dynamics point toward it


What were the pure Nash equilibria of the coordination game?


A

B

b, c

a, a

A

B

c, b

d, d


1

0


And the mixed strategy equilibrium is:


Replicator teaches us:

We end up at Nash

(…if we end)

AND not just any Nash

(e.g. not mixed Nash in coordination)


Let’s generalize this to three strategies:


Now…

is the number playing

is the number playing

is the number playing


Now…

is the proportion playing

is the proportion playing

is the proportion playing


The state of population is

where , , ,

and


For example,

Consider the

Rock-Paper-Scissors

Game:

With starting frequencies:

R

P

S

R

0

-1

1

P

1

0

-1

S

-1

1

0


Fitness for player playing is


In general, fitness for players with strategy R is:


The average fitness, f, of the population is:


Replicator is still:

Current frequency of strategy

Own fitness relative

to the average


Notice not asymptotically stable

It cycles


R

P

S

R

0

-1

2

P

2

0

-1

S

-1

2

0


Note now is asymptotically stable


For further readings, see:

Nowak Evolutionary Dynamics Ch. 4

WeibullEvolutionary Game Theory Ch. 3

Some notes:

Can be extended to any number of strategies

Doesn’t always converge, but when does converges to Nash

Thus, dynamics “justify Nash” but also give more info


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