The Complexity of Cooperation
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Transcript The Complexity of Cooperation
Evolving New
Strategies
The Evolution of Strategies in
the Prisoner’s Dilemma
-By Robert Axelrod
Why’s and What’s of
Evolution
Agents are not always fully
aware of there situations.
Instead, they must adapt to
them.
Methods for adapation in
nature are a combination of
natural selection and
mutation.
The Steps in a Model of
Evolution
Specify the Environment in which
the evolutionary process can
operate.
A method for simulating genetics.
A test to discovery whether
strategies diverge or converge in
similar situations.
Statisical Analysis of the computer
simulation
The Simulated
Environment
An iterated prisoner’s dilemma
of known length.
A determined number of
generations.
Adaptive agents that play
against eight successful rules of
Axelrod’s tournament.
The Genetic Algorithm
Each Agent has it’s own
‘Chromosome’.
The Strategies are deterministic
and based on a three turn
memory.
64 Corps Genes + 6 Additional
Genes to deal with the start of
the game.
There are 10 to the 21st
combinations of C’s and D’s.
The Genetic Algorithm
Cont’d
An initial random population is
selected and run.
The more successful individuals
are chosen to mate at random.
Crossover and mutation
determine the child’s genes.
The new generation replaces the
old one.
Results
A constant population of twenty
individuals meeting 151 times
with each of the eight rules ~
24,000 moves per generation.
The median adaptive agent was
as successful as the best rule in
Axelrod’s tournament
The adaptive agents act similar
to TFT.
A Surprising Result
In 11 of 50 generations the
Agents learned to exploit
there opponents.
In this case defecting first
payed.
These new strategies are
not robust.
Five Important traits for
each agent
“Don’t Rock the boat (C after
RRR).”
“Be provocable (D after RRS).”
“Acccept an apology (C after
TSR).”
“Forget (C after SRR).”
“Accept a rut (D after PPP).”
Where can we go from
here?
Dock our model with Axelrod’s
as a litmus test for its
usefulness.
Increasingly use our program to
run experiments.
Data analysis and
documentation will be
increasingly important
Some of Axelrod’s Ideas
for Expanding the Model
Adaptive Mutation Rates.
Several vs. Individual
Chromosomes (Coadapation).
Dominant and recessive genes.
Gradual vs. Rapid Evolution.
Population viscosity and mating.
Speciation and ecological
niches.