This n-person Iterated Prisoners' Dilemma captures some of the complexity
of real-world decision making. In this case each prisoner plays the
game with each of the surrounding 8 players and himself. The player
then takes on cooperator or defector status depending on the more successful
strategy. Each 2-person game is scored as follows:
both cooperate - each receives a score of 1.0
both defect - each receives a score of 0
one defects & receives a score of a variable b (here set
to1.85), and the cooperator (who goes to jail) receives a score of 0.
The game is set to b = 1.85 where b is the advantage to defection, also termed the "sucker's payoff".
Two grids are shown below. (Each grid wraps around at the edges, like a torus.) Use the buttons on the bottom right corner of each grid to populate the grid (top button), clear (middle button), or start/pause (bottom button) the simulation.
Victor Blue (March 25, 1999). Send email with comments to: vicblue@ulster.net
The color code is:
cooperated previously and is now a cooperator - magenta (purple)
cooperated previously and is now a defector - yellow
defected previously and is now a cooperator - green
defected previously and is now a defector - cyan (aquamarine)
Small group of defectors at center of field of cooperative players
starts the game:
The above pattern only lasts about 750 time steps. Try a clean
restart with a slightly different starting group by clicking the mouse
over cells you want to activate as defectors. By startting
with any symmetrical pattern, a symmetrical pattern results.
A very different evolution takes place with a randomized start (below).
The cutting edge of change is indicated by the yellow and green cells.
Random allocation of 10% defectors in field of cooperative players starts
the game:
A never-ending panoply of patterns in dynamic flux emerges from the
contest between cooperators and defectors.
Check out the dmoz
website for further links on the Iterated Prisoners' Dilemma..
Also try Exploring
Emergence and Artificial
Life Online for further explanations and examples of CA. Check
out TRANSIMS
for research ongoing with CA vehicular traffic simulations pioneered by
Kai Nagel. Also, the models of Craig
Reynolds and Dirk
Helbing, though not CA models, may be of interest because their models
deal with self-organization of individual, autonomous agents.
Click to go back to homepage , bidirectional,
or 4-directional pedestrian simulations.
Victor Blue (March 25, 1999). Send email with comments to: vicblue@ulster.net
For a well developed discussion see the books
by Robert Axelrod "The Evolution of Cooperation" and "The Complexity of
Cooperation" and the University of Michigan Complexity website.
Or check out the Scientific American article mentioned above.