#### NAME

sipd - simulate the spatial iterated Prisoner's Dilemma

#### SYNOPSIS

**sipd** **-help**
or
**sipd** **[-width** *integer***]** **[-height** *integer***]** **[-steps** *integer***]**
**[-rounds** *integer***]** **[-seed** *integer***]** **[-CC** *double***]** **[-CD**
*double***]** **[-DC** *double***]** **[-DD** *double***]** **[-Iallc** *double***]**
**[-Itft** *double***]** **[-Irand** *double***]** **[-Ipav** *double***]**
**[-Ialld** *double***]** **[-rcp** *double***]** **[-noise** *double***]**
**[-mute** *double***]** **[-stats]** **[-inv]** **[-mag** *integer***]**
**[-term** *string***]**

#### DESCRIPTION

The spatial iterated Prisoner's Dilemma is simulated and
plotted over time according to the specified parameters.
Each cell in a grid plays a specific strategy against its
eight neighbors for several rounds. At the end of the
last round, each cell copies the strategy of its most suc-
cesful neighbor, which is then used for the next time
step. Possible strategies include 'Always Cooperate,'
'Always Defect,'

#### OPTIONS

**-width** *integer*
Width of world.
**-height** *integer*
Height of world.
**-steps** *integer*
Number of steps to simulate.
**-rounds** *integer*
Number of rounds per step.
**-seed** *integer*
Random seed for initial state.
**-CC** *double*
Reward Payoff.
**-CD** *double*
Sucker Payoff.
**-DC** *double*
Temptation Payoff.
**-DD** *double*
Punish Payoff.
**-Iallc** *double*
Initial population of All-C.
**-Itft** *double*
Initial population of TFT.
**-Irand** *double*
Initial population of Random.
**-Ipav** *double*
Initial population of Pavlov.
**-Ialld** *double*
Initial population of All-D.
**-rcp** *double*
Probability of C for Random strategy.
**-noise** *double*
Probability of noise.
**-mute** *double*
Probability of mutation.
**-stats** Print statistics?
**-inv** Invert all colors?
**-mag** *integer*
Magnification factor.
**-term** *string*
How to plot points.

#### PAYOFFS

The payoff matrix for the Prisoner's Dilemma game is usu-
ally expressed as:
Player B's Move
+-----------+-----------+
Player A's Move | cooperate | defect |
+-----------+-----------+-----------+
| cooperate | CC, CC | CD, DC |
+-----------+-----------+-----------+
| defect | DC, CD | DD, DD |
+-----------+-----------+-----------+
where the table entries are (A's payoff, B's payoff) and
CC, CD, DC, and DD are the reward, sucker, temptation, and
punish payoffs, respectively. For each of these four out-
comes you will probably want the payoffs to reflect the
relationships:
(DC > CC > DD > CD) and ((CD + DC) / 2 < CC).

#### MISCELLANY

The option for the probability of mutation (-mute) corre-
sponds to the act of a cell spontaneously changing to a
randomly selected strategy independent of the outcome of
the most recent set of rounds.
random noise (via the -noise option) manifests itself as a
cell making a randomly selected move in a single round.
In this case, both the cell whose action was altered as
well as that cell's opponents "remember" what the random
move was on the next round.
The value supplied with the -term option may be "none," in
which case no graphic output is performed. This is useful
if you simply want the statistics to be calculated for
each time step (via the -stats option).
The initial population levels for all strategies will be
normalized, so the scaling of the option values is irrele-
vant.

#### BUGS

No sanity checks are performed to make sure that any of
the options make sense.

#### AUTHOR

Copyright (c) 1997, Gary William Flake.
Permission granted for any use according to the standard
GNU ``copyleft'' agreement provided that the author's com-
ments are neither modified nor removed. No warranty is
given or implied.