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Competition III–Another Iterated Prisoner's Dilemma Tournament

February 13, 2008

These results are a follow on to my last post on competition and iterated prisoner’s dilemma simulation. In the tournament below, I used the tournament rule that every agent plays every agent at each round.  This takes a lot longer to run and the results are different. AvgT4T is still the winner, but T4TForgive beats out T4T.

Tournament 1 was one in which everyone played once each round.  This seems more like a competitive environment in which the other players don’t get complete information about the how play proceeded in that round–maybe more like competing for jobs or making friends in high school. 

Tournament 2 seems to give more complete information at each round as a public auction or public disclosure pricing might provide.  I am sure the merits of using one style of tournament over another have been debated plenty.

The difference in outcome illustrates an important characteristic of complex interactions among agents: The initial conditions and the rules of play make all the difference in the world.  For those who are committed to free markets, there seems to be a parallel assumption that the ultimate achievement in free markets is one with now rules at all. Citizens of a connected and crowded planet may choose to design the rules of interacting based on our initial conditions. When I play with these simulations for awhile, I start to understand Case’s argument against depending on the simplistic dogma of free-markets to answer all questions of our common wellbeing.

Prisoner's Dilema Population by Round 

Data tables … 

Match Win-Loss-Tie

Random-T4TForgive: 1.000, 0.000, 0.000
Defect-Random: 1.000, 0.000, 0.000
Random-T4T: 1.000, 0.000, 0.000
AvgT4T-Random: 0.537, 0.460, 0.003
Random-Pred1: 0.701, 0.299, 0.000
T4TDefect-Random: 0.946, 0.006, 0.048
Defect-T4TForgive: 1.000, 0.000, 0.000
T4T-T4TForgive: 0.000, 0.000, 1.000
AvgT4T-T4TForgive: 0.000, 0.000, 1.000
Pred1-T4TForgive: 1.000, 0.000, 0.000
T4TDefect-T4TForgive: 0.998, 0.000, 0.002
Defect-T4T: 1.000, 0.000, 0.000
Defect-AvgT4T: 1.000, 0.000, 0.000
Defect-Pred1: 1.000, 0.000, 0.000
Defect-T4TDefect: 1.000, 0.000, 0.000
AvgT4T-T4T: 0.000, 0.000, 1.000
Pred1-T4T: 1.000, 0.000, 0.000
T4TDefect-T4T: 0.981, 0.000, 0.019
Pred1-AvgT4T: 1.000, 0.000, 0.000
T4TDefect-AvgT4T: 1.000, 0.000, 0.000
T4TDefect-Pred1: 0.970, 0.015, 0.015

Match Scores

T4T-T4TForgive: 0.500, 0.500
T4TDefect-T4TForgive: 0.510, 0.490
Random-T4TDefect: 0.490, 0.510
Defect-AvgT4T: 0.506, 0.494
T4TForgive-Random: 0.490, 0.510
Pred1-AvgT4T: 0.522, 0.478
Defect-T4T: 0.506, 0.494
T4TDefect-AvgT4T: 0.516, 0.484
Pred1-Random: 0.415, 0.585
Defect-Random: 0.857, 0.143
AvgT4T-T4TForgive: 0.500, 0.500
AvgT4T-T4T: 0.500, 0.500
T4TDefect-T4T: 0.504, 0.496
T4TDefect-Pred1: 0.511, 0.489
T4T-Random: 0.499, 0.501
Pred1-T4TForgive: 0.510, 0.490
Random-AvgT4T: 0.484, 0.516
Defect-T4TDefect: 0.506, 0.494
Defect-T4TForgive: 0.541, 0.459
Pred1-T4T: 0.503, 0.497
Defect-Pred1: 0.506, 0.494
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