How Models Fail
A Critical Look at the History of Computer Simulations of the Evolution of Cooperation

Eckhart Arnold
previousupnextTable of Contents
1 Introduction
2 The empirical failure of simulations of the evolution of cooperation
3 Justificatory narratives
4 Bad excuses for bad methods and why they are wrong
    4.1 “Our knowledge is limited, anyway”
    4.2 “One can always learn something from failure”
    4.3 “Models always rely on simplification”
    4.4 “There are no alternatives to modeling”
    4.5 “Modeling promotes a scientific habit of mind”
    4.6 “Division of labor in science exempts theoreticians from empirical work”
    4.7 “Success within the scientific community proves scientific validity”
    4.8 “Natural sciences do it just the same way”
    4.9 Concluding remarks
5 History repeats itself: Comparison with similar criticisms of naturalistic or scientistic approaches
Bibliography

4.2 “One can always learn something from failure”

Argument: Even if Axelrod's approach ultimately turned out to be a failure, we can still learn important lessons from it. Failure is at least as important for the progress of science as success.

Response: Unfortunately, it is not clear, whether the necessary lessons have already been learned. If Axelrod's computer tournament is still remembered as an “extremely effective means for investigating the evolution of cooperation” (Rendell et al. 2010a, 208) by the scientific community then it seems that the lessons have not been learned. And even if the lessons have been learned (by some) then the many dozens of inapplicable simulations that have kept scientists busy in the aftermath of Axelrod's book have surely been a rather long detour.

page topnext

t g+ f @