Simulating Science?

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Seminar paper from the year 2010 in the subject Philosophy - Theoretical (Realisation, Science, Logic, Language), grade: 1, 0, University of Stuttgart (Institut für Philosophie/Wissenschaftstheorie/Technikphilosophie), course: HS Philosophy of Simulation, language: English, abstract: In the early 1980s, Robert Axelrod published several articles on The Evolution of Cooperation, discussing and interpreting the results of his well-known computer tournaments and of a series of subsequent simulations. Both the tournaments and simulations were conducted in order to find a suitable, evolutionary stable strategy for the iterated prisoner's dilemma, which is generally considered an appropriate model of a certain type of social dilemma that arises when "the pursuit of self-interest by each leads to a poor outcome for all." The results of the tournaments and simulations led to a generalized theory of the evolution of cooperation, which claims to provide an explanation for various historical, social and biological phenomena. Axelrod`s work contributed extensively to popularizing computer simulation as a scientific method in the social sciences. Besides the fact that his approach had an unquestionably high impact on succeeding research and ushered in the "simulation era" in the social sciences, the use Axelrod made of computer simulations raises questions about their methodological and epistemological status: If, as Axelrod states in his paper "Advancing the Art of Simulation in the Social Sciences", simulation can serve the purposes of prediction, proof and even scientific discovery, what need is there for conducting experiments any longer? Can't we simulate science? Admittedly, this suggestion sounds somewhat exaggerated, but why exactly do most of us share the intuition that there are fundamental differences persisting between simulations and experiments? What are the characteristic features distinguishing them? Do computer simulations in general - and Axelrod's tournaments in particular - resemble experiments insofar as their potential to provide us with surprising results that permit further theorizing is concerned? Or are they nothing else than mere "number-crunching techniques", using brute-force computational means in order to generate data from theoretical knowledge and assumptions already built into the underlying model? The question where to draw the conceptual line between simulation and experiment has turned out to be of great interest to philosophy of science, not least since the categorization might be relevant to the way the results are assessed and used. The objective of this paper is to elaborate on the distinctive characteristics of simulations in contrast to experiments.