Cyrille Imbert

Whereas computer simulations involve no direct physical interaction between the machine they are run on and the physical systems they are used to investigate, they are often used as experiments and yield data about these systems. It is commonly argued that they do so because they are implemented on physical machines. We claim that physicality is not necessary for their representational and predictive capacities and that the explanation of why computer simulations generate desired information about their target system is only to be found in the detailed analysis of their semantic levels. We provide such an analysis and we determine the actual consequences of physical implementation for simulations

Cellular Automata (CA) based simulations are widely used in a great variety of domains, fromstatistical physics to social science. They allow for spectacular displays and numerical predictions. Are they forall that a revolutionary modeling tool, allowing for “direct simulation”, or for the simulation of “the phenomenon itself”? Or are they merely models "of a phenomenological nature rather than of a fundamental one”? How do they compareto other modeling techniques? In order to answer these questions, we present a systematic exploration of CA’s various uses.

Computer simulations are usually considered to be non-explanatory because, when a simulation reveals that a property is instantiated in a system, it does not enable the exact identification of what it is that brings this property out (relevance requirement). Conversely, analytical deductions are widely considered to yield explanations and understanding. In this paper, I emphasize that explanations should satisfy the relevance requirement and argue that the more they do so, the more they have explanatory value. Finally, I show that this emphasis on relevance has the unexpected consequence that simulations can sometimes be explanatory.