Cyrille Imbert

Computer simulations as experiments

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.

Introduction, SI of Synthese “The collective dimension of science”

Scientists are not isolated agents: they collaborate in laboratories, research networks and large-scale international projects. Apart from direct collaboration, scientists interact with each other in various ways: they follow entrenched research programs, trust their peers, embed their work into an existing paradigm, exchange concepts, methods and results, compete for grants or prestige, etc. The collective dimension of science has been discussed by philosophers of science in various ways, but until recently, the use of formal methods has been restricted to some particular areas, such as the treatment of the division of scientific labor, the study of reward schemes or the effects of network structures on the production of scientific knowledge. Given the great promise of these methods for modeling and understanding of the dynamics of scientific research, this blind spot struck us as surprising. At the same time, social aspects of the production and diffusion of knowledge have been

Unfolding in the empirical sciences: experiments, thought experiments and computer simulations

Experiments (E), computer simulations (CS) and thought experiments (TE) are usually seen as playing different roles in science and as having different epistemologies. Accordingly, they are usually analyzed separately. We argue in this paper that these activities can contribute to answering the same questions by playing the same epistemic role when they are used to unfold the content of a well-described scenario. We emphasize that in such cases, these three activities can be described by means of the same conceptual framework—even if each of them, because they involve different types of processes, fall under these concepts in different ways. We further illustrate our claims by presenting a threefold case study describing how a TE, a CS and an E were indeed used in the same role at different periods to answer the same questions about the possibility of a physical Maxwellian demon. We also point at fluid dynamics as another field where these activities seem to be playing the same unfolding role. We analyze the importance of unfolding as a general task of science and highlight how our description in terms of epistemic functions articulates in a noncommittal way with the epistemology of these three activities and accounts for their similarities and the existence of hybrid forms of activities. We finally emphasize that picturing these activities as functionally substitutable does not imply that they are epistemologically substitutable.

Scientific Collaboration: Do Two Heads Need to Be More Than Twice Better Than One?

Epistemic accounts of scientific collaboration usually assume that, one way or another, two heads really are more than twice better than one. We show that this hypothesis is unduly strong. We present a deliberately crude model with unfavorable hypotheses. We show that, even then, when the priority rule is applied, large differences in successfulness can emerge from small differences in efficiency, with sometimes increasing marginal returns. We emphasize that success is sensitive to the structure of competing communities. Our results suggest that purely epistemic explanations of the efficiency of collaborations are less plausible but have much more powerful socioepistemic versions.

Relevance, Not Invariance, Explanatoriness, Not Manipulability: Discussion of Woodward’s Views on Explanatory Relevance

According to Woodward’s causal model of explanation, explanatory information is relevant for manipulation purposes and indicates by means of invariant causal relations how to change the value of certain target explanandum variables by intervening on others. Therefore, the depth of an explanation is evaluated through the size of the domain of invariance of the generalization involved. In this article, I argue that Woodward’s account of explanatory relevance is still unsatisfactory and claim that the depth of an explanation should be explicated in terms of the size of the domain of circumstances which it designates as leaving the explanandum unchanged.

Computer Simulations and Computational Models in Science

Computational science and computer simulations have significantly changed the face of science in recent times, even though attempts to extend our computational capacities are by no means new and computer simulations are more or less accepted across scientific fields as legitimate ways of reaching results (Sect. 34.1). Also, a great variety of computational models and computer simulations can be met across science, in terms of the types of computers, computations, computational models, or physical models involved and they can be used for various types of inquiries and in different scientific contexts (Sect. 34.2). For this reason, epistemological analyses of computer simulations are contextual for a great part. Still, computer simulations raise general questions regarding how their results are justified, how computational models are selected, which type of knowledge is thereby produced (Sect. 34.3), or how computational accounts of phenomena partly challenge traditional expectations regarding the explanation and understanding of natural systems (Sect. 34.4). Computer simulations also share various epistemological features with experiments and thought experiments; hence, the need for transversal analyses of these activities (Sect. 34.5). Finally, providing a satisfactory and fruitful definition of computer simulations turns out to be more difficult than expected, partly because this notion is at the crossroads of difficult questions like the nature of representation and computation or the success of scientific inquiries (Sect. 34.6). Overall, a pointed analysis of computer simulations in parallel requires developing insights about the evolving place of human capacities and humans within (computational) science (Sect. 34.7).