Cyrille Imbert

Les simulations donnent l’impression de ressembler aux systèmes qu’elles représentent, au point d’en être peut-être des analogues. Dans cet article, je discute d’abord les différentes notions de temps qu’il faut distinguer pour bien analyser les simulations puis je montre sur cette base que, pour être de bonnes représentations scientifiques, les simulations ne doivent pas en général ressembler du point de vue temporel aux objets qu’elles représentent.Simulations often give the impression of being similar to the systems they represent. In this paper, I first show that it is necessary to distinguish between different notions of time in order to analyse simulations correctly. This enables me to argue that, in order to serve correctly their purpose as scientific representations, simulations must not in general be similar from a temporal point of view to the systems they represent

According to Hanne Andersen, "an analysis of goes beyond research ethics and includes important epistemological aspects" (p.1). Her purpose is to point at a new area for philosophy of science in practice, which she does by highlighting different epistemological issues about malpractices and showing how documenting them in a precise way is beneficial to their solution. She articulates in particular two questions, namely the issue of the identification of bad practices and malpractices, and the ways of preventing the latter from happening. I shall discuss how Andersen contributes to these issues, and make additional suggestions. Before going further, I first want to clarify a few points.

In this paper, I criticize Bedau's definition of `diachronically emergent properties', which says that a property is a DEP if it can only be predicted by a simulation and is nominally emergent. I argue at length that this definition is not complete because it fails to eliminate trivial cases. I discuss the features that an additional criterion should meet in order to complete the definition and I develop a notion, salience, which together with the simulation requirement can be used to characterize DEPs. In the second part of the paper, I sketch this notion. Basically, a property is salient when one can find an indicator, namely a descriptive function, that is such that its fitting description shifts from one elementary mathematical object to another when the property appears. Finally, I discuss restrictions that must be brought to what can count as DFs and EMOs if the definition of salience is to work and be non trivial. I conclude that salience can complete the definition of DEPs

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

Computers have transformed science and help to extend the boundaries of human knowledge. However, does the validation and diffusion of results of computational inquiries and computer simulations call for a novel epistemological analysis? I discuss how the notion of novelty should be cashed out to investigate this issue meaningfully and argue that a consequentialist framework similar to the one used by Goldman to develop social epistemologySocial epistemology can be helpful at this point. I highlight computational, mathematical, representational, and social stages on which the validity of simulation-based belief-generating processes hinges, and emphasize that their epistemic impact depends on the scientific practices that scientists adopt at these different stages. I further argue that epistemologists cannot ignore these partially novel issues and conclude that the epistemology of computational inquiries needs to go beyond that of models and scientific representations and has cognitive, social, and in the present case computational, dimensions.

ABSTRACTDeliberative and decisional groups play crucial roles in most aspects of social life. But it is not obvious how to organize these groups and various socio-cognitive mechanisms can spoil debates and decisions. In this paper we focus on one such important mechanism: the misrepresentation of views, i.e. when agents express views that are aligned with those already expressed, and which differ from their private opinions. We introduce a model to analyze the extent to which this behavioral pattern can warp deliberations and distort the decisions that are finally taken. We identify types of situations in which misrepresentation can have major effects and investigate how to reduce these effects by adopting appropriate deliberative procedures. We discuss the beneficial effects of holding a sufficient number of rounds of expression of views; choosing an appropriate order of speech, typically a random one; rendering the deliberation dissenter-friendly; having agents express fined-grained views. These applicable procedures help improve deliberations because they dampen conformist behavior, give epistemic minorities more opportunities to be heard, and reduce the number of cases in which an inadequate consensus or majority develops.

This paper shows that, under certain reasonable conditions, if the investigation of the behavior of a physical system is difficult, no scientific change can make it significantly easier. This impossibility result implies that complexity is then a necessary feature of models which truly represent the target system and of all models which are rich enough to catch its behavior and therefore that it is an inevitable element of any possible science in which this behavior is accounted for. I finally argue that complexity can then be seen as representing an intrinsic feature of the system itself.

In Woodward's causal model of explanation, explanatory information is information that is relevant to manipulation and control and that affords to change the value of some target explanandum variable by intervening on some other. Accordingly, the depth of an explanation is evaluated through the size of the domain of invariance of the generalization involved. In this paper, I argue that Woodward's treatment of explanatory relevance in terms of invariant causal relations is still wanting and suggest to evaluate the depth of an explanation through the size of the domain of circumstances that it designates as leaving the explanandum unchanged.

In this paper I investigate the potential explanatory value of computer simulations, which are often said to be suitable tools for the prediction, emulation or imitation of phenomena, but not for their explanation. Similarly, they are described as providing brute force (number-crunching) methods that are helpful to investigate the behaviour of physical systems but less so when it comes to understanding them. Be this as it may, wrongly or not simulations seem to be raising specific problems concerning their potential explanatory value. I try to systematically analyze these problems and for this purpose use existing theories of explanations as analytical guidelines. I first investigate how simulations fare concerning truth (section 2) and proceed by analyzing whether they satisfy the deductivity and nomicity requirements, which play a central role in Hempel’s model of explanation (section 3). I also study whether simulations are appropriate vehicles for relevant causal information which a good explanation is expected to provide (section 4). I then analyze how much the informational repleteness and the inferential or computational stodginess of computer simulations are problems for the development of explanatory knowledge and our understanding of phenomena (section 5). I finally analyze how much computer simulations can have a unificatory role as good explanations arguably do (section 6). Overall, I try to analyze why computer simulations still raise specific problems concerning explanations while seemingly being able to meet the criteria that explanations should fulfil. I suggest that some of these reasons are to be found in the epistemology of explanatory activity, in methodological expectations towards good explanations and in the specific uses that are made of computer simulations to investigate complex systems—in addition to the fact that computer simulations are not human-sized activities and that, as such, they may fail to provide first person understanding of phenomena.

The persistent pervasiveness of inappropriately small studies in empirical fields is regu-larly deplored in scientific discussions. Consensually, taken individually, higher-powered studies are more likely to be truth-conducive. However, are they also beneficial for the wider performance of truth-seeking communities? We study the impact of sample sizes on collective exploration dynamics under ordinary conditions of resource limita-tion. We find that large collaborative studies, because they decrease diversity, can have detrimental effects in certain realistic circumstances that we characterize precisely. We show how limited inertia mechanisms may partially solve this pooling dilemma and dis-cuss our findings briefly in terms of editorial policies.

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.

Scientific models need to be investigated if they are to provide valuable information about the systems they represent. Surprisingly, the epistemological question of what enables this investigation has hardly been investigated. Even authors who consider the inferential role of models as central, like Hughes or Bueno and Colyvan, content themselves with claiming that models contain mathematical resources that provide inferential power. We claim that these notions require further analysis and argue that mathematical formalisms contribute to this inferential role. We characterize formalisms, illustrate how they extend our mathematical resources, and highlight how distinct formalisms offer various inferential affordances.

This paper shows that, under certain reasonable conditions, if the investigation of the behavior of a physical system is difficult, no scientific change can make it significantly easier. This impossibility result implies that complexity is then a necessary feature of models which truly represent the target system and of all models which are rich enough to catch its behavior and therefore that it is an inevitable element of any possible science in which this behavior is accounted for. I finally argue that complexity can then be seen as representing an intrinsic feature of the system itself.

Why are some models, like the harmonic oscillator, the Ising model, a few Hamiltonian equations in quantum mechanics, the poisson equation, or the Lokta-Volterra equations, repeatedly used within and across scientific domains, whereas theories allow for many more modeling possibilities? Some historians and philosophers of science have already proposed plausible explanations. For example, Kuhn and Cartwright point to a tendency toward conservatism in science, and Humphreys emphasizes the importance of the intractability of what he calls “templates.” This paper investigates more systematically the reasons for this remarkable interdisciplinary recurrence. To this aim, the authors describe in more detail the phenomenon they focus on and review competing potentialexplanations. The authors disentangle the various assumptions underlying these explanations based on sensitivity to a computational constraints and assess its relationships with the other analyzed explanatons.

Although scientific models and simulations differ in numerous ways, they are similar in so far as they are posing essentially philosophical problems about the nature of representation. This collection is designed to bring together some of the best work on the nature of representation being done by both established senior philosophers of science and younger researchers. Most of the pieces, while appealing to existing traditions of scientific representation, explore new types of questions, such as: how understanding can be developed within computational science; how the format of representations matters for their use, be it for the purpose of research or education; how the concepts of emergence and supervenience can be further analyzed by taking into account computational science; or how the emphasis upon tractability--a particularly important issue in computational science--sheds new light on the philosophical analysis of scientific reasoning.

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 has increased over the past two centuries, a fact for which various explanations have been proposed. We offer a novel functional explanation of this increase in collaboration, grounded in a sequential model of scientific research where the priority rule applies. Robust patterns concerning the differential success of collaborative groups with respect to their competitors are derived, and it is argued that these patterns feed the development of collaboration. This general mechanism may trigger an ‘arms race’ and is compatible with a certain amount of decreased productivity in collaborative groups as well as some over-collaboration. The proposed explanation succeeds in integrating various factors usually associated with the rise of collaboration.

The roles models play in science have long been recognised and sparked rich and varied philosophical debates. In recent years attention has also been paid to the computational techniques used in the sciences, and the question arose what the implications were of the use of computer simulations for our understanding of scientific modelling, and science more generally. This was the subject of the conference “Models and Simulations”, which took place at the IHPST in Paris in June 2006. Selected papers of that conference appeared in a special issue of this journal (Synthese 169(3) (2009)). After the conference there was a general feeling that there still was much ground to cover, and so we decided to organize a follow up a year later. “Models and Simulations 2” took place in October 2007 at the Tilburg Center for Logic and Philosophy of Science (TiLPS) in the Netherlands. The conference was made possible due to generous financial support from IHPST (Paris, CNRS), Universitat de Barcelona, TiLPS (Tilburg Center for Logic and Philosophy of Science) and the Evert Willem Beth Foundation. The papers in this special issue were presented at the conference and selected after a double-blind review process. We would like to thank the authors and referees of the papers for their work, and Vincent Hendricks and John Symons for their support of the project of another special issue on models and simulations.

We analyze the effects of the introduction of new mathematical tools on an old branch of physics by focusing on lattice fluids, which are cellular automata (CA)-based hydrodynamical models. We examine the nature of these discrete models, the type of novelty they bring about within scientific practice and the role they play in the field of fluid dynamics. We critically analyze Rohrlich's, Fox Keller's and Hughes' claims about CA-based models. We distinguish between different senses of the predicates “phenomenological” and “theoretical” for scientific models and argue that it is erroneous to conclude, as they do, that CA-based models are necessarily phenomenological in any sense of the term. We conversely claim that CA-based models of fluids, though at first sight blatantly misrepresenting fluids, are in fact conservative as far as the basic laws of statistical physics are concerned and not less theoretical than more traditional models in the field. Based on our case-study, we propose a general discussion of the prospect of CA for modeling in physics. We finally emphasize that lattice fluids are not just exotic oddities but do bring about new advantages in the investigation of fluids' behavior.

ABSTRACT: Whereas relevance in scientific explanations is usually discussed as if it was a single problem, several criteria of relevance will be distinguished in this paper. Emphasis is laid upon the notion of intra-scientific relevance, which is illustrated using explanation of the law of areas as an example. Traditional accounts of explanation, such as the causal and unificationist accounts, are analyzed against these criteria of relevance. Particularly, it will be shown that these accounts fail to indicate which explanations fulfill the condition of intra-scientific relevance. Finally, the significance of this latter criterion is emphasized, and the epistemic benefits of explanations that fulfill it are highlighted

Computer simulations have changed the face of many scientific disciplines. This has attracted the attention of a number of philosophers of science, who have started discussing the philosophical implications of the use of computational methods in science. It was the aim of the conference Models and Simulations that took place at the IHPST in Paris, France, on 12 and 13 June 2006 to bring researchers working in this field together and provide a platform to discuss these issues in a focused way. The papers in this special issue were presented at this conference. We would like to thank the IHPST and the CNRS (département SHS, ATIP “Physique et Calcul”) for generous financial support of the conference. We also would like to thank the authors and referees of the papers for their work, and Vincent Hendricks and John Symons for their support of the project of a special issue of this journal to the topic of the conference.

Various errors can affect scientific code and detecting them is a central concern within computational science. Could formal verification methods, which are now available tools, be widely adopted to guarantee the general reliability of scientific code? After discussing their benefits and drawbacks, we claim that, absent significant changes as regards features like their user-friendliness and versatility, these methods are unlikely to be adopted throughout computational science, beyond certain specific contexts for which they are well-suited. This issue exemplifies the epistemological heterogeneity of computational science: profoundly different practices can be appropriate to meet the reliability challenge that rises for scientific code.

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.

Various errors can affect scientific code and detecting them is a central concern within computational science. Could formal verification methods, which are now available tools, be widely adopted to guarantee the general reliability of scientific code? After discussing their benefits and drawbacks, we claim that, absent significant changes as regards features like their user-friendliness and versatility, these methods are unlikely to be adopted throughout computational science, beyond certain specific contexts for which they are well-suited. This issue exemplifies the epistemological heterogeneity of computational science: profoundly different practices can be appropriate to meet the reliability challenge that rises for scientific code.