Anouk Barberousse

Comment comprendre les enonces statistiques et probabilistes? Cette question concerne aussi bien le domaine scientifique que notre vie quotidienne. Nous attribuons constamment des degres de possibilite aux evenements que nous envisageons, et nous tirons des inferences a partir de ces attributions. Comment ces inferences sont-elles justifiees? Quelle est la signification de leurs conclusions? Ce livre tente d'apporter une reponse philosophique a ces questions, en explorant divers aspects de la philosophie des probabilites. Les probabilites jouent en outre un role considerable dans l'activite scientifique depuis pres de deux siecles. Une enquete sur leur signification se doit donc de prendre en compte leurs usages scientifiques aussi bien que leurs usages quotidiens. A cette fin, l'exemple de la mecanique statistique est etudie en detail, tant sous l'aspect historique que sous l'aspect philosophique. Cette etude permet de presenter les solutions qui ont ete apportees dans ce domaine particulier de la physique aux problemes de la philosophie des probabilites.

This volume is the best available tool to compare and appraise the different approaches of today’s biology and their conceptual frameworks, serving as a springboard for new research on a clarified conceptual basis. It is expected to constitute a key reference work for biologists and philosophers of biology, as well as for all scientists interested in understanding what is at stake in the present transformations of biological models and theories. The volume is distinguished by including, for the first time, self-reflections and exchanges of views on practice and theoretical attitudes by important participants in recent biological debates. The questions of how biological models and theories are constructed, how concepts are chosen and how different models can be articulated, are asked. Then the book explores some of these convergences between different models or theoretical frameworks. Confronting views on adaptive complexity are investigated, as well as the role of self-organization in evolution; niche construction meets developmental biology; the promises of the emergent field of ecological-evolutionary-development are examined. In sum, this book is a marvellous account of the dynamism of today’s theoretical biology. Foreword: Carving Nature at its Joints? Richard Lewontin Chapter 1: Introduction Anouk Barberousse, Michel Morange, Thomas Pradeu Chapter 2: Articulating Different Modes of Explanation: The Present Boundary in Biological Research Michel Morange Chapter 3: Compromising Positions: The Minding of Matter Susan Oyama Chapter 4:ions, Idealizations, and Evolutionary Biology Peter Godfrey-Smith Chapter 5: The Adequacy of Model Systems for Evo-Devo: Modeling the Formation Of Organisms / Modeling the Formation Of Society Scott F. Gilbert Chapter 6: Niche Construction in Evolution, Ecosystems and Developmental Biology John Odling-Smee Chapter 7: Novelty, Plasticity and Niche Construction: The Influence of Phenotypic Variation on Evolution Kim Sterelny Chapter 8: The Evolution of Complexity Mark A. Bedau Chapter 9: Self-Organization, Self-Assembly, and the Origin of Life Evelyn Fox Keller Chapter 10: Self-Organization and Complexity in Evolutionary Theory, or, In this Life the Bread Always Falls Jammy Side Down Michael Ruse.

Whereas experiments and computer simulations seem very different at first view because the former, but not the latter, involve interactions with material properties, we argue that this difference is not so important with respect to validation, as far as epistemologyEpistemology is concerned. Major differences remain nevertheless from the methodological point of view. We present and defend this distinction between epistemology and methodology. We illustrate this distinction and related claims by comparing how experiments and simulations are validated in evolutionary studies, a domain in which both experiments in the lab and computer simulations are relatively new but mutually reinforcing.

What is a natural kind? This old yet lasting philosophical question has recently received new competing answers (e.g., Chakravartty, 2007; Magnus, 2014; Khalidi, 2013; Slater, 2015; Ereshefsky & Reydon, 2015). We show that the main ingredients of an encompassing and coherent account of natural kinds are actually on the table, but in need of the right articulation. It is by adopting a non-reductionist, naturalistic and non-conceptualist approach that, in this paper, we elaborate a new synthesis of all these ingredients. Our resulting proposition is a multiple-compartment theory of natural kinds that defines them in purely ontological terms, clearly distinguishes and relates ontological and epistemological issues —more precisely, two grains of ontological descriptions and two grains of explanatory success of natural kinds—, and which sheds light on why natural kinds play an epistemic role both within science and in everyday life.

L’étude de l’évolution du climat passe nécessairement par des simulations numériques. Leur utilisation a été remise en cause en raison des incertitudes qui affectent leurs résultats. Cet article présente les différentes composantes des simulations numériques utilisées en climatologie et en propose une analyse épistémologique. La conclusion en est que ces simulations numériques procèdent des exigences scientifiques plus généralement à l’œuvre dans la science contemporaine.In order to study the evolution of global climate, computer simulation is required. The use of computer simulation has been criticized since the results are uncertain. In this paper, I present the various components of the simulations used in climate studies and I analyze those components from an epistemological point of view. I conclude in showing that these simulations obey the scientific requirements usually governing contemporary science.

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.

The Developmental Systems Theory (DST) presented by its proponents as a challenging approach in biology is aimed at transforming the workings of the life sciences from both a theoretical and experimental point of view (see, in particular, Oyama [1985] 2000; Oyama et al. 2001). Even though some may have the impression that the enthusiasm surrounding DST has faded in very recent years, some of the key concepts, ideas, and visions of DST have in fact pervaded biology and philosophy of biology. It seems crucial to us both to establish which of these ideas are truly specific to DST, and to shift through these ideas in order to determine the criticisms they have drawn, or may draw (e.g., Sterelny et al. 1996; Griesemer 2000; Sterelny 2000; Kitcher 2001; Keller 2005; Waters 2007)

Generalmente si ritiene che la rilevazione e identificazione delle specie presenti in una determinata area fornisca un corpus di conoscenza di base che permette ai biologi di sviluppare pezzi di conoscenza ulteriori. Tuttavia, si rivela sorprendentemente difficile ottenere inventari biologici che soddisfino i criteri inerenti a tale conoscenza di base. Il nostro obiettivo in questo articolo è di mettere in luce come la pratica corrente di inventariazione biologica sia condizionata da varie limitazioni e potenziali pregiudizi; cosa che ci conduce a riconsiderare le funzioni degli inventari all’inizio del ventunesimo secolo. A tale scopo, presentiamo l’esempio degli inventari della fauna degli ambienti marini profondi dell’Oceano Pacifico e ci concentriamo su una fonte di pregiudizio: la perdurante influenza della cosiddetta “ipotesi azoica”, secondo la quale non ci sarebbe vita al di sotto dei 600 metri. Mostriamo come l’ipotesi azoica, nonostante sia stata rapidamente confutata, abbia fortemente influenzato la concezione della fauna degli ambienti profondi. Nelle conclusioni, guardiamo come i limiti economici influenzino le pratiche correnti di inventario biologico degli ambienti marini profondi.

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.

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.

Computer simulations are widely used in current scientific practice, as a tool to obtain information about various phenomena. Scientists accordingly rely on the outputs of computer simulations to make statements about the empirical world. In that sense, simulations seem to enable scientists to acquire empirical knowledge. The aim of this paper is to assess whether computer simulations actually allow for the production of empirical knowledge, and how. It provides an epistemological analysis of present-day empirical science, to which the traditional epistemological categories cannot apply in any simple way. Our strategy consists in acknowledging the complexity of scientific practice, and trying to assess its rationality. Hence, while we are careful not to abstract away from the details of scientific practice, our approach is not strictly descriptive: our goal is to state in what conditions empirical science can rely on computer simulations. In order to do so, we need to adopt a renewed epistemological framework, whose categories would enable us to give a finer-grained, and better-fitted analysis of the rationality of scientific practice.