Par un procédé d'objections/réponses, nous passons d'abord en revue certains des arguments en faveur ou en défaveur du caractère empirique de la simulation informatique. A l'issue de ce chemin clarificateur, nous proposons des arguments en faveur du caractère concret des objets simulés en science, ce qui légitime le fait que l'on parle à leur sujet d'une expérience, plus spécifiquement d'une expérience concrète du second genre.

The credibility of digital computer simulations has always been a problem. Today, through the debate on verification and validation, it has become a key issue. I will review the existing theses on that question. I will show that, due to the role of epistemological beliefs in science, no general agreement can be found on this matter. Hence, the complexity of the construction of sciences must be acknowledged. I illustrate these claims with a recent historical example. Finally I temperate this diversity (...) by insisting on recent trends in environmental sciences and in industrial sciences. (shrink)

Artificial life (also known as “ALife”) is a broad, interdisciplinary endeavor that studies life and life-like processes through simulation and synthesis. The goals of this activity include modelling and even creating life and life-like systems, as well as developing practical applications using intuitions and methods taken from living systems. Artificial life both illuminates traditional philosophical questions and raises new philosophical questions. Since both artificial life and philosophy investigate the essential nature of certain fundamental aspects of reality like life and adaptation, (...) artificial life offers philosophy a new perspective on these phenomena. This chapter provides an introduction to current research in artificial life and explains its philosophical implications. (shrink)

In this work, we propose a computational approach to the triadic model of Peircean semiosis (meaning processes). We investigate theoretical constraints about the feasibility of simulated semiosis. These constraints, which are basic requirements for the simulation of semiosis, refer to the synthesis of irreducible triadic relations (Sign–Object–Interpretant). We examine the internal organization of the triad S–O–I, that is, the relative position of its elements and how they relate to each other. We also suggest a multi-level approach based on self-organization principles. (...) In this context, semiosis is described as an emergent process. Nevertheless, the term ‘emergence’ is often used in a very informal way in the so called ‘emergent’ computation, without clear explanations and/or definitions. In this paper, we discuss in some detail the meaning of the theoretical terms ‘emergence’ and ‘emergent’, showing how such an analysis can lead to improvements of the algorithm proposed. (shrink)

Both the irreducible complexity of biological phenomena and the aim of a universalized biology (life-as-it-could-be) have lead to a deep methodological shift in the study of life; represented by the appearance of ALife, with its claim that computational modelling is the main tool for studying the general principles of biological phenomenology. However this methodological shift implies important questions concerning the aesthetic, engineering and specially the epistemological status of computational models in scientific research: halfway between the well established categories of theory (...) and experiment. ALife models become powerful epistemic artefacts allowing the simulation of emergent phenomena, the interaction between different levels of organization and the integration of different causal factors in the very same manipulable object. The use of computational models in ALife can be classified in four main categories depending on their position between theoretical and empirical practices: generic, conceptual, functional and mechanistic. For each of these categories we analyse their epistemic value and select paradigmatic examples that illustrate how ALife models can be fruitfully inserted in the study of life. (shrink)

There are many different kinds of model and scientists do all kind of things with them. This diversity of model type and model use is a good thing for science. Indeed, it is crucial especially for the biological and cognitive sciences, which have to solve many different problems at many different scales, ranging from the most concrete of the structural details of a DNA molecule to the most abstract and generic principles of self-organization in networks. Getting a grip (or more (...) likely many separate grips) on this range of topics calls for a teeming forest of techniques, including many different modeling techniques. Barbara Webb’s target article strikes us as a proposal for clear-cutting the forest. We think clear-cutting here would be as good for science as it is for non-metaphorical forests. Our argument for this is primarily a recitation of a few of the ways that diversity has been useful. Recently, looking at the actual practice of artificial life modelers, one of us distinguished four uses of simulation models classified in terms of the position the models take up between theory and data (see Figure 1). The classification is not exhaustive, and the barriers between kinds are not absolute. Rather, the purpose of the taxonomy is to open up the view for an epistemic ecology of modeling practices. First, and closest to the empirical domain, there are mechanistic models, in which there is an almost one-to-one correspondence between variables in the model and observables in the target system and its environment. Webb’s.. (shrink)

Yapay zekâ sistemlerinin davranışında bir Ahlâki kodun uygulanmasının sadece soyut bir zekâ değil, belirli bir insan ve yapay zekâ biçimine ihtiyaç duyduğu tezini tartışıyoruz. Zekâyı, içyapısı ve Ahlâki sistemin yapısal seviyeleri olan bir sistem olarak sunuyoruz ve aynı zamanda yapay zekâ sahibi faillerin belli başlı özelliklerini sunarak, onlara nasıl davranılması gerektiğini sıralıyoruz: 1- bireysel varlıklar, 2- Ahlâki zekâ gibi çeşitli ve hatta çoklu zekâ formlarına sahip varlıklar, 3- açık ve hatta serbest davranışlı performans gösteren sistemler, 4- sadece öğretime değil, eğitime (...) de açık sistemler, 5- sadece “durum” değil “hayat bilgisi” olan varlıklar, 6- sadece otomatizmlerle değil, inançlarla donatılmış, 7- hatta düşünebilen ve yansıtabilen, 8- bazı gerçek toplulukların unsurları/üyeleri, 9- ve kültürel varlıklar: çünkü özgür davranış, "doğal" veya yapay bir varlığın eylemlerine kültürel değer katar. Bu tür özelliklerin uygulanması, insanlar gibi olan makineleri tasarlama, inşa etme ve eğitme çabalarını zorunlu olarak gerektirmez. İnsan Ahlâki kodu telafi edilemez bir şekilde kusurludur: bu; etik sistemlerin icadıyla, ideal değerlerin dolaşımıyla ve etik eğitimle giderilemeyecek tercihin, hesap vermenin ve özgürlüksüzlüğün Ahlâkıdır. Ancak böyle kusurlu bir Ahlâk, uygulanması için mükemmel araçlara ihtiyaç duyar: özel mantık alanlarının; makineye sadece zekâ değil, aynı zamanda vicdan ve hatta ruh kazandıracak etkili psikolojik kazanımların; nesnel kararı öznel bir kararla tamamlamak için kapsamlı teknik araçların uygulamaları. Makine etiği en yüksek kalitede olabilir/olacaktır çünkü bilimlerden türetilecek, tekniklerle modellenecek ve teknolojilerle başarılacaktır. Yapay bir Ahlâki davranışın uygulanması için gerekli olan belirli bir Ahlâki zekâ hakkındaki kuramsal hipotezimiz doğruysa, bazı kuramsal ve teknik sorunlar ortaya çıkar, ancak aşağıdaki çalışma hipotezleri mümkündür: yapısal, işlevsel ve davranışsal. İnsan ve/veya yapay Ahlâkın geleceği tahmin edilmelidir. (shrink)

This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s. -/- Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows how (...) and why computers, data treatment devices and programming languages have occasioned a gradual but irresistible and massive shift from mathematical models to computer simulations. -/- . (shrink)

Many religions and religious philosophies say that ultimate reality is a kind of primal energy. This energy is often described as a vital power animating living things, as a spiritual force directing the organization of matter, or as a divine creative power which generates all things. By refuting older conceptions of primal energy, modern science opens the door to new and more precise conceptions. Primal energy is referred to here as ‘spirit’. But spirit is a natural power. A naturalistic theory (...) of spirit is developed using ideas from information theory and thermodynamics, such as the maximum entropy production principle. Spirit drives the evolution of complexity at all levels of existence. (shrink)

This paper argues against the view that the standard explanation of phase transitions in statistical mechanics may be considered a causal explanation, a distortion that can nevertheless successfully represent causal relations.

Artificial life (ALife) is the attempt to create artificial instances of life in a variety of media, but primarily within the digital computer. As such, the field brings together computationally-minded biologists and biologically-minded computer scientists. I argue that this new field is filled with interesting philosophical issues. However, there is a dearth of philosophers actively conducting research in this area. I discuss two books on the new field: Margaret A. Boden's The philosophy of artificial life and Christopher G. Langton's Artificial (...) life: an overview. They cover three areas of philosophical interest: the definition of life, the relationship between life and mind, and the possibility of creating life within a computational environment. This discussion allows me to critique past work in the philosophy of ALife that tends to see the field as a proving ground for traditional arguments from the philosophy of artificial intelligence. Instead, I suggest, what is interesting about ALife is how it differs from artificial intelligence and that the most interesting philosophical issues in the area are those derived from biology, not psychology. I recommend that these two books taken together constitute an interesting introduction to ALife and the wealth of philosophical issues found therein. (shrink)

The nature and status of psychological laws are a long-standing controversy. I will argue that part of the controversy stems from the distinctive nature of an important subset of those laws, which I’ll call “supple laws.” An emergent-model strategy taken by the new interdisciplinary field of artificial life provides a strikingly successful understanding of analogously supple laws in biology. So, after reviewing the failures of the two evident strategies for understanding supple psychological laws, I’ll turn for inspiration to emergent-models explanations (...) of supple laws in biology. I’ll conclude by inferring what an emergent model of supple laws in psychology should be like. (shrink)

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. (shrink)

To surmount the notorious difficulties of defining life, we should evaluate theories of life not by whether they provide necessary and sufficient conditions for our current preconceptions about life but by how well they explain living phenomena and how satisfactorily they resolve puzzles about life. On these grounds, the theory of life as supple adaptation (Bedau 1996) gets support from its natural and compelling resolutions of the following four puzzles: (1) How are different forms of life at different levels of (...) the vital hierarchy related? (2) Is there a continuum between life and non-life? (3) Does life essentially concern a living entity’s material composition or its form? (4) Are life and mind intrinsically connected? (shrink)