Les efforts pour définir la vie se sont intensifiés ces dernières années dans les sciences biologiques. Partant d’une interrogation sur les motivations qui président à ces efforts, nous montrons que l’on doit distinguer deux projets qui s’exposent à des difficultés philosophiques très différentes : ceux présentant l’obtention d’une définition de la vie comme un moyen nécessaire pour l’enquête scientifique ; ceux qui pensent celle-ci comme une fin. Puis nous analysons les usages qui sont faits du terme « vie ». Tandis (...) que l’ensemble des analyses l’ont considéré comme un terme de classe dont les organismes seraient des instances, nous montrons qu’en pratique il existe un second usage implicite de ce terme : dans certains contextes « vie » sert à désigner un individu dont les organismes sont des parties. (shrink)

Building upon a non-standard understanding of evolutionary process focusing on variation and persistence, I will argue that communities and ecosystems can evolve by natural selection as emergent individuals. Evolutionary biology has relied ever increasingly on the modeling of population dynamics. Most have taken for granted that we all agree on what is a population. Recent work has reexamined this perceived consensus. I will argue that there are good reasons to restrict the term “population” to collections of monophyletically related replicators and (...) interactors, which explains why many existing models in population biology exclude by definition many genuine evolving biological individuals such as communities and ecosystems. By studying a case of community evolution (a symbiotic termite–fungus community), we will see that it is variation that is important to evolutionary processes, not populations. Variation within a population is only one of many types of variation that can lead to evolution by natural selection. The upshot of focusing on variation is that cases of community and ecosystem adaptive change become tractable in evolutionary terms. I will show that complex emergent individuals such as communities and ecosystems cannot be fully accommodated by conventional population/reproduction models but can be accommodated by variation/persistence models. (shrink)

Latour is a world famous and widely published French sociologist who has written with great eloquence and perception about the relationship between people, science, and technology. He is also closely associated with the school of thought known as Actor Network Theory. In this book he sets out for the first time in one place his own ideas about Actor Network Theory and its relevance to management and organization theory.

Martin Heidegger (1889–1976) influenced the work of such diverse thinkers as Sartre and Derrida. In Tool-Being, Graham Harman departs from the prevailing linguistic approach to analytic and continental philosophy in favor of Heideggerian object-oriented research into the secret contours of objects. Written in a colorful style, it will be of interest to anyone open to new trends in present-day philosophy.

Willard van Orman Quine once said that he had a preference for a desert ontology. This was in an earlier day when concerns with logical structure and ontological simplicity reigned supreme. Ontological genocide was practiced upon whole classes of upper-level or ‘derivative’ entities in the name of elegance, and we were secure in the belief that one strayed irremediably into the realm of conceptual confusion and possible error the further one got from ontic fundamentalism. In those days, one paid more (...) attention to generic worries about possible errors than to actual errors derived from distancing oneself too far from the nitty-gritty details of actual theory, actual inferences from actual data, the actual conditions under which we posited and detected entities, calibrated and ‘burned in’ instruments, identified and rejected artifacts, debugged programs and procedures, explained the mechanisms behind regularities, judged correlations to be spurious, and in general, to the real complexities and richness of actual scientific practice. (shrink)

From the book: What is to be done with political ecology? Nothing. What is to be done? Political ecology!

In this paper I defend an etiological theory of biological functions (according to which the proper function of a trait is the effect for which it was selected by natural selection) against three objections which have been influential. I argue, contrary to Millikan, that it is wrong to base our defense of the theory on a rejection of conceptual analysis, for conceptual analysis does have an important role in philosophy of science. I also argue that biology requires a normative notion (...) of a "proper function", and that a normative notion is not ahistorical. (shrink)

I defend the historical definition of "function" originally given in my Language, Thought and Other Biological Categories (1984a). The definition was not offered in the spirit of conceptual analysis but is more akin to a theoretical definition of "function". A major theme is that nonhistorical analyses of "function" fail to deal adequately with items that are not capable of performing their functions.

This classic work is reissued with a new preface by the author. Written for non-scientists the idea is put forward that life on Earth functions as a single organism.

Here I advance two related evolutionary propositions. (1) Natural selection is most often considered to require competition between reproducing “individuals”, sometimes quite broadly conceived, as in cases of clonal, species or multispecies-community selection. But differential survival of non-competing and non-reproducing individuals will also result in increasing frequencies of survival-promoting “adaptations” among survivors, and thus is also a kind of natural selection. (2) Darwinists have challenged the view that the Earth’s biosphere is an evolved global homeostatic system. Since there is only (...) one biosphere, reproductive competition cannot have been involved in selection for such survival-promoting adaptations, they claim. But natural selection through survival could reconcile Gaia with evolutionary theory. (shrink)

Foreword -- Preface -- Introductory -- What is Gaia? -- Exploring Daisyworld -- The Archean -- The middle ages -- Modern times -- The contemporary environment -- The second home -- God and Gaia -- Gaia since 1988 -- Epilog -- References -- Further Reading -- Index.

Altruism is generally understood to be behavior that benefits others at a personal cost to the behaving individual. However, within evolutionary biology, different authors have interpreted the concept of altruism differently, leading to dissimilar predictions about the evolution of altruistic behavior. Generally, different interpretations diverge on which party receives the benefit from altruism and on how the cost of altruism is assessed. Using a simple trait-group framework, we delineate the assumptions underlying different interpretations and show how they relate to one (...) another. We feel that a thorough examination of the connections between interpretations not only reveals why different authors have arrived at disparate conclusions about altruism, but also illuminates the conditions that are likely to favor the evolution of altruism. (shrink)

This article on the epistemology of computational models stems from an analysis of the Gaia hypothesis (GH). It begins with James Kirchner’s criticisms of the central computational model of GH: Daisyworld. Among other things, the model has been criticized for being too abstract, describing fictional entities (fictive daisies on an imaginary planet) and trying to answer counterfactual (what-if) questions (how would a planet look like if life had no influence on it?). For these reasons the model has been considered not (...) testable and therefore not legitimate in science, and in any case not very interesting since it explores non actual issues. This criticism implicitly assumes that science should only be involved in the making of models that are ‘‘actual’’ (by opposition to what-if) and ‘‘specific’’ (by opposition to abstract). I challenge both of these criticisms in this article. First by showing that although the testability—understood as the comparison of model output with empirical data—is an important procedure for explanatory models, there are plenty of models that are not testable. The fact that these are not testable (in this restricted sense) has nothing to do with their being ‘‘abstract’’ or ‘‘what-if’’ but with their being predictive models. Secondly, I argue that ‘‘abstract’’ and ‘‘what-if’’ models aim at (respectable) epistemic purposes distinct from those pursued by ‘‘actual and specific models’’. Abstract models are used to propose how-possibly explanation or to pursue theorizing. What-if models are used to attribute causal or explanatory power to a variable of interest. The fact that they aim at different epistemic goals entails that it may not be accurate to consider the choice between different kinds of model as a ‘‘strategy‘‘. (shrink)