Despite many attempts to achieve an adequate definition of living systems by means of a set of necessary and sufficient conditions, the opinion that such an enterprise is inexorably destined to fail is increasingly gaining support. However, we believe options do not just come down to either having faith in a future success or endorsing skepticism. In this paper, we aim to redirect the discussion of the problem by shifting the focus of attention from strict definitions towards a philosophical framework (...) that allows conceiving of living systems as a natural kind, but whereby natural kinds are not to be defined by fixed necessary and sufficient conditions. We argue for a property-cluster kind approach according to which living systems constitute a natural kind with vague boundaries, capable of changing, and whose members do not need to instantiate every property. We draw from Boyd’s homeostatic property-cluster theory and introduce two modifications, one regarding homeostatic mechanisms and another related to the scientific role of kinds. Thus, our view overcomes some difficulties of Boyd’s theory and we are able to account for the natural kindhood of living things. We also emphasize the most appealing features of our approach for specific research fields and address three objections to this sort of approach. (shrink)

The concept of “life” certainly is of some use to distinguish birds and beavers from water and stones. This pragmatic usefulness has led to its construal as a categorical predicate that can sift out living entities from non-living ones depending on their possessing specific properties—reproduction, metabolism, evolvability etc. In this paper, we argue against this binary construal of life. Using text-mining methods across over 30,000 scientific articles, we defend instead a degrees-of-life view and show how these methods can contribute to (...) experimental philosophy of science and concept explication. We apply topic-modeling algorithms to identify which specific properties are attributed to a target set of entities (bacteria, archaea, viruses, prions, plasmids, phages and the molecule of adenine). Eight major clusters of properties were identified together with their relative relevance for each target entity (two that relate to metabolism and catalysis, one to genetics, one to evolvability, one to structure, and—rather unexpectedly—three that concern interactions with the environment broadly construed). While aligning with intuitions—for instance about viruses being less alive than bacteria—these quantitative results also reveal differential degrees of performance that have so far remained elusive or overlooked. Taken together, these analyses provide a conceptual “lifeness space” that makes it possible to move away from a categorical construal of life by empirically assessing the relative lifeness of more-or-less alive entities. (shrink)

In several disciplines within science—evolutionary biology, molecular biology, astrobiology, synthetic biology, artificial life—and outside science—primarily ethics—efforts to define life have recently multiplied. However, no consensus has emerged. In this article, I argue that this is no accident. I propose a dilemma showing that the project of defining life is either impossible or pointless. The notion of life at stake in this project is either the folk concept of life or a scientific concept. In the former case, empirical evidence shows that (...) life cannot be defined. In the latter case, I argue that, although defining life may be possible, it is pointless. I conclude that scientists, philosophers, and ethicists should discard the project of defining life. (shrink)

One of the central problems in philosophy of artificial life (AL) is whether the artificial life entities we create can be genuine life. Proponents of strong AL believe that the artificial life entities exhibiting characteristics of natural life in a physical or a virtual environment can be real life. Opponents of strong artificial life, however, think that artificial life entities are not real life or just simulation of natural life. The aim of this paper is to demonstrate which view of (...) strong artificial life is valid. The method is to use philosophical theory and logics to analyze the opponents’ arguments. The conclusion is that the opponents’ arguments for denying strong AL are insufficient to exclude the possibility of strong AL. (shrink)

The attempt to define life has gained new momentum in the wake of novel fields such as synthetic biology, astrobiology, and artificial life. In a series of articles, Cleland, Chyba, and Machery claim that definitions of life seek to provide necessary and sufficient conditions for applying the concept of life—something that such definitions cannot, and should not do. We argue that this criticism is largely unwarranted. Cleland, Chyba, and Machery approach definitions of life as classifying devices, thereby neglecting their other (...) epistemic roles. We identify within the discussions of the nature and origin of life three other types of definitions: theoretical, transdisciplinary, and diagnostic definitions. The primary aim of these definitions is not to distinguish life from nonlife, although they can also be used for classificatory purposes. We focus on the definitions of life within the budding field of astrobiology, paying particular attention to transdisciplinary definitions, and diagnostic definitions in the search for biosignatures from other planets. (shrink)

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)

I investigate the relationship between adaptation, as defined in evolutionary theory through natural selection, and the concept of emergence. I argue that there is an essential correlation between the former, and “emergence” defined in the field of algorithmic simulations. I first show that the computational concept of emergence (in terms of incompressible simulation) can be correlated with a causal criterion of emergence (in terms of the specificity of the explanation of global patterns). On this ground, I argue that emergence in (...) general involves some sort of selective processes. Finally, I show that a second criterion, concerning novel explanatory regularities following the emergence of a pattern, captures the robustness of emergence displayed by some cases of emergence (according to the first criterion). Emergent processes fulfilling both criteria are therefore exemplified in evolutionary biology by some so-called “innovations”, and mostly by the new units of fitness or new kinds of adaptations (like sexual reproduction, multicellular organisms, cells, societies) sometimes called “major transitions in evolution”, that recent research programs (Maynard-Smith and Szathmary 1995 ; Michod 1999 ) aims at explaining. (shrink)

Among many properties distinguishing emergence, such as novelty, irreducibility and unpredictability, computational accounts of emergence in terms of computational incompressibility aim first at making sense of such unpredictability. Those accounts prove to be more objective than usual accounts in terms of levels of mereology, which often face objections of being too epistemic. The present paper defends computational accounts against some objections, and develops what such notions bring to the usual idea of unpredictability. I distinguish the objective unpredictability, compatible with determinism (...) and entailed by emergence, and various possibilities of predictability at emergent levels. This makes sense of practices common in complex systems studies that forge qualitative predictions on the basis of comparisons of simulations with multiple values of parameters. I consider robustness analysis as a way to ensure the ontological character of computational emergence. Finally, I focus on the property of novelty, as it is displayed by biological evolution, and ask whether computer simulations of evolution can produce the same kind of emergence as the open-ended evolution attested in Phanerozoic records. (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)

This article on the epistemology of computational models stems from an analysis of the Gaïa hypothesis. 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 and trying to answer counterfactual questions. 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” and “specific”. 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 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 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)

All of the attempts to date to find a set of necessary and sufficient conditions for life, in order to provide an essential definition of life, have failed. We only have at our disposal series of lists that contain diverse characteristics usually found in living beings. Some authors have drawn from this fact the conclusion that life is not a natural kind. It will be argued here that this conclusion is too hasty and that if life is understood as a (...) natural kind in the non-essentialist sense of a homeostatic property cluster, it is easy to see why the attempts to find a unique and essentialist definition of life have failed. Understanding life in this way would imply that the pretension to find such a type of definition should be abandoned. (shrink)

The question ‘what is life?’ has long been a source of philosophical debate and in recent years has taken on increasing scientific importance. The most popular approach among both philosophers and scientists for answering this question is to provide a “definition” of life. In this article I explore a variety of different definitional approaches, both traditional and non-traditional, that have been used to “define” life. I argue that all of them are deeply flawed. It is my contention that a scientifically (...) compelling understanding of the nature of life presupposes an empirically adequate scientific theory (vs. definition) of life; as I argue, scientific theories are not the sort of thing that can be encapsulated in definitions. Unfortunately, as I also discuss, scientists are currently in no position to formulate even a tentative version of such a theory. Recent discoveries in biology and biochemistry have revealed that familiar Earth life represents a single example that may not be representative of life. If this is the case, life on Earth today provides an empirically inadequate foundation for theorizing about life considered generally. I sketch a strategy for procuring the needed additional examples of life without the guidance of a definition or theory of life, and close with an application to NASA’s fledgling search for extraterrestrial life. (shrink)

In his recent book on Darwinism, Daniel Dennett has offered up a species of a priori selectionism that he calls algorithmic. He used this view to challenge a number of positions advocated by Stephen J. Gould. I examine his algorithmic conception, review his unqualified enthusiasm for the a priori selectionist position, challenge Dennett's main metaphors (cranes vs. skyhooks and a design space), examine ways in which his position has lead him to misunderstand or misrepresent Gould (spandrels, exaptation, punctuated equilibrium, contingency (...) and disparity), and discuss recent results in developmental biology that suggest that an a priori position does not fill the demands of an evolutionary biology. I conclude by insisting that evolutionary biology is many leveled, complicated, and is carried on an ever shifting and expanding empirical base that when disregarded results in caricature. (shrink)

A number of areas of biology raise questions about what is of value in the natural environment and how we ought to behave towards it: conservation biology, environmental science, and ecology, to name a few. Based on my experience teaching students from these and similar majors, I argue that the field of environmental ethics has much to teach these students. They come to me with pent-up questions and a feeling that more is needed to fully engage in their subjects, and (...) I believe some exposure to environmental ethics can help focus their interests and goals. I identify three primary areas in which environmental ethics can con- tribute to their education. The first is an examination of who (or what) should be considered to be part of our moral community (i.e., the community to whom we owe direct duties). Is it humans only? Or does it include all sentient life? Or all life? Or ecosystems considered holistically? Often, readings implicitly assume one or more of these answers; the goal is to make the student more sensitive to these implicit claims and to get them to think about the different reasons that support them. The second area, related to the first, is the application of the different answers concerning the extent of the ethical community to real environmental issues and problems. Students need to be aware of how the different answers concerning the moral community can imply conflicting answers for how we should act in certain cases and to think about ways to move toward conflict resolution. The third area in which environmental ethics can contribute is a more conceptual one, focusing on central concepts such as biodiversity, sustainability, species, and ecosystems. Exploring and evaluating various meanings of these terms will make students more reflective and thoughtful citizens and biologists, sensitive to the implications that different conceptual choices make. (shrink)