Aristotle is oftentimes viewed through a strictly philosophical lens as heir to Plato and has having introduced logical rigor where an emphasis on the theory of Forms formerly prevailed. It must be appreciated that Aristotle was the son of a physician, and that his inculcation of the thought of other Greek philosophers addressing health and the natural elements led to an extremely broad set of biologically- and medically-related writings. As this article proposes, Aristotle deepened the fourfold theory of the elements (...) with anatomic and physiologic observations. In books like History of Animals, Parts of Animals, and the lost Anatomai, he actively dissected organisms and recorded his findings. The corpus of medically-related literature Aristotle developed had a direct influence on subsequent Greek thinkers, including Galen, and on Medieval Islamic and modern Western practitioners such as William Harvey. Aristotle's ideas continue to influence modern medical thinkers in Europe and America through both his interpretation by European philosophers gaining the attention of medical humanists, and his writings' enduring impact on medical researchers balancing scientific with more personalistic approaches to medicine. (shrink)

In Making Sense of Life , Keller emphasizes several differences between biology and physics. Her analysis focuses on significant ways in which modelling practices in some areas of biology, especially developmental biology, differ from those of the physical sciences. She suggests that natural models and modelling by homology play a central role in the former but not the latter. In this paper, I focus instead on those practices that are importantly similar, from the point of view (...) of epistemology and cognitive science. I argue that concrete and abstract models are significant in both disciplines, that there are shared selection criteria for models in physics and biology, e.g. familiarity, and that modelling often occurs in a similar fashion. (shrink)

Propositions alone are not constitutive of science. But is the "non-propositional" side of science theoretically superfluous: must philosophy of science consider it in order to adequately account for science? I explore the boundary between the propositional and non-propositional sides of biological theory, drawing on three cases: Grinnell's remnant models of faunas, Wright's path analysis, and Weismannism's role in the generalization of evolutionary theory. I propose a picture of material model-building in biology in which manipulated systems of material objects (...) function as theoretical models. In each of the cases, material systems such as diagrams play important generative as well as presentational roles. (shrink)

In recent years, there has much attention given by philosophers to the ubiquitous role of models and modeling in the biological sciences. Philosophical debates has focused on several areas of discussion. First, what are models in the biological sciences? The term ‘model’ is applied to mathematical structures, graphical displays, computer simulations, and even concrete organisms. Is there an account which unifies these disparate structures? Second, scientists routinely distinguish between theories and models; however, this distinction is more difficult (...) to draw in the biological sciences since biologists often only have a variety of models and rarely have something like a fundamental theory. What then is a theory in biology? Third, how are models related to empirical or “target” systems? (shrink)

In Making Sense of Life, Keller emphasizes several differences between biology and physics. Her analysis focuses on significant ways in which modelling practices in some areas of biology, especially developmental biology, differ from those of the physical sciences. She suggests that natural models and modelling by homology play a central role in the former but not the latter. In this paper, I focus instead on those practices that are importantly similar, from the point of view of (...) epistemology and cognitive science. I argue that concrete and abstract models are significant in both disciplines, that there are shared selection criteria for models in physics and biology, e.g. familiarity, and that modelling often occurs in a similar fashion. (shrink)

The biological sciences have become increasingly reliant on so-called 'model organisms'. I argue that in this domain, the concept of a descriptive model is essential for understanding scientific practice. Using a case study, I show how such a model was formulated in a preexplanatory context for subsequent use as a prototype from which explanations ultimately may be generated both within the immediate domain of the original model and in additional, related domains. To develop this concept of a descriptive model, I (...) focus on use of the nematode worm Caenorhabditis elegans and the wiring diagrams that were developed as models of its neural structure. In addition, implications of the concept of a descriptive model, particularly its relevance for the data-phenomena distinction as well as its relation to long-standing debates on realism, are briefly examined. (shrink)

Instead of arguing for a specific animal model, Striedter will review the history and philosophy of animal models in biomedical research, examining their various advantages and limitations.

Propositions are no more constitutive of science than they are of any activity: a body of knowledge is not all there is to the life of science. Thus I take the premise underlying the topic of this symposium to be uncontroversial, there is a “non-propositional” side of science and of biology in particular. From time to time, however, philosophers ask whether the “non-propositional” side of science is theoretically superfluous, or as Duhem put it, logically dispensable. What they mean to (...) ask is whether science can be fully analyzed in propositional terms; must philosophy of science, in other words, consider the non-propositional side in order to adequately account for science?Negative answers to the question often rest on the tacit view that the most (or only) important thing about science is scientific theories. (shrink)

Since the early 20th century underdetermination has been one of the most contentious problems in the philosophy of science. In this article I relate the underdetermination problem to models in biology and defend two main lines of argument: First, the use of models in this discipline lends strong support to the underdetermination thesis. Second, models and theories in biology are not determined strictly by the logic of representation of the studied phenomena, but also by other (...) constraints such as research traditions, backgrounds of the scientists, aims of the research and available technology. Convincing evidence for the existence of underdetermination in biology, where models abound, comes both from the fact that for a natural phenomenon we can create a number of candidate models but also from the fact that we do not have a universal rule that would adjudicate among them. This all makes a strong case for the general validity of underdetermination thesis. (shrink)

From the perspective of biological cybernetics, “real world” robots have no fundamental advantage over computer simulations when used as models for biological behavior. They can even weaken biological relevance. From an engineering point of view, however, robots can benefit from solutions found in biological systems. We emphasize the importance of this distinction and give examples for artificial systems based on insect biology.

Many apparently complex mechanisms in biology, especially in embryology and molecular biology, can be explained easily by reasoning at the level of the “efficient cause” of the observed phenomenology: the mechanism can then be explained by a simple geometrical argument or a variational principle, leading to the solution of an optimization problem, for example, via the co-existence of a minimization and a maximization problem . Passing from a microscopic level to the macroscopic level often involves an averaging effect (...) that gives birth to a global functional feature . We will illustrate these general principles by building in four different domains of application “a minima” models and showing the main properties of their solutions: extraction of a minimal RNA structure functioning as the first “peptidic machine,” a kind of ancestral ribosome; study of a genetic regulatory network of Drosophila centred on Engrailed gene and expressing successively two genes inside a limit cycle; study of a genetic network regulating neural activity and proliferation in mammals; and study of a simple geometric model of epiboly in zebrafish. (shrink)

The aim of this article is to investigate the relevance and implications of synthetic models for the study of the interactive dimension of minimal life and cognition, by taking into consideration how the use of artificial systems may contribute to an understanding of the way in which interactions may affect or even contribute to shape biological identities. To do so, this article analyzes experimental work in synthetic biology on different types of interactions between artificial and natural systems, more (...) specifically: between protocells and between biological living cells and protocells. It discusses how concepts such as control, cognition, communication can be used to characterize these interactions from a theoretical point of view, which criteria of relevance and evaluation of synthetic models can be applied to these cases, and what are their limits. (shrink)

Catastrophe Theory was developed in an attempt to provide a form of Mathematics particularly apt for applications in the biological sciences. It was claimed that while it could be applied in the more conventional physical way, it could also be applied in a new metaphysical way, derived from the Structuralism of Saussure in Linguistics and Lévi-Strauss in Anthropology.Since those early beginnings there have been many attempts to apply Catastrophe Theory to Biology, but these hopes cannot be said to have (...) been fully realised. (shrink)

Biological processes span several scales in space, from the single molecules to organisms and ecosystems. Multiscale modelling approaches in biology are useful to take into account the complex interactions between different organisation levels in those systems. We review several single- and multiscale models, from the most simple to the complex ones, and discuss their properties from a multiscale point of view. Approaches based on master equations for stochastic processes, individual-based models, hybrid continuous-discrete models and structured PDE (...) models are presented. (shrink)

In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual and hypothetical conditionals that can exhibit different "degrees of contingency". We use this idea to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, this structure explains why idealizations, despite being deformations of reality, are so successful in scientific practice. For (...) illustrative purposes, we provide an example from population genetics, the Wright-Fisher Model. (shrink)

The biological sciences have become increasingly reliant on so-called ‘model organisms’. I argue that in this domain, the concept of a descriptive model is essential for understanding scientific practice. Using a case study, I show how such a model was formulated in a preexplanatory context for subsequent use as a prototype from which explanations ultimately may be generated both within the immediate domain of the original model and in additional, related domains. To develop this concept of a descriptive model, I (...) focus on use of the nematode worm Caenorhabditis elegans and the wiring diagrams that were developed as models of its neural structure. In addition, implications of the concept of a descriptive model, particularly its relevance for the data-phenomena distinction as well as its relation to long-standing debates on realism, are briefly examined. (shrink)

In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual and hypothetical conditionals that can exhibit different “degrees of contingency”. We use this idea to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, this structure explains why idealizations, despite being deformations of reality, are so successful in scientific practice. For (...) illustrative purposes, we provide an example from population genetics, the Wright-Fisher Model. (shrink)

Mechanistic models in molecular systems biology are generally mathematical models of the action of networks of biochemical reactions, involving metabolism, signal transduction, and/or gene expression. They can be either simulated numerically or analyzed analytically. Systems biology integrates quantitative molecular data acquisition with mathematical models to design new experiments, discriminate between alternative mechanisms and explain the molecular basis of cellular properties. At the heart of this approach are mechanistic models of molecular networks. We focus on (...) the articulation and development of mechanistic models, identifying five constraints which guide the articulation of models in molecular systems biology. These constraints are not independent of one another, with the result that modeling becomes an iterative process. We illustrate the use of these constraints in the modeling of the mechanism for bistability in the lac operon. (shrink)

Explanation in biology has long been characterized as being very different from explanation in other scientific disciplines, very much so from explanation in physics. One of the reasons was the existence in biology of explanation types that were unheard of in the physical sciences: teleological explanations (e.g. Hull 1974), evolutionary explanations (e.g. Mayr 1988), or even functional explanations (e.g. Neander 1991). More recently, and owing much to the rise of molecular biology, biological explanations have been depicted as (...) mechanisms (e.g; Machamer, Darden and Craver 2000). The aim of this volume is to shed some new light on the diversity of explanation types in biology. What are the different types of explanation that occur in biology? Are these types of explanation specific to particular sub-disciplines of biology, or to particular types of problems across biology? How do they relate to each another? Do they compete with one another for answering the same questions? Or do they complement each other, providing insights to different questions? What are the reasons for such diversity? Can this diversity be overcome by a broader unifying model of explanation or is it more profound and irreducible? Why? This volume aims at making sense of this diversity of types of explanations that are found in biology, of their relationship with one another. After all, explanation in biology may prove not only different from explanation in the physical sciences, but also much more diverse than originally anticipated. (shrink)

Biosemiotics is a growing fi eld that investigates semiotic processes in the living realm in an attempt to combine the fi ndings of the biological sciences and semiotics. Semiotic processes are more or less what biologists have typically referred to as “ signals, ” “ codes, ”and “ information processing ”in biosystems, but these processes are here understood under the more general notion of semiosis, that is, the production, action, and interpretation of signs. Thus, biosemiotics can be seen as (...) class='Hi'>biology interpreted as a study of living sign systems — which also means that semiosis or sign process can be seen as the very nature of life itself. In other words, biosemiotics is a field of research investigating semiotic processes (meaning, signification, communication, and habit formation in living systems) and the physicochemical preconditions for sign action and interpretation. -/- (...). (shrink)

Three metascientific concepts that have been object of philosophical analysis are the concepts oflaw, model and theory. The aim ofthis article is to present the explication of these concepts, and of their relationships, made within the framework of Sneedean or Metatheoretical Structuralism (Balzer et al. 1987), and of their application to a case from the realm of biology: Population Dynamics. The analysis carried out will make it possible to support, contrary to what some philosophers of science in general and (...) of biology in particular hold, the following claims: a) there are "laws" in biological sciences, b) many of the heterogeneous and different "models" of biology can be accommodated under some "theory", and c) this is exactly what confers great unifying power to biological theories. (shrink)

We used agent-based modelling to highlight the advantages and disadvantages of several management styles in biology, ranging from centralized to egalitarian ones. In egalitarian groups, all team members are connected with each other, while in centralized ones, they are only connected with the principal investigator. Our model incorporated time constraints, which negatively influenced weakly connected groups such as centralized ones. Moreover, our results show that egalitarian groups outperform others if the questions addressed are relatively simple or when the communication (...) among agents is limited. Complex epistemic spaces are explored best by centralized groups. They outperform other team structures because the individual members can develop their own ideas with less interference of the opinions of others. The optimal ratio between time spent on experimentation and dissemination varies between different organizational structures. Furthermore, if the evidence is shared only after a relevant degree of certainty is reached, all investigated groups epistemically profit. We discovered that the introduction of seminars to the model changes the epistemic performance in favour of weakly connected teams. Finally, the abilities of the principal investigator do not seem to outperform cognitive diversity, as group performances were not strongly influenced by the increase of her abilities. (shrink)

A common view of how-possibly explanations in biology treats them as explanatorily incomplete. In addition to this interpretation of how-possibly explanation, I argue that there is another interpretation, one which features what I term “explanatory strategies.” This strategy-centered interpretation of how-possibly explanation centers on there being a different explanatory context within which how-possibly explanations are offered. I contend that, in conditions where this strategy context is recognized, how-possibly explanations can be understood as complete explanations. I defend this alternative interpretation (...) by analyzing the explanatory value of simple physical models the nineteenth century developmental biologist Wilhelm His constructed for animal development. (shrink)

Models are of central importance in many scientific contexts. The centrality of models such as inflationary models in cosmology, general-circulation models of the global climate, the double-helix model of DNA, evolutionary models in biology, agent-based models in the social sciences, and general-equilibrium models of markets in their respective domains is a case in point (the Other Internet Resources section at the end of this entry contains links to online resources that discuss these (...) models). Scientists spend significant amounts of time building, testing, comparing, and revising models, and much journal space is dedicated to interpreting and discussing the implications of models. In short, models are one of the principal instruments of modern science. (shrink)

In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual and hypothetical conditionals that can exhibit different “degrees of contingency”. We use this idea to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, this structure explains why idealizations, despite being deformations of reality, are so successful in scientific practice. For (...) illustrative purposes, we provide an example from population genetics, the Wright-Fisher Model. (shrink)

This article considers claims that biology should seek general theories similar to those found in physics but argues for an alternative framework for biological theories as collections of prototypical interlevel models that can be extrapolated by analogy to different organisms. This position is exemplified in the development of the Hodgkin‐Huxley giant squid model for action potentials, which uses equations in specialized ways. This model is viewed as an “emergent unifier.” Such unifiers, which require various simplifications, involve the types (...) of heuristics discussed in Wimsatt’s writings on reduction, but with a twist. Here, the heuristics are used to generate emergent rather than reductive explanations. †To contact the author, please write to: Department of History and Philosophy of Science, University of Pittsburgh, 1017 Cathedral of Learning, Pittsburgh, PA 15260; e‐mail: [email protected]. (shrink)

The notion of how-possibly explanations emerged with William Dray in response to Carl Hempel’s influential deductive-nomological model of scientific explanation. Dray’s aim was to distinguish explanations of states of affairs that might occur, in contrast to the aim of D-N explanations working to establish that states of affairs must actually occur. More recently, interest in how-possibly explanations has been particularly keen among philosophers of biology. One of the concerns philosophers of biology have focused on is whether how-possibly explanations (...) are “complete” explanations. Dray considered how-possibly explanations to be complete explanations, but the view that they are incomplete explanations became prominent with Robert Brandon’s articulation of successful adaptation explanation in evolutionary biology. According to Brandon, how-possibly adaptation explanations are incomplete, since they lack the requisite empirical evidence. Moreover, Brandon’s characterization of how-possibly explanation can be seen as having set the stage for thinking broadly about how-possibly explanations in biology. Echoing Brandon’s account for adaptation explanations, for example, David Resnik contends that how-possibly explanations are inherently incomplete, and yet they serve an important heuristic role throughout biology by setting out an agenda for future research. (shrink)

In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual and hypothetical conditionals that can exhibit different “degrees of contingency”. We use this idea to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, this structure explains why idealizations, despite being deformations of reality, are so successful in scientific practice. For (...) illustrative purposes, we provide an example from population genetics, the Wright-Fisher Model. (shrink)

The ArgumentThe claim that Jan Swammerdam's empirical research did not support his theory of biological preformation is shown to rest on a notion of evidence narrower than that used by many seventeenth-century natural philosophers. The principles of evidence behind the use of mechanical models are developed. It is then shown that the Cartesian theory of biological reproduction and embryology failed to gain acceptance because it did not meet the evidential requirements of these principles. The problems in this and other (...) mechanistic theories prior to Swammerdam are found to arise from certain difficulties and tensions in the mechanical conception of nature, which Swammerdam's theory is able to resolve. The relation between Swammerdam's empirical research and his theory is examined and shown to satisfy the required notion of evidence.Leibniz' puzzling appeals to Swammerdam's research in support of his metaphysical doctrine of the spontaneous development of individual substances are then examined and shown to fall within the parameters of the notion of evidence. (shrink)

This essay analyzes and develops recent views about explanation in biology. Philosophers of biology have parted with the received deductive-nomological model of scientific explanation primarily by attempting to capture actual biological theorizing and practice. This includes an endorsement of different kinds of explanation (e.g., mathematical and causal-mechanistic), a joint study of discovery and explanation, and an abandonment of models of theory reduction in favor of accounts of explanatory reduction. Of particular current interest are philosophical accounts of complex (...) explanations that appeal to different levels of organismal organization and use contributions from different biological disciplines. The essay lays out one model that views explanatory integration across different disciplines as being structured by scientific problems. I emphasize the philosophical need to take the explanatory aims pursued by different groups of scientists into account, as explanatory aims determine whether different explanations are competing or complementary and govern the dynamics of scientific practice, including interdisciplinary research. I distinguish different kinds of pluralism that philosophers have endorsed in the context of explanation in biology, and draw several implications for science education, especially the need to teach science as an interdisciplinary and dynamic practice guided by scientific problems and explanatory aims. (shrink)

The geosciences include a wide spectrum of disciplines ranging from paleontology to climate science, and involve studies of a vast range of spatial and temporal scales, from the deep-time history of microbial life to the future of a system no less immense and complex than the entire Earth. Modeling is thus a central and indispensable tool across the geosciences. Here, we review both the history and current state of model-based inquiry in the geosciences. Research in these fields makes use of (...) a wide variety of models, such as conceptual, physical, and numerical models, and more specifically cellular automata, artificial neural networks, agent-based models, coupled models, and hierarchical models. We note the increasing demands to incorporate biological and social systems into geoscience modeling, challenging the traditional boundaries of these fields. Understanding and articulating the many different sources of scientific uncertainty – and finding tools and methods to address them – has been at the forefront of most research in geoscience modeling. We discuss not only structuralmodel uncertainties, parameter uncertainties, and solution uncertainties, but also the diverse sources of uncertainty arising from the complex nature of geoscience systems themselves. Without an examination of the geosciences, our philosophies of science and our understanding of the nature of model-based science are incomplete. (shrink)

This open access book assesses the prospects of (re)adopting organization as a pivotal concept in biology. It shows how organization can nourish biological thinking and practice, by reconnecting with the idea of biology as the science of organized systems. The book provides a comprehensive state-of-the-art picture of the characterizations and uses of the concept of organization in both biological science and philosophy of biology. It also deals with a variety of themes – including evolution, organogenesis, heredity, cognition (...) and ecology – with respect to which the concept of organization can guide the elaboration of original models and new experimental protocols. It will be of interest to biologists and scholars working in philosophy of science alike. (shrink)

This paper analyses conceptual and experimental work in synthetic biology on different types of interactions considered as minimal examples or models of communication. It discusses their pertinence and relevance for the wider understanding of this biological and cognitive phenomenon. It critically analyses their limits and it argues that a conceptual framework is needed. As a possible solution, it provides a theoretical account of communication based on the notion of organisation, and characterised in terms of the functional influence exerted (...) by the sender upon the receiver. It shows that this account can be operationalised in synthetic biology, and that it can supply criteria and guidelines for the design and evaluation of synthetic models. (shrink)

One important aspect of biological explanation is detailed causal modeling of particular phenomena in limited experimental background conditions. Recognising this allows one to appreciate that a sufficient condition for a reduction in biology is a molecular model of (1) only the demonstrated causal parameters of a biological model and (2) only within a replicable experimental background. These identities—which are ubiquitous in biology and form the basis of ruthless reductions (Bickle, Philosophy and neuroscience: a ruthlessly reductive account, 2003)—are criticised (...) as merely “local” (Sullivan, Synthese 167:511–539, 2009) or “fragmentary” (Schaffner, Synthese, 151(3):377–402, 2006). However, in an instructive case, a biological model is preserved in molecular terms, demonstrating a complex phenomenon that has been successfully reduced. (shrink)

In biology a great variety of models can be distinguished: there is a gradation scale from the more realistic to the more idealistic ones. The place of a model on this scale depends on the role of the fundamental ideas, apriorisms and empirisms, which inspire the direction of thought. The relation between reality, models and explanatory theory is worked out. The interplay between model and ideas makes it understandable why in biology several kinds of explanation are (...) possible. (shrink)

Recent research in philosophy of science has shown that scientists rely on a plurality of strategies to develop successful explanations of different types of phenomena. In the case of biology, most of these strategies go far beyond the traditional and reductionistic models of scientific explanation that have proven so successful in the fundamental sciences. Concretely, in the last two decades, philosophers of science have discovered the existence of at least two different types of scientific explanation at work in (...) the biological sciences, namely: mechanistic and structural explanations. Despite the growing evidence about the radically different nature of these two types of explanation, no inquiry has been conducted to date to determine the ontological reasons that might underlie these differences, nor the way in which these types of explanations can be systematically related with each other. Here, we aim to cover this gap by connecting this plurality of research strategies with the existence of emergent levels of reality. We argue that the existence of these different—and apparently incompatible—explanatory strategies to account for biological phenomena derives from the existence of “ontological jumps” in nature, which generate different regimes of causation that in turn demand the development of different explanatory frameworks. We identify two of these strategies—mechanistic modelling and network modelling—and connect them to the existence of two ontological regimes of causation. Finally, we relate them with each other in a systematic way. In this vein, our paper provides an ontological justification for the plurality of explanatory strategies that we see in the life sciences. (shrink)

Mathematical models of biological patterns are central to contemporary biology. This paper aims to consider what these models contribute to biology through the detailed consideration of an important case: Hamilton’s selfish herd. While highly abstract and idealized, Hamilton’s models have generated an extensive amount of research and have arguably led to an accurate understanding of an important factor in the evolution of gregarious behaviors like herding and flocking. I propose an account of what these (...) class='Hi'>models are able to achieve and how they can support a successful scientific research program. I argue that the way these models are interpreted is central to the success of such programs. (shrink)

Model organisms became an indispensable part of experimental systems in molecular developmental and cell biology, constructed to investigate physiological and pathological processes. They are thought to play a crucial role for the elucidation of gene function, complementing the sequencing of the genomes of humans and other organisms. Accordingly, historians and philosophers paid considerable attention to various issues concerning this aspect of experimental biology. With respect to the representational features of model organisms, that is, their status as models, (...) the main focus was on generalization of phenomena investigated in such experimental systems. Model organisms have been said to be models for other organisms or a higher taxon. This, however, presupposes a representation of the phenomenon in question. I will argue that prior to generalization, model organisms allow researchers to built generative material models of phenomena – structures, processes or the mechanisms that explain them – through their integration in experimental set-ups that carve out the phenomena from the whole organism and thus represent them. I will use the history of zebrafish biology to show how model organism systems, from around 1970 on, were developed to construct material models of molecular mechanisms explaining developmental or physiological processes. (shrink)

In a recent paper, Potochnik (Biol Philos 24(2):183–197, 2009) analyses some uses of optimality modelling in light of the anti-adaptationism criticism. She distinguishes two broad classes of such uses (weak and strong) on the basis of assumptions held by biologists about the role and the importance of natural selection. This is an interesting proposal that could help in the epistemological characterisation of some biological practices. However, Potochnik’s distinction also rests on the assumption that all optimality modelling represent the selection dynamic (...) involved in the system of phenomena being considered. Since this assumption does not hold for models belonging to optimal foraging theory (OFT)—one of behavioural ecology’s important modelling traditions—Potochnik’s proposal has to be critically reappraised. In this paper, we briefly discuss what is optimality modelling and what it means for a model to represent a dynamic of selection or of evolution. Then, we demonstrate that OFT modelling is unable to represent either past or contemporary selection dynamics. In order to make this point, we carefully delineate the theory’s rationale. This allows us to identify and analyse the assumptions on which the theory is built, and to circumscribe precisely the role that natural selection plays in it. Next, we show that the distinction of weak and strong uses of optimality modelling is seriously weakened when OFT modelling is taken into account. More precisely, the distinction is either irrelevant (if the assumption that selection dynamics are represented in all optimality modelling is held) or of a modest utility (if the assumption is dropped). However, we suggest that Potochnik’s original proposal could be saved, and that it even constitutes a tool to appraise the marks left in the literature by the evolution of optimality modelling practices in the last four decades, provided that it is made into a tripartite distinction. (shrink)

Spatial evolutionary game theory explains how cooperative traits can survive the intense competition in biological systems. If the spatial distribution allows cooperators to interact with each other frequently, the benefits of cooperation will outweigh the losses due to exploitation by selfish organisms. However, for a cooperative behavior to get established in a system, it needs to be found initially in a sufficiently large cluster to allow a high frequency of intracooperator interactions. Since mutations are rare events, this poses the question (...) of how cooperation can arise in a biological system in the first place. We present a simple model which captures two concepts from genetics that can explain how evolution overcomes the emergence problem. The first concept is, often in nature, a gene may not express its phenotype except under specific environmental conditions, rendering it to be a “silent” gene. The second key idea is that a neutral gene, one that does not harm or improve an organism’s survival chances, can still spread through a population if it is physically near to another gene that is positively selected. Through these two ideas, our model offers a possible solution to the fundamental problem of emergence of cooperation in biological systems. (shrink)

The aim of this article is to examine the scientific and public functions of two- andthree-dimensional models in the context of three episodes from nineteenth-century biology. Iargue that these models incorporate both data and theory by presenting theoretical assumptions inthe light of concrete data or organizing data through theoretical assumptions. Despite their diverseroles in scientific practice, they all can be characterized as mediators between data and theory.Furthermore, I argue that these different mediating functions often reflect their different (...) audiencesthat included specialized scientists, students, and the general public. In this sense, models innineteenth-century biology can be understood as mediators between theory, data, and their diverseaudiences. (shrink)

Fictionalists believe that scientific models are about model systems that are imaginary. Michael Weisberg has claimed that fictionalism is indefensible because many scientific models are about model systems that are unimaginable. According to a certain account of imagination, what Weisberg says is plausible. According to another, more defensible account of imagination, it is not. I discuss these issues within the context of an allegedly unimaginable model system in ecology, but the conclusions I draw are more general. I then (...) describe how fictionalism should be recast in order to deal with Weisberg’s critique. (shrink)