Abstraction and idealization are the two notions that are most often discussed in the context of assumptions employed in the process of model building. These notions are also routinely used in philosophical debates such as that on the mechanistic account of explanation. Indeed, an objection to the mechanistic account has recently been formulated precisely on these grounds: mechanists cannot account for the common practice of idealizing difference-making factors in models in molecular biology. In this paper I revisit the debate and (...) I argue that the objection does not stand up to scrutiny. This is because it is riddled with a number of conceptual inconsistencies. By attempting to resolve the tensions, I also draw several general lessons regarding the difficulties of applying abstraction and idealization in scientific practice. Finally, I argue that more care is needed only when speaking of abstraction and idealization in a context in which these concepts play an important role in an argument, such as that on mechanistic explanation. (shrink)

According to mainstream philosophical views causal explanation in biology and neuroscience is mechanistic. As the term ‘mechanism’ gets regular use in these fields it is unsurprising that philosophers consider it important to scientific explanation. What is surprising is that they consider it the only causal term of importance. This paper provides an analysis of a new causal concept—it examines the cascade concept in science and the causal structure it refers to. I argue that this concept is importantly different from the (...) notion of mechanism and that this difference matters for our understanding of causation and explanation in science. (shrink)

In the last two decades few topics in philosophy of science have received as much attention as mechanistic explanation. A significant motivation for these accounts is that scientists frequently use the term “mechanism” in their explanations of biological phenomena. While scientists appeal to a variety of causal concepts in their explanations, many philosophers argue or assume that all of these concepts are well understood with the single notion of mechanism. This reveals a significant problem with mainstream mechanistic accounts– although philosophers (...) use the term “mechanism” interchangeably with other causal concepts, this is not something that scientists always do. This paper analyses two causal concepts in biology–the notions of “mechanism” and “pathway”–and how they figure in biological explanation. I argue that these concepts have unique features, that they are associated with distinct strategies of causal investigation, and that they figure in importantly different types of explanation. (shrink)

Although the analogy between macroscopic machines and biological molecular devices plays an important role in the conceptual framework of both neo-mechanistic accounts and nanotechnology, it has recently been claimed that certain complex molecular devices cannot be considered machines since they are subject to physicochemical forces that are different from those of macroscopic machines. However, the structural and physicochemical conditions that allow both macroscopic machines and microscopic devices to work and perform new functions, through a combination of elemental functional parts, have (...) not yet been examined. In order to fill this void, this paper has a threefold aim: first, to clarify the structural and organisational conditions of macroscopic machines and microscopic devices; second, to determine whether the machine-like analogy fits nanoscale devices; and third, to assess whether the machine-like analogy is appropriate for describing the behaviour of some biological macromolecules. Finally, the paper gives an account of ‘machine’ which, while acknowledging the physicochemical and organisational differences between man-made machines and biological microscopic devices, nevertheless identifies a common conceptual core that allows us to consider the latter ‘machines’. (shrink)

Species are considered the basic unit of biological classification and evolution. Hence, they are used as a benchmark in several fields, although the ontological status of such a category has always been a matter of debate. This paper aims to discuss the problem of the definition of species within the new mechanistic approach. Nevertheless, the boundary between entities, activities, and mechanisms remains difficult to establish and always requires an analysis of what is meant by explanation. As a case study, the (...) paper describes the debate concerning the species category by considering different kingdoms: Animals, Bacteria, and Fungi. The inherently biological differences between those groups prevent the use of a single, universally applicable concept of species that could fit the mechanisms responsible for the variability present in these kingdoms. The same issue is encountered within each group, as highlighted through a focus on mammals and microbes. This controversy has given rise to opposite approaches, namely: monism, which looks for a single definition that might account for all species, and pluralism, which admits that different groups of organisms require somewhat different definitions. In order to develop an adequate definition of species, we propose to apply a new mechanistic framework, which considers the ontic-epistemic dimensions of scientific explanation in close parallel. The apt correlation between epistemic and the ontic aspects highlights the way in which the concept of species and the reference to data are strictly co-determined. This suggests that the concept of species is better understandable within a dual ontic-epistemic approach. (shrink)

Idealization and abstraction are central concepts in the philosophy of science and in science itself. My goal in this paper is suggest an account of these concepts, building on and refining an existing view due to Jones Idealization XII: correcting the model. Idealization and abstraction in the sciences, vol 86. Rodopi, Amsterdam, pp 173–217, 2005) and Godfrey-Smith Mapping the future of biology: evolving concepts and theories. Springer, Berlin, 2009). On this line of thought, abstraction—which I call, for reasons to be (...) explained, abstractness—involves the omission of detail, whereas idealization consists in a deliberate mismatch between a description and the world. I will suggest that while the core idea underlying these authors’ view is correct, they make several assumptions and stipulations that are best avoided. For one thing, they tie abstractness too close to truth. For another, they do not allow sufficient room to the difference between idealization and error. Taking these points into account leads to a refined account of the distinction, in which abstractness is seen in terms of relative richness of detail, and idealization is seen as closely connected with the knowledge and intentions of idealizers. I lay out these accounts in turn, and then discuss the relationship between the two concepts, and several other upshots of the present way of construing the distinction. (shrink)

This paper argues that scale-dependence of physical and biological processes offers resistance to reductionism and has implications that support a specific kind of downward causation. I demonstrate how insights from multiscale modeling can provide a concrete mathematical interpretation of downward causation as boundary conditions for models used to represent processes at lower scales. The autonomy and role of macroscale parameters and higher-level constraints are illustrated through examples of multiscale modeling in physics, developmental biology, and systems biology. Drawing on these examples, (...) I defend the explanatory importance of constraining relations for understanding the behavior of biological systems. (shrink)

A common reductionist assumption is that macro-scale behaviors can be described "bottom-up" if only sufficient details about lower-scale processes are available. The view that an "ideal" or "fundamental" physics would be sufficient to explain all macro-scale phenomena has been met with criticism from philosophers of biology. Specifically, scholars have pointed to the impossibility of deducing biological explanations from physical ones, and to the irreducible nature of distinctively biological processes such as gene regulation and evolution. This paper takes a step back (...) in asking whether bottom-up modeling is feasible even when modeling simple physical systems across scales. By comparing examples of multi-scale modeling in physics and biology, we argue that the “tyranny of scales” problem presents a challenge to reductive explanations in both physics and biology. The problem refers to the scale-dependency of physical and biological behaviors that forces researchers to combine different models relying on different scale-specific mathematical strategies and boundary conditions. Analyzing the ways in which different models are combined in multi-scale modeling also has implications for the relation between physics and biology. Contrary to the assumption that physical science approaches provide reductive explanations in biology, we exemplify how inputs from physics often reveal the importance of macro-scale models and explanations. We illustrate this through an examination of the role of biomechanics modeling in developmental biology. In such contexts, the relation between models at different scales and from different disciplines is neither reductive nor completely autonomous, but interdependent. (shrink)

We argue that artificial networks are explainable and offer a novel theory of interpretability. Two sets of conceptual questions are prominent in theoretical engagements with artificial neural networks, especially in the context of medical artificial intelligence: Are networks explainable, and if so, what does it mean to explain the output of a network? And what does it mean for a network to be interpretable? We argue that accounts of “explanation” tailored specifically to neural networks have ineffectively reinvented the wheel. In (...) response to, we show how four familiar accounts of explanation apply to neural networks as they would to any scientific phenomenon. We diagnose the confusion about explaining neural networks within the machine learning literature as an equivocation on “explainability,” “understandability” and “interpretability.” To remedy this, we distinguish between these notions, and answer by offering a theory and typology of interpretation in machine learning. Interpretation is something one does to an explanation with the aim of producing another, more understandable, explanation. As with explanation, there are various concepts and methods involved in interpretation: Total or Partial, Global or Local, and Approximative or Isomorphic. Our account of “interpretability” is consistent with uses in the machine learning literature, in keeping with the philosophy of explanation and understanding, and pays special attention to medical artificial intelligence systems. (shrink)

Much of the recent philosophical debate on causation and causal explanation in the biological and biomedical sciences has focused on the notion of mechanism. Mechanisms, their nature and epistemic roles have been tackled by a range of so-called neo-mechanistic theories, and widely discussed. Without denying the merits of this approach, our paper aims to show how lately it has failed to give proper credit to processes, which are central to the field, especially of contemporary molecular biology. Processes can be summed (...) up in the notion of ‘pathway’, which is far from being just equivalent to that of ‘mechanism’ and has a profound epistemological and explanatory relevance. It is argued that an adequate consideration of pathways impels some rethinking of scientific explanation in molecular biology, namely its functional and contextual features. A number of examples are given to suggest that the focus of philosophical attention in this disciplinary field should shift from the notion of mechanism to the notion of pathway. (shrink)

Organization figures centrally in the understanding of biological systems advanced by both new mechanists and proponents of the autonomy framework. The new mechanists focus on how components of mechanisms are organized to produce a phenomenon and emphasize productive continuity between these components. The autonomy framework focuses on how the components of a biological system are organized in such a way that they contribute to the maintenance of the organisms that produce them. In this paper we analyze and compare these two (...) accounts of organization and argue that understanding biological organisms as cohesively integrated systems benefits from insights from both. To bring together the two accounts, we focus on the notions of control and regulation as bridge concepts. We start from a characterization of biological mechanisms in terms of constraints and focus on a specific type of mechanism, control mechanisms, that operate on other mechanisms on the basis of measurements of variables in the system and its environment. Control mechanisms are characterized by their own set of constraints that enable them to sense conditions, convey signals, and effect changes on constraints in the controlled mechanism. They thereby allow living organisms to adapt to internal and external variations and to coordinate their parts in such a manner as to maintain viability. Because living organisms contain a vast number of control mechanisms, a central challenge is to understand how they are themselves organized. With the support of examples from both unicellular and multicellular systems we argue that control mechanisms are organized heterarchically, and we discuss how this type of control architecture can, without invoking top-down and centralized forms of organizations, succeed in coordinating internal activities of organisms. (shrink)