General Theory of Topological Explanations and Explanatory Asymmetry

In this paper, I present a general theory of topological explanations, and illustrate its fruitfulness by showing how it accounts for explanatory asymmetry. My argument is developed in three steps. In the first step, I show what it is for some topological property A to explain some physical or dynamical property B. Based on that, I derive three key criteria of successful topological explanations: a criterion concerning the facticity of topological explanations, i.e. what makes it true of a particular system; a criterion for describing counterfactual dependencies in two explanatory modes, i.e. the vertical and the horizontal; and, finally, a third perspectival one that tells us when to use the vertical and when to use the horizontal mode. In the second step, I show how this general theory of topological explanations accounts for explanatory asymmetry in both the vertical and horizontal explanatory modes. Finally, in the third step, I argue that this theory is universally applicable across biological sciences, which helps to unify essential concepts of biological networks

The Turing Test and the Zombie Argument

In this paper I shall try to put some implications concerning the Turing's test and the so-called Zombie arguments into the context of philosophy of mind. My intention is not to compose a review of relevant concepts, but to discuss central problems, which originate from the Turing's test - as a paradigm of computational theory of mind - with the arguments, which refute sustainability of this thesis. In the first section (Section I), I expose the basic computationalist presuppositions; by examining the premises of the Turing Test (TT) I argue that the TT, as a functionalist paradigm concept, underlies the computational theory of mind. I treat computationalism as a thesis that defines the human cognitive system as a physical, symbolic and semantic system, in such a manner that the description of its physical states is isomorphic with the description of its symbolic conditions, so that this isomorphism is semantically interpretable. In the second section (Section II), I discuss the Zombie arguments, and the epistemological-modal problems connected with them, which refute sustainability of computationalism. The proponents of the Zombie arguments build their attack on the computationalism on the basis of thought experiments with creatures behaviorally, functionally and physically indistinguishable from human beings, though these creatures do not have phenomenal experiences. According to the consequences of these thought experiments - if zombies are possible, then, the computationalism doesn't offer a satisfying explanation of consciousness. I compare my thesis from Section 1, with recent versions of Zombie arguments, which claim that computationalism fails to explain qualitative phenomenal experience. I conclude that despite the weaknesses of computationalism, which are made obvious by zombie-arguments, these arguments are not the last word when it comes to explanatory force of computationalism

Unifying the essential concepts of biological networks: biological insights and philosophical foundations

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels, and network hierarchies

Minimal structure explanations, scientific understanding and explanatory depth

In this paper, I outline a heuristic for thinking about the relation between explanation and understanding that can be used to capture various levels of “intimacy”, between them. I argue that the level of complexity in the structure of explanation is inversely proportional to the level of intimacy between explanation and understanding, i.e. the more complexity the less intimacy. I further argue that the level of complexity in the structure of explanation also affects the explanatory depth in a similar way to intimacy between explanation and understanding, i.e. the less complexity the greater explanatory depth and vice versa

The Explanatory Gap Account and Intelligibility of Explanation

This paper examines the explanatory gap account. The key notions for its proper understanding are analysed. In particular, the analysis is concerned with the role of “thick” and “thin” modes of presentation and “thick” and “thin” concepts which are relevant for the notions of “thick” and “thin” conceivability, and to that effect relevant for the gappy and non-gappy identities. The last section of the paper discusses the issue of the intelligibility of explanations. One of the conclusions is that the explanatory gap account only succeeds in establishing the epistemic gap. The claim that psychophysical identity is not intelligibly explicable, and thus opens the explanatory gap, would require an indepen- dent argument which would prove that intelligible explanations stem only from conceptual analysis. This, I argue, is not the case

Mechanistic and topological explanations: an introduction

In the last twenty years or so, since the publication of a seminal paper by Watts and Storgatz (1998), an interest in topological explanations has spread like a wild fire over many areas of science, e.g. ecology, evolutionary biology, medicine, and cognitive neuroscience. The aim of this special issue is to discuss the relationship between mechanistic and topological approaches to explanation and their prospects

Unifying the debates: mathematical and non-causal explanations

In the last couple of years, a few seemingly independent debates on scientific explanation have emerged, with several key questions that take different forms in different areas. For example, the question what makes an explanation distinctly mathematical and are there any non-causal explanations in sciences (i.e. explanations that don’t cite causes in the explanans) sometimes take a form of the question what makes mathematical models explanatory, especially whether highly idealized models in science can be explanatory and in virtue of what they are explanatory. These questions raise further issues about counterfactuals, modality and explanatory asymmetries, i.e. do mathematical and non-causal explanations support counterfactuals, and how to understand explanatory asymmetries in non-causal explanations. Even though these are very common issues in the philosophy of physics and mathematics, they can be found in different guises in the philosophy of biology, where there is the statistical interpretation of the Modern Synthesis theory of evolution, according to which the post-Darwinian theory of natural selection explains evolutionary change by citing statistical properties of populations and not the causes of changes. These questions also arise in the philosophy of ecology or neuroscience in regard to the nature of topological explanations. The question here is whether in network models in biology, ecology, neuroscience, and computer science the mathematical or more precisely topological properties can be explanatory of physical phenomena, or they are just different ways to represent causal structures. The aim of the special issue is to unify all these debates around several overlapping questions

Unifying the essential concepts of biological networks: biological insights and philosophical foundations

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels, and network hierarchies

Unifying the essential concepts of biological networks: biological insights and philosophical foundations

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels, and network hierarchies

Unifying the debates: mathematical and non-causal explanations

In the last couple of years, a few seemingly independent debates on scientific explanation have emerged, with several key questions that take different forms in different areas. For example, the question what makes an explanation distinctly mathematical and are there any non-causal explanations in sciences (i.e. explanations that don’t cite causes in the explanans) sometimes take a form of the question what makes mathematical models explanatory, especially whether highly idealized models in science can be explanatory and in virtue of what they are explanatory. These questions raise further issues about counterfactuals, modality and explanatory asymmetries, i.e. do mathematical and non-causal explanations support counterfactuals, and how to understand explanatory asymmetries in non-causal explanations. Even though these are very common issues in the philosophy of physics and mathematics, they can be found in different guises in the philosophy of biology, where there is the statistical interpretation of the Modern Synthesis theory of evolution, according to which the post-Darwinian theory of natural selection explains evolutionary change by citing statistical properties of populations and not the causes of changes. These questions also arise in the philosophy of ecology or neuroscience in regard to the nature of topological explanations. The question here is whether in network models in biology, ecology, neuroscience, and computer science the mathematical or more precisely topological properties can be explanatory of physical phenomena, or they are just different ways to represent causal structures. The aim of the special issue is to unify all these debates around several overlapping questions