Theories of explanation need to account for a puzzling feature of our explanatory practices: the fact that we prefer explanations that are relatively abstract but only moderately so. Contra Franklin-Hall ([2016]), I argue that the interventionist account of explanation provides a natural and elegant explanation of this fact. By striking the right balance between specificity and generality, moderately abstract explanations optimally subserve what interventionists regard as the goal of explanation, namely identifying possible interventions that would have changed the explanandum.

A number of philosophers have recently suggested that some abstract, plausibly non-causal and/or mathematical, explanations explain in a way that is radically dif- ferent from the way causal explanation explain. Namely, while causal explanations explain by providing information about causal dependence, allegedly some abstract explanations explain in a way tied to the independence of the explanandum from the microdetails, or causal laws, for example. We oppose this recent trend to regard abstractions as explanatory in some sui generis way, and (...) argue that a prominent ac- count of causal explanation can be naturally extended to capture explanations that radically abstract away from microphysical and causal-nomological details. To this end, we distinguish di erent senses in which an explanation can be more or less abstract, and analyse the connection between explanations’ abstractness and their explanatory power. According to our analysis abstract explanations have much in common with counterfactual causal explanations. (shrink)

I consider three explanatory strategies from recent systems biology that are driven by mathematics as much as mechanistic detail. Analysis of differential equations drives the first strategy; topological analysis of network motifs drives the second; mathematical theorems from control engineering drive the third. I also distinguish three abstraction types: aggregations, which simplify by condensing information; generalizations, which simplify by generalizing information; and structurations, which simplify by contextualizing information. Using a common explanandum as reference point—namely, the robust perfect adaptation (...) of chemotaxis in Escherichia coli—I argue that each strategy invokes a different combination of abstraction types and that each targets its abstractions to different mechanistic details. (shrink)

Physical reality as an explanatory model is an abstraction of the mind. Every perceptual system is a user interface, like the dashboard of an aeroplane or the desktop of a computer. We do not see or otherwise perceive reality but only interface with reality. The user interface concept is a starting point for a critical dialogue with those epistemic theories that present themselves as veridical and take explanatory abstractions as ontological primitives. At the heart of any scientific (...) model are assumptions about which things exist, how they are related, and how we can know them. Scientific models take our knowledge beyond ordinary experience toward explanatory abstractions. The main problem with veridical models lies in why we cannot express our theories and the explanatory abstractions associated with them other than through classical perceptual interface symbols. This study analyses the limits, possibilities and constraints of explanatory abstractions. (shrink)

Mayr’s proximate–ultimate distinction has received renewed interest in recent years. Here we discuss its role in arguments about the relevance of developmental to evolutionary biology. We show that two recent critiques of the proximate–ultimate distinction fail to explain why developmental processes in particular should be of interest to evolutionary biologists. We trace these failures to a common problem: both critiques take the proximate–ultimate distinction to neglect specific causal interactions in nature. We argue that this is implausible, and that the distinction (...) should instead be understood in the context of explanatory abstractions in complete causal models of evolutionary change. Once the debate is reframed in this way, the proximate–ultimate distinction’s role in arguments against the theoretical significance of evo-devo is seen to rely on a generally implicit premise: that the variation produced by development is abundant, small and undirected. We show that a “lean version” of the proximate–ultimate distinction can be maintained even when this isotropy assumption does not hold. Finally, we connect these considerations to biological practice. We show that the investigation of developmental constraints in evolutionary transitions has long relied on a methodology which foregrounds the explanatory role of developmental processes. It is, however, entirely compatible with the lean version of the proximate–ultimate distinction. (shrink)

In this paper, we address the question of what current computers are from the point of view of human-computer interaction. In the early days of computing, the Turing machine (TM) has been the cornerstone of the understanding of computers. The TM defines what can be computed and how computation can be carried out. However, in the last decades, computers have evolved and increasingly become interactive systems, reacting in real-time to external events in an ongoing loop. We argue that the TM (...) does not provide a mechanistic explanation for interactive computing. The reason is that the fundamental phenomena relevant to interactive computing are out of the scope of classical computability theory. Part of the explanatory power of the TM relies on what we propose to call an execution model. An execution model belongs to a level of abstraction where it is possible to describe both the functional architecture and the execution in mechanistic terms. An updated execution model is warranted to provide the minimal mechanistic description for interactive computation as a counterpart of what the TM could explain regarding Church-Turing computation. It would support an explanation of the ubiquitous computing devices we know - those interacting with humans, e.g., through digital interfaces. We show that such a model is not available within interactive models of computation and that relevant abstractions and concerns are available in computer engineering but need to be identified and gathered. To fill this void, we propose to reflect on the level of abstraction required to support the mechanistic description of an interactive execution and propose some preliminary requirements. (shrink)

Though it is held that some models in science have explanatory value, there is no conclusive agreement on what provides them with this value. One common view is that models have explanatory value vis-à-vis some target systems because they are developed using an abstraction process. Though I think this is correct, I believe it is not the whole picture. In this paper, I argue that, in addition to the well-known process of abstraction understood as an omission (...) of features or information, there is also a family of abstraction processes that involve aggregation of features or information and that these processes play an important role in endowing the models they are used to build with explanatory value. After offering a taxonomy of abstraction processes involving aggregation, I show by considering in detail several models drawn from different sciences that the abstraction processes involving aggregation that are used to build these models are responsible for their having explanatory value. (shrink)

Some explanations are relatively abstract: they abstract away from the idiosyncratic or messy details of the case in hand. The received wisdom in philosophy is that this is a virtue for any explanation to possess. I argue that the apparent consensus on this point is illusory. When philosophers make this claim, they differ on which of four alternative varieties of abstractness they have in mind. What’s more, for each variety of abstractness there are several alternative reasons to think that the (...) variety of abstractness in question is a virtue. I identify the most promising reasons, and dismiss some others. The paper concludes by relating this discussion to the idea that explanations in biology, psychology and social science cannot be replaced by relatively micro explanations without loss of understanding. (shrink)

Non-reductive physicalists have long held that the special sciences offer explanations of some phenomena that are objectively superior to physical explanations. This explanatory “autonomy” has largely been based on the multiple realizability argument. Recently, in the face of the local reduction and disjunctive property responses to multiple realizability, some defenders of non-reductive physicalism have suggested that autonomy can be grounded merely in human cognitive limitations. In this paper, I argue that this is mistaken. By distinguishing between two kinds of (...) abstraction I show that the greater explanatory relevance of some special science predicates (to certain explananda) is both non-anthropocentric and not solely based on considerations of multiple realizability. This shows that the explanatory autonomy of the special sciences is safe from the local reduction and disjunctive property strategies, even if they are successful responses to the multiple realizability argument. (shrink)

Anti-reductionists hold that special science explanations of some phenomena are objectively better than physical explanations of those phenomena. Prominent defenses of this claim appeal to the multiple realizability of special science properties. I argue that special science explanations can be shown to be better, in one respect, than physical explanations in a way that does not depend on multiple realizability. Namely, I discuss a way in which a special science explanation may be more abstract than a competing physical explanation, even (...) if it is not multiply realizable, and I argue that this kind of abstraction can be used to support the idea that that special science explanation omits explanatorily irrelevant detail. (shrink)

Abstract: This paper argues that a central explanatory role for the concept of Umwelt in theoretical biology is to be found in developmental biology, in particular in the effort to understand development as a goal-directed and adaptive process that is controlled by the organism itself. I will reach this conclusion in two (interrelated) ways. The first is purely theoretical and relates to the current scenario in the philosophy of biology. Challenging neo-Darwinism requires a new understanding of the various components (...) involved in natural selection processes. An important prerequisite for the explanation is the ability to understand development in a teleological way. Here, the concept of Umwelt plays a crucial role: if organisms are responsible for generating adaptive variation in specific environments, we need a theory that explains the context-dependent nature of adaptively oriented processes. The Umwelt is thus a central element in determining the goal that an adaptive process pursues. The second path in my analysis also has a historical dimension. I will present Karl Ernst von Baer’s reflections on teleological development and his influence on von Uexküll's thinking. I will present various ideas developed by von Baer, such as the distinction between Ziel and Zweck and the use of musical metaphors, which can help to understand development teleologically and give von Uexküll’s theory a central place in this framework. (shrink)

Abstract According to John Dupré, the metaphysics underpinning modern science posits a deterministic, fully law?governed and potentially fully intelligible structure that pervades the entire universe. To reject such a metaphysical framework for science is to subscribe to ?the disorder of things?, and the latter, according to Dupré, entails the impossibility of a unified science. Dupré's argument rests crucially upon purported disunities evident in the explanatory practices of science. I critically examine the implied project of drawing metaphysical conclusions from epistemological (...) premisses concerning the nature of our explanatory practices. I then argue that Dupré fails to answer a particular argument for the ontological unity of science that rests upon assumptions about the causal structure of the world. This ?causal? argument for the unity of science might be countered by a more radical metaphysical revisionism. The latter, however, seems unable to account for features of our explanatory practices that testify to a measure of explanatory unity in science. I conclude by sketching a strategy that might enable the revisionist to overcome such difficulties. (shrink)

I defend an account of explanatory depth according to which explanations in the non-fundamental sciences can be deeper than explanations in fundamental physics.

In this book, Zalta attempts to lay the axiomatic foundations of metaphysics by developing and applying a (formal) theory of abstract objects. The cornerstones include a principle which presents precise conditions under which there are abstract objects and a principle which says when apparently distinct such objects are in fact identical. The principles are constructed out of a basic set of primitive notions, which are identified at the end of the Introduction, just before the theorizing begins. The main reason for (...) producing a theory which defines a logical space of abstract objects is that it may have a great deal of explanatory power. It is hoped that the data explained by means of the theory will be of interest to pure and applied metaphysicians, logicians and linguists, and pure and applied epistemologists. (shrink)

ABSTRACT When scientists seek further confirmation of their results, they often attempt to duplicate the results using diverse means. To the extent that they are successful in doing so, their results are said to be ‘robust’. This article investigates the logic of such ‘robustness analysis’. The most important and challenging question an account of RA can answer is what sense of evidential diversity is involved in RAs. I argue that prevailing formal explications of such diversity are unsatisfactory. I propose a (...) unified, explanatory account of diversity in RAs. The resulting account is, I argue, truer to actual cases of RA in science; moreover, this account affords us a helpful new foothold on the logic undergirding RAs. 1Robustness Analysis in Science 2RA Diversity and Independence 2.1Unconditional probabilistic independence 2.2Reliability independence 2.3Confirmational and conditional independence 2.4Partial independence? 3Robustness Analysis as Explanatory Reasoning 3.1Explanatorily discriminating means of detection 3.2The logic of robustness analysis 3.2.1Mutually exclusive competitors 3.2.2Consistent epistemic competitors 4Conclusion Appendix. (shrink)

> Context • The alleged dichotomy between mind and matter is pervasive. Therefore, the attempt to explain mat- ter in terms of mind (idealism) is often considered a mirror image of that of explaining mind in terms of mat- ter (mainstream physicalism), in the sense of being structurally equivalent despite being reversely arranged. > Problem • I argue that this is an error arising from language artifacts, for dichotomies must reside in the same level of abstraction. > Method • (...) I show that, because matter outside mind is not an empirical observation but rather an explanatory model, the epistemic symmetry between the two is broken. Consequently, matter and mind cannot reside in the same level of abstraction. > Results • It then becomes clear that attempting to explain mind in terms of matter is epistemically more costly than attempting to explain matter in terms of mind. > Implications • The qualities of experience are suggested to be not only epistemically, but also ontologically primary. > Constructivist content • I high- light the primacy of perceptual constructs over explanatory abstraction on both epistemic and ontic levels. > Key words • Idealism, physicalism, pancomputationalism, anti-realism, hard problem of consciousness, epistemic symmetry, explanatory abstraction, levels of abstraction. (shrink)

Explainable artificial intelligence (XAI) aims to help people understand black box algorithms, particularly of their outputs. But what are these explanations and when is one explanation better than another? The manipulationist definition of explanation from the philosophy of science offers good answers to these questions, holding that an explanation consists of a generalization that shows what happens in counterfactual cases. Furthermore, when it comes to explanatory depth this account holds that a generalization that has more abstract variables, is broader (...) in scope and/or more accurate is better. By applying these definitions and contrasting them with alternative definitions in the XAI literature I hope to help clarify what a good explanation is for AI. (shrink)

Among the factors necessary for the occurrence of some event, which of these are selectively highlighted in its explanation and labeled as causes — and which are explanatorily omitted, or relegated to the status of background conditions? Following J. S. Mill, most have thought that only a pragmatic answer to this question was possible. In this paper I suggest we understand this ‘causal selection problem’ in causal-explanatory terms, and propose that explanatory trade-offs between abstraction and stability can (...) provide a principled solution to it. After sketching that solution, it is applied to a few biological examples, including to a debate concerning the ‘causal democracy’ of organismal development, with an anti-democratic (though not a gene-centric) moral. (shrink)

Proponents of mechanistic explanation all acknowledge the importance of organization. But they have also tended to emphasize specificity with respect to parts and operations in mechanisms. We argue that in understanding one important mode of organization—patterns of causal connectivity—a successful explanatory strategy abstracts from the specifics of the mechanism and invokes tools such as those of graph theory to explain how mechanisms with a particular mode of connectivity will behave. We discuss the connection between organization, abstraction, and mechanistic (...) explanation and illustrate our claims by looking at an example from recent research on so-called network motifs. (shrink)

argumentation has been shown to be a powerful tool within many fields such as artificial intelligence, logic and legal reasoning. In this paper we enhance Dung’s well-known abstract argumentation framework with explanatory capabilities. We show that an explanatory argumentation framework (EAF) obtained in this way is a useful tool for the modeling of scientific debates. On the one hand, EAFs allow for the representation of explanatory and justificatory arguments constituting rivaling scientific views. On the other hand, different (...) procedures for selecting arguments, corresponding to different methodological and epistemic requirements of theory evaluation, can be formulated in view of our framework. (shrink)

This article considers the popular thesis that a more proportional relationship between a cause and its effect yields a more abstract causal explanation of that effect, which in turn produces a deeper explanation. This thesis is taken to have important implications for choosing the optimal granularity of explanation for a given explanandum. In this article, I argue that this thesis is not generally true of probabilistic causal relationships. In light of this finding, I propose a pragmatic, interest-relative measure of (...) class='Hi'>explanatory depth. This measure uses a decision-theoretic model of information pricing to determine the optimal granularity of explanation for a given explanandum, agent, and decision problem. (shrink)

Scientists and philosophers frequently speak about levels of description, levels of explanation, and ontological levels. In this paper, I propose a unified framework for modelling levels. I give a general definition of a system of levels and show that it can accommodate descriptive, explanatory, and ontological notions of levels. I further illustrate the usefulness of this framework by applying it to some salient philosophical questions: (1) Is there a linear hierarchy of levels, with a fundamental level at the bottom? (...) And what does the answer to this question imply for physicalism, the thesis that everything supervenes on the physical? (2) Are there emergent properties? (3) Are higher-level descriptions reducible to lower-level ones? (4) Can the relationship between normative and non-normative domains be viewed as one involving levels? Although I use the terminology of “levels”, the proposed framework can also represent “scales”, “domains”, or “subject matters”, where these are not linearly but only partially ordered by relations of supervenience or inclusion. (shrink)

Several modern accounts of explanation acknowledge the importance of abstraction and idealization for our explanatory practice. However, once we allow a role for abstraction, questions remain. I ask whether the relation between explanations at different theoretical levels should be thought of wholly in terms of abstraction, and argue that changes of the quantities in terms of which we describe a system can lead to novel explanations that are not merely abstractions of some more detailed picture. I (...) use the example of phase transitions as described by statistical mechanics and thermodynamics to illustrate this, and to demonstrate some details of the relationship between abstraction, idealization, and novel explanation. (shrink)

Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the classical (...) problem of opacity in artificial neural networks (ANNs) explodes with the adoption of current Deep Learning techniques [LeCun, Bengio, Hinton, 2015]. In this paper we argue that the explanatory deficit of such techniques represents an important problem, that limits their adoption in the cognitive modelling and computational cognitive science arena. In particular we will show how the current attempts of providing explanations of the deep nets behaviour (see e.g. [Ritter et al. 2017] are not satisfactory. As a possibile way out to this problem, we present two different research strategies. The first strategy aims at dealing with the opacity problem by providing a more abstract interpretation of neural mechanisms and representations. This approach is adopted, for example, by the biologically inspired SPAUN architecture [Eliasmith et al., 2012] and by other proposals suggesting, for example, the interpretation of neural networks in terms of the Conceptual Spaces framework [Gärdenfors 2000, Lieto, Chella and Frixione, 2017]. All such proposals presuppose that the neural level of representation can be considered somehow irrelevant for attacking the problem of explanation [Lieto, Lebiere and Oltramari, 2017]. In our opinion, pursuing this research direction can still preserve the use of deep learning techniques in artificial cognitive models provided that novel and additional results in terms of “transparency” are obtained. The second strategy is somehow at odds with respect to the previous one and tries to address the explanatory issue by avoiding to directly solve the “opacity” problem. In this case, the idea is that one of resorting to pre-compiled plausible explanatory models of the word used in combination with deep-nets (see e.g. [Augello et al. 2017]). We argue that this research agenda, even if does not directly fits the explanatory needs of Computational Cognitive Science, can still be useful to provide results in the area of applied AI aiming at shedding light on the models of interaction between low level and high level tasks (e.g. between perceptual categorization and explanantion) in artificial systems. (shrink)

Abstract The article sketches a theoretical model which explains how it is possible that fundamentalist beliefs can emerge as a result of an individual rational adaptation to the context of special living conditions. The model is based on the insight that most of our knowledge is acquired by trusting the testimony of some kind of authority. If a social group is characterized by a high degree of mistrust towards the outer society or other groups, then the members of this group (...) will rely solely on the authorities of their own group for their acquisition of knowledge. In this way they can adopt a corpus of beliefs which may seem absurd from an external point of view. However, they may be locked in a “fundamentalist equilibrium” in which particularistic trust, common sense plausibility, epistemic seclusion, social isolation and fundamentalist beliefs are mutually reinforcing - and in which individuals who adopt the “fundamentalist truths” of their group do not behave more irrationally than individuals in an open society who accept the “enlightened” worldview of their culture. (shrink)

In addition to their core explanatory and predictive assumptions, scientific models include simplifying assumptions, which function as idealizations, approximations, and abstractions. There are methods to investigate whether simplifying assumptions bias the results of models, such as robustness analyses. However, the equally important issue – the focus of this paper – has received less attention, namely, what are the methodological and epistemic strengths and limitations associated with different simplifying assumptions. I concentrate on one type of simplifying assumption, the use of (...) mega parameters as abstractions in ecological models. First, I argue that there are two kinds of mega parameters qua abstractions, sufficient parameters and aggregative parameters, which have gone unnoticed in the literature. The two are associated with different heuristics, holism and reductionism, which many view as incompatible. Second, I will provide a different analysis of abstractions and the associated heuristics than previous authors. Reductionism and holism and the accompanying abstractions have different methodological and epistemic functions, strengths, and limitations, and the heuristics should be viewed as providing complementary research perspectives of cognitively limited beings. This is then, third, used as a premise to argue for epistemic and methodological pluralism in theoretical ecology. Finally, the presented taxonomy of abstractions is used to comment on the current debate whether mechanistic accounts of explanation are compatible with the use of abstractions. This debate has suffered from an abstract discussion of abstractions. With a better taxonomy of abstractions the debate can be resolved. (shrink)

Noam Chomsky pursues a methodology in linguistics that abstracts from substantial amounts of data about actual language use in a way that has met considerable resistance from many other linguists. This chapter argues that Chomsky's observation in fact accords with good explanatory practice elsewhere in science, but it does conflict with a traditional methodology in linguistics. It's striking that the main features of Chomsky's Galilean style are independently taken to be rather obvious features of scientific method in contemporary philosophy (...) of science. The chapter first looks in more detail at the roles that idealization plays in contemporary generative grammar. It then proceeds to explore the congruence between the Galilean style in linguistics and aspects of good scientific methodology. Finally, the chapter sets out and responds briefly to some of the arguments that have been raised against the Galilean style. (shrink)

Are there genuine mathematical explanations of physical phenomena, and if so, how can mathematical theories, which are typically thought to concern abstract mathematical objects, explain contingent empirical matters? The answer, I argue, is in seeing an important range of mathematical explanations as structural explanations, where structural explanations explain a phenomenon by showing it to have been an inevitable consequence of the structural features instantiated in the physical system under consideration. Such explanations are best cast as deductive arguments which, by virtue (...) of their form, establish that, given the mathematical structure instantiated in the physical system under consideration, the explanandum had to occur. Against the claims of platonists such as Alan Baker and Mark Colyvan, I argue that formulating mathematical explanations as structural explanations in this way shows that we can accept that mathematics can play an indispensable explanatory role in empirical science without committing to the existence of any abstract mathematical objects. (shrink)

Probabilistic argumentation and neuro-argumentative systems offer new computational perspectives for the theory and applications of argumentation, but their principled construction involves two entangled problems. On the one hand, probabilistic argumentation aims at combining the quantitative uncertainty addressed by probability theory with the qualitative uncertainty of argumentation, but probabilistic dependences amongst arguments as well as learning are usually neglected. On the other hand, neuro-argumentative systems offer the opportunity to couple the computational advantages of learning and massive parallel computation from neural networks (...) with argumentative reasoning and explanatory abilities, but the relation of probabilistic argumentation frameworks with these systems has been ignored so far. Towards the construction of neuro-argumentative systems based on probabilistic argumentation, we associate a model of abstract argumentation and the graphical model of Boltzmann machines in order to (i... (shrink)

Cases or examples can be often explained by the interplay of arguments in favour or against their outcomes. This paper addresses the problem of finding explanations for a collection of cases where an explanation is a labelled argumentation graph consistent with the cases, and a case is represented as a statement labelling. The focus is on semi-abstract argumentation graphs specifying attack and subargument relations between arguments, along with particular complete argument labellings taken from probabilistic argumentation where arguments can be excluded. (...) The framework and computational considerations lead to an iterated local iterative deepening depth-first search to collect ‘interesting’ semi-abstract argumentation graphs consistent with a collection of statement labellings. Finally, the framework is illustrated and evaluated with a proof-of-concept implementation. (shrink)

On the neo-Fregean approach to the foundations of mathematics, elementary arithmetic is analytic in the sense that the addition of a principle wliich may be held to IMJ explanatory of the concept of cardinal number to a suitable second-order logical basis suffices for the derivation of its basic laws. This principle, now commonly called Hume's principle, is an example of a Fregean abstraction principle. In this paper, I assume the correctness of the neo-Fregean position on elementary aritlunetic and (...) seek to explain one way in which it may be extended to encompass the theory of real numbers, introducing the reals, by means of suitable further abstraction principles, as ratios of quantities. (shrink)

I survey several counterexamples (by Raz and MacCormick) to Hohfeld's conjecture that a claim-right is correlative to a directed duty and (by Cornell and Frick) to Bentham's suggestion that a claim-right is correlative to a wronging. We can vindicate these claims of correlativity if we acknowledge that entitlements like claim-rights and directed duties admit of degrees of abstraction: that they may be general rather than specific, unspecified rather than specified, or indefinite rather than definite. I provide an error theory (...) consisting in linguistic and practical reasons for why we articulate normative incidents in ways that threaten correlativity. And I deny that abstraction imposes a heavy metaphysical cost on rights theory, though I leave open whether abstraction excludes certain explanatory accounts of rights such as the interest theory or will theory. (shrink)

Some tools introduced by Linnebo to show that mathematical entities are thin objects can also be applied to non-mathematical entities, which have been thought to be thin as well for a variety of reasons. In this paper, I discuss some difficulties and opportunities concerning the application of abstraction and interpretational modalities to mereological sums. In particular, I show that on one hand some prima facie attractive candidates for the role of an explanatory plural abstraction principle for mereological (...) sums (in terms of pluralities of summed entities) are not really explanatory; on the other hand, singular abstraction principles (in terms of single summed entities) are materially inadequate. Nonetheless, explanatory criteria of identity and conditions of existence for mereological sums are provided by classical extensional mereology independent of abstraction principles. Thus, given classical extensional mereology, the reasons why, according to Linnebo, mathematical abstracted entities are thin also hold for mereological sums. Finally, I contend that interpretational modalities can be used to characterise the process by which a subject adds sums of previously admitted entities to the domain of quantification. (shrink)

Context: In this paper I expand aspects of the generalized bottom-up explanatory approach devised in Part I to expound the natural emergence of composite self-organized dynamic systems endowed with self-produced embodied boundaries and with observed degrees of autonomous behavior. In Part I, the focus was on the rules defined by Varela, Maturana & Uribe (VM&U rules), viewed as a validation test to assess if an observed system is autopoietic. This was accomplished by referring to Maturana’s ontological-epistemological frame and by (...) defining distinctions, concepts, and abstractions necessary to describe dynamic systems in any observational domain. This approach concentrates on pure causation flow rather than on domain-specific interaction mechanisms. Problem: It is essential to analyze the requirements imposed by the VM&U rules on the “intra-boundaries” phenomenology for compliance with the self-production capabilities expected from an autopoietic system. Beyond what is merely implied by the compact wording of the VM&U rules, a key point needs to be addressed explicitly: how to describe some “peculiar” capabilities that the components should be endowed with to participate in new component production (as macro-molecules do in the biological domain) so that system’s self-production can be assessed. Method: Using this approach, I first describe the process of constituting self-organized dynamic structures provided with embodied boundaries. Then I explain how a capability of self-organization emerges and how this results in ephemeral configurations that may evolve into self-regulated long-lasting dynamic system stability within a continuous causation flow inside the boundaries, up to the emergence of some “specialized” subsets of components. This explication allows us to distinguish the medium, the boundaries, and the core of a self-organized dynamic system and to focus attention on the “intra-boundaries” phenomenology that should be at the heart of self-production capabilities, as prescribed by the 5th and 6th VM&U rules. Results: I propose an abstract, domain-free description of the “peculiar” composition and decomposition transformation capabilities that components should possess while subject to state transitions triggered within the “intra-boundaries” causation flow. This is combined with a discussion concerning the “intra-boundaries” causation structure’s possible topological layouts that could be compliant with the 6th rule. Implications: The above-mentioned results allow us to improve our analytic criteria when observing dynamic systems existing in non-biological domains in order to assess their autopoietic nature. They also reveal that the task of consistently identifying possible non-biological autopoietic systems is harder than merely identifying self-organized dynamic systems provided with boundaries and some observable autonomous behavioral capabilities in a given observational domain. More implications will be discussed further in Part III. (shrink)

This article reconstructs and defends Theodor Adorno’s social theory by motivating the central role of abstract domination within it. Whereas critics such as Axel Honneth have charged Adorno with adhering to a reductive model of personal domination, I argue that the latter rather understands domination as a structural and de-individualized feature of capitalist society. If Adorno’s social theory is to be explanatory, however, it must account for the source of the abstractions that dominate modern individuals and, in particular, that (...) of value. While such an account remains undeveloped in Adorno, Marx provides resources for its development, in positing the constitution of value neither in production nor exchange alone, but in the social totality. This article argues that Marx’s account is compatible with Adorno’s, and that it may be used to render Adorno’s theory of domination more credible on explanatory grounds. (shrink)

Context: This paper is intended for readers familiar with Humberto Maturana’s theory of autopoietic systems and with the still unresolved debate concerning the existence of non-biological autopoietic systems. Because the seminal work of the Chilean biologist has not yet been fully and correctly understood in other disciplines, I consider that it is necessary to offer a more generalized concept of the autopoietic system, derived by implication from Maturana’s grounding definition. Problem: The above-mentioned debate is rooted in a deficient application of (...) some rigorous distinctions, definitions, and epistemological considerations introduced by Maturana when he coined the term “autopoiesis.” Some researchers think that social or economic organizations could be considered as autopoietic systems of a higher order because they appear to behave autonomously and be self-organized and self-producing. However, in practice some precise distinctions would need to be verified through observation in order to claim properly their autopoietic nature. These distinctions were defined by Varela, Maturana and Uribe in 1974 as a set of six decisional rules (“MV&U rules”) whereby an observer may possibly justify this stand. My aim is to pinpoint clearly the basic cognitive tasks that an observer should perform in order to ascertain such a claim. Method: I accomplish this with a thorough analysis of the entailments derived from each rule when applied to the most general case – when the observational domain where the system manifests itself is not specified. A bottom-up approach is used to avoid referring to “apparent autopoietic behavior” as a starting point distinction (top-down approach): the aim is to distinguish “autopoietic behavior” as an outcome of more basic distinctions, not as a premise for these. These may be used as abstract tools to facilitate a rigorous description of observations and lead to precise explanations of the emergence of complex self-generated dynamic systems. In theory, this conceptual frame is not limited to the macro-molecular domain and may therefore be applicable to non-biological systems. Results: According to MV&U rules, the most important distinctions are those that refer to intra-boundary phenomenology: this focus is necessary to explain how the key processes involved in the emergence of autopoiesis actually manifest themselves. These explanations are crucial to validating a claim about the “autopoietic nature” of an observed system. Implications: This work could help multidisciplinary researchers to apply properly the theory of autopoietic systems beyond the realm of biology and to settle ongoing debates. It could also help investigations related to the specifications of software simulation processes for modeling a minimal artificial autopoietic system. However, the rigorous focus on the role of intra-boundary phenomenology and self-production of components reveals that our chances of detecting “natural” meta-molecular autopoietic systems are scarce. (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)

In Metaphysics A, Aristotle offers some objections to Plato’s theory of Forms to the effect that Plato’s Forms would not be explanatory in the right way, and seems to suggest that they might even make the explanatory project worse. One interesting historical puzzle is whether Aristotle can avoid these same objections to his own theory of universals. The concerns Aristotle raises are, I think, cousins of contemporary concerns about the usefulness and explanatoriness of abstract objects, some of which (...) have recently been receiving attention in the philosophy of mathematics. After discussing Aristotle’s objections and their contemporary cousins, the paper discusses some of the main available lines of response to these sorts of challenges, before concluding with an examination of whether these responses could assist Plato or Aristotle in responding to these challenges. (shrink)

Explanations of biological phenomena such as cell division, protein synthesis or circadian rhythms commonly take the form of models of the responsible mechanisms. Recently philosophers of science have attempted to analyze this practice, presenting mechanisms as organized collections of parts performing operations that together produce the phenomenon. But in some cases what researchers seek to explain is not a general phenomenon, but a specific feature of a more fine-grained phenomenon. In some of these cases, it is not the model of (...) the mechanism that performs the explanatory work. I consider a case in which the investigator offered an abstract representation of a fine-grained phenomenon to show why in had the feature in question. I consider a second case in which a researcher abstracted from the mechanism to identify a design principle that explains why the functioning mechanism exhibits a specific feature. (shrink)

This paper addresses the methodological problem of analysing what it is to explain observed behaviours of engineered computing systems, focusing on the crucial role that abstraction and idealization play in explanations of both correct and incorrect BECS. First, it is argued that an understanding of explanatory requests about observed miscomputations crucially involves reference to the rich background afforded by hierarchies of functional specifications. Second, many explanations concerning incorrect BECS are found to abstract away from descriptions of physical components (...) and processes of computing systems that one finds below the logic circuit and gate layer of functional specification hierarchies. Third, model-based explanations of both correct and incorrect BECS that are provided in the framework of formal verification methods often involve idealizations. Moreover, a distinction between restrictive and permissive idealizations is introduced and their roles in BECS explanations are analysed. (shrink)

New mechanical philosophy posits that explanations in the life sciences involve the decomposition of a system into its entities and their respective activities and organization that are responsible for the explanandum phenomenon. This mechanistic account of explanation has proven problematic in its application to mathematical models, leading the mechanists to suggest different ways of aligning abstract models with the mechanist program. Initially, the discussion centered on whether the Hodgkin-Huxley model is explanatory. Network models provided another complication, as they apply (...) to a wide number of materially diverse systems. In this article, we examine the various attempts to integrate abstract models within the mechanist program, also presenting a further challenge: the Heimburg-Jackson model, which was introduced as an alternative to the Hodgkin-Huxley model. We argue that although the notion of abstraction as the omission of irrelevant mechanistic details appears to give a mechanistic solution for accommodating abstract models, this notion does not suit models whose epistemic strategy is not decompositional. As a result, the mechanist has to choose whether to dilute the mechanistic approach nearly beyond recognition or to claim that many, if not most, abstract theoretical models do not deliver mechanistic explanations, or qualify as explanatory at all. (shrink)

Mathematicians sometimes judge a mathematical proof to be beautiful and in doing so seem to be making a judgement of the same kind as aesthetic judgements of works of visual art, music or literature. Mathematical proofs are also appraised for explanatoriness: some proofs merely establish their conclusions as true, while others also show why their conclusions are true. This paper will focus on the prima facie plausible assumption that, for mathematical proofs, beauty and explanatoriness tend to go together. To make (...) headway we need to have some grip on what it is for a proof to be beautiful, and for that we need some account of judgements of beauty in general. That is the concern of the first section. The second section faces the problem that it is far from obvious how abstract entities, such as mathematical proofs, can be beautiful, strictly and literally speaking. Reasons are given for the view that they can be. The third section introduces the distinction between proofs which explain their conclusions and proofs which do not. Finally, the question whether, for mathematical proofs, the beautiful and the explanatory tend to coincide is addressed. It is argued that we have reason to doubt that explanatory proofs tend to be beautiful, and insufficient reason to believe or disbelieve that beautiful proofs tend to be explanatory. (shrink)

This paper's purpose is to set forth the conditions of explanation in the domain of formal modelling of social action. Explanation is defined as an adequate account of the underlying factors bringing about a phenomenon. The modelling of a social phenomenon can claim explanatory value in this sense if the following two conditions are fulfilled. (1) The generative mechanisms involved translate the effects of real factors abstracted from their phenomenal context, not those of purely ideal ones. (2) The (...) class='Hi'>explanatory hypotheses, which account for the effects of explanatory factors, and the purely descriptive hypotheses, which introduce conceptual simplifications and summarise complex secondary mechanisms, are relatively independent from each other with regard to the phenomenon represented. This condition subjects the model to testing by alternatives through the development of purely descriptive hypotheses in the sense of explanatory or analytical realism. (shrink)

The scope of Platonism is extended by introducing the concept of a “Platonic computer” which is incorporated in metacomputics. The theoretical framework of metacomputics postulates that a Platonic computer exists in the realm of Forms and is made by, of, with, and from metaconsciousness. Metaconsciousness is defined as the “power to conceive, to perceive, and to be self-aware” and is the formless, con-tentless infinite potentiality. Metacomputics models how metaconsciousness generates the perceived actualities including abstract entities and physical and nonphysical realities. (...) It is postulated that this is achieved via digital computation using the Platonic computer. The introduction of a Platonic computer into the realm of Forms thus bridges the “inverse explanatory gap” and therefore solves the “inverse hard problem of consciousness” in the philosophy of mind. (shrink)

The scope of Platonism is extended by introducing the concept of a “Platonic computer” which is incorporated in metacomputics. The theoretical framework of metacomputics postulates that a Platonic computer exists in the realm of Forms and is made by, of, with, and from metaconsciousness. Metaconsciousness is defined as the “power to conceive, to perceive, and to be self-aware” and is the formless, con-tentless infinite potentiality. Metacomputics models how metaconsciousness generates the perceived actualities including abstract entities and physical and nonphysical realities. (...) It is postulated that this is achieved via digital computation using the Platonic computer. The introduction of a Platonic computer into the realm of Forms thus bridges the “inverse explanatory gap” and therefore solves the “inverse hard problem of consciousness” in the philosophy of mind. (shrink)

Philosophers like Duhem and Cartwright have argued that there is a tension between laws' abilities to explain and to represent. Abstract laws exemplify the first quality, phenomenological laws the second. This view has both metaphysical and methodological aspects: the world is too complex to be represented by simple theories; supplementing simple theories to make them represent reality blocks their confirmation. We argue that both aspects are incompatible with recent developments in nonlinear dynamics. Confirmation procedures and modelling strategies in nonlinear dynamics (...) show that there are simple, abstract theories that can be confirmed without encountering the problems pointed to by Cartwright. (shrink)

This paper identifies one way that a mathematical proof can be more explanatory than another proof. This is by invoking a more abstract kind of entity than the topic of the theorem. These abstract mathematical explanations are identified via an investigation of a canonical instance of modern mathematics: the Galois theory proof that there is no general solution in radicals for fifth-degree polynomial equations. I claim that abstract explanations are best seen as describing a special sort of dependence relation (...) between distinct mathematical domains. This case study highlights the importance of the conceptual, as opposed to computational, turn of much of modern mathematics, as recently emphasized by Tappenden and Avigad. The approach adopted here is contrasted with alternative proposals by Steiner and Kitcher. (shrink)