Since the birth of computing as an academic discipline, the disciplinary identity of computing has been debated fiercely. The most heated question has concerned the scientific status of computing. Some consider computing to be a natural science and some consider it to be an experimental science. Others argue that computing is bad science, whereas some say that computing is not a science at all. This survey article presents viewpoints for and against computing as a science. Those viewpoints are analyzed against (...) basic positions in the philosophy of science. The article aims at giving the reader an overview, background, and a historical and theoretical frame of reference for understanding and interpreting some central questions in the debates about the disciplinary identity of computer science. The article argues that much of the discussion about the scientific nature of computing is misguided due to a deep conceptual uncertainty about science in general as well as computing in particular. (shrink)

Some philosophers argue that we should eschew cross-explanatory integrations of mechanistic, dynamicist, and psychological explanations in cognitive science, because, unlike integrations of mechanistic explanations, they do not deliver genuine, cognitive scientific explanations. Here I challenge this claim by comparing the theoretical virtues of both kinds of explanatory integrations. I first identify two theoretical virtues of integrations of mechanistic explanations—unification and greater qualitative parsimony—and argue that no cross-explanatory integration could have such virtues. However, I go on to argue that this is (...) only a problem for those who think that cognitive science aims to specify one fundamental structure responsible for cognition. For those who do not, cross-explanatory integration will have at least two theoretical virtues to a greater extent than integrations of mechanistic explanations: explanatory depth and applicability. I conclude that one’s views about explanatory integration in cognitive science cannot be segregated from one’s views about the explanatory task of cognitive science. (shrink)

Cybernetics promoted machine-supported investigations of adaptive sensorimotor behaviours observed in biological systems. This methodological approach receives renewed attention in contemporary robotics, cognitive ethology, and the cognitive neurosciences. Its distinctive features concern machine experiments, and their role in testing behavioural models and explanations flowing from them. Cybernetic explanations of behavioural events, regularities, and capacities rely on multiply realizable mechanism schemata, and strike a sensible balance between causal and unifying constraints. The multiple realizability of cybernetic mechanism schemata paves the way to principled (...) comparisons between biological systems and machines. Various methodological issues involved in the transition from mechanism schemata to their machine instantiations are addressed here, by reference to a simple sensorimotor coordination task. These concern the proper treatment of ceteris paribus clauses in experimental settings, the significance of running experiments with correct but incomplete machine instantiations of mechanism schemata, and the advantage of operating with real machines ??? as opposed to simulated ones ??? immersed in real environments. (shrink)

McKay & Dennett (M&D) look for adaptive misbeliefs that result from the normal, though fallible, functioning of human cognition. Their account can be substantially improved by the addition of two elements: (1) significance of a belief's testability for its functionality, and (2) an account of reason appropriate to understanding systemic misbelief. Together, these points show why religion probably is an adaptive misbelief.

O presente texto tem como propósito identificar as características elementares da explicação tecnológica. Assim, num primeiro momento procuramos justificar a importância de estabelecermos uma investigação filosófica sobre a explicação tecnológica, contextualizando a temática, demonstrando a multiplicidade de elementos que podem ser contemplados por ela e salientando suas especificidades. Em síntese, a primeira parte do trabalho será dedicada às perguntas e aos tipos de questionamentos que a explicação tecnológica pode responder. No segundo momento, esclareceremos que ela contempla necessariamente dois elementos dos (...) artefatos técnicos, a saber, o estrutural e o funcional. Em síntese, a explicação tecnológica precisa elucidar os componentes físicos e materiais constituintes dos artefatos técnicos que, por sua vez, são portadores de uma determinada função. Além disso, faz-se necessário ilustrar como é possível atribuir e/ou implementar uma função em determinado artefato. Por fim, busca-se apresentar os desafios e os problemas em aberto que ainda perpassam a explicação tecnológica. (shrink)

Mathematics educators suggest that students of all ages need to participate in productive forms of mathematical argument (NCTM, 2000). Accordingly, we developed two complementary frameworks for analyzing the emergence of mathematical argumentation in one second‐grade classroom. Children attempted to resolve contesting claims about the “space covered” by three different‐looking rectangles of equal area measure. Our first analysis renders the topology of the semantic structure of the classroom conversation as a directed graph. The graph affords clear “at a glance” visualization of (...) how various senses of mathematics—as imagined, as performed, and as historically rooted—were interrelated. The graph represents the emergence and intercoordination between conceptual and procedural knowledge in the ebb and flow of classroom conversation. The graph has not just descriptive, but also predictive power: Interconnectedness among the nodes of the graph representing the first 40 min of conversation predicted the structure of recall in the final ten minutes by children who had played little overt role in the conversation up to that point. The second, complementary framework draws upon Goffman's expanded repertoire of roles in speech to demonstrate how the teacher orchestrated this classroom conversation to establish coherent argument. In this second analysis, we establish how the teacher mediated between the everyday talk of her students and the discourse of mathematics. Her revoicing of student talk created interstices for identity and participation in the formation of the argument. Together, the two forms of analysis illuminate the emergence of mathematical argument at two levels: as collective structure and concurrently, as individual activity. (shrink)

To understand the behavior of a complex system, you must understand the interactions among its parts. Doing so is difficult for non-decomposable systems, in which the interactions strongly influence the short-term behavior of the parts. Science's principal tool for dealing with non-decomposable systems is a variety of probabilistic analysis that I call EPA. I show that EPA's power derives from an assumption that appears to be false of non-decomposable complex systems, in virtue of their very non-decomposability. Yet EPA is extremely (...) successful. I aim to find an interpretation of EPA's assumption that is consistent with, indeed that explains, its success. (shrink)

Amongst philosophers and cognitive scientists, modularity remains a popular choice for an architecture of the human mind, primarily because of the supposed explanatory value of this approach. Modular architectures can vary both with respect to the strength of the notion of modularity and the scope of the modularity of mind. We propose a dilemma for modular architectures, no matter how these architectures vary along these two dimensions. First, if a modular architecture commits to the informational encapsulation of modules, as it (...) is the case for modularity theories of perception, then modules are on this account impenetrable. However, we argue that there are genuine cases of the cognitive penetrability of perception and that these cases challenge any strong, encapsulated modular architecture of perception. Second, many recent massive modularity theories weaken the strength of the notion of module, while broadening the scope of modularity. These theories do not require any robust informational encapsulation, and thus avoid the incompatibility with cognitive penetrability. However, the weakened commitment to informational encapsulation greatly weakens the explanatory force of the theory and, ultimately, is conceptually at odds with the core of modularity. (shrink)

Engineering is often said to be ‘scientific’, but the nature of knowledge in engineering is different to science. Engineering has a different ontological basis—its theories address different entities and are judged by different criteria. In this paper I use Popper’s three worlds ontological framework to propose a model of engineering theories, and provide an abstract logical view of engineering theories analogous to the deductive-nomological view of scientific theories. These models frame three key elements from definitions of engineering: requirements, designs of (...) artefacts, and theories for reasoning about how artefacts will meet requirements. In a subsequent paper I use this ontological basis to explore methodological issues in the growth of engineering knowledge from the perspective of critical rationalism. (shrink)

Jerry Fodor argues, in The mind doesn't work that way (Cambridge, MA: MIT Press, 2000, that the computational theory of mind is undermined by the pervasive context sensitivity of human cognition. His objections can be easily met, however, by noting the properties of appropriately structured representation hierarchies.

The examples and concepts that Shoemaker cites are rather heterogeneous. Some distinctions need to be drawn. An optimality thesis involves not just an ordering of options, but a value judgment about them. So let us begin by distinguishing minimality from optimality. And the concept of minimality can play a variety of roles, among which I distinguish between extremum descriptions, statements hypothesizing an optimizing process, and methodological recommendations. Finally, I consider how the three categories relate to Shoemaker’s question that “Who is (...) optimizing: the scientist or nature?” . (shrink)

Examples of extended cognition typically involve the use of technologically low-grade bio-external resources (e.g., the use of pen and paper to solve long multiplication problems). The present paper describes a putative case of extended cognizing based around a technologically advanced mixed reality device, namely, the Microsoft HoloLens. The case is evaluated from the standpoint of a mechanistic perspective. In particular, it is suggested that a combination of organismic (e.g., the human individual) and extra-organismic (e.g., the HoloLens) resources form part of (...) a common mechanism that realizes a bona fide cognitive routine. In addition to demonstrating how the theoretical resources of neo-mechanical philosophy might be used to evaluate extended cognitive systems, the present paper illustrates one of the ways in which mixed reality devices, virtual objects (i.e., holograms), and online (Internet-accessible) computational routines might be incorporated into human cognitive processes. This, it is suggested, speaks to the recent interest in mixed/virtual reality technologies across a number of disciplines. It also introduces us to issues that cross-cut disparate fields of philosophical research, such as the philosophy of science and the philosophy of technology. (shrink)

ABSTRACT This paper draws upon the already extensive epistemology of science, both to provide a yardstick of comparison for the emergent epistemology of design, and to establish some starting points from which we might begin to construct the epistemology of design. In approaching the problem of how designers design, the paper employs Heidegger's distinction between ‘know‐how’and ‘knowledge‐that’, popularised by Ryle, and shows it to be central to the distinction between the implicit processes of design employed by craft technologies, and the (...) explicit processes employed by modern scientific technology. Borrowing from recent work by post‐structuralists, it explores the nature of the phases of analysis or ‘deconstruction’, synthesis or construction, and evaluation, by which scientifically informed design is supposed to proceed, paying particular attention to the relationship between deconstructive analysis and constructive synthesis, in order to re‐construct the creative moment/movement by which designers ‘leap’ from problem to solution. (shrink)

A recurrent theme in the characterization of synthetic biology is the role of engineering. This theme is widespread in the accounts of scholars studying this field and the biologists working in it, in those of the biologists themselves, as well as in policy documents. The aim of this article is to open this black-box of engineering that is supposed to influence and change contemporary life sciences. Too often, both synthetic biologists and their critics assume a very narrow understanding of what (...) engineering is about, resulting in an unfruitful debate about whether synthetic biology possesses genuine engineering methodologies or not. By looking in more detail to the diversity of engineering conceptions in debates concerning synthetic biology, a richer perspective can be developed. In this article, I will examine five influential ways in which engineering is understood in these debates, namely engineering as applied science, as rational methodology, context-sensitive practice, cunning activity or design. The claim is first of all thus to argue that engineering must not be seen as something stable or characterized by a fixed essence. It rather has multiple meanings and interpretations. Secondly, the claim is that most of the debates on synthetic biology cannot be indifferent towards the question which conception of engineering is at play, since the specific questions and concerns that pop up depend to a great extent on the precise conception of engineering one has in account. Many of the existing debates around synthetic biology can thus be reinterpreted and readdressed once one is aware of which conception of engineering is at play. (shrink)

Cognitive science is, of course, not really a new discipline, but a recognition of a fundamental set of common concerns shared by the disciplines of psychology, computer science, linguistics, economics, epistemology, and the social sciences generally. All of these disciplines are concerned with information processing systems, and all of them are concerned with systems that are adaptive—that are what they are from being ground between the nether millstone of their physiology or hardware, as the case may be, and the upper (...) millstone of a complex environment in which they exist. Systems that are adaptive may equally well be described as “artificial,” for as environments change, they can be expected to change too, as though they were deliberately designed to fit those environments (as indeed they sometimes are).The task of empirical science is to discover and verify invariants in the phenomena under study. The artificiality of information processing systems creates a subtle problem in defining empirical invariants in such systems. For observed regularities are very likely invariant only within a limited range of variation in their environments, and any accurate statement of the laws of such systems must contain reference to their relativity to environmental features. It is a common experience in experimental psychology, for example, to discover that we are studying sociology—the effects of the past histories of our subjects—when we think we are studying physiology—the effects of properties of the human nervous system. Similarly, business cycle economists are only now becoming aware of the extent to which the parameters of the system they are studying are dependent on the experiences of a population with economic events over the previous generation.In artificial sciences, the descriptive and the normative are never far apart. Thus, in economics, the “principle of rationality” is sometimes asserted as a descriptive invariant, sometimes as advice to decision makers. Similarly, in psychology, the processes of adaptation (learning) have always been a central topic, at one time a topic that dominated the whole field of research. Linguistics, too, has suffered its confusions between descriptive and normative attitudes towards its subject. But we must avoid the error, in studying information processing systems, of thinking that the adaptive processes themselves must be invariant; and we must be prepared to face the complexities of regression in the possibility that they themselves may be subject to improvement and adaptation.It might have been necessary a decade ago to argue for the commonality of the information processes that are employed by such disparate systems as computers and human nervous systems. The evidence for that commonality is now overwhelming, and the remaining questions about the boundaries of cognitive science have more to do with whether there also exist nontrivial commonalities with information processing in genetic systems than with whether men and machines both think. (shrink)

This paper presents a way of classifying different forms of naturalness and unnaturalness. Three main forms of (un)naturalness are found as the following: history- based (un)naturalness, property-based (un)naturalness and relation-based (un)naturalness. Numerous subforms (and some subforms of the subforms) of each are presented. The subforms differ with respect to the entities that are found (un)natural, with respect to their all-inclusiveness, and whether (un)naturalness is seen as all-or-nothing affair, or a continuous gradient. This kind of conceptual analysis is needed, first, because (...) discussion concerning (un)naturalness is common in current bioethics and environmental ethics, and second, because the terms natural and unnatural are highly ambiguous. Thus, the lack of an exact definition of the type of (un)naturalness may lead into equivocation, other forms of bad argumentation, or at least vagueness. (shrink)

In light of recent criticisms by Woodward (2017) and Rescorla (2018), we examine the relationship between mechanistic explanation and phenomenological laws. We disambiguate several uses of the phrase “phenomenological law” and show how a mechanistic theory of explanation sorts them into those that are and are not explanatory. We also distinguish the problem of phenomenological laws from arguments about the explanatory power of purely phenomenal models, showing that Woodward and Rescorla conflate these problems. Finally, we argue that the temptation to (...) pit mechanistic and interventionist theories of explanation against one another occludes important and scientifically relevant research questions. (shrink)