Philosophy of Computing

Biologically inspired cognitive systems have successfully been used for various purposes in recent years. There is, however, a considerable amount of work to be done using certain biological organisms to understand cognitions, especially in cases in which biological structures have been employed to create useful computational models that nevertheless are not thought to be explanations of cognitive capabilities. Superorganisms are at the basis of the swarm intelligence approach to artificial intelligence and robotics, but are also likely to lend themselves to the investigation of emergent phenomena involved in biology and in the foundation of cognitive capabilities. In this paper, I shall try to formulate hypotheses on some peculiar features of superorganisms that may help to shed light on understanding and building cognitive (artificial and natural) entities.

This chapter deals with those fields that study computing systems. Among these computational sciences are computer science, computational cognitive science, computational neuroscience, and artificial intelligence. In the first part of the chapter, it is shown that there are varieties of computation, such as human computation, algorithmic machine computation, and physical computation. There are even varieties of versions of the Church-Turing thesis. The conclusion is that different computational sciences are often about different kinds of computation. The second part of the chapter discusses three specific philosophical issues. One is whether computers are natural kinds.
Another issue is the nature of computational theories and explanations. The last section of the chapter relates remarkable results in computational complexity theory to problems of verification and confirmation.

The aim of this paper is to take a look at Péter's talk Rekursivität und Konstruktivität delivered at the Constructivity in Mathematics Col-loquium in 1957, where she challenged Church's Thesis from a constructive point of view. The discussion of her argument and motivations is then connected to her earlier work on recursion theory as well as her later work on theoretical computer science.

This paper deals with the question: which notion of computation (if any) is essential for explaining cognition? Five answers are discussed in the paper.
1. The classicist answer: symbolic (digital) computation is required for explaining cognition.
2. The broad digital computationalist answer: digital computation broadly construed is required for explaining cognition.
3. The connectionist answer: sub-symbolic computation is required for explaining cognition.
4. The computational neuroscientist answer: neural computation (that, strictly, is neither digital nor analogue) is required for explaining cognition.
5. The extreme dynamicist answer: computation is not required for explaining cognition.
The first four answers are only accurate to a first approximation. But the “devil” is in the details. The last answer cashes in on the parenthetical “if any” in the question above. The classicist argues that cognition is symbolic computation. But digital computationalism need not be equated with classicism. Indeed, computationalism can, in principle, range from digital (and analogue) computationalism through (the weaker thesis of) generic computationalism to (the even weaker thesis of) digital (or analogue) pancomputationalism. Connectionism, which has traditionally been criticised by classicists for being non-computational, can be plausibly construed as being either analogue or digital computationalism (depending on the type of connectionist networks used). Computational neuroscience invokes the notion of neural computation that may (possibly) be interpreted as a sui generis type of computation. The extreme dynamicist argues that the time has come for a post-computational cognitive science. This paper is an attempt to shed some light on this debate by examining various conceptions and misconceptions of (particularly digital) computation.

THE CHURCH-TURING THESIS (CTT) underlies tantalizing open questions concerning the fundamental place of computing in the physical universe. For example, is every physical system computable? Is the universe essentially computational in nature? What are the implications for computer science of recent speculation about physical uncomputability? Does CTT place a fundamental logical limit on what can be computed, a computational "barrier" that cannot be broken, no matter how far and in what multitude of ways computers develop? Or could new types of hardware, based perhaps on quantum or relativistic phenomena, lead to radically new computing paradigms that do breach the Church-Turing barrier, in which the uncomputable becomes computable , in an upgraded sense of "com-putable"? Before addressing these questions , we first look back to the 1930s to consider how Alonzo Church and Alan Turing formulated, and sought to justify , their versions of CTT. With this necessary history under our belts, we then turn to today's dramatically more powerful versions of CTT.

This paper deals with the question: what are the key requirements for a physical system to perform digital computation?
Time and again cognitive scientists are quick to employ the notion of computation simpliciter when asserting basically that cognitive activities are computational. They employ this notion as if there was or is a consensus on just what it takes for a physical system to perform computation, and in particular digital computation. Some cognitive scientists in referring to digital computation simply adhere to Turing's notion of computability. Classical computability theory studies what functions on the natural numbers are computable, and what mathematical problems are undecidable. Whilst a mathematical formalism of computability may perform a methodological function of evaluating computational theories of certain cognitive capacities, concrete computation in physical systems seems to be required for explaining cognition as an embodied phenomenon.
There are many non-equivalent accounts of digital computation in physical systems. I examine only a handful of those in this paper: 1. Turing's account; 2. The triviality "account"; 3. Reconstructing Smith's account of participatory computation; 4. The Algorithm Execution account. My goal in this paper is twofold. First, it is to identify and clarify some of the underlying key requirements mandated by these accounts. I argue that these differing requirements justify a demand that one commits to a particular account when employing the notion of computation in regard to physical systems. Second, it is to argue that despite the informative role that mathematical formalisms of computability may play in cognitive science, they do not specify the relationship between abstract and concrete computation.

It is common in cognitive science to equate computation (and in particular digital computation) with information processing. Yet, it is hard to find a comprehensive explicit account of concrete digital computation in information processing terms. An Information Processing account seems like a natural candidate to explain digital computation. But when "information" comes under scrutiny, this account becomes a less obvious candidate.
Four interpretations of information are examined here as the basis for an Information Processing account of digital computation, namely Shannon information, algorithmic information, factual information and instructional information. I argue that any plausible account of concrete computation has to be capable of explaining at least the three key algorithmic notions of input, output and procedures. Whilst algorithmic information fares better than Shannon information, the most plausible candidate for an Information Processing account is instructional information.

n this thesis, we aim at contributing to the theory of conceptual modeling and ontology representation. Our main objective here is to provide ontological foundations for the most fundamental concepts in conceptual modeling. These foundations comprise a number of ontological theories, which are built on established work on philosophical ontology, cognitive psychology, philosophy of language and linguistics. Together these theories amount to a system of categories and formal relations known as a foundational ontology

Computer pioneer Konrad Zuse (1910-1995) built the world's first working program controlled general-purpose digital computer in Berlin in 1941. After the Second World War he supplied Europe with cheap relay-based computers, and later transistorized computers. Mathematical logician Robin Gandy (1919-1995) proved a number of major results in recursion theory and set theory. He was Alan Turing's only PhD student. Mathematician Roger Penrose (1931- ) is famous for his work with Stephen Hawking. What we call Zuse's thesis, Gandy's thesis, and Penrose's thesis are three fundamental theses concerning computation and physics. Zuse hypothesized that the physical universe is a computer. Gandy offered a profound analysis supporting the thesis that every discrete deterministic physical assembly is computable (assuming that there is an upper bound on the speed of propagation of effects and signals, and a lower bound on the dimensions of an assembly's components). Penrose argued that the physical universe is in part uncomputable. We explore these three theses. Zuse's thesis we believe to be false: the universe might have consisted of nothing but a
giant computer, but in fact does not. Gandy viewed his claim as a relatively apriori one, provable on the basis of a set-theoretic argument that makes only very general physical assumptions about decomposability into parts and the nature of causation. We maintain
that Gandy's argument does not work, and that Gandy's thesis is best viewed, like Penrose's, as an open empirical hypothesis.

Situated approaches to cognition maintain that cognition is embodied, embedded, enactive, and affective (and extended, but that is not relevant here). Situated approaches are often pitched as alternatives to computational and representational approaches, according to which cognition is computation over representations. I argue that, far from being opposites, situatedness and neural representation are more deeply intertwined than anyone suspected. To show this, I introduce a neurocomputational account of cognition that relies on neural representations. I argue not only that this account is compatible with (non-question-begging) situated approaches, but also that it requires embodiment, embeddedness, enaction, and affect at its very core. That is, constructing neural representations and their semantic content, and learning computational processes appropriate for their content, requires a tight dynamic interaction between nervous system, body, and environment. Most importantly, I argue that situatedness is needed to give a satisfactory account of neural representation: neurocognitive systems that are embodied, embedded, affective, dynamically interact with their environment, and use feedback from their interaction to shape their own representations and computations (1) can construct neural representations with original semantic content, (2) their neural vehicles and the way they are processed are automatically coordinated with their content, (3) such content is causally efficacious, (4) is determinate enough for the system's purposes, (5) represents the distal stimulus, and (6) can misrepresent. This proposal hints at what is needed to build artifacts with some of the basic cognitive capacities possessed by neurocognitive systems.

The question ‘What is computation?’ might seem a trivial one to many, but this is far from being in consensus in philosophy of mind, cognitive science and even in physics. The lack of consensus leads to some interesting, yet contentious, claims, such as that cognition or even the universe is computational. Some have argued, though, that computation is a subjective phenomenon: whether or not a physical system is computational, and if so, which computation it performs, is entirely a matter of an observer choosing to view it as such. According to one view, which we dub bold anti-realist pancomputationalism, every physical object (can be said to) computes every computer program. According to another, more modest view, some computational systems can be ascribed multiple computational descriptions. We argue that the first view is misguided, and that the second view need not entail observer-relativity of computation. At least to a large extent, computation is an objective phenomenon. Construed as a form of information processing, we argue that information-processing considerations determine what type of computation takes place in physical systems.

The Information Systems discipline (IS) is usually regarded as a social science because it includes research on human-related aspects of these systems. However, a limited number of IS research outputs use approaches that are typical of the traditional arts and humanities. Little recognition has been given to the arts and humanities-informed stream of the IS discipline. This article aims to clarify the subtle distinctions between these scientific constellations and IS's place in it. It highlights the cluster of arts, humanities and IS in the inter-linked world of scientific disciplines and makes some recommendations to build further on these accomplishments.

Abstract. In recent years there has been an increase in the general public awareness of the ethical aspects of technology. The attention given by the media to computer-related disasters in technical systems such as the explosion of the Ariane 5 rocket in 1996 and the Therac-25 computerized radiation machine overdoses has stimulated interest in Computer Ethics.

In his famous paper, An Unsolvable Problem of Elementary Number Theory , Alonzo Church (1936) identified the intuitive notion of effective calculability with the mathematically precise notion of recursiveness. This proposal, known as Church's Thesis, has been widely accepted. Only a few papers have been written against it. One of these is László Kalmár's An Argument Against the Plausibility of Church's Thesis from 1959. The aim of this paper is to present Kalmár's argument and to fill in missing details based on his general philosophical thoughts on mathematics.

This paper deals with the question: what are the key requirements for a physical system to perform digital computation? Oftentimes, cognitive scientists are quick to employ the notion of computation simpliciter when asserting basically that cognitive activities are computational. They employ this notion as if there is a consensus on just what it takes for a physical system to compute. Some cognitive scientists in referring to digital computation simply adhere to Turing computability. But if cognition is indeed computational, then it is concrete computation that is required for explaining cognition as an embodied phenomenon. Three accounts of computation are examined here: 1. Formal Symbol Manipulation. 2. Physical Symbol Systems and 3. The Mechanistic account. I argue that the differing requirements implied by these accounts justify the demand that one commits to a particular account when employing the notion of digital computation in regard to physical systems, rather than use these accounts interchangeably.

The semantic view of computation is the claim that semantic properties play an essential role in the individuation of physical computing systems such as laptops and brains. The main argument for the semantic view (" the master argument ") rests on the fact that some physical systems simultaneously implement different automata at the same time, in the same space, and even in the very same physical properties ("simultaneous implementation"). Recently, several authors have challenged this argument (Piccinini 2008, 2015; Coelho Mollo 2018; Dewhurst 2018). They accept the premise of simultaneous implementation but reject the semantic conclusion. In this paper, I aim to explicate the semantic view and to address these objections. I first characterize the semantic view and distinguish it from other, closely related views. Then, I contend that the master argument for the semantic view survives the counter-arguments against it. One counter-argument is that computational individuation is not forced to choose between the implemented automata but rather always picks out a more basic computational structure. My response is that this move might undermine the notion of computational equivalence. Another counter-argument is that while computational individuation is forced to rely on extrinsic features, these features need not be semantic. My reply is that the semantic view better accounts for these extrinsic features than the proposed non-semantic alternatives.

This paper deals with the question: how is computation best individuated? The semantic view of computation: computation is best individuated by its semantic properties. The causal view of computation: computation is best individuated by its causal properties. The functional view of computation: computation is best individuated by its functional properties. Some scientific theories explain the capacities of brains by appealing to computations that they supposedly perform. The reason for that is usually that computation is individuated semantically. I criticize the reasons in support of this view and its presupposition of representation and semantics. Furthermore, I argue that the only justified appeal to a representational individuation of computation might be that it is partly individuated by implicit intrinsic representations.

There are currently considerable confusion and disarray about just how we should view computationalism, connectionism and dynamicism as explanatory frameworks in cognitive science. A key source of this ongoing conflict among the central paradigms in cognitive science is an equivocation on the notion of computation simpliciter. ‘Computation’ is construed differently by computationalism, connectionism, dynamicism and computational neuroscience. I claim that these central paradigms, properly understood, can contribute to an integrative cognitive science. Yet, before this claim can be defended, a better understanding of ‘computation’ is required.
‘Digital computation’ is an ambiguous concept. It is not just the classical dichotomy between analogue and digital computation that is the basis for the equivocation on ‘computation’ simpliciter in cognitive science, but also the diversity of extant accounts of digital computation. What does it take for a physical system to perform digital computation? There are many answers to this question ranging from Turing machine computation, through the formal manipulation of symbols, the execution of algorithms and others, to strong pancomputationalism, according to which every physical system computes every Turing-computable function. Despite some overlap among them, extant accounts of concrete digital computation are intensionally and extensionally non- equivalent, thereby rendering ‘digital computation’ ambiguous.
The objective of this book is twofold. First, it is to promote a clearer understanding of concrete digital computation. Accordingly, the main underlying thesis of the book is that not only are extant accounts of concrete digital computation non-equivalent, but most of them are inadequate. In the course of examining several key accounts of concrete digital computation, I also propose the instructional information processing account, according to which nontrivial digital computation is the processing of discrete data in accordance with finite instructional information. The second objective is to establish the foundational role of computation in cognitive science whilst rejecting the extrinsically representational nature of computation proper.

Bilgisayar bilimi çok yeni bir bilim olduğu için ve günümüzde bu konuda üretilen bilgi katlanarak arttığından dolayı ele alınan kavramlar müellifler tarafından farklı yorumlanabilmektedir. Bu yüzden dijital ontolojiyi belirlemesi adına bilgisayar, algoritma, program, yazılım, donanım gibi kavramlara yönelik var olan yaklaşımları incelemek, dijital ontolojinin bina edildiği temeli anlamakta önemli bir adımdır. Konu çok yeni olduğundan dolayı akademik dünyada yapılan çalışmaları bir başlık altında toplayan ve özellikle felsefi bir bakış açısıyla inceleyen çok fazla eser bulunmamaktadır. Bu açığı kapatmak adına Doç. William Rapaport tarafından yapılan bu literatür derlemesi çok değerli ve kıymetlidir. Üretilen kavramlar ve bu kavramlara bağlı oluşan değerlere eleştirel bir açıdan bakabilmemiz için öncelikle kavramların konu edilen bağlamlara göre hangi anlamlarda kullanıldığını bilmemiz gerekir. Bu yüzden bilgisayar bilimi odaklı dijital ontoloji anlayabilmek adına bilgisayar bilimin yapıtaşlarını Rapaport’un eseri(2015) ışığında inceleyeceğim.

This chapter provides an account of realization within a mechanistic framework and introduces the notions of variable realizability, multiple realizability, and medium independence. I argue that realization is the relation between a higher-level property and the lower-level properties of which it is a part. Variable realizability occurs when the same higher-level property can be realized in different lower-level properties—the same property part embedded in different property wholes. Variable realizability is ubiquitous yet insufficient for multiple realizability proper. Multiple realizability occurs when the same higher-level property can be realized in different lower-level properties that constitute different mechanisms for that property at the immediately lower mechanistic level. Medium independence is an even stronger condition than multiple realizability: it occurs when not only is a higher-level property multiply realizable; in addition, the inputs and outputs that define the higher-level property are also multiply realizable. Thus, all that matters to defining a medium-independent higher-level property is the manipulation of certain degrees of freedom. In sum, medium independence entails multiple realizability, which in turn entails variable realizability, but variable realizability does not entail multiple realizability, which in turn does not entail medium independence.

In philosophy, the term ontology has been used since the 17 century to refer both to a
philosophical discipline (Ontology with a capital “O”), and as a domain-independent system of categories that can be used in the conceptualization of domain-specific scientific theories. In the past decades there has been a growing interest in the subject of ontology in computer and information sciences. In the last few years, this interest has expanded considerably in the context of the Semantic Web and MDA (Model-Driven Architecture) research efforts, and due to the role ontologies are perceived to play in these initiatives. In this paper, we explore the relations between Ontology and ontologies in the philosophical sense with domain ontologies in computer science. Moreover, we elaborate on formal characterizations for the notions of ontology, conceptualization and metamodel, as well as on the relations between these notions. Additionally, we discuss a set of criteria that a modeling language should meet in order to be considered a suitable language to model phenomena in a given domain, and present a systematic framework for language evaluation and design. Furthermore, we argue for the importance of ontology in both philosophical senses aforementioned for designing and evaluating a suitable general ontology representation language, and we address the question whether the so-called Ontology Web languages can be considered as suitable general ontology representation languages. Finally, we motivate the need for two complementary classes of modeling languages in Ontology Engineering addressing two separate sets of concerns.

This paper deals with the question: What are the criteria that an adequate theory of computation has to meet? (1) Smith’s answer: it has to meet the empirical criterion (i.e. doing justice to computational practice), the conceptual criterion (i.e. explaining all the underlying concepts) and the cognitive criterion (i.e. providing solid grounds for computationalism). (2) Piccinini’s answer: it has to meet the objectivity criterion (i.e. identifying computation as a matter of fact), the explanation criterion (i.e. explaining the computer’s behaviour), the right things compute criterion, the miscomputation criterion (i.e. accounting for malfunctions), the taxonomy criterion (i.e. distinguishing between different classes of computers) and the empirical criterion. (3) Von Neumann’s answer: it has to meet the precision and reliability of computers criterion, the single error criterion (i.e. addressing the impacts of errors) and the distinction between analogue and digital computers criterion. (4) “Everything” computes answer: it has to meet the implementation theory criterion by properly explaining the notion of implementation.
According to computationalists, minds are computational. Before we can judge the plausibility of any particular computationalist theory, we need to understand what notion of computation this theory employs. Although there are extant accounts of computation, any of which may, in principle, serve as a basis for computationalism, it isn’t clear that they’re all equivalent or even adequate as accounts of computation proper. By examining plausible alternatives to Smith’s adequacy criteria, our goal here is to resist his claim that no adequate account of computation proper is possible.

I argue that good metaphysics and good metascience go hand in hand and go on to clarify my egalitarian ontology of levels, defend the aspect view of realization, insist that computation must be medium independent and neurocognitive processes are largely computational even though some medium-dependent phenomena affect neural computation, and sketch an account of mental representation in terms of neural representation.

Do the dynamics of a physical system determine what function the system computes? Except in special cases, the answer is no: it is often indeterminate what function a given physical system computes. Accordingly, care should be taken when the question 'What does a particular neural system do?' is answered by hypothesising that the system computes a particular function. The phenomenon of the indeterminacy of computation has important implications for the development of computational explanations of biological systems. Additionally, the phenomenon lends some support to the idea that a single neural structure may perform multiple cognitive functions, each subserved by a different computation. We provide an overarching conceptual framework in order to further the philosophical debate on the nature of computational indeterminacy and computational explanation.

The behavior of some systems is noncomputable in a precise new sense.  One infamous problem is that of the stability of the solar system:  Given the initial positions and velocities of several mutually gravitating bodies, will any eventually collide or be thrown off to infinity?  Many have made vague suggestions that this and similar problems are undecidable:  No finite procedure can reliably determine whether a given configuration will eventually prove unstable.  But taken in the most natural way, this is trivial.  The state of a system corresponds to a point in a continuous space, and virtually no set of points in space is strictly decidable.  A new, more pragmatic concept is therefore introduced:  A set is decidable up to measure zero (d.m.z.) if there is a procedure to decide whether a point is in that set and it only fails on some points that form a set of zero volume.  This is motivated by the intuitive correspondence between volume and probability:  We can ignore a zero-volume set of states because the state of an arbitrary system almost certainly will not fall in that set.  D.m.z. is also closer to the intuition of decidability than other notions in the literature, which are either less strict or apply only to special sets, like closed sets.  Certain complicated sets are not d.m.z., most remarkably including the set of know stable orbits for planetary systems (the KAM tori).  This suggests that the stability problem is indeed undecidable in the precise sense of d.m.z.  Carefully extending decidability concepts from idealized models to actual systems, we see that even deterministic aspects of physical behaviour can be undecidable in a clear and significant sense.

This essay re-problematises the Aristotelian concept of technē in terms of the mathematical function to demonstrate the centrality of agency in technics. This schema animates computational theory as an agential operator or computer, to situate agency's necessity to the technical apparatus and the production of abstract relations.

A chronicled approach to the notion of computer simulations shows that there are two predominant interpretations in the specialized literature. According to the first interpretation, computer simulations are techniques for finding the set of solutions to a mathematical model. I call this first interpretation the problem-solving technique viewpoint (PST). In its second interpretation, computer simulations are considered to describe patterns of behavior of a target system. I call this second interpretation the description of patterns of behavior viewpoint of computer simulations (DPB). This article explores these two interpretations of computer simulations from three different angles. First, I collect a series of definitions of computer simulation from the historical record. I track back definitions to the early 1960s and show how each viewpoint shares similar interpretations of computer simulations-ultimately clustering into the two viewpoints aforementioned. This reconstruction also includes the most recent literature. Second, I unpack the philosophical assumptions behind each viewpoint, with a special emphasis on their differences. Third, I discuss the philosophical implications of each viewpoint in the context of the recent discussion on the logic of scientific explanation for computer simulations.

Abstract: More than a hundred years ago, the American philosopher CS Peirce suggested the idea of pragmatism as a logical criterion to analyze what words and concepts express through their practical meaning. Many words have been said on creative processes and reasoning, especially in the case of scientific practices. In fact, philosophers have usually offered a number of ways of construing hypotheses generation, but all aim at demonstrating that the activity of generating hypotheses is paradoxical, illusory or obscure, and then not ...

Although software plays an essential role in modern society, its ontolog-ical nature is still unclear. For many, software is just code, but this is not illuminat-ing. Several researchers have attempted to understand the core nature of software and programs in terms of concepts such as code, copy, medium and execution. More recently, a proposal was made to consider software as an abstract artifact, distinct from code, just because code may change while the software remains the same. We explore in this paper the implications of such a proposal in the light of software engineering and requirements engineering literature. We make a sharp distinction between different kinds of software artifacts (code, program, software system, and software product), and describe the ways they are inter-connected in the context of a software engineering process.

Some things compute, others do not. Digital computers do, and perhaps minds, but not rocks. Or so it seems. Determining what conditions a physical system must satisfy in order to compute is the focus of theories of computational implementation, or physical computation. In this chapter we explore some implications of these theories for the Computational Theory of Mind (CTM). After highlighting some general features of implementation, we review the theories on offer and how they address the challenge of pancomputationalism, namely, the thesis that every physical system computes. We argue that satisfying minimal desiderata for theories of implementation—to avoid the most damaging form of pancomputationalism—sharply limits the degree to which psychology is autonomous from neuroscience. In particular, if the mind is computational and physically implemented, then psychology constrains the kinds of structure and organization to be found in the nervous system, and neuroscience constrains the kinds of computation to be posited by psychologists.

In this book, Marcin Milkowski argues that the mind can be explained computationally because it is itself computational—whether it engages in mental arithmetic, parses natural language, or processes the auditory signals that allow us to experience music. Defending the computational explanation against objections to it—from John Searle and Hilary Putnam in particular—Milkowski writes that computationalism is here to stay but is not what many have taken it to be. It does not, for example, rely on a Cartesian gulf between software and hardware, or mind and brain. Milkowski's mechanistic construal of computation allows him to show that no purely computational explanation of a physical process will ever be complete. Computationalism is only plausible, he argues, if you also accept explanatory pluralism.

Milkowski sketches a mechanistic theory of implementation of computation against a background of extant conceptions, describing four dissimilar computational models of cognition. He reviews other philosophical accounts of implementation and computational explanation and defends a notion of representation that is compatible with his mechanistic account and adequate vis à vis the four models discussed earlier. Instead of arguing that there is no computation without representation, he inverts the slogan and shows that there is no representation without computation—but explains that representation goes beyond purely computational considerations. Milkowski's arguments succeed in vindicating computational explanation in a novel way by relying on mechanistic theory of science and interventionist theory of causation.

Computer Science is a rather young discipline, and as usual with new disciplines, in its early stage there were important discussions about its aim, scope and methodology. Throughout these debates, it was claimed at different times that computer science belongs to the natural sciences, mathematics, or engineering. Questions about the organization of the field were raised as well: is there a need for computer science departments, or for separate computer science majors at the university level? The history of these debates has been documented rather well in recent years. However, the literature focuses mostly on sources from the US and Western Europe. The aim of this paper is to include the stance of eminent Hungarian logician and computer scientist László Kalmár in the history of this discussion. Kalmár's view is reconstructed based on recently found, formerly unpublished archival materials from 1970-1971: a conference abstract and his correspondence about Hungarian computer science education. In this paper, I will also situate Kalmár's view among the positions of other prominent scholars in these debates.

Non-functional requirements (NFRs) have been the focus of research in Requirements Engineering (RE) for more than 20 years. Despite this attention, their ontological nature is still an open question, thereby hampering efforts to de- velop concepts, tools and techniques for eliciting, modeling, and analyzing them, in order to produce a specification for a system-to-be. In this paper, we propose to treat NFRs as qualities, based on definitions of the UFO foundational ontology. Furthermore, based on these ontological definitions, we provide guidelines for dis- tinguishing between non-functional and functional requirements, and sketch a syn- tax of a specification language that can be used for capturing NFRs.

Do the dynamics of a physical system determine what function the system computes? Except in special cases, the answer is no: it is often indeterminate what function a given physical system computes. Accordingly, care should be taken when the question ‘What does a particular neuronal system do?’ is answered by hypothesising that the system computes a particular function. The phenomenon of the indeterminacy of computation has important implications for the development of computational explanations of biological systems. Additionally, the phenomenon lends some support to the idea that a single neuronal structure may perform multiple cognitive functions, each subserved by a different computation. We provide an overarching conceptual framework in order to further the philosophical debate on the nature of computational indeterminacy and computational explanation.

Several philosophical issues in connection with computer simulations rely on the assumption that results of simulations are trustworthy. Examples of these include the debate on the experimental role of computer simulations [Parker, 2009, Morrison, 2009], the nature of computer data [Barberousse and Vorms, 2013, Humphreys, 2013], and the explanatory power of computer simulations [Krohs, 2008, Durán, 2017]. The aim of this article is to show that these authors are right in assuming that the ​results of computer simulations are to be trusted when computer simulations are reliable processes. After a short reconstruction of the problem of epistemic opacity, the article elaborates extensively on computational reliabilism, a specified form of process reliabilism with computer simulations located at the center. The article ends with a discussion of four sources for computational reliabilism, namely, verification and validation, robustness analysis for computer simulations, a history of (un)successful implementations, and the role of expert knowledge in simulations.