Turner argues that computer programs must have purposes, that implementation is not a kind of semantics, and that computers might need to understand what they do. I respectfully disagree: Computer programs need not have purposes, implementation is a kind of semantic interpretation, and neither human computers nor computing machines need to understand what they do.

Argument-mapping software abounds, and one of the reasons is that using the software has been shown to teach/promote/improve critical thinking skills. These positive results are very encouraging, but they also raise the question of whether the computer tutorial environment is producing these results, or whether learning argument mapping, even with just paper and pencil, is sufficient. Based on the results of two empirical studies, I argue that the basic skill of being able to represent an argument diagrammatically plays an (...) important role in the improvement of critical-thinking skills. While these studies do not offer a direct comparison between the two methods, it is important for anyone wishing to employ argument mapping in the classroom to know that significant results can be obtained even with the most rudimentary of tools. (shrink)

As a first step in the larger project of charting the ontology of computer programs, we pose three central questions: (1) Can programs, hardware, and metaprograms be organized into a meaningful taxonomy? (2) To what ontology are computer programs committed? (3) What explains the proliferation of programming languages and how do they come about? Taking the complementary perspectives software engineering and mathematical logic, we take inventory of programs and related objects and conclude that the notions of abstraction (...) and concretization take a central role in this investigation. (shrink)

This review of Turner’s “Computational Artifacts” focuses on one of the key novelties of the book, namely the proposal to understand the nature of computer programs as a “trinity” of specification, symbolic program, and physical process, replacing the traditional dualist views of programs as functional/structural or as symbolic/physical. This trinitarian view is found to be robust and helpful to solve typical issues of dualist views. Drawing comparisons with Simon’s view of the artifact as an interface, the author suggests that (...) this trinitarian view may characterize not only computational artifacts but also artifacts in general. One ambiguity is however noticed on the denotation of what Turner actually calls the “physical process.”. (shrink)

European Computing and Philosophy conference, 2–4 July Barcelona The Seventh ECAP (European Computing and Philosophy) conference was organized by Jordi Vallverdu at Autonomous University of Barcelona. The conference started with the IACAP (The International Association for CAP) presidential address by Luciano Floridi, focusing on mechanisms of knowledge production in informational networks. The first keynote delivered by Klaus Mainzer made a frame for the rest of the conference, by elucidating the fundamental role of complexity of informational structures that can (...) be analyzed on different levels of organization giving place for variety of possible approaches which converge in this cross-disciplinary and multi-disciplinary research field. Keynotes by Kevin Warwick about re-embodiment of rats’ neurons into robots, Raymond Turner on syntax and semantics in programming languages, Roderic Guigo on Biocomputing Sciences and Francesco Subirada on the past and future of supercomputing presented different topics of philosophical as well as practical aspects of computing. Vonference tracks included: Philosophy of Information (Patrick Allo), Philosophy of Computer Science (Raymond Turner), Computer and Information Ethics (Johnny Søraker and Alison Adam), Computational Approaches to the Mind (Ruth Hagengruber), IT and Cultural Diversity (Jutta Weber and Charles Ess), Crossroads (David Casacuberta), Robotics, AI & Ambient Intelligence (Thomas Roth-Berghofer), Biocomputing, Evolutionary and Complex Systems (Gordana Dodig Crnkovic and Søren Brier), E-learning, E-science and Computer-Supported Cooperative Work (Annamaria Carusi) and Technological Singularity and Acceleration Studies (Amnon Eden). (shrink)

Philosophy and Computing explores each of the following areas of technology: the digital revolution; the computer; the Internet and the Web; CD-ROMs and Mulitmedia; databases, textbases, and hypertexts; Artificial Intelligence; the future of computing. Luciano Floridi shows us how the relationship between philosophy and computing provokes a wide range of philosophical questions: is there a philosophy of information? What can be achieved by a classic computer? How can we define complexity? What are the limits of (...) quantam computers? Is the Internet an intellectual space or a polluted environment? What is the paradox in the Strong Artificial Intelligence program? Philosophy and Computing is essential reading for anyone wishing to fully understand both the development and history of information and communication technology as well as the philosophical issues it ultimately raises. (shrink)

Because the label "computing and philosophy" can seem like an ad hoc attempt to tie computing to philosophy, it is important to explain why it is not, what it studies (or does) and how it differs from research in, say, "computing and history," or "computing and biology". The American Association for History and Computing is "dedicated to the reasonable and productive marriage of history and computer technology for teaching, researching and representing history through scholarship and public history" (...) (http://theaahc.org). More pervasive, work in computing and biology enjoys the convenient name of "bioinformatics...the science of using information to understand biology..., a subset of the larger field of computational biology, the application of quantitative analytical techniques in modeling biological systems" (http://oreilly.com/catalog/bioskills/chapter/ ch01.html). The recent venture of the Association for Computing Machinery and the Institute of Electrical and Electronics Engineers to publish the Transactions on Computational Biology and Bioinformatics (TCBB) bears witness to the reach of computing and biology and underscores its objective. TCBB intends to report "archival research results related to the algorithmic, mathematical, statistical, and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development and optimization of biological databases; and important biological results that are obtained from the use of these methods, programs, and databases" (http://tcbb.acm.org). In the case of "computing and history" and "bioinformatics," each discipline stands in a particular relationship to computers that raises questions unique to itself. But both are devoted to the development of computational tools to aid discovery.. (shrink)

Powerful transformer models based on neural networks such as GPT-4 have enabled huge progress in natural language processing. This paper identifies three challenges for computer programs dealing with metaphors. First, the phenomenon of Twice-Apt-Metaphors shows that metaphorical interpretations do not have to be triggered by syntactical, semantic or pragmatic tensions. The detection of these metaphors seems to involve a sense of aesthetic pleasure or a higher-order theory of mind, both of which are difficult to implement into computer programs. (...) Second, the contexts relative to which metaphors are interpreted are not simply given but must be reconstructed based on pragmatic considerations that can involve presuppositional pretence. If computer programs cannot produce or understand such a form of pretence, they will have problems dealing with certain metaphors. Finally, adequately interpreting and reacting to some metaphors seems to require the ability to have internal, first-personal experiential and affective states. Since it is questionable whether computer programs have such mental states, it can be assumed that they will have problems with these kinds of metaphors. (shrink)

This volume is a collection of papers that explore various areas of common interest between philosophy, computing, and cognition. The book illustrates the rich intrigue of this fascinating recent intellectual story. It begins by providing a new analysis of the ideas related to computer ethics, such as the role in information technology of the so-called moral mediators, the relationship between intelligent machines and warfare, and the new opportunities offered by telepresnece, for example in teaching and learning. The book (...) also ties together the concerns of epistemology and logic, showing, for example, the connections between computers, bio-robotics, and scientific research and between computational programs and scientific discovery. Important results coming from recent computational models of deduction, the dynamic nature of meaning, and the role of reasoning and learning in spatial, visual and exemplar-based compuational frameworks are also addressed. Some stimulating papers carefully study how the interplay between computing and philosophy has also shed new light on the role of rational acceptance in the logic of belief and on the status of old philosophical topics like embodiment and consciousness, the role of information and the problem of realism in the new digital world. Finally, a considerable part of the book addresses the role of intenal and external representations in scientific reasoning and creative inferences as well as the place of manipulation of objects and artifacts in human cognition. Taking these topics together this book describes an aspect of an emerging agenda which is likely to carry the interaction between philosophy, cognition and computing forward into the twenty-first century. The volume is based on the papers that were presented at the International European Conference Computing and Philosophy, E-CAP2004, Italy, held at the University of Paiva, Paiv, Italy in June 2004, chaired by Lorenzo Magnani. (shrink)

This paper contains a discussion of striking similarities between influential philosophical concepts of the past and the approaches currently employed in selected areas of computer science. In particular, works of the Pythagoreans, Plato, Abelard, Ash’arites, Malebranche and Berkeley are presented and contrasted with such computer science ideas as digital computers, object-oriented programming, the modelling of an object’s actions and causality in virtual environments, and 3D graphics rendering. The intention of this paper is to provoke the computer (...) science community to go off the beaten path in order to find inspiration for the development of new approaches in software engineering. (shrink)

In a society where computers have become ubiquitous, it is necessary to develop a broader understanding of the nature of computing and programming, not just from a technical viewpoint but also from a historical and philosophical perspective. Computers and computer programs do not exist in a vacuum. Instead, they are a part of a rich socio-technological context that provides ways for understanding computers and reasoning about programs. This includes not only formal logic, mathematics, sciences, and technology but also (...) cognitive sciences and sociology. The focus of this special issue is on questions that arise when we consider computing and programming in a wider context. In particular, the papers in this special issue explore the interplay between computing or programming and mathematics, formal logic, sciences, technology, and society. (shrink)

In this paper I attempt to cast the current program verification debate within a more general perspective on the methodologies and goals of computer science. I show, first, how any method involved in demonstrating the correctness of a physically executing computer program, whether by testing or formal verification, involves reasoning that is defeasible in nature. Then, through a delineation of the senses in which programs can be run as tests, I show that the activities of testing and formal (...) verification do not necessarily share the same goals and thus do not always constitute alternatives. The testing of a program is not always intended to demonstrate a program's correctness. Testing may seek to accept or reject nonprograms including algorithms, specifications, and hypotheses regarding phenomena. The relationship between these kinds of testing and formal verification is couched in a more fundamental relationship between two views of computer science, one properly containing the other. (shrink)

What do philosophy and computer science have in common? It turns out, quite a lot! In providing an introduction to computer science (using Python), Daniel Lim presents in this book key philosophical issues, ranging from external world skepticism to the existence of God to the problem of induction. These issues, and others, are introduced through the use of critical computational concepts, ranging from image manipulation to recursive programming to elementary machine learning techniques. In illuminating some of (...) the overlapping conceptual spaces of computer science and philosophy, Lim teaches readers fundamental programming skills and allows them to develop the critical thinking skills essential for examining some of the enduring questions of philosophy. (shrink)

The prelims comprise: Introduction Cognitive Modeling Engineering AI Theory of Computation What Computing Adds to Philosophy of Science.

Technological advances in computer science have secured the computer metaphor status of a heuristic methodological tool used to answer the question about the nature of mind. Nevertheless, some philosophers strongly support opposite opinions. Anti-computationalism in the philosophy of mind is a methodological program that uses extremely heterogeneous grounds for argumentation, deserving analysis and discussion. This article provides an overview and interpretation of the traditional criticism of the computational theory of mind ; its basic theses have been formed (...) in Western philosophy in the last quarter of the 20th century. The main goal is to reveal the content of the arguments of typical anti-computationalist programs and expand their application to the framework of the semantic problems of the Classic Computational Theory of Mind. The main fault of the symbolic ap-proach in the classical computationalism is the absence of a full-fledged theory of semantic properties. The relevance of considering these seemingly outdated problems is justified by the fact that the problem of meaning remains in the core of the latest developments in various areas of AI and the principles of human-computer interaction. (shrink)

Computational models can aid in the development of philosophical views concerning the structure and growth of scientific knowledge. In cognitive psychology, computational models have proved valuable for describing the structures and processes of thought and for testing these models by writing and running computer programs using the techniques of artificial intelligence. Similarly, in the philosophy of science models can be developed that shed light on the structure, discovery, and justification of scientific theories. This paper briefly describes a computational (...) model of problem solving and learning that has been used to simulate several kinds of scientific reasoning. (shrink)

Alan M. Turing, pioneer of computing and WWII codebreaker, is one of the most important and influential thinkers of the twentieth century. In this volume for the first time his key writings are made available to a broad, non-specialist readership. They make fascinating reading both in their own right and for their historic significance: contemporary computational theory, cognitive science, artificial intelligence, and artificial life all spring from this ground-breaking work, which is also rich in philosophical and logical insight. An introduction (...) by leading Turing expert Jack Copeland provides the background and guides the reader through the selection. About Alan Turing Alan Turing FRS OBE, (1912-1954) studied mathematics at King's College, Cambridge. He was elected a Fellow of King's in March 1935, at the age of only 22. In the same year he invented the abstract computing machines - now known simply as Turing machines - on which all subsequent stored-program digital computers are modelled. During 1936-1938 Turing continued his studies, now at Princeton University. He completed a PhD in mathematical logic, analysing the notion of 'intuition' in mathematics and introducing the idea of oracular computation, now fundamental in mathematical recursion theory. An 'oracle' is an abstract device able to solve mathematical problems too difficult for the universal Turing machine. In the summer of 1938 Turing returned to his Fellowship at King's. When WWII started in 1939 he joined the wartime headquarters of the Government Code and Cypher School (GC&CS) at Bletchley Park, Buckinghamshire. Building on earlier work by Polish cryptanalysts, Turing contributed crucially to the design of electro-mechanical machines ('bombes') used to decipher Enigma, the code by means of which the German armed forces sought to protect their radio communications. Turing's work on the version of Enigma used by the German navy was vital to the battle for supremacy in the North Atlantic. He also contributed to the attack on the cyphers known as 'Fish'. Based on binary teleprinter code, Fish was used during the latter part of the war in preference to morse-based Enigma for the encryption of high-level signals, for example messages from Hitler and other members of the German High Command. It is estimated that the work of GC&CS shortened the war in Europe by at least two years. Turing received the Order of the British Empire for the part he played. In 1945, the war over, Turing was recruited to the National Physical Laboratory (NPL) in London, his brief to design and develop an electronic computer - a concrete form of the universal Turing machine. Turing's report setting out his design for the Automatic Computing Engine (ACE) was the first relatively complete specification of an electronic stored-program general-purpose digital computer. Delays beyond Turing's control resulted in NPL's losing the race to build the world's first working electronic stored-program digital computer - an honour that went to the Royal Society Computing Machine Laboratory at Manchester University, in June 1948. Discouraged by the delays at NPL, Turing took up the Deputy Directorship of the Royal Society Computing Machine Laboratory in that year. Turing was a founding father of modern cognitive science and a leading early exponent of the hypothesis that the human brain is in large part a digital computing machine, theorising that the cortex at birth is an 'unorganised machine' which through 'training' becomes organised 'into a universal machine or something like it'. He also pioneered Artificial Intelligence. Turing spent the rest of his short career at Manchester University, being appointed to a specially created Readership in the Theory of Computing in May 1953. He was elected a Fellow of the Royal Society of London in March 1951 (a high honour). (shrink)

This book deals with a major problem in the study of language: the problem of reference. The ease with which we refer to things in conversation is deceptive. Upon closer scrutiny, it turns out that we hardly ever tell each other explicitly what object we mean, although we expect our interlocutor to discern it. Amichai Kronfeld provides an answer to two questions associated with this: how do we successfully refer, and how can a computer be programmed to achieve this? (...) Beginning with the major theories of reference, Dr Kronfeld provides a consistent philosophical view which is a synthesis of Frege's and Russell's semantic insights with Grice's and Searle's pragmatic theories. This leads to a set of guiding principles, which are then applied to a computational model of referring. The discussion is made accessible to readers from a number of backgrounds: in particular, students and researchers in the areas of computational linguistics, artificial intelligence and the philosophy of language will want to read this book. (shrink)

We explore the possibility of teaching philosophy through the teaching of computer programming. It is pedagogically useful to use programming because it is extremely popular (especially due to the recent breakthroughs in machine learning), and it can provide a novel, interesting, and clear introduction to a variety of classic philosophical issues. We discuss two examples. The first is using programming to solve digital image manipulation tasks as a way of introducing and clarifying debates over external (...) world skepticism. The second is programming a simulation of a simple 2-D cellular automaton as a way of introducing and clarifying debates over determinism and free will. (shrink)

Gualtiero Piccinini articulates and defends a mechanistic account of concrete, or physical, computation. A physical system is a computing system just in case it is a mechanism one of whose functions is to manipulate vehicles based solely on differences between different portions of the vehicles according to a rule defined over the vehicles. Physical Computation discusses previous accounts of computation and argues that the mechanistic account is better. Many kinds of computation are explicated, such as digital vs. analog, serial vs. (...) parallel, neural network computation, program-controlled computation, and more. Piccinini argues that computation does not entail representation or information processing although information processing entails computation. Pancomputationalism, according to which every physical system is computational, is rejected. A modest version of the physical Church-Turing thesis, according to which any function that is physically computable is computable by Turing machines, is defended. (shrink)

The issue of proper functioning of operative computing and the utility of program verification, both in general and of specific methods, has been discussed a lot. In many of those discussions, attempts have been made to take mathematics as a model of knowledge and certitude achieving, and accordingly infer about the suitable ways to handle computing. I shortly review three approaches to the subject, and then take a stance by considering social factors which affect the epistemic status of both mathematics (...) and computing. I use the analogy between mathematics and computing in reverse—that is to say, I consider operative computing as a form of making mathematics, and so attempt to learn from computing to mathematics in general. I conclude that mathematics engineering is a field to be both developed for practical improvement of doing mathematics and taken into consideration while philosophizing about mathematics as well. (shrink)

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. (shrink)

This book looks at the ways in which conditionals, an integral part of philosophy and logic, can be of practical use in computer programming. It analyzes the different types of conditionals, including their applications and potential problems. Other topics include defeasible logics, the Ramsey test, and a unified view of consequence relation and belief revision. Its implications will be of interest to researchers in logic, philosophy, and computer science, particularly artificial intelligence.

We examine the philosophical disputes among computer scientists concerning methodological, ontological, and epistemological questions: Is computer science a branch of mathematics, an engineering discipline, or a natural science? Should knowledge about the behaviour of programs proceed deductively or empirically? Are computer programs on a par with mathematical objects, with mere data, or with mental processes? We conclude that distinct positions taken in regard to these questions emanate from distinct sets of received beliefs or paradigms within the discipline: (...) – The rationalist paradigm, which was common among theoretical computer scientists, defines computer science as a branch of mathematics, treats programs on a par with mathematical objects, and seeks certain, a priori knowledge about their ‘correctness’ by means of deductive reasoning. – The technocratic paradigm, promulgated mainly by software engineers and has come to dominate much of the discipline, defines computer science as an engineering discipline, treats programs as mere data, and seeks probable, a posteriori knowledge about their reliability empirically using testing suites. – The scientific paradigm, prevalent in the branches of artificial intelligence, defines computer science as a natural (empirical) science, takes programs to be entities on a par with mental processes, and seeks a priori and a posteriori knowledge about them by combining formal deduction and scientific experimentation. We demonstrate evidence corroborating the tenets of the scientific paradigm, in particular the claim that program-processes are on a par with mental processes. We conclude with a discussion in the influence that the technocratic paradigm has been having over computer science. (shrink)

This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty ; and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: logic programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of input/output logic for dealing with uncertainty in (...) dynamic normative contexts. (shrink)

After the First World War mathematics and the organisation of ballistic computations at Aberdeen Proving Ground changed considerably. This was the basis for the development of a number of computing aids that were constructed and used during the years 1920 to 1950. This article looks how the computational organisation forms and changes the instruments of calculation. After the differential analyzer relay-based machines were built by Bell Labs and, finally, the ENIAC, one of the first electronic computers, was built, to satisfy (...) the need for computational power in ballistics during the second World War. (shrink)

The duality of computer programs is characterized, on the one hand, by their physical implementations on physical devices, and, on the other, by the conceptual implementations in programmers’ minds of the objects making up the computational processes they conceive. We contend that central to programmers’ conceptual implementations are (i) the concept of type, at both the programming and the design level, and (ii) metaphors created to facilitate these implementations.

Depending on what one means by the main connective of logic, the -if..., then... -, several systems of logic result: classic and modal logics, intuitionistic logic or relevance logic. This book presents the underlying ideas, the syntax and the semantics of these logics. Soundness and completeness are shown constructively and in a uniform way. Attention is paid to the interdisciplinary role of logic: its embedding in the foundations of mathematics and its intimate connection with philosophy, in particular the (...) class='Hi'>philosophy of language. Set theory is presented both as a conditio sine qua non for logic and as a interesting exact ontology. The study of infinite sets yields perplexing results. Formalization of informal number theory results in formal number theory; Godel's incompleteness is treated. At appropriate places attention is paid to paradoxes, intuitionism, conditionals, the historical development of logic, to logic programming and automated theorem proving for classical logic.". (shrink)

Bayesian networks are computer programs which represent probabilitistic relationships graphically as directed acyclic graphs, and which can use those graphs to reason probabilistically , often at relatively low computational cost. Almost every expert system in the past tried to support probabilistic reasoning, but because of the computational difficulties they took approximating short-cuts, such as those afforded by MYCIN's certainty factors. That all changed with the publication of Judea Pearl's Probabilistic Reasoning in Intelligent Systems, in 1988, which synthesized a decade (...) of research making accurate graphical probabilistic reasoning computationally achievable.Bayesian network technology is now one of the fastest growing fields of research in artificial intelligence. That it has become a publication industry in its own right is shown by a search on Google scholar :This development, together with a parallel related growth in the use of causal discovery algorithms which automate the learning of Bayesian networks from sample data, has generated considerable interest, and controversy, within the philosophy-of-science community.Three central questions bringing together AI researchers and philosophers of science are: Are Bayesian networks Bayesian? What is the relation between probability and causality? Are the assumptions behind causal discovery of Bayesian networks realistic or fantastical?Jon Williamson, as a philosopher of science with a keen interest in the technology, asks and answers these questions in his new book. Although it is self-contained, his book is not very likely as an introduction to the technology , nor is it optimal even as an introduction to the philosophical problems in interpreting Bayesian networks . Rather Williamson's book is an attempt to move the debate forward by solving the central problems of the …. (shrink)

What is the mind? How does it work? How does it influence behavior? Some psychologists hope to answer such questions in terms of concepts drawn from computer science and artificial intelligence. They test their theories by modeling mental processes in computers. This book shows how computer models are used to study many psychological phenomena--including vision, language, reasoning, and learning. It also shows that computer modeling involves differing theoretical approaches. Computational psychologists disagree about some basic questions. For instance, (...) should the mind be modeled by digital computers, or by parallel-processing systems more like brains? Do computer programs consist of meaningless patterns, or do they embody (and explain) genuine meaning? (shrink)

The prelims comprise: Introduction: Two Semantic Projects History The Uses of Semantics Conclusions.

In the 21st century, digitalization is a global challenge of mankind. Even for the public, it is obvious that our world is increasingly dominated by powerful algorithms and big data. But, how computable is our world? Some people believe that successful problem solving in science, technology, and economies only depends on fast algorithms and data mining. Chances and risks are often not understood, because the foundations of algorithms and information systems are not studied rigorously. Actually, they are deeply rooted in (...) logics, mathematics, computer science and philosophy. Therefore, this book studies the foundations of mathematics, computer science, and philosophy, in order to guarantee security and reliability of the knowledge by constructive proofs, proof mining and program extraction. We start with the basics of computability theory, proof theory, and information theory. In a second step, we introduce new concepts of information and computing systems, in order to overcome the gap between the digital world of logical programming and the analog world of real computing in mathematics and science. The book also considers consequences for digital and analog physics, computational neuroscience, financial mathematics, and the Internet of Things (IoT). (shrink)

Searle’s celebrated Chinese room thought experiment was devised as an attempted refutation of the view that appropriately programmed digital computers literally are the possessors of genuine mental states. A standard reply to Searle, known as the “robot reply” (which, I argue, reflects the dominant approach to the problem of content in contemporary philosophy of mind), consists of the claim that the problem he raises can be solved by supplementing the computational device with some “appropriate” environmental hookups. I argue that (...) not only does Searle himself casts doubt on the adequacy of this idea by applying to it a slightly revised version of his original argument, but that the weakness of this encoding-based approach to the problem of intentionality can also be exposed from a somewhat different angle. Capitalizing on the work of several authors and, in particular, on that of psychologist Mark Bickhard, I argue that the existence of symbol-world correspondence is not a property that the cognitive system itself can appreciate, from its own perspective, by interacting with the symbol and therefore, not a property that can constitute intrinsic content. The foundational crisis to which Searle alluded is, I conclude, very much alive. (shrink)

Taken at face value, a programming language is defined by a formal grammar. But, clearly, there is more to it. By themselves, the naked strings of the language do not determine when a program is correct relative to some specification. For this, the constructs of the language must be given some semantic content. Moreover, to be employed to generate physical computations, a programming language must have a physical implementation. How are we to conceptualize this complex package? Ontologically, what (...) kind of thing is it? In this paper, we shall argue that an appropriate conceptualization is furnished by the notion of a technical artifact. (shrink)

In the paper I discuss the importance of relativistic hypercomputation for the philosophy of mathematics, in particular for our understanding of mathematical knowledge. I also discuss the problem of the explanatory role of mathematics in physics and argue that relativistic computation fits very well into the so-called programming account. Relativistic computation reveals an interesting interplay between the empirical realm and the realm of very abstract mathematical principles that even exceed standard mathematics and suggests, that such principles might play (...) an explanatory role. I also argue that relativistic computation does not have some of the weaknesses of other hypercomputational models, thus it is particularly attractive for the philosophy of mathematics. (shrink)

It has been known since the seventies that the formulas of modal logic are invariant for bisimulations between possible worlds models — while conversely, all bisimulation-invariant first-order formulas are modally definable. In this paper, we extend this semantic style of analysis from modal formulas to dynamic program operations. We show that the usual regular operations are safe for bisimulation, in the sense that the transition relations of their values respect any given bisimulation for their arguments. Our main result is a (...) complete syntactic characterization of all first-order definable program operations that are safe for bisimulation. This is a semantic functional completeness result for programming, which may be contrasted with the more usual analysis in terms of computational power. The 'Safety Theorem' can be modulated in several ways. We conclude with a list of variants, extensions, and further developments. (shrink)

The purpose of this paper is to set forth a sense in which programs can and do explain behavior, and to distinguish from this a number of senses in which they do not. Once we are tolerably clear concerning the sort of explanatory strategy being employed, two rather interesting facts emerge; (1) though it is true that programs are "internally represented," this fact has no explanatory interest beyond the mere fact that the program is executed; (2) programs which are couched (...) in information processing terms may have an explanatory interest for a given range of behavior which is independent of physiological explanations of the same range of behavior. (shrink)

Studies of scientific practice demonstrate that the development of scientific models is an enactive and emergent process. Scientists make meaning through processes such as perspective taking, finding patterns, and following intuitions. In this paper, we focus on how a group of fourth grade learners and their teacher engaged in interpretation in ways that align with core ideas and practices in kinematics and computing. Cycles of measuring and modeling––including computer programming––helped to support classroom interactions that highlighted the interpretive nature (...) of modeling and participation in model construction as a knowledge-building process. We draw on literature from the history and philosophy of science in order to analyze the students’ interpretive actions as forms of epistemic and representational agency, constituting a construct we term disciplined interpretation. We demonstrate how students’ disciplined interpretative moves help to position them as owners of their own design decisions and their rights to interpret the phenomena they were modeling, data collected from those phenomena, and the scientific and computational models themselves. We present four extended episodes that characterize the nature of activity in the classroom and the development of students’ disciplined interpretations in terms of learning to recognize scientific patterns amid complex perceptual fields, and to represent them in ways that support sensemaking. (shrink)

Ethnographic study of the constitution of algorithms.