Computational philosophy is the use of mechanized computational techniques to unearth philosophical insights that are either difficult or impossible to find using traditional philosophical methods. Computational metaphysics is computational philosophy with a focus on metaphysics. In this paper, we (a) develop results in modal metaphysics whose discovery was computer assisted, and (b) conclude that these results work not only to the obvious benefit of philosophy but also, less obviously, to the benefit of computer science, since the new computational techniques (...) that led to these results may be more broadly applicable within computer science. The paper includes a description of our background methodology and how it evolved, and a discussion of our new results. (shrink)

Colburn (computer science, U. of Minnesota-Duluth) has a doctorate in philosophy and an advanced degree in computer science; he's worked as a philosophy professor, a computer programmer, and a research scientist in artificial intelligence. Here he discusses the philosophical foundations of artificial intelligence; the new encounter of science and philosophy (logic, models of the mind and of reasoning, epistemology); and the philosophy of computer science (touching on math, abstraction, software, and ontology).

Explanations in cognitive science and computational neuroscience rely predominantly on computational modeling. Although the scientific practice is systematic, and there is little doubt about the empirical value of numerous models, the methodological account of computational explanation is not up-to-date. The current chapter offers a systematic account of computational explanation in cognitive science and computational neuroscience within a mechanistic framework. The account is illustrated with a short case study of modeling of the mirror neuron system in terms of predictive (...) coding. (shrink)

Attempts to lay a foundation for the sciences based on modern mathematics are questioned. In particular, it is not clear that computer science should be based on set-theoretic mathematics. Set-theoretic mathematics has difficulties with its own foundations, making it reasonable to explore alternative foundations for the sciences. The role of computation within an alternative framework may prove to be of great potential in establishing a direction for the new field of computer science.Whitehead''s theory of reality is re-examined (...) as a foundation for the sciences. His theory does not simply attempt to add formal rigor to the sciences, but instead relies on the methods of the biological and social sciences to construct his world-view. Whitehead''s theory is a rich source of notions that are intended to explain every element of experience. It is a product of Whitehead''s earlier attempt to provide a mathematical foundation for the physical sciences and is still consistent with modern physics. (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)

Epistemic logic has grown from its philosophical beginnings to find diverse applications in computer science, and as a means of reasoning about the knowledge and belief of agents. This book provides a broad introduction to the subject, along with many exercises and their solutions. The authors begin by presenting the necessary apparatus from mathematics and logic, including Kripke semantics and the well-known modal logics K, T, S4 and S5. Then they turn to applications in the context of distributed systems (...) and artificial intelligence. These include the notions of common knowledge, distributed knowledge, explicit and implicit belief, the interplays between knowledge and time, and knowledge and action, as well as a graded (or numerical) variant of the epistemic operators. The authors also discuss extensively the problem of logical omniscience. They cover Halpern & Moses' theory of honest formulas, and they make a digression into the realm of nonmonotonic reasoning and preferential entailment. They discuss Moore's autoepistemic logic, together with Levesque's related logic of "all I know". Furthermore, they show how one can base default and counterfactual reasoning on epistemic logic. Graduate students in philosophy or in computer science, especially those with an interest in AI, will find this book useful. (shrink)

The recent trend in cognitive robotics experiments on language learning, symbol grounding, and related issues necessarily entails a reduction of sensorimotor aspects from those provided by a human body to those that can be realized in machines, limiting robotic models of symbol grounding in this respect. Here, we argue that there is a need for modeling work in this domain to explicitly take into account the richer human embodiment even for concrete concepts that prima facie relate merely to simple actions, (...) and illustrate this using distributional methods from computational linguistics which allow us to investigate grounding of concepts based on their actual usage. We also argue that these techniques have applications in theories and models of grounding, particularly in machine implementations thereof. Similarly, considering the grounding of concepts in human terms may be of benefit to future work in computational linguistics, in particular in going beyond “grounding” concepts in the textual modality alone. Overall, we highlight the overall potential for a mutually beneficial relationship between the two fields. (shrink)

This volume contains the proceedings of three special sessions: Algebra and Computer Science, held during the Joint AMS-EMS-SPM meeting in Porto, Portugal, June 10–13, 2015; Groups, Algorithms, and Cryptography, held during the Joint Mathematics Meeting in San Antonio, TX, January 10–13, 2015; and Applications of Algebra to Cryptography, held during the Joint AMS-Israel Mathematical Union meeting in Tel-Aviv, Israel, June 16–19, 2014. Papers contained in this volume address a wide range of topics, from theoretical aspects of algebra, namely group (...) theory, universal algebra and related areas, to applications in several different areas of computer science. From the computational side, the book aims to reflect the rapidly emerging area of algorithmic problems in algebra, their computational complexity and applications, including information security, constraint satisfaction problems, and decision theory. The book gives special attention to recent advances in quantum computing that highlight the need for a variety of new intractability assumptions and have resulted in a new area called group-based cryptography. (shrink)

Computationalism says that brains are computing mechanisms, that is, mechanisms that perform computations. At present, there is no consensus on how to formulate computationalism precisely or adjudicate the dispute between computationalism and its foes, or between different versions of computationalism. An important reason for the current impasse is the lack of a satisfactory philosophical account of computing mechanisms. The main goal of this dissertation is to offer such an account.
I also believe that the history of computationalism sheds light on the (...) current debate. By tracing different versions of computationalism to their common historical origin, we can see how the current divisions originated and understand their motivation. Reconstructing debates over computationalism in the context of their own intellectual history can contribute to philosophical progress on the relation between brains and computing mechanisms and help determine how brains and computing mechanisms are alike, and how they differ. Accordingly, my dissertation is divided into a historical part, which traces the early history of computationalism up to 1946, and a philosophical part, which offers an account of computing mechanisms.
The two main ideas developed in this dissertation are that (1) computational states are to be identified functionally not semantically, and (2) computing mechanisms are to be studied by functional analysis. The resulting account of computing mechanism, which I call the functional account of computing mechanisms, can be used to identify computing mechanisms and the functions they compute. I use the functional account of computing mechanisms to taxonomize computing mechanisms based on their different computing power, and I use this taxonomy of computing mechanisms to taxonomize different versions of computationalism based on the functional properties that they ascribe to brains. By doing so, I begin to tease out empirically testable statements about the functional organization of the brain that different versions of computationalism are committed to. I submit that when computationalism is reformulated in the more explicit and precise way I propose, the disputes about computationalism can be adjudicated on the grounds of empirical evidence from neuroscience. (shrink)

In this full review paper, the recent emerging trends in Computing Structures, Software Science, and System Applications have been reviewed and explored to address the recent topics and contributions in the era of the Software and Computing fields. This includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art, framework, implemented approaches and techniques research projects in the areas of Software Technology & Automation, Networking, Systems, Computing Sciences and Software Engineering, Big Data and E-learning. Based on this (...) systematic review, we have put some recommendations and suggestions for researchers, practitioners and scholars to improve their research quality in this area. (shrink)

There is a widely extended image of computer software as some sort of ‘black box,’ where it does not matter how it internally works, but rather what sort of results are obtained given certain input values. By approaching computer software this way, many philosophical issues are hidden, neglected, or simply misunderstood. This article discusses three units of analysis of computer software, namely, specifications, algorithms, and computer processes. The aim is to understand the scientific and engineering practices supporting each unit of (...) analysis, as well as to analyze their methodology, ontology, and epistemology. (shrink)

Connectionism provides hope for unifying work in neuroscience, computer science, and cognitive psychology. This promise has met with some resistance from Classical Computionalists, which may have inspired Connectionists to retaliate with bold, inflationary claims on behalf of Connectionist models. This paper demonstrates, by examining three intimately connected issues, that these inflationary claims made on behalf of Connectionism are wrong. This should not be construed as an attack on Connectionism, however, since the inflated claims made on its behalf have the (...) look of cures for which there are no ailments. There is nothing wrong with Connectionism for its failure to solve illusory problems. (shrink)

The subject of this study is the problem of the failure of attempts by the scientific community to come to a common understanding of what exactly information can be as something encoded into material structures and moved along with them. At the same time, the following aspects of this problem are considered in detail: what is the immediate cause of the information problem; what are the objective and subjective prerequisites for its appearance; why the unresolved nature of this problem does (...) not interfere with the creation and development of communication systems, control and other "smart" devices; is there any general guideline for finding solutions to such problems, and what is it; what role did the philosophy of information play in turning the problem of information from a private scientific problem into a problem of the ideological level. The main results of the conducted research are as follows: the immediate cause of the information problem has been identified and a comprehensively substantiated solution to this problem has been given; it has been revealed that the incompleteness of the materialistic theory of cognition has become a fertile ground for the appearance of this problem; the circumstances that prevented its completion have been identified; the natural mechanism of control and cognition has been revealed, as a result of which the materialistic theory of cognition has been further developed the source of the ideal in the material world is revealed; it is shown that the mechanistic idea of information coding made the concept of information incompatible with the presence of any objective content; it is revealed what is actually hidden behind the words about information coding and its measurement; the nature of signals and signs is revealed; a kind of bias of the philosophy of information and a general error is revealed all known concepts of information. (shrink)

This book addresses key conceptual issues relating to the modern scientific and engineering use of computer simulations. It analyses a broad set of questions, from the nature of computer simulations to their epistemological power, including the many scientific, social and ethics implications of using computer simulations. The book is written in an easily accessible narrative, one that weaves together philosophical questions and scientific technicalities. It will thus appeal equally to all academic scientists, engineers, and researchers in industry interested in questions (...) related to the general practice of computer simulations. (shrink)

A recent issue of Time magazine (March 29, 1999) was devoted to the twenty greatest "thinkers" of the twentieth century -- scientists, inventors, and engineers. There is one interesting omission: there are no cognitive psychologists or cognitive scientists. (Cognitive science is an amalgam of cognitive, neuro, and developmental psychology, artificial intelligence, philosophy, linguistics, biology, and anthropology.) Freud is there, to be sure. But, while he was very influential, it is not even clear that he was a scientist, let alone (...) a cognitive scientist. There are those who regard Freud as somewhere between incompetent and a charlatan (see Glymour, 1988). In any case, though Freud's positive proposal for the mind's architecture -- namely, that it contains the unconscious -- seems correct as far as it goes, it does appear that all the details are wrong. For example: (1) there is a lot more to the mind than the mere unconscious; (2) it is doubtful that there is an id, ego, and superego; (3) most dreams may very likely be meaningless; and (4) human motivation, even unconscious motivation, is about a lot more than sex. In the end, because he was most interested in certain kinds of human mental malfunctioning, Freud is probably best thought of as a physician, a proponent and early explorer of human mental health; he was not an experimental cognitive psychologist. (shrink)

This book constitutes the strictly refereed post-workshop proceedings of the 11th International Workshop on Computer Science Logic, CSL '97, held as the 1997 Annual Conference of the European Association on Computer Science Logic, EACSL, in Aarhus, Denmark, in August 1997. The volume presents 26 revised full papers selected after two rounds of refereeing from initially 92 submissions; also included are four invited papers. The book addresses all current aspects of computer science logics and its applications and thus (...) presents the state of the art in the area. (shrink)

The process of constructing mathematical models is examined and a case made that the construction process is an integral part of the justification for the model. The role of heuristics in testing and modifying models is described and some consequences for scientific methodology are drawn out. Three different ways of constructing the same model are detailed to demonstrate the claims made here.

Digital computers play a special role in cognitive science—they may actually be instances of the phenomenon they are being used to model. This paper surveys some of the main issues involved in understanding the relationship between digital computers and cognition. It sketches the role of digital computers within orthodox computational cognitive science, in the light of a recently emerging alternative approach based around dynamical systems.

Open peer commentary on the article “Exploration of the Functional Properties of Interaction: Computer Models and Pointers for Theory” by Etienne B. Roesch, Matthew Spencer, Slawomir J. Nasuto, Thomas Tanay & J. Mark Bishop. Upshot: Why computer models of constructivist processes can enhance constructivist matters even though the models will always “seem incomplete.”.

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)

Logic has pride of place in mathematics and its 20th century offshoot, computer science. Modern symbolic logic was developed, in part, as a way to provide a formal framework for mathematics: Frege, Peano, Whitehead and Russell, as well as Hilbert developed systems of logic to formalize mathematics. These systems were meant to serve either as themselves foundational, or at least as formal analogs of mathematical reasoning amenable to mathematical study, e.g., in Hilbert’s consistency program. Similar efforts continue, but have (...) been expanded by the development of sophisticated methods to study the properties of such systems using proof and model theory. In parallel with this evolution of logical formalisms as tools for articulating mathematical theories (broadly speaking), much progress has been made in the quest for a mechanization of logical inference and the investigation of its theoretical limits, culminating recently in the development of new foundational frameworks for mathematics with sophisticated computer-assisted proof systems. In addition, logical formalisms developed by logicians in mathematical and philosophical contexts have proved immensely useful in describing theories and systems of interest to computer scientists, and to some degree, vice versa. Three examples of the influence of logic in computer science are automated reasoning, computer verification, and type systems for programming languages. (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 social sciences and humanities are fragmented into specialized areas, each with their own parlance and procedures. This hinders information sharing and the growth of a coherent body of knowledge. Modeling and simulation can be the scientific lingua franca, or shared technical language, that can unite, integrate, and relate relevant parts of these diverse disciplines.Models are well established in the scientific community as mediators, contributors, and enablers of scientific knowledge. We propose a potentially revolutionary linkage between social sciences, humanities and (...) computer simulation, forging what we call “human simulation.” We explore three facets of human simulation, namely: the simulation of humans, the design of simulations for human use, and simulations that include humans as well as simulated agents among the actors. We describe the potential of human simulation using several illuminating examples. We also discuss computational, epistemological, and hermeneutical challenges constraining the use of human simulation. (shrink)

Experiments (E), computer simulations (CS) and thought experiments (TE) are usually seen as playing different roles in science and as having different epistemologies. Accordingly, they are usually analyzed separately. We argue in this paper that these activities can contribute to answering the same questions by playing the same epistemic role when they are used to unfold the content of a well-described scenario. We emphasize that in such cases, these three activities can be described by means of the same conceptual (...) framework—even if each of them, because they involve different types of processes, fall under these concepts in different ways. We further illustrate our claims by presenting a threefold case study describing how a TE, a CS and an E were indeed used in the same role at different periods to answer the same questions about the possibility of a physical Maxwellian demon. We also point at fluid dynamics as another field where these activities seem to be playing the same unfolding role. We analyze the importance of unfolding as a general task of science and highlight how our description in terms of epistemic functions articulates in a noncommittal way with the epistemology of these three activities and accounts for their similarities and the existence of hybrid forms of activities. We finally emphasize that picturing these activities as functionally substitutable does not imply that they are epistemologically substitutable. (shrink)

Extract from Hofstadter's revew in Bulletin of American Mathematical Society : http://www.ams.org/journals/bull/1980-02-02/S0273-0979-1980-14752-7/S0273-0979-1980-14752-7.pdf -/- "Aaron Sloman is a man who is convinced that most philosophers and many other students of mind are in dire need of being convinced that there has been a revolution in that field happening right under their noses, and that they had better quickly inform themselves. The revolution is called "Artificial Intelligence" (Al)-and Sloman attempts to impart to others the "enlighten- ment" which he clearly regrets not having (...) experienced earlier himself. Being somewhat of a convert, Sloman is a zealous campaigner for his point of view. Now a Reader in Cognitive Science at Sussex, he began his academic career in more orthodox philosophy and, by exposure to linguistics and AI, came to feel that all approaches to mind which ignore AI are missing the boat. I agree with him, and I am glad that he has written this provocative book. The tone of Sloman's book can be gotten across by this quotation (p. 5): "I am prepared to go so far as to say that within a few years, if there remain any philosophers who are not familiar with some of the main developments in artificial intelligence, it will be fair to accuse them of professional incom- petence, and that to teach courses in philosophy of mind, epistemology, aesthetics, philosophy of science, philosophy of language, ethics, metaphysics, and other main areas of philosophy, without discussing the relevant aspects of artificial intelligence will be as irresponsible as giving a degree course in physics which includes no quantum theory." -/- (The author now regrets the extreme polemical tone of the book.). (shrink)

F. H. George is Professor of Cybernetics at Brunel University in England. His book comprises eight chapters originally developed as lectures for a non-specialist audience. He points out the position of computer science among the sciences, explains its aims, procedures, and achievements to date, and speculates on its long-term implications for science in particular and society in general. Among the topics discussed are biological simulation and organ replacement, automated education, and the new philosophy of science. Each chapter (...) concludes with a brief summary. George's treatment of the technical details of his speciality is both illuminating and readable, thus serving as an excellent primer on one of the new technology's most important components. His wider forays into philosophy, economics, sociology, and religion are less happy, however; and unfortunately they take up a large part of the text. In general, they reveal that George identifies the methods of human advancement with the methods of the natural sciences in an equation whose rigidity would make even B. F. Skinner blush. Yet, the reader cannot claim that he was not forewarned; for in the introduction, D. J. Stewart, Chairman of the Rationalist Press Association, suggests that the current "swing of interest among young people away from the physical and biological sciences and towards the behavioural and social sciences... represents a symptom of disillusionment with science and technology and an attempted escape into irrationality."--J. M. V. (shrink)

Aspiring Minds Computer Adaptive Test (AMCAT) is a computer adaptive test used widely in India to assess the employability skill level of engineering students. Prefinal and final year students belonging to different streams take up this online test. The present study investigates the perception of students regarding the conduct of AMCAT. Furthermore, the study explores the benefits that they obtain through the test. The comparison is made among the students belonging to the two different streams, engineering and computer science. (...) The conclusion drawn facilitates the readers to understand the utility of AMCAT. (shrink)

Edited in collaboration with FoLLI, the Association of Logic, Language and Information this book constitutes the refereed proceedings of the 22nd Workshop on Logic, Language, Information and Computation, WoLLIC 2015, held in the campus of Indiana University, Bloomington, IN, USA in July 2015. The 14 contributed papers, presented together with 8 invited lectures and 4 tutorials, were carefully reviewed and selected from 44 submissions. The focus of the workshop was on interdisciplinary research involving formal logic, computing and programming theory, (...) and natural language and reasoning. (shrink)

Many researchers insist that computational methods will transform the historical profession, while an equally large number reject these claims as unwarranted hype. This study attempts to place the debate in historical and social context. The essay is divided into three parts. The first part offers a brief review of computational history. It asks whether the “computational turn” bears any resemblance to quantitative history and how it fits within the digital humanities. The second part describes the authors’ recent attempts to apply (...) computational methods to an existing project in the history of science using a standardized workflow. It demonstrates that each step of the workflow adds another layer of subjectivity. The third part reflects on what computational methods mean for the historical profession. It systematically reviews the positive aspects of computational history (open access, interdisciplinary collaboration, and new perspectives) as well as the negative aspects (inequality, fragility, and the threat of automation) and offers prescriptions based on the authors’ experiences. (shrink)

This book features papers from CEPE-IACAP 2015, a joint international conference focused on the philosophy of computing. Inside, readers will discover essays that explore current issues in epistemology, philosophy of mind, logic, and philosophy of science from the lens of computation. Coverage also examines applied issues related to ethical, social, and political interest. -/- The contributors first explore how computation has changed philosophical inquiry. Computers are now capable of joining humans in exploring foundational issues. Thus, we can (...) ponder machine-generated explanation, thought, agency, and other quite fascinating concepts. The papers are also concerned with normative aspects of the computer and information technology revolution. They examine technology-specific analyses of key challenges, from Big Data to autonomous robots to expert systems for infrastructure control and financial services. -/- The virtue of a collection that ranges over philosophical questions, such as this one does, lies in the prospects for a more integrated understanding of issues. These are early days in the partnership between philosophy and information technology. Philosophers and researchers are still sorting out many foundational issues. They will need to deploy all of the tools of philosophy to establish this foundation. This volume admirably showcases those tools in the hands of some excellent scholars. -/- . (shrink)

At a conference, two engineering professors and a philosophy professor discussed the teaching of ethics in engineering and computer science. The panelists considered the integration of material on ethics into technical courses, the role of ethical theory in teaching applied ethics, the relationship between cases and codes of ethics, the enlisting of support of engineering faculty, the background needed to teach ethics, and the assessment of student outcomes. Several audience members contributed comments, particularly on teaching ethical theory and on (...) student assessment. (shrink)

Driven by the question, 'What is the computational content of a (formal) proof?', this book studies fundamental interactions between proof theory and computability. It provides a unique self-contained text for advanced students and researchers in mathematical logic and computer science. Part I covers basic proof theory, computability and Gödel's theorems. Part II studies and classifies provable recursion in classical systems, from fragments of Peano arithmetic up to Π11-CA0. Ordinal analysis and the (Schwichtenberg-Wainer) subrecursive hierarchies play a central role and (...) are used in proving the 'modified finite Ramsey' and 'extended Kruskal' independence results for PA and Π11-CA0. Part III develops the theoretical underpinnings of the first author's proof assistant MINLOG. Three chapters cover higher-type computability via information systems, a constructive theory TCF of computable functionals, realizability, Dialectica interpretation, computationally significant quantifiers and connectives and polytime complexity in a two-sorted, higher-type arithmetic with linear logic. (shrink)

This volume on the semantic complexity of natural language explores the question why some sentences are more difficult than others. While doing so, it lays the groundwork for extending semantic theory with computational and cognitive aspects by combining linguistics and logic with computations and cognition. -/- Quantifier expressions occur whenever we describe the world and communicate about it. Generalized quantifier theory is therefore one of the basic tools of linguistics today, studying the possible meanings and the inferential power of quantifier (...) expressions by logical means. The classic version was developed in the 1980s, at the interface of linguistics, mathematics and philosophy. Before this volume, advances in "classic" generalized quantifier theory mainly focused on logical questions and their applications to linguistics, this volume adds a computational component, the third pillar of language use and logical activity. This book is essential reading for researchers in linguistics, philosophy, cognitive science, logic, AI, and computer science. (shrink)

Interest in the computational aspects of modeling has been steadily growing in philosophy of science. This paper aims to advance the discussion by articulating the way in which modeling and computational errors are related and by explaining the significance of error management strategies for the rational reconstruction of scientific practice. To this end, we first characterize the role and nature of modeling error in relation to a recipe for model construction known as Euler’s recipe. We then describe a general (...) model that allows us to assess the quality of numerical solutions in terms of measures of computational errors that are completely interpretable in terms of modeling error. Finally, we emphasize that this type of error analysis involves forms of perturbation analysis that go beyond the basic model-theoretical and statistical/probabilistic tools typically used to characterize the scientific method; this demands that we revise and complement our reconstructive toolbox in a way that can affect our normative image of science. (shrink)

Within the Computer Science community, many ethical issues have emerged as significant and critical concerns. Computer ethics is an academic field in its own right and there are unique ethical issues associated with information technology. It encompasses a range of issues and concerns including privacy and agency around personal information, Artificial Intelligence and pervasive technology, the Internet of Things and surveillance applications. As computing technology impacts society at an ever growing pace, there are growing calls for more computer ethics (...) content to be included in Computer Science curricula. In this paper we present the results of a survey that polled faculty from Computer Science and related disciplines about teaching practices for computer ethics at their institutions. The survey was completed by respondents from 61 universities across 23 European countries. Participants were surveyed on whether or not computer ethics is taught to Computer Science students at each institution, the reasons why computer ethics is or is not taught, how computer ethics is taught, the background of staff who teach computer ethics and the scope of computer ethics curricula. This paper presents and discusses the results of the survey. (shrink)

Following Marr’s famous three-level distinction between explanations in cognitive science, it is often accepted that focus on modeling cognitive tasks should be on the computational level rather than the algorithmic level. When it comes to mathematical problem solving, this approach suggests that the complexity of the task of solving a problem can be characterized by the computational complexity of that problem. In this paper, I argue that human cognizers use heuristic and didactic tools and thus engage in cognitive processes (...) that make their problem solving algorithms computationally suboptimal, in contrast with the optimal algorithms studied in the computational approach. Therefore, in order to accurately model the human cognitive tasks involved in mathematical problem solving, we need to expand our methodology to also include aspects relevant to the algorithmic level. This allows us to study algorithms that are cognitively optimal for human problem solvers. Since problem solving methods are not universal, I propose that they should be studied in the framework of enculturation, which can explain the expected cultural variance in the humanly optimal algorithms. While mathematical problem solving is used as the case study, the considerations in this paper concern modeling of cognitive tasks in general. (shrink)

Computer models provide formal techniques that are highly relevant to philosophical issues in epistemology, metaphysics, and ethics. Such models can help philosophers to address both descriptive issues about how people do think and normative issues about how people can think better. The use of computer models in ways similar to their scientific applications substantially extends philosophical methodology beyond the techniques of thought experiments and abstract reflection. For formal philosophy, computer models offer a much broader range of representational techniques than are (...) found in traditional logic, probability, and set theory, taking into account the important roles of imagery, analogy, and emotion in human thinking. Computer models make possible investigation of the dynamics of inference, not just abstract formal relations. (shrink)

Cognitive science has been dominated by the computational conception that cogniton is computation across representations. To the extent to which cognition is supposed to be a purposive, meaningful, algorithmic, problem-solving activity, however, computers appear to be incapable of cognition. They are devices that can facilitate computations on the basis of semantic grounding relations as special kinds of signs. Even their algorithmic, problem-solving character arises from ther interpretation by human users. Strictly speaking, computers as such–apart from human users–are not (...) only incapable of cognition but even incapable of computation, properly construed. If we want to understand the nature of thought, we are going to have to study thinking, not computing, because they are not the same thing. (shrink)