There are many branches of philosophy called “the philosophy of X,” where X = disciplines ranging from history to physics. The philosophy of artificial intelligence has a long history, and there are many courses and texts with that title. Surprisingly, the philosophy of computer science is not nearly as well-developed. This article proposes topics that might constitute the philosophy of computer science and describes a course covering those topics, along with suggested readings and assignments.

Investigations into inter-level relations in computer science, biology and psychology call for an *empirical* turn in the philosophy of mind. Rather than concentrate on *a priori* discussions of inter-level relations between 'completed' sciences, a case is made for the actual study of the way inter-level relations grow out of the developing sciences. Thus, philosophical inquiries will be made more relevant to the sciences, and, more importantly, philosophical accounts of inter-level relations will be testable by confronting them with what (...) really happens in science. Hence, close observation of the ever-changing reduction relations in the developing sciences, and revision of philosophical positions based on these empirical observations, may, in the long run, be more conducive to an adequate understanding of inter-level relations than a traditional *a priori* approach. (shrink)

The paper describes the design of an intelligent tutoring system for teaching Introduction to Computer Science-a compulsory curriculum in Al-Azhar University of Gaza to students who attend the university. The basic idea of this system is a systematic introduction into computer science. The system presents topics with examples. The system is dynamically checks student's individual progress. An initial evaluation study was done to investigate the effect of using the intelligent tutoring system on the performance of students (...) enrolled in computer science curriculum at Al-Azhar University, Gaza. The results showed a positive impact on the evaluators. (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)

While calls to integrate ethics into computer science education go back decades, recent high-profile ethical failures related to computing technology by large technology companies, governments, and academic institutions have accelerated the adoption of computer ethics education at all levels of instruction. Discussions of how to integrate ethics into existing computer science programmes often focus on the structure of the intervention—embedded modules or dedicated courses, humanists or computer scientists as ethics instructors—or on the specific content (...) to be included—lists of case studies and essential topics to cover. While proponents of computer ethics education often emphasize the importance of closely connecting ethical and technical content in these initiatives, most do not reflect in depth on the variety of ways in which the disciplines can be combined. In this paper, I deploy a framework from cross- disciplinary studies that categorizes academic projects that work across disciplines as multidisciplinary, interdisciplinary, or transdisciplinary, depending on the degree of integration. When applied to computer ethics education, this framework is orthogonal to the structure and content of the initiative, as I illustrate using examples of dedicated ethics courses and embedded modules. It therefore highlights additional features of cross-disciplinary teaching that need to be considered when planning a computer ethics programme. I argue that computer ethics education should aim to be at least interdisciplinary—multidisciplinary initiatives are less aligned with the pedagogical aims of computer ethics—and that computer ethics educators should experiment with fully transdisciplinary education that could transform computer science as a whole for the better. (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)

This paper presents folk impressions of the University of Manchester’s difficulties in becoming a great university, but by means of a fiction imitating a distinguished writer from the Indian subcontinent. The impressions concern past efforts and the difficulties they faced.

Embedding ethics modules within computer science courses has become a popular response to the growing recognition that CS programs need to better equip their students to navigate the ethical dimensions of computing technologies like AI, machine learning, and big data analytics. However, the popularity of this approach has outpaced the evidence of its positive outcomes. To help close that gap, this empirical study reports positive results from Northeastern’s program that embeds values analysis modules into CS courses. The resulting (...) data suggest that such modules have a positive effect on students’ moral attitudes and that students leave the modules believing they are more prepared to navigate the ethical dimensions they’ll likely face in their eventual careers. Importantly, these gains were accomplished at an institution without a philosophy doctoral program, suggesting this strategy can be effectively employed by a wider range of institutions than many have thought. (shrink)

In this paper I argue that whether or not a computer can be built that passes the Turing test is a central question in the philosophy of mind. Then I show that the possibility of building such a computer depends on open questions in the philosophy of computer science: the physical Church-Turing thesis and the extended Church-Turing thesis. I use the link between the issues identified in philosophy of mind and philosophy of computer science (...) to respond to a prominent argument against the possibility of building a machine that passes the Turing test. Finally, I respond to objections against the proposed link between questions in the philosophy of mind and philosophy of computer science. (shrink)

Community detection is a major issue in network analysis. This paper combines a socio-historical approach with an experimental reconstruction of programs to investigate the early automation of clique detection algorithms, which remains one of the unsolved NP-complete problems today. The research led by the archaeologist Jean-Claude Gardin from the 1950s on non-numerical information and graph analysis is retraced to demonstrate the early contributions of social sciences and humanities. The limited recognition and reception of Gardin's innovative computer application to the (...) humanities are addressed through two factors, in addition to the effects of historiography and bibliographies on the recording, discoverability, and reuse of scientific productions: (1) funding policies, evidenced by the transfer of research effort on graph applications from temporary interdisciplinary spaces to disciplinary organizations related to the then-emerging field of computer science; and (2) the erratic careers of algorithms, in which efficiency, flaws, corrections, and authors’ status, were determining factors. (shrink)

Abstract: The paper describes the design of an intelligent tutoring system for teaching Introduction to Computer Science-a compulsory curriculum in Al-Azhar University of Gaza to students who attend the university. The basic idea of this system is a systematic introduction into computer science. The system presents topics with examples. The system is dynamically checks student's individual progress. An initial evaluation study was done to investigate the effect of using the intelligent tutoring system on the performance of (...) students enrolled in computer science curriculum at Al-Azhar University, Gaza. The results showed a positive impact on the evaluators. (shrink)

The discipline of ontology has enjoyed a checkered history since 1606, with a significant expansion in recent years. We focus here on those developments in the recent history of philosophy which are most relevant to the understanding of the increased acceptance of ontology, and especially of realist ontology, as a valuable method also outside the discipline of philosophy.

ITS (Intelligent Tutoring System) is a computer software that supplies direct and adaptive training or response to students without, or with little human teacher interfering. The main target of ITS is smoothing the learning-teaching process using the ultimate technology in computer science. The proposed system will be implemented using the “ITSB” Authoring tool. The book "Introduction To Computer Science" is taught in Al-Azhar University in Gaza as a compulsory subject for students who study at humanities (...) faculties. In this thesis, the researcher demonstrates an intelligent tutoring system for teaching the above mentioned subject. The system was assessed by a group of teachers and students and the results were promising . (shrink)

(See also the separate entry for the volume itself.) This introduction has three parts. The first providing an overview of some main lines of research in deontic logic: the emergence of SDL, Chisholm's paradox and the development of dyadic deontic logics, various other puzzles/challenges and areas of development, along with philosophical applications. The second part focus on some actual and potential fruitful interactions between deontic logic, computer science and artificial intelligence. These include applications of deontic logic to AI (...) knowledge representation in legal systems, to modelling computer systems where it is expected that sub-ideal states will emerge and require countermeasures, to norm-governed human interactions with computer systems, and to the representation of some features of multi-agent systems where different agent-like computer systems interact with one another. The third and final part briefly groups and previews the papers in the anthology. (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)

We offer philosophical motivations for a method we call Virtual World Cognitive Science (VW CogSci), in which researchers use virtual embodied agents that are embedded in virtual worlds to explore questions in the field of Cognitive Science. We focus on questions about mental and linguistic representation and the ways that such computational modeling can add rigor to philosophical thought experiments, as well as the terminology used in the scientific study of such representations. We find that this method forces (...) researchers to take a god’s-eye view when describing dynamical relationships between entities in minds and entities in an environment in a way that eliminates the need for problematic talk of belief and concept types, such as the belief that cats are silly, and the concept CAT, while preserving belief and concept tokens in individual cognizers’ minds. We conclude with some further key advantages of VW CogSci for the scientific study of mental and linguistic representation and for Cognitive Science more broadly. (shrink)

I develop a theory of counterfactuals about relative computability, i.e. counterfactuals such as 'If the validity problem were algorithmically decidable, then the halting problem would also be algorithmically decidable,' which is true, and 'If the validity problem were algorithmically decidable, then arithmetical truth would also be algorithmically decidable,' which is false. These counterfactuals are counterpossibles, i.e. they have metaphysically impossible antecedents. They thus pose a challenge to the orthodoxy about counterfactuals, which would treat them as uniformly true. What’s more, I (...) argue that these counterpossibles don’t just appear in the periphery of relative computability theory but instead they play an ineliminable role in the development of the theory. Finally, I present and discuss a model theory for these counterfactuals that is a straightforward extension of the familiar comparative similarity models. (shrink)

This paper is a follow-up of the first part of the persons reply to the Chinese Room Argument. The first part claims that the mental properties of the person appearing in that argument are what matter to whether computational cognitive science is true. This paper tries to discern what those mental properties are by applying a series of hypothetical psychological and strengthened Turing tests to the person, and argues that the results support the thesis that the Man performing the (...) computations characteristic of understanding Chinese actually understands Chinese. The supposition that the Man does not understand Chinese has gone virtually unquestioned in this foundational debate. The persons reply acknowledges the intuitive power behind that supposition, but knows that brute intuitions are not epistemically sacrosanct. Like many intuitions humans have had, and later deposed, this intuition does not withstand experimental scrutiny. The second part of the persons reply consequently holds that computational cognitive science is confirmed by the Chinese Room thought experiment. (shrink)

This paper explores the challenges and innovations in optimizing data science workflows within cloud computing environments. It begins by highlighting the critical role of data science in modern industries and the pivotal contribution of cloud computing in enabling scalable and efficient data processing. The primary focus lies in identifying and analyzing the key challenges encountered in current data science workflows deployed in cloud infrastructures. These challenges include scalability issues related to handling large volumes of data, resource management (...) complexities in optimizing computational resources, cost management strategies to balance performance with expenses, and ensuring robust data security and privacy measures. The manuscript then delves into innovative solutions and techniques aimed at addressing these challenges. It discusses advancements such as workflow automation tools and frameworks that streamline repetitive tasks, containerization technologies like Docker and Kubernetes for efficient application deployment and management, and the utilization of serverless architectures to enhance scalability and reduce operational costs. Additionally, it explores the benefits of parallel processing frameworks such as Apache Spark and Hadoop in optimizing data processing tasks. The integration of machine learning algorithms for dynamic workflow optimization and effective data management strategies in cloud environments are also examined. Through detailed case studies and application examples across various domains, the manuscript illustrates the practical implementation and outcomes of these optimization strategies. Furthermore, it discusses emerging trends in cloud technologies, the role of AI-driven automation in enhancing workflow efficiencies, and ethical considerations surrounding data science operations in cloud computing. The manuscript concludes with a summary of findings, practical recommendations for organizations seeking to enhance their data science workflows in the cloud, and insights into future research directions to address evolving challenges. (shrink)

A response to a recent critique by Cem Bozşahin of the theory of syntactic semantics as it applies to Helen Keller, and some applications of the theory to the philosophy of computer science.

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)

Recent advances in neuroscience lead to a wider realm for philosophy to include the science of the Darwinian-evolved computational brain, our inner world producing organ, a non-recursive super- Turing machine combining 100B synapsing-neuron DNA-computers based on the genetic code. The whole system is a logos machine offering a world map for global context, essential for our intentional grasp of opportunities. We start from the observable contrast between the chaotic universe vs. our orderly inner world, the noumenal cosmos. So far, (...) philosophy has been rehearsing our thoughts, our human-internal world, a grand painting of the outer world, how we comprehend subjectively our experience, worked up by the logos machine, but now we seek a wider horizon, how humans understand the world thanks to Darwinian evolution to adapt in response to the metaphysical gap, the chasm between the human animal and its environment, shaping the organism so it can deal with its variable world. This new horizon embraces global context coded in neural structures that support the noumenal cosmos, our inner mental world, for us as denizens of the outer environment. Kant’s inner and outer senses are fundamental ingredients of scientific philosophy. Several sections devoted to Heidegger, his lizard debunked, but his version of the metaphysical gap & his doctrine of the logos praised. Rorty and others of the behaviorist school discussed also. (shrink)

What is philosophy of science? Numerous manuals, anthologies or essays provide carefully reconstructed vantage points on the discipline that have been gained through expert and piecemeal historical analyses. In this paper, we address the question from a complementary perspective: we target the content of one major journal of the field—Philosophy of Science—and apply unsupervised text-mining methods to its complete corpus, from its start in 1934 until 2015. By running topic-modeling algorithms over the full-text corpus, we identified 126 key (...) research topics that span across 82 years. We also tracked their evolution and fluctuating significance over time in the journal articles. Our results concur with and document known and lesser-known episodes of the philosophy of science, including the rise and fall of logic and language-related topics, the relative stability of a metaphysical and ontological questioning (space and time, causation, natural kinds, realism), the significance of epistemological issues about the nature of scientific knowledge as well as the rise of a recent philosophy of biology and other trends. These analyses exemplify how computational text-mining methods can be used to provide an empirical large-scale and data-driven perspective on the history of philosophy of science that is complementary to other current historical approaches. (shrink)

Mathematical models are a well established tool in most natural sciences. Although models have been neglected by the philosophy of science for a long time, their epistemological status as a link between theory and reality is now fairly well understood. However, regarding the epistemological status of mathematical models in the social sciences, there still exists a considerable unclarity. In my paper I argue that this results from specific challenges that mathematical models and especially computer simulations face in the (...) social sciences. The most important difference between the social sciences and the natural sciences with respect to modeling is that in the social sciences powerful and well confirmed background theories (like Newtonian mechanics, quantum mechanics or the theory of relativity in physics) do not exist in the social sciences. Therefore, an epistemology of models that is formed on the role model of physics may not be appropriate for the social sciences. I discuss the challenges that modeling faces in the social sciences and point out their epistemological consequences. The most important consequences are that greater emphasis must be placed on empirical validation than on theoretical validation and that the relevance of purely theoretical simulations is strongly limited. (shrink)

Climatology is a paradigmatic complex systems science. Understanding the global climate involves tackling problems in physics, chemistry, economics, and many other disciplines. I argue that complex systems like the global climate are characterized by certain dynamical features that explain how those systems change over time. A complex system's dynamics are shaped by the interaction of many different components operating at many different temporal and spatial scales. Examining the multidisciplinary and holistic methods of climatology can help us better understand the (...) nature of complex systems in general. -/- Questions surrounding climate science can be divided into three rough categories: foundational, methodological, and evaluative questions. "How do we know that we can trust science?" is a paradigmatic foundational question (and a surprisingly difficult one to answer). Because the global climate is so complex, questions like "what makes a system complex?" also fall into this category. There are a number of existing definitions of `complexity,' and while all of them capture some aspects of what makes intuitively complex systems distinctive, none is entirely satisfactory. Most existing accounts of complexity have been developed to work with information-theoretic objects (signals, for instance) rather than the physical and social systems studied by scientists. -/- Dynamical complexity, a concept articulated in detail in the first third of the dissertation, is designed to bridge the gap between the mathematics of contemporary complexity theory (in particular the formalism of "effective complexity" developed by Gell-Mann and Lloyd [2003]) and a more general account of the structure of science generally. Dynamical complexity provides a physical interpretation of the formal tools of mathematical complexity theory, and thus can be used as a framework for thinking about general problems in the philosophy of science, including theories, explanation, and lawhood. -/- Methodological questions include questions about how climate science constructs its models, on what basis we trust those models, and how we might improve those models. In order to answer questions about climate modeling, it's important to understand what climate models look like and how they are constructed. Climate model families are significantly more diverse than are the model families of most other sciences (even sciences that study other complex systems). Existing climate models range from basic models that can be solved on paper to staggeringly complicated models that can only be analyzed using the most advanced supercomputers in the world. I introduce some of the central concepts in climatology by demonstrating how one of the most basic climate models might be constructed. I begin with the assumption that the Earth is a simple featureless blackbody which receives energy from the sun and releases it into space, and show how to model that assumption formally. I then gradually add other factors (e.g. albedo and the greenhouse effect) to the model, and show how each addition brings the model's prediction closer to agreement with observation. After constructing this basic model, I describe the so-called "complexity hierarchy" of the rest of climate models, and argue that the sense of "complexity" used in the climate modeling community is related to dynamical complexity. -/- With a clear understanding of the basics of climate modeling in hand, I then argue that foundational issues discussed early in the dissertation suggest that computation plays an irrevocably central role in climate modeling. "Science by simulation" is essential given the complexity of the global climate, but features of the climate system--the presence of non-linearities, feedback loops, and chaotic dynamics--put principled limits on the effectiveness of computational models. This tension is at the root of the staggering pluralism of the climate model hierarchy, and suggests that such pluralism is here to stay, rather than an artifact of our ignorance. Rather than attempting to converge on a single "best fit" climate model, we ought to embrace the diversity of climate models, and view each as a specialized tool designed to predict and explain a rather narrow range of phenomena. Understanding the climate system as a whole requires examining a number of different models, and correlating their outputs. This is the most significant methodological challenge of climatology. -/- Climatology's role contemporary political discourse raises an unusually high number of evaluative questions for a physical science. The two leading approaches to crafting policy surrounding climate change center on mitigation (i.e. stopping the changes from occurring) and adaptation (making post hoc changes to ameliorate the harm caused by those changes). Crafting an effective socio-political response to the threat of anthropogenic climate change, however, requires us to integrate multiple perspectives and values: the proper response will be just as diverse and pluralistic as the climate models themselves, and will incorporate aspects of both approaches. I conclude by offering some concrete recommendations about how to integrate this value pluralism into our socio-political decision making framework. (shrink)

Marr’s seminal distinction between computational, algorithmic, and implementational levels of analysis has inspired research in cognitive science for more than 30 years. According to a widely-used paradigm, the modelling of cognitive processes should mainly operate on the computational level and be targeted at the idealised competence, rather than the actual performance of cognisers in a specific domain. In this paper, we explore how this paradigm can be adopted and revised to understand mathematical problem solving. The computational-level approach applies methods (...) from computational complexity theory and focuses on optimal strategies for completing cognitive tasks. However, human cognitive capacities in mathematical problem solving are essentially characterised by processes that are computationally sub-optimal, because they initially add to the computational complexity of the solutions. Yet, these solutions can be optimal for human cognisers given the acquisition and enactment of mathematical practices. Here we present diagrams and the spatial manipulation of symbols as two examples of problem solving strategies that can be computationally sub-optimal but humanly optimal. These aspects need to be taken into account when analysing competence in mathematical problem solving. Empirically informed considerations on enculturation can help identify, explore, and model the cognitive processes involved in problem solving tasks. The enculturation account of mathematical problem solving strongly suggests that computational-level analyses need to be complemented by considerations on the algorithmic and implementational levels. The emerging research strategy can help develop algorithms that model what we call enculturated cognitive optimality in an empirically plausible and ecologically valid way. (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)

The classical theory of computation does not represent an adequate model of reality for simulation in the social sciences. The aim of this paper is to construct a methodological perspective that is able to conciliate the formal and empirical logic of program verification in computer science, with the interpretative and multiparadigmatic logic of the social sciences. We attempt to evaluate whether social simulation implies an additional perspective about the way one can understand the concepts of program and computation. (...) We demonstrate that the logic of social simulation implies at least two distinct types of program verifications that reflect an epistemological distinction in the kind of knowledge one can have about programs. Computer programs seem to possess a causal capability (Fetzer, 1999) and an intentional capability that scientific theories seem not to possess. This distinction is associated with two types of program verification, which we call empirical and intentional verification. We demonstrate, by this means, that computational phenomena are also intentional phenomena, and that such is particularly manifest in agent-based social simulation. Ascertaining the credibility of results in social simulation requires a focus on the identification of a new category of knowledge we can have about computer programs. This knowledge should be considered an outcome of an experimental exercise, albeit not empirical, acquired within a context of limited consensus. The perspective of intentional computation seems to be the only one possible to reflect the multiparadigmatic character of social science in terms of agent-based computational social science. We contribute, additionally, to the clarification of several questions that are found in the methodological perspectives of the discipline, such as the computational nature, the logic of program scalability, and the multiparadigmatic character of agent-based simulation in the social sciences. (shrink)

It is argued that logic, and in particular mathematical logic, should play a key role in the undergraduate curriculum for students in the computing fields, which include electrical engineering (EE), computer engineering (CE), and computer science (CS). This is based on 1) the history of the field of computing and its close ties with logic, 2) empirical results showing that students with better logical thinking skills perform better in tasks such as programming and mathematics, and 3) the (...) skills students are expected to have in the job market. Further, the authors believe teaching logic to students explicitly will improve student retention, especially involving underrepresented minorities. Though this work focuses specifically on the computing fields, these results demonstrate the importance of logic education to STEM (science, technology, engineering, and mathematics) as a whole. (shrink)

Natural Computing is a field of research in Computer Science aimed at reinterpreting biological phenomena as computing mechanisms. This allows unconventional computing architectures to be proposed in which computations are performed by atoms, DNA strands, cells, insects or other biological elements. Membrane Computing is a branch of Natural Computing in which biological phenomena of interest are related with interactions between molecules inside cells. The research in Membrane Computing has lead to very important theoretical results that show how, in (...) principle, cells could be used to solve any (computable) computational problem with performances that cannot be obtained by conventional computers. However, the implementation of a cell-based computational architecture seems not easily achievable. On the other hand, models of Membrane Computing have found an alternative application to the description of biological systems, with the aim of developing simulators and other analysis tools for the study of biological problems. (shrink)

This work addresses a broad range of questions which belong to four fields: computation theory, general philosophy of science, philosophy of cognitive science, and philosophy of mind. Dynamical system theory provides the framework for a unified treatment of these questions. ;The main goal of this dissertation is to propose a new view of the aims and methods of cognitive science--the dynamical approach . According to this view, the object of cognitive science is a particular set of (...) dynamical systems, which I call "cognitive systems". The goal of a cognitive study is to specify a dynamical model of a cognitive system, and then use this model to produce a detailed account of the specific cognitive abilities of that system. The dynamical approach does not limit a-priori the form of the dynamical models which cognitive science may consider. In particular, this approach is compatible with both computational and connectionist modeling, for both computational systems and connectionist networks are special types of dynamical systems. ;To substantiate these methodological claims about cognitive science, I deal first with two questions in two different fields: What is a computational system? What is a dynamical explanation of a deterministic process? ;Intuitively, a computational system is a deterministic system which evolves in discrete time steps, and which can be described in an effective way. In chapter 1, I give a formal definition of this concept which employs the notions of isomorphism between dynamical systems, and of Turing computable function. In chapter 2, I propose a more comprehensive analysis which is based on a natural generalization of the concept of Turing machine. ;The goal of chapter 3 is to develop a theory of the dynamical explanation of a deterministic process. By a "dynamical explanation" I mean the specification of a dynamical model of the system or process which we want to explain. I start from the analysis of a specific type of explanandum--dynamical phenomena--and I then use this analysis to shed light on the general form of a dynamical explanation. Finally, I analyze the structure of those theories which generate explanations of this form, namely dynamical theories. (shrink)

In what follows I propose to bring out certain methodological properties of projects of modelling the tacit realm that bear on the kinds of modelling done in connection with scientific cognition by computer as well as by ethnomethodological sociologists, both of whom must make some claims about the tacit in the course of their efforts to model cognition. The same issues, I will suggest, bear on the project of a cognitive psychology of science as well.

According to some critics, if biology is a kind of reverse engineering for the nature, it is quite poorly prepared for the task. Thus, the issue is more likely with its ontology. Multiple hypotheses and conjectures found in papers on methodological issues claim that living systems should be viewed as complex networks of signal-transmitting paths, both neural and non-neural, that feature modularity and feedback circuits and are prone to emergent properties and increasing complexity. If so, we are on the eve (...) of a new stage in computer models development where not only computers are used to emulate life, but life itself is construed as a complex network of interacting natural computers. (shrink)

What structure of scientific communication and cooperation, between what kinds of investigators, is best positioned to lead us to the truth? Against an outline of standard philosophical characteristics and a recent turn to social epistemology, this paper surveys highlights within two strands of computational philosophy of science that attempt to work toward an answer to this question. Both strands emerge from abstract rational choice theory and the analytic tradition in philosophy of science rather than postmodern sociology of (...) class='Hi'>science. The first strand of computational research models the effect of communicative networks within groups, with conclusions regarding the potential benefit of limited communication. The second strand models the potential benefits of cognitive diversity within groups. Examples from each strand of research are used in analyzing what makes modeling of this sort both promising and distinctly philosophical, but are also used to emphasize possibilities for failure and inherent limitations as well. (shrink)

In computational complexity theory, decision problems are divided into complexity classes based on the amount of computational resources it takes for algorithms to solve them. In theoretical computer science, it is commonly accepted that only functions for solving problems in the complexity class P, solvable by a deterministic Turing machine in polynomial time, are considered to be tractable. In cognitive science and philosophy, this tractability result has been used to argue that only functions in P can feasibly (...) work as computational models of human cognitive capacities. One interesting area of computational complexity theory is descriptive complexity, which connects the expressive strength of systems of logic with the computational complexity classes. In descriptive complexity theory, it is established that only first-order (classical) systems are connected to P, or one of its subclasses. Consequently, second-order systems of logic are considered to be computationally intractable, and may therefore seem to be unfit to model human cognitive capacities. This would be problematic when we think of the role of logic as the foundations of mathematics. In order to express many important mathematical concepts and systematically prove theorems involving them, we need to have a system of logic stronger than classical first-order logic. But if such a system is considered to be intractable, it means that the logical foundation of mathematics can be prohibitively complex for human cognition. In this paper I will argue, however, that this problem is the result of an unjustified direct use of computational complexity classes in cognitive modelling. Placing my account in the recent literature on the topic, I argue that the problem can be solved by considering computational complexity for humanly relevant problem solving algorithms and input sizes. (shrink)

Computer and information ethics, as well as other fields of applied ethics, need ethical theories which coherently unify deontological and consequentialist aspects of ethical analysis. The proposed theory of just consequentialism emphasizes consequences of policies within the constraints of justice. This makes just consequentialism a practical and theoretically sound approach to ethical problems of computer and information ethics.

Computing and information, and their philosophy in the broad sense, play a most important scientific, technological and conceptual role in our world. This book collects together, for the first time, the views and experiences of some of the visionary pioneers and most influential thinkers in such a fundamental area of our intellectual development.

Traditionally, computational theory (CT) and dynamical systems theory (DST) have presented themselves as opposed and incompatible paradigms in cognitive science. There have been some efforts to reconcile these paradigms, mainly, by assimilating DST to CT at the expenses of its anti-representationalist commitments. In this paper, building on Piccinini’s mechanistic account of computation and the notion of functional closure, we explore an alternative conciliatory strategy. We try to assimilate CT to DST by dropping its representationalist commitments, and by inviting CT (...) to recognize the functionally closed nature of some computational systems. (shrink)

For centuries, philosophers studying the great mysteries of human subjectivity have focused on the mind/body problem and the difference between human beings and animals. Now a new ontological question takes center stage: to what extent can a manufactured object (a computer) exhibit qualities of mind? There have been passionate exchanges between those who believe that a "manufactured mind" is possible and those who believe that mind cannot exist except as a living, socially situated, embodied person. As with earlier arguments, (...) this one shows no sign of being resolved. But the fight over computationalism (the belief that all mental processes can be generated by computer programs) has immediate, "hard" consequences for technological research and development, social- and cognitive-science methodology, and for our everyday experience of the world and ourselves. This special issue consists of papers presented at a conference of the same title held at the Center for Cognitive Science at the University at Buffalo, 22-23 May 1990. The authors come from a variety of disciplinary backgrounds (Computer Science, Linguistics, Philosophy, Psychology, Sociology, etc.), and bring a wide variety of perspectives to the topic. (shrink)

This review essay analyzes the book by Giuseppe Primiero, On the foundations of computing. Oxford: Oxford University Press (ISBN 978-0-19-883564-6/hbk; 978-0-19-883565-3/pbk). xix, 296 p. (2020). It gives a critical view from the perspective of physical computing as a foundation of computing and argues that the neglected pillar of material computation (Stepney) should be brought centerstage and computing recognized as the fourth great domain of science (Denning).

This dissertation examines aspects of the interplay between computing and scientific practice. The appropriate foundational framework for such an endeavour is rather real computability than the classical computability theory. This is so because physical sciences, engineering, and applied mathematics mostly employ functions defined in continuous domains. But, contrary to the case of computation over natural numbers, there is no universally accepted framework for real computation; rather, there are two incompatible approaches --computable analysis and BSS model--, both claiming to formalise algorithmic (...) computation and to offer foundations for scientific computing. -/- The dissertation consists of three parts. In the first part, we examine what notion of 'algorithmic computation' underlies each approach and how it is respectively formalised. It is argued that the very existence of the two rival frameworks indicates that 'algorithm' is not one unique concept in mathematics, but it is used in more than one way. We test this hypothesis for consistency with mathematical practice as well as with key foundational works that aim to define the term. As a result, new connections between certain subfields of mathematics and computer science are drawn, and a distinction between 'algorithms' and 'effective procedures' is proposed. -/- In the second part, we focus on the second goal of the two rival approaches to real computation; namely, to provide foundations for scientific computing. We examine both frameworks in detail, what idealisations they employ, and how they relate to floating-point arithmetic systems used in real computers. We explore limitations and advantages of both frameworks, and answer questions about which one is preferable for computational modelling and which one for addressing general computability issues. -/- In the third part, analog computing and its relation to analogue (physical) modelling in science are investigated. Based on some paradigmatic cases of the former, a certain view about the nature of computation is defended, and the indispensable role of representation in it is emphasized and accounted for. We also propose a novel account of the distinction between analog and digital computation and, based on it, we compare analog computational modelling to physical modelling. It is concluded that the two practices, despite their apparent similarities, are orthogonal. (shrink)

There is an ongoing debate on whether or to what degree computer simulations can be likened to experiments. Many philosophers are sceptical whether a strict separation between the two categories is possible and deny that the materiality of experiments makes a difference (Morrison 2009, Parker 2009, Winsberg 2010). Some also like to describe computer simulations as a “third way” between experimental and theoretical research (Rohrlich 1990, Axelrod 2003, Kueppers/Lenhard 2005). In this article I defend the view that (...) class='Hi'>computer simulations are not experiments but that they are tools for evaluating the consequences of theories and theoretical assumptions. In order to do so the (alleged) similarities and differences between simulations and experiments are examined. It is found that three fundamental differences between simulations and experiments remain: 1) Only experiments can generate new empirical data. 2) Only Experiments can operate directly on the target system. 3) Experiments alone can be employed for testing fundamental hypotheses. As a consequence, experiments enjoy a distinct epistemic role in science that cannot completely be superseded by computer simulations. This finding in connection with a discussion of border cases such as hybrid methods that combine measurement with simulation shows that computer simulations can clearly be distinguished from empirical methods. It is important to understand that computer simulations are not experiments, because otherwise there is a danger of systematically underestimating the need for empirical validation of simulations. (shrink)

In an attempt to determine the epistemic status of computer simulation results, philosophers of science have recently explored the similarities and differences between computer simulations and experiments. One question that arises is whether and, if so, when, simulation results constitute novel empirical data. It is often supposed that computer simulation results could never be empirical or novel because simulations never interact with their targets, and cannot go beyond their programming. This paper argues against this position by (...) examining whether, and under what conditions, the features of empiricality and novelty could be displayed by computer simulation data. I show that, to the extent that certain familiar measurement results have these features, so can some computer simulation results. (shrink)

In this paper a possible general framework for the representation of concepts in cognitive artificial systems and cognitive architectures is proposed. The framework is inspired by the so called proxytype theory of concepts and combines it with the heterogeneity approach to concept representations, according to which concepts do not constitute a unitary phenomenon. The contribution of the paper is twofold: on one hand, it aims at providing a novel theoretical hypothesis for the debate about concepts in cognitive sciences by providing (...) unexplored connections between different theories; on the other hand it is aimed at sketching a computational characterization of the problem of concept representation in cognitively inspired artificial systems and in cognitive architectures. (shrink)

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory (...) to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year. (shrink)

Computational modeling should play a central role in philosophy. In this introduction to our topical collection, we propose a small topology of computational modeling in philosophy in general, and show how the various contributions to our topical collection fit into this overall picture. On this basis, we describe some of the ways in which computational models from other disciplines have found their way into philosophy, and how the principles one found here still underlie current trends in the field. Moreover, we (...) argue that philosophers contribute to computational modeling not only by building their own models, but also by thinking about the various applications of the method in philosophy and the sciences. In this context, we note that models in philosophy are usually simple, while models in the sciences are often more complex and empirically grounded. Bridging certain methodological gaps that arise from this discrepancy may prove to be challenging and fruitful for the further development of computational modeling in philosophy and beyond. (shrink)