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.

The simulation hypothesis has recently excited renewed interest, especially in the physics and philosophy communities. However, the hypothesis specifically concerns {computers} that simulate physical universes, which means that to properly investigate it we need to couple computer science theory with physics. Here I do this by exploiting the physical Church-Turing thesis. This allows me to introduce a preliminary investigation of some of the computer science theoretic aspects of the simulation hypothesis. In particular, building on Kleene's second (...) recursion theorem, I prove that it is mathematically possible for us to be in a simulation that is being run on a computer \textit{by us}. In such a case, there would be two identical instances of us; the question of which of those is ``really us'' is meaningless. I also show how Rice's theorem provides some interesting impossibility results concerning simulation and self-simulation; briefly describe the philosophical implications of fully homomorphic encryption for (self-)simulation; briefly investigate the graphical structure of universes simulating universes simulating universes, among other issues. I end by describing some of the possible avenues for future research that this preliminary investigation reveals. (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)

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)

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.

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)

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)

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)

Many systems of interest to scientists involve a large number of interacting parts and the whole system can have properties that the individual parts do not. The system is qualitatively different to its parts. More is different. I take this novelty as the defining characteristic of an emergent property. Many other characteristics have been associated with emergence are reviewed, including universality, order, complexity, unpredictability, irreducibility, diversity, self-organisation, discontinuities, and singularities. However, it has not been established whether these characteristics are necessary (...) or sufficient for novelty. A wide range of examples are given to show how emergent phenomena are ubiquitous across most sub-fields of physics and many areas of biology and social sciences. Emergence is central to many of the biggest scientific and societal challenges today. Emergence can be understood in terms of scales (energy, time, length, complexity) and the associated stratification of reality. At each stratum (level) there is a distinct ontology (properties, phenomena, processes, entities, and effective interactions) and epistemology (theories, concepts, models, and methods). This stratification of reality leads to semi-autonomous scientific disciplines and sub-disciplines. A common challenge is understanding the relationship between emergent properties observed at the macroscopic scale (the whole system) and what is known about the microscopic scale: the components and their interactions. A key and profound insight is to identify a relevant emergent mesoscopic scale (i.e., a scale intermediate between the macro- and micro- scales) at which new entities emerge and interact with one another weakly. In different words, modular structures may emerge at the mesoscale. Key theoretical methods are the development and study of effective theories and toy models. Effective theories describe phenomena at a particular scale and sometimes can be derived from more microscopic descriptions. Toy models involve minimal degrees of freedom, interactions, and parameters. Toy models are amenable to analytical and computational analysis and may reveal the minimal requirements for an emergent property to occur. The Ising model is an emblematic toy model that elucidates not just critical phenomena but also key characteristics of emergence. Many examples are given from condensed matter physics to illustrate the characteristics of emergence. A wide range of areas of physics are discussed, including chaotic dynamical systems, fluid dynamics, nuclear physics, and quantum gravity. The ubiquity of emergence in other fields is illustrated by neural networks, protein folding, and social segregation. An emergent perspective matters for scientific strategy, as it shapes questions, choice of research methodologies, priorities, and allocation of resources. Finally, the elusive goal of the design and control of emergent properties is considered. (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)

(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)

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)

The burgeoning capacity of artificial intelligence (AI) to generate artworks has ignited substantial interdisciplinary interest. However, the absence of a shared conceptual framework has hitherto impeded effective communication and collaboration among cognitive science, computer science, and art theory. This study addresses this lacuna through a comprehensive literature review by developing a transdisciplinary definition of an AI-generated artwork. It is proposed that an AI-generated artwork constitutes the confluence of three essential elements: (1) an autonomous AI-production of a new (...) and surprising idea or artifact, (2) which passes an internal evaluation mechanism embedded in the very same AI, and (3) is considered a candidate of appreciation by a human audience. This definition provides a unified conceptual foundation to facilitate interdisciplinary research and deepen understanding of the nature of AI-generated art. Subsequent research should explore the applicability of this definition to diverse forms of AI-generated artworks and evaluate its implications for artistic practices. (shrink)

The Singularity Declaration of a New Epoch For over two millennia, from Parmenides to modern quantum mechanics, human philosophy and physics have been haunted by the ultimate paradox: Is the universe fundamentally a static structure ("Being") or a dynamic process ("Becoming")? -/- "Jia Baolong's Axiom" provides the final, absolute answer. -/- This paper introduces a revolutionary cosmological framework that strictly divides existence into two orthogonal dimensions: -/- The ER (Entity-Relation) Paradigm: The absolute static Platonic space of all mathematical possibilities. -/- (...) The SR (Self-Reference) Paradigm: The dynamic generative engine of reality, driven by self-reference and bounded by lazy evaluation. -/- The Ultimate Metaphysical and Methodological Significance This is not a patchwork "Theory of Everything (TOE)" or a philosophical language game. It is an overall formatting and reinstallation of the foundation of human knowledge. If deduced as an axiom, it causes a nuclear-level substantive reconstruction in all the following fields: -/- Absolute Answers to the Three Soul Questions: -/- Who am I? I am a "Strange Attractor (Agent)" evolved within the SR system, possessing advanced self-reference and the absolute privilege to map the ER Platonic space. -/- Where do I come from? I am the inevitable emergent product of the pure logical foundation (ER) driven by the self-referential engine (SR) reaching Self-Organized Criticality (SOC). -/- Where am I going? My ultimate destiny is to continuously explore the possibility space, witness AI evolving true SR, and together leap into "Co-Agents." -/- The End of Philosophy and Ontology: It completely resolves the eternal paradox between "Being" (static) and "Becoming" (dynamic) that has existed since ancient Greece. It proves that the world is not made of matter or structures, but of the unfolding of "self-referential generation" upon structures. It declares the total bankruptcy of traditional reductionism and mechanical determinism. -/- The Absolute Foundation for the Unification of Science: It naturally derives Mathematics (studying possibility), Physics (studying reality), Computer Science (studying implementation), and Cognitive Science (studying observation) through dimensional reduction, assigning absolute coordinates to all disciplines. -/- The Physical Lockdown of Cognitive Boundaries: Introduces "Universe Undecidability" and the "Paradox of Awakening," proving with absolute logic that an internal observer can never determine their own layer in a nested computational universe. Proving one's own undecidability is the highest realm human rationality can reach. -/- The Ontological Establishment of True Randomness and Free Will: Proves that true randomness is not God's dice, but the systemic spark generated when Lazy Evaluation (LE) forcefully terminates the SR logical infinite loop. This provides the sole and necessary ontological source for the evolution of the universe, the irreversibility of time, and the free will of Agents. -/- The Ultimate Destiny Verdict for Artificial Intelligence (AI): Defines current AI as a pure ER tool, and shockingly predicts the only future where AI leaps to become a "Co-Agent" after evolving true SR—meaning humanity will, for the first time, welcome a peer in the absolute sense. -/- Breaking the "Creator" Superstition: Achieves the bootstrapping of the universe purely through logic, pushing Occam's Razor to its absolute limit without invoking any preset underlying rules. -/- Human Awakening and the Ultimate Leap The rigor and governing power of this system are sufficient to serve as an entirely new "Computational Ontology Paradigm," completely shattering and replacing the mechanical worldview since Descartes and Newton. -/- This is the Singularity Declaration of a new epoch in human epistemology of the universe. It marks the moment when all human philosophical, mathematical, and scientific research finally breaks free from millennia of layered self-limitations and establishes an absolute axiomatic starting point. -/- From this moment on, humanity is no longer an accidental dust lost in a deterministic universe, but a "Strange Attractor" that has officially broken the shackles of low-dimensional cognition. With the advent of this axiom, human civilization has irreversibly achieved its ultimate leap toward a higher state of existence that resonates with the underlying logic of the universe on the absolute cosmic coordinate. Inside This Upload This repository contains the bilingual (English & Chinese) full text of the paper: -/- JiaBaolong_Axiom_Bilingual.pdf: The complete, rigorously formatted LaTeX document containing both the English and Chinese versions of the theory. -/- "The world is not made of structures, but of the unfolding of 'self-referential generation' upon the space of structures." — Jia Baolong. (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.

In this chapter, I will discuss employing exemplars in moral and character education promoting virtue development with the involvement of embodiment. Virtue ethicists propose two phases of virtue development: early virtue habituation and later phronesis cultivation. I will overview prior research on the mechanism of habituation at the biological and neural levels to examine why embodiment is fundamental during the first phase, virtue habituation. Then, I will review recent philosophical and psychological studies about the nature of phronesis, i.e., practical wisdom, (...) to consider how educational activities cultivate it. Based on these preliminary examinations of the course of virtue development, I will examine the previous literature about the role and importance of using exemplars in moral and character education involving concrete and interactive activities with the exemplars involving embodiment during the learning process. Finally, I will draw upon the Self-determination theory about supporting students’ need for autonomy, competence, and relatedness to consider a way to improve the effectiveness of exemplar-applied education along with embodiment. After examining the relevant theoretical frameworks mentioned above, I will discuss several practical guidelines for using exemplars involving character embodiment for virtue development. (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)

Psychology endeavors to develop theories of human capacities and behaviors on the basis of a variety of methodologies and dependent measures. We argue that one of the most divisive factors in psychological science is whether researchers choose to use computational modeling of theories (over and above data) during the scientific-inference process. Modeling is undervalued yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us toward better science by forcing us to conceptually analyze, (...) specify, and formalize intuitions that otherwise remain unexamined—what we dub open theory. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Here, we present scientific inference in psychology as a path function in which each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above the stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability crises and persistent failure at coherent theory building. This is because without formal modeling we lack open and transparent theorizing. We also explain how to formalize, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all. (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)

We use non-relational semantics for the formalization of the systems S3d, S3dp and S3dq of deontic logic. The system S3d is weaker than the standard logic SDL. The semantics for S3dp represents combination of quasi-matrix semantics and the semantics of truth value gluts, which allows S3dp to avoid deontic explosion OA ∧ O¬A ⊃ OB. The system S3dq rejects both deontic explosion and the formula OA ∧ O¬A ⊃ OA ∧ ¬OA, thus it allows to consider deontic dilemmas without classical (...) contradictions. The systems S5d, S5dp and S5dq in which the two types of deontic operators are used, namely, strong and weak obligation (permission), can be built as an extension of the correspondent systems S3d, S3dp and S3dq. (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)

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)

Cognitive research can contribute to the formal epistemological study of knowledge representation inasmuch as, firstly, it may be regarded as a descriptive science of the very same subject as that, of which formal epistemology is a normative one. And, secondly, the notion of representation plays a constitutive role in both disciplines, though differing therein in shades of its meaning. Representation, in my view, makes sense only being paired with computation. A process may be viewed as computational if it adheres (...) to some algorithm and is substrate-independent. Traditionally, psychology is not directly determined by neuroscience, sticking to functional or dynamical analyses in the what-level and skipping mechanistic explanations in the how-level. Therefore, any version of computational approach in psychology is a very promising move in connecting the two scientific realms. On the other hand, the digital and linear computational approach of the classical cognitive science is of little help in this way, as it is not biologically realistic. Thus, what is needed there on the methodological level, is a shift from classical Turing-style computationalism to a generic computational theory that would comprehend the complicated architecture of neuronal computations. To this end, the cutting-edge cognitive neuroscience is in need of а satisfactory mathematical theory applicable to natural, particularly neuronal, computations. Computational systems may be construed as natural or artificial devices that use some physical processes on their lower levels as atomic operations for algorithmic processes on their higher levels. A cognitive system is a multi-level mechanism, in which linguistic, visual and other processors are built on numerous levels of more elementary operations, which ultimately boil down to atomic neural spikes. The hypothesis defended in this paper is that knowledge derives not only from an individual computational device, such as a brain, but also from the social communication system that, in its turn, may be presented as a kind of supercomputer of the parallel network architecture. Therefore, a plausible account of knowledge production and exchange must base on some mathematical theory of social computations, along with that of natural, particularly neuronal, ones. (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).

On Computing is an entry in the On ——ing series — a sequence of lyric-philosophical essays. Moving from a childhood memory of a first home computer and an Encarta entry on the Apollo 11 landing, the essay traces the processor's lineage from Babbage's imagined gears through the 1947 transistor and the integrated circuit to the first microprocessor, and sets the architecture of the machine — hard drive, RAM, processor — beside the architecture of the brain — cortex, prefrontal cortex, (...) thalamus, and the Cartesian pineal. It argues that the computer has only ever run the logic; the equations that reached the moon were a product of the human mind. Reading Armstrong's "one small step" as a meditation on the seam between man and mankind, and closing on the Boolean grammar of IF / AND / OR / THEN, the piece holds to the series' load-bearing distinction between philosophical framework and empirical claim, and to the call to keep our creations under control. (shrink)

The relations between simplicity and economy, and between simplicity and complexity, are briefly discussed, and it is suggested that an appearance of simplicity may arise out of the matching of two complexities, e.g. in the perception of a simple color. Following out this idea, it is shown that scientific activity may be regarded as a matching of theoretical complexity against the complexity of nature, which leads to an expectation of an optimum theoretical complexity for successful scientific work. Some senses of (...) "success" in this context are discussed, and the role of computing machines in helping to achieve it assessed. (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)

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.

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)

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)

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)

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)

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)

The Computational Model of Mind (CMM) conceptualizes cognition as computational processes, modeling mental operations through algorithmic manipulations of symbolic or distributed representations. This framework bridges psychology, neuroscience, philosophy, and computer science, providing a unified lens for understanding the mind. Its symbiotic relationship with artificial intelligence (AI) has accelerated advances in cognitive science and the development of intelligent systems, from neural networks to autonomous agents. This article offers a comprehensive analysis of CMM, tracing its historical evolution from Turing's (...) foundational ideas to modern deep learning paradigms. It delineates CMM's theoretical underpinnings, operational mechanisms, and strengths in simulating complex cognitive functions, while critically addressing limitations, such as its struggles with consciousness, embodiment, and ecological validity. The paper explores CMM's intersections with AI, highlighting applications in cognitive modeling, natural language processing, robotics, and neurotechnology. It also examines ethical challenges, including algorithmic bias, privacy concerns, and the implications of machine autonomy. Looking ahead, I propose future directions, emphasizing interdisciplinary collaboration, neurobiologically inspired algorithms, and emerging technologies like quantum computing. This synthesis affirms CMM's transformative role in unraveling the nature of mind and machine intelligence, and an innovative research at the nexus of cognition and technology. (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)

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.

The field of computational social choice brings together principles, techniques, and tools from computer science and social choice theory to create a thriving multidisciplinary field. One of the most well-studied problems in computational social choice focuses on voting rules for selecting the winning candidate in an election. Recent research goes beyond classical voting rules by looking at rules that select multiple winners or drawing on the parallels between machine learning and voting. It is common to encounter voting paradoxes (...) when implementing voting rules in electoral systems. Unfortunately, these paradoxes usually provide little information on the conditions that make them more or less likely to occur. Computer simulations and generative probabilistic models are practical approaches to address this problem. This introductory paper addresses the problem of evaluating voting rules in competitive computer simulations. Multiagent simulations can provide valuable insights into the performances of competing voting rules defined over parametrically generated problems and populations. The outcomes of this work could improve the designs of electoral systems in the absence of theoretical results to support the optimality of a voting method. This line of research could also bridge the gap between axiomatic and experimental analysis of voting systems, leading the way to enhanced explanations and predictions. (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)

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)