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

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

The article deals with Russian scientific classifications (GRNTI, VAK) of computer science in comparison with Western scien­tific classifications Fields of Science and Technology (FOS) and Universal Decimal Classification (UDS). The author analyzes the basics and principles of these classifications, identifies their strong and weak points as well as their influence on the devel­opment of computer sciences. She also provides some recom­mendations on adjustments of Russian scientific classifications aiming to make them more flexible and adaptive to the (...) faster scientific and technological development and interdisciplinary cooperation. (shrink)

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

Computational methods such as computer simulations, Monte Carlo methods, and agent-based modeling have become the dominant techniques in many areas of science. Extending Ourselves contains the first systematic philosophical account of these new methods, and how they require a different approach to scientific method. Paul Humphreys draws a parallel between the ways in which such computational methods have enhanced our abilities to mathematically model the world, and the more familiar ways in which scientific instruments have expanded our access (...) to the empirical world. This expansion forms the basis for a new kind of empiricism, better suited to the needs of science than the older anthropocentric forms of empiricism. Human abilities are no longer the ultimate standard of epistemological correctness.Humphreys also includes arguments for the primacy of properties rather than objects, the need to consider technological constraints when appraising scientific methods, and a detailed account of how the path from computational template to scientific application is constructed. This last feature allows us to hold a form of selective realism in which anti-realist arguments based on formal reconstructions of theories can be avoided. One important consequence of the rise of computational methods is that the traditional organization of the sciences is being replaced by an organization founded on computational templates.Extending Ourselves will be of interest to philosophers of science, epistemologists, and to anyone interested in the role played by computers in modern science. (shrink)

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

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)

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.

This volume presents the proceedings of the workshop CSL '91 (Computer Science Logic) held at the University of Berne, Switzerland, October 7-11, 1991. This was the fifth in a series of annual workshops on computer sciencelogic (the first four are recorded in LNCS volumes 329, 385, 440, and 533). The volume contains 33 invited and selected papers on a variety of logical topics in computer science, including abstract datatypes, bounded theories, complexity results, cut elimination, denotational (...) semantics, infinitary queries, Kleene algebra with recursion, minimal proofs, normal forms in infinite-valued logic, ordinal processes, persistent Petri nets, plausibility logic, program synthesis systems, quantifier hierarchies, semantics of modularization, stable logic, term rewriting systems, termination of logic programs, transitive closure logic, variants of resolution, and many others. (shrink)

I critically review Raymond Turner’s Computational Artifacts – Towards a Philosophy of Computer Science by placing beside his position a rather different one, according to which computer science is a branch of, and is therefore subsumed by, immaterial formal logic.

The prelims comprise: Introduction Computer Science and Mathematics The Formal Verification Debate Abstraction in Computer Science Conclusion.

We characterize abstraction in computer science by first comparing the fundamental nature of computer science with that of its cousin mathematics. We consider their primary products, use of formalism, and abstraction objectives, and find that the two disciplines are sharply distinguished. Mathematics, being primarily concerned with developing inference structures, has information neglect as its abstraction objective. Computer science, being primarily concerned with developing interaction patterns, has information hiding as its abstraction objective. We show that (...) abstraction through information hiding is a primary factor in computer science progress and success through an examination of the ubiquitous role of information hiding in programming languages, operating systems, network architecture, and design patterns. (shrink)

What is computer-science about? CS is obviously the science of computers. But what exactly are computers? We know that there are physical computers, and, perhaps, also abstract computers. Let us limit the discussion here to physical entities and ask: What are physical computers? What does it mean for a physical entity to be a computer? The answer, it seems, is that physical computers are physical dynamical systems that implement formal entities such as Turing-machines. I do not (...) think that this answer is false. But it invites another, and troubling, question: What distinguishes computers from other physical dynamical systems? The difficulty is that, on the one hand, every physical system im-plements abstract formal entities such as sets of differential equations, while on the other hand we certainly do not want to count every dynamical system as a computer. After all, if CS is somehow distinctive, then there must be a difference between computers and other systems such as solar systems, stomachs, and carburetors. But what is the difference? (shrink)

The related fields of fractal image encoding and fractal image analysis have blossomed in recent years. This book, originating from a NATO Advanced Study Institute held in 1995, presents work by leading researchers. It is developing the subjects at an introductory level, but it also has some recent and exciting results in both fields. The book contains a thorough discussion of fractal image compression and decompression, including both continuous and discrete formulations, vector space and hierarchical methods, and algorithmic optimizations. The (...) book also discusses multifractal approaches to image analysis, segmentation, and recognition, including medical applications. (shrink)

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

Recently, information retrieval is shown to be a science by mapping information retrieval scientific study to scientific study abstracted from physics. The exercise was rather tedious and lengthy. Instead of dealing with the nitty gritty, this paper looks at the insights into how computer science can be made into a science by using that methodology. That is by mapping computer science scientific study to the scientific study abstracted from physics. To show the mapping between (...) computer science and physics, we need to define what is engineering science which computer science belongs to. Some principles and assumptions of engineering science theory are presented. To show computer science is a science, we presented two approaches. Approach 1 considers computer science as simulation of human behaviour similar to the goal of artificial intelligence. Approach 2 is closely related to the actual activities in computer science, and this approach considers computer science based on the theory of computation. Finally, we answer some of the common outstanding issues about computer science to convince our reader that computer science is a science. (shrink)

This paper aims to connect Stiegler’s reflections on theoretical computer science with his practical propositions for the design of digital technologies. Indeed, Stiegler’s theory of exosomatization implies a new conception of artificial intelligence, which is not based on an analogical paradigm (which compares organisms and machines, as in cybernetics, or which compares thought and computing, as in cognitivism) but on an organological paradigm, which studies the co-evolution of living organisms (individuals), artificial organs (tools), and social organizations (institutions). Such (...) a perspective does not compare human capacities to machine performances but studies the way in which the evolution of material and technical supports affects and transforms psychological, cognitive or noetic faculties (intuition, memory, understanding, imagination, sensibility, reason, etc.), as well as the constitution of different kinds of knowledge. This new theoretical paradigm implies a new ‘design’ for digital technologies, which considers their social role and their impact on psychological, cognitive or noetic faculties. (shrink)

This reader contains the extended abstracts of the seminars organised for the “Computer Science and IT with/for Biology” Seminar Series, held at the Faculty of Computer Science, Free University of Bozen-Bolzano, from October to December 2005. Slides of the presentations are available online at: www.inf.unibz.it/krdb/biology.

This workshop on stochastic theory and adaptive control assembled many of the leading researchers on stochastic control and stochastic adaptive control to increase scientific exchange and cooperative research between these two subfields of stochastic analysis. The papers included in the proceedings include survey and research. They describe both theoretical results and applications of adaptive control. There are theoretical results in identification, filtering, control, adaptive control and various other related topics. Some applications to manufacturing systems, queues, networks, medicine and other topics (...) are gien. (shrink)

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

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

What is computer-science about? CS is obviously the science of computers. But what exactly are computers? We know that there are physical computers, and, perhaps, also abstract computers. Let us limit the discussion here to physical entities and ask: What are physical computers? What does it mean for a physical entity to be a computer? The answer, it seems, is that physical computers are physical dynamical systems that implement formal entities such as Turing-machines. I do not (...) think that this answer is false. But it invites another, and troubling, question: What distinguishes computers from other physical dynamical systems? The difficulty is that, on the one hand, every physical system im-plements abstract formal entities such as sets of differential equations, while on the other hand we certainly do not want to count every dynamical system as a computer. After all, if CS is somehow distinctive, then there must be a difference between computers and other systems such as solar systems, stomachs, and carburetors. But what is the difference? (shrink)

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

The philosophy of computer science is concerned with issues that arise from reflection upon the nature and practice of the discipline of computer science. This book presents an approach to the subject that is centered upon the notion of computational artefact. It provides an analysis of the things of computer science as technical artefacts. Seeing them in this way enables the application of the analytical tools and concepts from the philosophy of technology to the (...) technical artefacts of computer science. With this conceptual framework the author examines some of the central philosophical concerns of computer science including the foundations of semantics, the logical role of specification, the nature of correctness, computational ontology and abstraction, formal methods, computational epistemology and explanation, the methodology of computer science, and the nature of computation. The book will be of value to philosophers and computer scientists. (shrink)

The essays included in the special issue dedicated to the philosophy of computer science examine new philosophical questions that arise from reflection upon conceptual issues in computer science and the insights such an enquiry provides into ongoing philosophical debates.

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

In this paper I hope to show that the idea of teaching philosophy through teaching computer science is a project worth pursuing. In the first section I will sketch a variety of ways in which philosophy and computer science might interact. Then I will give a brief rationale for teaching philosophy through teaching computer science. Then I will introduce three philosophical issues (among others) that have pedagogically useful analogues in computer science: (i) (...) external world skepticism, (ii) numerical vs. qualitative identity, and (iii) the existence of God. (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)

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)

Part I presents a model of interactive computation and a metric for expressiveness, Part II relates interactive models of computation to physics, and Part III considers empirical models from a philosophical perspective. Interaction machines, which extend Turing Machines to interaction, are shown in Part I to be more expressive than Turing Machines by a direct proof, by adapting Gödel's incompleteness result, and by observability metrics. Observation equivalence provides a tool for measuring expressiveness according to which interactive systems are more expressive (...) than algorithms. Refinement of function equivalence by observation of outer interactive behavior and inner computation steps is examined. The change of focus from algorithms specified by computable functions to interaction specified by observation equivalence captures the essence of empirical computer science. Part II relates interaction in models of computation to observation in the natural sciences. Explanatory power in physics is specified by the same observability metric as expressiveness in interactive systems. Realist models of inner structure are characterized by induction, abduction, and Occam's Razor. Interactive realism extends the hidden-variable model of Einstein to hidden interfaces that provide extra degrees of freedom to formulate hypotheses with testable predictions conforming with quantum theory. Greater expressiveness of collaborative computational observers (writers) than single observers implies that hidden-interface models are more expressive than hidden-variable models. By providing a common foundation for empirical computational and physical models we can use precise results about computational models to establish properties of physical models. Part III shows that the evolution in computing from algorithms to interaction parallels that in physics from rationalism to empiricism. Plato's cave metaphor is interactively extended from Platonic rationalism to empiricism. The Turing test is extended to TMs with hidden interfaces that express interactive thinking richer than the traditional Turing test. Interactive (nonmonotonic) extensions of logic such as the closed-world assumption suggest that interactiveness is incompatible with monotonic logical inference. Procedure call, atomicity of transactions, and taking a fixed point are techniques for closing open systems similar to "preparation" followed by "observation" of a physical system. Pragmatics is introduced as a framework for extending logical models with a fixed syntax and semantics to multiple-interface models that support collaboration among clients sharing common resources. (shrink)

Analogicity in computer science is understood in two, not mutually exclusive ways: 1) with regard to the continuity feature (of data or computations), 2) with regard to the analogousness feature (i.e. similarity between certain natural processes and computations). Continuous computations are the subject of three methodological questions considered in the paper: 1a) to what extent do their theoretical models go beyond the model of the universal Turing machine (defining digital computations), 1b) is their computational power greater than that (...) of the universal Turing machine, 1c) under what conditions are continuous computations realizable in practice? The analogue-analogical computations lead to two other issues: 2a) in what sense and to what extent their accuracy depends on the adequacy of certain theories of empirical sciences, 2b) are there analogue-analogical computations in nature that are also continuous? The above issues are an important element of the philosophical discussion on the limitations of contemporary computer science. (shrink)

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

Linear Logic is a branch of proof theory which provides refined tools for the study of the computational aspects of proofs. These tools include a duality-based categorical semantics, an intrinsic graphical representation of proofs, the introduction of well-behaved non-commutative logical connectives, and the concepts of polarity and focalisation. These various aspects are illustrated here through introductory tutorials as well as more specialised contributions, with a particular emphasis on applications to computer science: denotational semantics, lambda-calculus, logic programming and concurrency (...) theory. The volume is rounded-off by two invited contributions on new topics rooted in recent developments of linear logic. The book derives from a summer school that was the climax of the EU Training and Mobility of Researchers project 'Linear Logic in Computer Science'. It is an excellent introduction to some of the most active research topics in the area. (shrink)

Computer science is an engineering science whose objective is to determine how to best control interactions among computational objects. We argue that it is a fundamental computer science value to design computational objects so that the dependencies required by their interactions do not result in couplings, since coupling inhibits change. The nature of knowledge in any science is revealed by how concepts in that science change through paradigm shifts, so we analyze classic paradigm (...) shifts in both natural and computer science in terms of decoupling. We show that decoupling pervades computer science both at its core and in the wider context of computing at large, and lies at the very heart of computer science’s value system. (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)

Computer science only became established as a field in the 1950s, growing out of theoretical and practical research begun in the previous two decades. The field has exhibited immense creativity, ranging from innovative hardware such as the early mainframes to software breakthroughs such as programming languages and the Internet. Martin Gardner worried that "it would be a sad day if human beings, adjusting to the Computer Revolution, became so intellectually lazy that they lost their power of creative (...) thinking" (Gardner, 1978, p. vi-viii). On the contrary, computers and the theory of computation have provided great opportunities for creative work. This chapter examines several key aspects of creativity in computer science, beginning with the question of how problems arise in computer science. We then discuss the use of analogies in solving key problems in the history of computer science. Our discussion in these sections is based on historical examples, but the following sections discuss the nature of creativity using information from a contemporary source, a set of interviews with practicing computer scientists collected by the Association of Computing Machinery’s on-line student magazine, Crossroads. We then provide a general comparison of creativity in computer science and in the natural sciences. (shrink)

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

A useful logic in which to specify normative system behaviour, deontic logic has a broad spectrum of possible applications within the field: from legal expert systems to natural language processing, database integrity to electronic contracting and the specification of fault-tolerant software.

A primary goal of quantum computer science is to find an explanation for the fact that quantum computers are more powerful than classical computers. In this paper I argue that to answer this question is to compare algorithmic processes of various kinds and to describe the possibility spaces associated with these processes. By doing this, we explain how it is possible for one process to outperform its rival. Further, in this and similar examples little is gained in subsequently (...) asking a how-actually question. Once one has explained how-possibly, there is little left to do. (shrink)