Results for 'Ltd Capilano Computing Systems'

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  1. Early Computer Models of Cognitive Systems and the Beginnings of Cognitive Systems Dynamics.G. Mallen - 2013 - Constructivist Foundations 9 (1):137-138.
    Open peer commentary on the article “A Cybernetic Computational Model for Learning and Skill Acquisition” by Bernard Scott & Abhinav Bansal. Upshot: The target paper acknowledges some early computer modelling that I did in the years 1966–1968 when working with Pask at System Research Ltd in Richmond. In the commentary, I revisit the roots of this kind of modelling and follow the trajectory from then to today’s growing understanding of the dynamics of cognitive systems.
     
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  2.  29
    Computational significance of the cellular mechanisms for synaptic plasticity in Purkinje cells.James C. Houk & Simon Alford - 1996 - Behavioral and Brain Sciences 19 (3):457-461.
    The data on the cellular mechanism of LTD that is presented in four target articles is synthesized into a new model of Purkinje cell plasticity. This model attempts to address credit assignment problems that are crucial in learning systems. Intracellular signal transduction mechanisms may provide the mechanism for a 3-factor learning rule and a trace mechanism. The latter may permit delayed information about motor error to modify the prior synaptic events that caused the error. This model may help to (...)
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  3. Computer systems and responsibility: A normative look at technological complexity.Deborah G. Johnson & Thomas M. Powers - 2005 - Ethics and Information Technology 7 (2):99-107.
    In this paper, we focus attention on the role of computer system complexity in ascribing responsibility. We begin by introducing the notion of technological moral action (TMA). TMA is carried out by the combination of a computer system user, a system designer (developers, programmers, and testers), and a computer system (hardware and software). We discuss three sometimes overlapping types of responsibility: causal responsibility, moral responsibility, and role responsibility. Our analysis is informed by the well-known accounts provided by Hart and Hart (...)
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  4. Transparency in Complex Computational Systems.Kathleen A. Creel - 2020 - Philosophy of Science 87 (4):568-589.
    Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s...
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  5. Computer systems: Moral entities but not moral agents. [REVIEW]Deborah G. Johnson - 2006 - Ethics and Information Technology 8 (4):195-204.
    After discussing the distinction between artifacts and natural entities, and the distinction between artifacts and technology, the conditions of the traditional account of moral agency are identified. While computer system behavior meets four of the five conditions, it does not and cannot meet a key condition. Computer systems do not have mental states, and even if they could be construed as having mental states, they do not have intendings to act, which arise from an agent’s freedom. On the other (...)
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  6.  7
    Computational systems as higher-order mechanisms.Jorge Ignacio Fuentes - 2024 - Synthese 203 (2):1-26.
    I argue that there are different orders of mechanisms with different constitutive relevance and individuation conditions. In common first-order mechanistic explanations, constitutive relevance norms are captured by the matched-interlevel-experiments condition (Craver et al. (2021) Synthese 199:8807–8828). Regarding individuation, we say that any two mechanisms are of the same type when they have the same concrete components performing the same activities in the same arrangement. By contrast, in higher-order mechanistic explanations, we formulate the decompositions in terms of generalized basic components (GBCs). (...)
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  7. Enactive autonomy in computational systems.Mario Villalobos & Joe Dewhurst - 2018 - Synthese 195 (5):1891-1908.
    In this paper we will demonstrate that a computational system can meet the criteria for autonomy laid down by classical enactivism. The two criteria that we will focus on are operational closure and structural determinism, and we will show that both can be applied to a basic example of a physically instantiated Turing machine. We will also address the question of precariousness, and briefly suggest that a precarious Turing machine could be designed. Our aim in this paper is to challenge (...)
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  8.  15
    Computer systems fit for the legal profession?Sylvie Delacroix - 2018 - Legal Ethics 21 (2):119-135.
    ABSTRACTThis essay aims to contribute robust grounds to question the Susskinds’ influential, consequentialist logic when it comes to the legitimacy of automation within the legal profession. It does so by questioning their minimalist understanding of the professions. If it is our commitment to moral equality that is at stake every time lawyers hail the specific vulnerability inherent in their professional relationship, the case for wholesale automation is turned on its head. One can no longer assume that, as a rule, wholesale (...)
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    " Lntelllgent" computer systems and theory comparison.Piotr Giza - 2000 - In Adam Jonkisz & Leon Koj (eds.), On Comparing and Evaluating Scientific Theories. Rodopi. pp. 72--89.
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  10.  89
    Implementing a Computing System: A Pluralistic Approach.Syed AbuMusab - 2023 - Global Philosophy 33 (1):1-19.
    In chapter eleven of "On The Foundation of Computing," Primiero takes on the implementation debate in computer science. He contrasts his theory with two other views—the Semantic and the specification—artifact. In this paper, I argue that there is a way to fine-tune the implementation concept further. Firstly, contrary to Primiero, I claim it is problematic to separate the implementation relationship from the conditions which make it correct. Secondly, by taking a pluralistic approach to implementation, I claim it is a (...)
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  11. Scientific Theories of Computational Systems in Model Checking.Nicola Angius & Guglielmo Tamburrini - 2011 - Minds and Machines 21 (2):323-336.
    Model checking, a prominent formal method used to predict and explain the behaviour of software and hardware systems, is examined on the basis of reflective work in the philosophy of science concerning the ontology of scientific theories and model-based reasoning. The empirical theories of computational systems that model checking techniques enable one to build are identified, in the light of the semantic conception of scientific theories, with families of models that are interconnected by simulation relations. And the mappings (...)
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  12. Organisations as Computing Systems.David Strohmaier - 2020 - Journal of Social Ontology 6 (2):211-236.
    Organisations are computing systems. The university’s sports centre is a computing system for managing sports teams and facilities. The tenure committee is a computing system for assigning tenure status. Despite an increasing number of publications in group ontology, the computational nature of organisations has not been recognised. The present paper is the first in this debate to propose a theory of organisations as groups structured for computing. I begin by describing the current situation in group (...)
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  13. Symbol grounding in computational systems: A paradox of intentions.Vincent C. Müller - 2009 - Minds and Machines 19 (4):529-541.
    The paper presents a paradoxical feature of computational systems that suggests that computationalism cannot explain symbol grounding. If the mind is a digital computer, as computationalism claims, then it can be computing either over meaningful symbols or over meaningless symbols. If it is computing over meaningful symbols its functioning presupposes the existence of meaningful symbols in the system, i.e. it implies semantic nativism. If the mind is computing over meaningless symbols, no intentional cognitive processes are available (...)
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  14.  8
    The Computer System User: An Information Need Model.Glynn Harmon - 1974 - In Donald E. Washburn & Dennis R. Smith (eds.), Coping with increasing complexity: implications of general semantics and general systems theory. New York: Gordon & Breach. pp. 115.
  15.  5
    Computer systems that learn.Alberto Segre & Geoffrey Gordon - 1993 - Artificial Intelligence 62 (2):363-378.
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  16. Situatedness and Embodiment of Computational Systems.Marcin Miłkowski - 2017 - Entropy 19 (4):162.
    In this paper, the role of the environment and physical embodiment of computational systems for explanatory purposes will be analyzed. In particular, the focus will be on cognitive computational systems, understood in terms of mechanisms that manipulate semantic information. It will be argued that the role of the environment has long been appreciated, in particular in the work of Herbert A. Simon, which has inspired the mechanistic view on explanation. From Simon’s perspective, the embodied view on cognition seems (...)
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  17.  56
    Explaining Engineered Computing Systems’ Behaviour: the Role of Abstraction and Idealization.Nicola Angius & Guglielmo Tamburrini - 2017 - Philosophy and Technology 30 (2):239-258.
    This paper addresses the methodological problem of analysing what it is to explain observed behaviours of engineered computing systems, focusing on the crucial role that abstraction and idealization play in explanations of both correct and incorrect BECS. First, it is argued that an understanding of explanatory requests about observed miscomputations crucially involves reference to the rich background afforded by hierarchies of functional specifications. Second, many explanations concerning incorrect BECS are found to abstract away from descriptions of physical components (...)
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  18.  35
    Mental Algorithms: Are Minds Computational Systems?James H. Fetzer - 1994 - Pragmatics and Cognition 2 (1):1-29.
    The idea that human thought requires the execution of mental algorithms provides a foundation for research programs in cognitive science, which are largely based upon the computational conception of language and mentality. Consideration is given to recent work by Penrose, Searle, and Cleland, who supply various grounds for disputing computationalism. These grounds in turn qualify as reasons for preferring a non-computational, semiotic approach, which can account for them as predictable manifestations of a more adquate conception. Thinking does not ordinarily require (...)
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  19.  37
    Mental algorithms: Are minds computational systems?James H. Fetzer - 1994 - Pragmatics and Cognition 21 (1):1-29.
    The idea that human thought requires the execution of mental algorithms provides a foundation for research programs in cognitive science, which are largely based upon the computational conception of language and mentality. Consideration is given to recent work by Penrose, Searle, and Cleland, who supply various grounds for disputing computationalism. These grounds in turn qualify as reasons for preferring a non-computational, semiotic approach, which can account for them as predictable manifestations of a more adquate conception. Thinking does not ordinarily require (...)
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  20. How minds can be computational systems.William J. Rapaport - 1998 - Journal of Experimental and Theoretical Artificial Intelligence 10 (4):403-419.
    The proper treatment of computationalism, as the thesis that cognition is computable, is presented and defended. Some arguments of James H. Fetzer against computationalism are examined and found wanting, and his positive theory of minds as semiotic systems is shown to be consistent with computationalism. An objection is raised to an argument of Selmer Bringsjord against one strand of computationalism, namely, that Turing-Test± passing artifacts are persons, it is argued that, whether or not this objection holds, such artifacts will (...)
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  21.  16
    Modelling interactive computing systems: Do we have a good theory of what computers are?Alice Martin, Mathieu Magnaudet & Stéphane Conversy - 2022 - Zagadnienia Filozoficzne W Nauce 73:77-119.
    Computers are increasingly interactive. They are no more transformational systems producing a final output after a finite execution. Instead, they continuously react in time to external events that modify the course of computing execution. While philosophers have been interested in conceptualizing computers for a long time, they seem to have paid little attention to the specificities of interactive computing. We propose to tackle this issue by surveying the literature in theoretical computer science, where one can find explicit (...)
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  22. Cognitive and Computer Systems for Understanding Narrative Text.William J. Rapaport, Erwin M. Segal, Stuart C. Shapiro, David A. Zubin, Gail A. Bruder, Judith Felson Duchan & David M. Mark - manuscript
    This project continues our interdisciplinary research into computational and cognitive aspects of narrative comprehension. Our ultimate goal is the development of a computational theory of how humans understand narrative texts. The theory will be informed by joint research from the viewpoints of linguistics, cognitive psychology, the study of language acquisition, literary theory, geography, philosophy, and artificial intelligence. The linguists, literary theorists, and geographers in our group are developing theories of narrative language and spatial understanding that are being tested by the (...)
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  23. Evolution: The Computer Systems Engineer Designing Minds.Aaron Sloman - 2011 - Avant: Trends in Interdisciplinary Studies 2 (2):45-69.
    What we have learnt in the last six or seven decades about virtual machinery, as a result of a great deal of science and technology, enables us to offer Darwin a new defence against critics who argued that only physical form, not mental capabilities and consciousness could be products of evolution by natural selection. The defence compares the mental phenomena mentioned by Darwin’s opponents with contents of virtual machinery in computing systems. Objects, states, events, and processes in virtual (...)
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  24.  53
    Argument Schemes in Computer System Safety Engineering.Tangming Yuan & Tim Kelly - 2011 - Informal Logic 31 (2):89-109.
    Safe Safety arguments are key components in a safety case. Too often, safety arguments are constructed without proper reasoning. To address this, we argue that informal logic argument schemes have important roles to play in safety argument construction and reviewing process. Ten commonly used reasoning schemes in computer system safety domain are proposed. The role of informal logic dialogue games in computer system safety arguments reviewing is also discussed and the intended work in this area is proposed. It is anticipated (...)
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  25.  36
    ‘Adaptive’ and ‘Cooperative’ computer systems — A challenge for sociological research.Michael Paetau - 1991 - AI and Society 5 (1):61-70.
    The vision of the new generation of office systems is based on the hypothesis that an automatic support system is all the more useful and acceptable, the more systems behaviour and performance are in accordance with features ofhuman behaviour. Consequently recent development activities are influenced by the paradigm of the computer as man's “cooperative assistant”. The metaphors ofassistance andcooperation illustrate some major requirements to be met by new office systems. Cooperative office systems will raise a set (...)
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  26.  6
    The Executioner Paradox: understanding self-referential dilemma in computational systems.Sachit Mahajan - forthcoming - AI and Society:1-8.
    As computational systems burgeon with advancing artificial intelligence (AI), the deterministic frameworks underlying them face novel challenges, especially when interfacing with self-modifying code. The Executioner Paradox, introduced herein, exemplifies such a challenge where a deterministic Executioner Machine (EM) grapples with self-aware and self-modifying code. This unveils a self-referential dilemma, highlighting a gap in current deterministic computational frameworks when faced with self-evolving code. In this article, the Executioner Paradox is proposed, highlighting the nuanced interactions between deterministic decision-making and self-aware code, (...)
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  27.  84
    Imaginary computational systems: queer technologies and transreal aesthetics. [REVIEW]Zach Blas & Micha Cárdenas - 2013 - AI and Society 28 (4):559-566.
  28.  15
    Locating'Agency'Within Ubiquitous Computing Systems.Adam Glen Swift - 2007 - International Review of Information Ethics 8:36-41.
    The final shape of the "Internet of Things" ubiquitous computing promises relies on a cybernetic system of inputs , computation or decision making , and outputs . My interest in this paper lies in the computational intelligences that suture these positions together, and how positioning these intelligences as autonomous agents extends the dialogue between human-users and ubiquitous computing technology. Drawing specifically on the scenarios surrounding the employment of ubiquitous computing within aged care, I argue that agency is (...)
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  29.  15
    On modelling in programmed computing systems.Jerzy Janusz Hallay - 1970 - Studia Logica 26 (1):45 - 72.
  30.  12
    Role of constrained computational systems in natural language processing.Aravind K. Joshi - 1998 - Artificial Intelligence 103 (1-2):117-132.
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  31.  25
    On board computing system for AMS-02 mission.Data Link Lrdl - 2005 - In Alan F. Blackwell & David MacKay (eds.), Power. Cambridge University Press. pp. x2.
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  32.  4
    An interactive computer system for retrieving faces.J. W. Shepherd - 1986 - In H. Ellis, M. Jeeves, F. Newcombe & Andrew W. Young (eds.), Aspects of Face Processing. Martinus Nijhoff. pp. 398--409.
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  33.  2
    A flexible efficient computer system to answer human questions.Daniel Chester - 1976 - Artificial Intelligence 7 (4):363-365.
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  34.  21
    Causality in naturally occurring computational systems.William Sulis - 1995 - World Futures 44 (2):129-148.
  35.  21
    Naturally occurring computational systems.William Sulis - 1994 - World Futures 39 (4):225-241.
  36.  44
    Caracolomobile: affect in computer systems[REVIEW]Tania Fraga - 2013 - AI and Society 28 (2):167-176.
    This essay presents and reflects upon the construction of a few experimental artworks, among them Caracolomobile , that looks for poetic, aesthetic and functional possibilities to bring computer systems to the sensitive universe of human emotions, feelings and expressions. Modern and Contemporary Art have explored such qualities in unfathomable ways and nowadays is turning towards computer systems and their co-related technologies. This universe characterizes and is the focus of these experimental artworks; artworks dealing with entwined subjective and objective (...)
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  37.  37
    Roles for knowledge-based computer systems: Case studies in maternity care. [REVIEW]M. Harris, A. P. Jagodzinski & K. R. Greene - 2001 - AI and Society 15 (4):386-395.
    The design of medical knowledge-based computer systems requires effective interdisciplinary communication for the development of a community sharing common goals and a common language for design. Over the past 9 years the Perinatal Research Group, an interdisciplinary team of computer scientists, engineers and clinicians, have developed a prototype knowledge-based computer system to aid clinicians in the care of women in labour. The group were uncertain which approach to adopt to progress this system from a prototype to a useful clinical (...)
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  38. Argumentation and risk communication about genetic testing: Challenges for healthcare consumers and implications for computer systems.Nancy L. Green - 2012 - Journal of Argumentation in Context 1 (1):113-129.
    As genetic testing for the presence of potentially health-affecting mutations becomes available for more genetic conditions, many people will soon be faced with the decision of whether or not to have a genetic test. Making an informed decision requires an understanding and evaluation of the arguments for and against having the test. As a case in point, this paper considers argumentation involving the decision of whether to have a BRCA gene test, one of the first commercially available genetic tests. First, (...)
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  39.  6
    Metaheuristic algorithms for optimization of resilient overlay computing systems.K. Walkowiak, W. Charewicz, M. Donajski & J. Rak - 2015 - Logic Journal of the IGPL 23 (1):31-44.
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  40.  4
    R1: A rule-based configurer of computer systems.John McDermott - 1982 - Artificial Intelligence 19 (1):39-88.
  41.  29
    The Central Role of Heuristic Search in Cognitive Computation Systems.Wai-Tat Fu - 2016 - Minds and Machines 26 (1-2):103-123.
    This paper focuses on the relation of heuristic search and level of intelligence in cognitive computation systems. The paper begins with a review of the fundamental properties of a cognitive computation system, which is defined generally as a control system that generates goal-directed actions in response to environmental inputs and constraints. An important property of cognitive computations is the need to process local cues in symbol structures to access and integrate distal knowledge to generate a response. To deal with (...)
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  42.  3
    When Is a Work-Around? Conflict and Negotiation in Computer Systems Development.Neil Pollock - 2005 - Science, Technology, and Human Values 30 (4):496-514.
    The notion of a “work-around” is a much-used resource within the sociology of technology, reflecting an interest in showing how users are not simply shaped by technologies but how they, through adopting artifacts in ways other than those for which they were designed or intended, are also shapers of technology. Using the language and concerns of actor-network theory and focusing on recent developments within computer-systems implementation, this article seeks to explore and add to our understanding of work-arounds through unpacking (...)
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  43. Report on DARPA Workshop on Self-Aware Computer Systems.Michael L. Anderson - unknown
    Self Aware Computer Systems is an area of basic research, and we are only in the initial stages of our understanding of what it means: What it means to be self aware; what a self aware system can do that a system without it cannot do; and what are some of the immediate practical applications and challenge problems. This paper is a report capturing some of the salient points discussed during the DARPA workshop on Self Aware Computer Systems (...)
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  44. Why Build a Virtual Brain? Large-scale Neural Simulations as Test-bed for Artificial Computing Systems.Matteo Colombo - 2015 - In D. C. Noelle, R. Dale, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings & P. P. Maglio (eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 429-434.
    Despite the impressive amount of financial resources invested in carrying out large-scale brain simulations, it is controversial what the payoffs are of pursuing this project. The present paper argues that in some cases, from designing, building, and running a large-scale neural simulation, scientists acquire useful knowledge about the computational performance of the simulating system, rather than about the neurobiological system represented in the simulation. What this means, why it is not a trivial lesson, and how it advances the literature on (...)
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  45. Moral responsibility for harm caused by computer system failures.Douglas Birsch - 2004 - Ethics and Information Technology 6 (4):233-245.
    When software is written and then utilized in complex computer systems, problems often occur. Sometimes these problems cause a system to malfunction, and in some instances such malfunctions cause harm. Should any of the persons involved in creating the software be blamed and punished when a computer system failure leads to persons being harmed? In order to decide whether such blame and punishment are appropriate, we need to first consider if the people are “morally responsible”. Should any of the (...)
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  46.  11
    The applicability of mathematics in computational systems biology and its experimental relations.Miles MacLeod - 2021 - European Journal for Philosophy of Science 11 (3):1-21.
    In 1966 Richard Levins argued that applications of mathematics to population biology faced various constraints which forced mathematical modelers to trade-off at least one of realism, precision, or generality in their approach. Much traditional mathematical modeling in biology has prioritized generality and precision in the place of realism through strategies of idealization and simplification. This has at times created tensions with experimental biologists. The past 20 years however has seen an explosion in mathematical modeling of biological systems with the (...)
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  47.  19
    Establishing norms with metanorms in distributed computational systems.Samhar Mahmoud, Nathan Griffiths, Jeroen Keppens, Adel Taweel, Trevor J. M. Bench-Capon & Michael Luck - 2015 - Artificial Intelligence and Law 23 (4):367-407.
    Norms provide a valuable mechanism for establishing coherent cooperative behaviour in decentralised systems in which there is no central authority. One of the most influential formulations of norm emergence was proposed by Axelrod :1095–1111, 1986). This paper provides an empirical analysis of aspects of Axelrod’s approach, by exploring some of the key assumptions made in previous evaluations of the model. We explore the dynamics of norm emergence and the occurrence of norm collapse when applying the model over extended durations. (...)
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  48.  12
    Sources of error and accountability in computer systems: Comments on “accountability in a computerized society”.Dr Peter Szolovits - 1996 - Science and Engineering Ethics 2 (1):43-46.
    Sources of error and accountability in computer systems: Comments on “accountability in a computerized society”.
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  49.  2
    Commentary: Reichenbach’s Verbal Tenses in the Context of Discovery About Computing Systems.Guglielmo Tamburrini - 2016 - In Flavia Santoianni (ed.), The Concept of Time in Early Twentieth-Century Philosophy. Springer Verlag.
    This contribution analyzes present applications of temporal logics that are meaningfully related to Hans Reichenbach's groundbreaking work on verbal tenses and their underlying logical structure. Specifically, some formal methods in theoretical computer science will be discussed that enable one to advance empirical hypotheses and to make predictions about the temporal evolution of computing system’s behaviors.
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  50.  37
    Computers Ltd: What They Really Can't Do.David Harel - 2003 - Oxford University Press.
    In Computers Ltd, David Harel, best-selling author of Algorithmics, explains and illustrates one of the most fundamental, yet under-exposed facets of computers - their inherent limitations. Looking at the bad news that is proven, lasting, and robust, discussing limitations that no amounts of hardware, software, talents, or resources can overcome, the book presents a disturbing and provocative view of computing at the start of the 21st century.
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