Results for 'computational complexity'

841 found
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  1. Epistemic Virtues, Metavirtues, and Computational Complexity.Adam Morton - 2004 - Noûs 38 (3):481–502.
    I argue that considerations about computational complexity show that all finite agents need characteristics like those that have been called epistemic virtues. The necessity of these virtues follows in part from the nonexistence of shortcuts, or efficient ways of finding shortcuts, to cognitively expensive routines. It follows that agents must possess the capacities – metavirtues –of developing in advance the cognitive virtues they will need when time and memory are at a premium.
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  2.  98
    Complexity of Judgment Aggregation.Ulle Endriss, Umberto Grandi & Daniele Porello - 2012 - Journal of Artificial Intelligence Research 45:481--514.
    We analyse the computational complexity of three problems in judgment aggregation: (1) computing a collective judgment from a profile of individual judgments (the winner determination problem); (2) deciding whether a given agent can influence the outcome of a judgment aggregation procedure in her favour by reporting insincere judgments (the strategic manipulation problem); and (3) deciding whether a given judgment aggregation scenario is guaranteed to result in a logically consistent outcome, independently from what the judgments supplied by the individuals (...)
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  3. Against the Possibility of a Formal Account of Rationality.Shivaram Lingamneni - manuscript
    I analyze a recent exchange between Adam Elga and Julian Jonker concerning unsharp (or imprecise) credences and decision-making over them. Elga holds that unsharp credences are necessarily irrational; I agree with Jonker's reply that they can be rational as long as the agent switches to a nonlinear valuation. Through the lens of computational complexity theory, I then argue that even though nonlinear valuations can be rational, they come in general at the price of computational intractability, and that (...)
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  4.  63
    Kolmogorov Randomness, Complexity and the Laws of Nature.Giovanni Sommazzi - 2016 - Dissertation,
    A formal introduction to Kolmogorov complexity is given, along with its fundamental theorems. Most importantly the theorem of undecidability of a random string and the information-theoretic reformulation of Gödel’s first theorem of incompleteness, stated by Chaitin. Then, the discussion moves on to inquire about some philosophical implications the concept randomness has in the fields of physics and mathematics. Starting from the notion of “understanding as compression” of information, as it is illuminated by algorithmic information theory, it is investigated (1) (...)
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  5. The Computable Universe: From Prespace Metaphysics to Discrete Quantum Mechanics.Martin Leckey - 1997 - Dissertation, Monash University
    The central motivating idea behind the development of this work is the concept of prespace, a hypothetical structure that is postulated by some physicists to underlie the fabric of space or space-time. I consider how such a structure could relate to space and space-time, and the rest of reality as we know it, and the implications of the existence of this structure for quantum theory. Understanding how this structure could relate to space and to the rest of reality requires, I (...)
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  6. Intractability and the Use of Heuristics in Psychological Explanations.Iris Rooij, Cory Wright & Todd Wareham - 2012 - Synthese 187 (2):471-487.
    Many cognitive scientists, having discovered that some computational-level characterization f of a cognitive capacity φ is intractable, invoke heuristics as algorithmic-level explanations of how cognizers compute f. We argue that such explanations are actually dysfunctional, and rebut five possible objections. We then propose computational-level theory revision as a principled and workable alternative.
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  7.  94
    Tractability and the Computational Mind.Rineke Verbrugge & Jakub Szymanik - 2018 - In Mark Sprevak & Matteo Colombo (eds.), The Routledge Handbook of the Computational Mind. Oxford, UK: pp. 339-353.
    We overview logical and computational explanations of the notion of tractability as applied in cognitive science. We start by introducing the basics of mathematical theories of complexity: computability theory, computational complexity theory, and descriptive complexity theory. Computational philosophy of mind often identifies mental algorithms with computable functions. However, with the development of programming practice it has become apparent that for some computable problems finding effective algorithms is hardly possible. Some problems need too much (...) resource, e.g., time or memory, to be practically computable. Computational complexity theory is concerned with the amount of resources required for the execution of algorithms and, hence, the inherent difficulty of computational problems. An important goal of computational complexity theory is to categorize computational problems via complexity classes, and in particular, to identify efficiently solvable problems and draw a line between tractability and intractability. -/- We survey how complexity can be used to study computational plausibility of cognitive theories. We especially emphasize methodological and mathematical assumptions behind applying complexity theory in cognitive science. We pay special attention to the examples of applying logical and computational complexity toolbox in different domains of cognitive science. We focus mostly on theoretical and experimental research in psycholinguistics and social cognition. (shrink)
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  8. Lightning in a Bottle: Complexity, Chaos, and Computation in Climate Science.Jon Lawhead - 2014 - Dissertation, Columbia University
    Climatology is a paradigmatic complex systems science. Understanding the global climate involves tackling problems in physics, chemistry, economics, and many other disciplines. I argue that complex systems like the global climate are characterized by certain dynamical features that explain how those systems change over time. A complex system's dynamics are shaped by the interaction of many different components operating at many different temporal and spatial scales. Examining the multidisciplinary and holistic methods of climatology can help us better understand the nature (...)
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  9.  76
    Computational Logic. Vol. 1: Classical Deductive Computing with Classical Logic.Luis M. Augusto - 2018 - London: College Publications.
    This is the first of a two-volume work combining two fundamental components of contemporary computing into classical deductive computing, a powerful form of computation, highly adequate for programming and automated theorem proving, which, in turn, have fundamental applications in areas of high complexity and/or high security such as mathematical proof, software specification and verification, and expert systems. Deductive computation is concerned with truth-preservation: This is the essence of the satisfiability problem, or SAT, the central computational problem in computability (...)
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  10. No Rationality Through Brute-Force.Danilo Fraga Dantas - 2017 - Filosofia Unisinos 18 (3):195-200.
    All reasoners described in the most widespread models of a rational reasoner exhibit logical omniscience, which is impossible for finite reasoners (real reasoners). The most common strategy for dealing with the problem of logical omniscience is to interpret the models using a notion of beliefs different from explicit beliefs. For example, the models could be interpreted as describing the beliefs that the reasoner would hold if the reasoner were able reason indefinitely (stable beliefs). Then the models would describe maximum rationality, (...)
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  11. Free Energy and Virtual Reality in Psychoanalysis and Neuroscience: A Complexity Theory of Dreaming and Mental Disorder.Jim Hopkins - 2016 - Frontiers in Psychology 7.
    This paper compares the free energy neuroscience now advocated by Karl Friston and his colleagues with that hypothesised by Freud, arguing that Freud's notions of conflict and trauma can be understood in terms of computational complexity. It relates Hobson and Friston's work on dreaming and the reduction of complexity to contemporary accounts of dreaming and the consolidation of memory, and advances the hypothesis that mental disorder can be understood in terms of computational complexity and the (...)
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  12.  9
    By Considering Fuzzy Time, P=BPP (P*=BPP*).Farzad Didehvar - manuscript
    The reason ability of considering time as a fuzzy concept is demonstrated in [7],[8]. One of the major questions which arise here is the new definitions of Complexity Classes. In [1],[2],…,[11] we show why we should consider time a fuzzy concept. It is noticeable to mention that that there were many attempts to consider time as a Fuzzy concept, in Philosophy, Mathematics and later in Physics but mostly based on the personal intuition of the authors or as a style (...)
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  13. The Epsilon Calculus and Herbrand Complexity.Georg Moser & Richard Zach - 2006 - Studia Logica 82 (1):133-155.
    Hilbert's ε-calculus is based on an extension of the language of predicate logic by a term-forming operator εx. Two fundamental results about the ε-calculus, the first and second epsilon theorem, play a rôle similar to that which the cut-elimination theorem plays in sequent calculus. In particular, Herbrand's Theorem is a consequence of the epsilon theorems. The paper investigates the epsilon theorems and the complexity of the elimination procedure underlying their proof, as well as the length of Herbrand disjunctions of (...)
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  14.  77
    Seven Properties of Self-Organization in the Human Brain.Birgitta Dresp-Langley - 2020 - Big Data and Cognitive Computing 2 (4):10.
    The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional (...)
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  15. Objects and Processes: Two Notions for Understanding Biological Information.Agustín Mercado-Reyes, Pablo Padilla Longoria & Alfonso Arroyo-Santos - forthcoming - Journal of Theoretical Biology.
    In spite of being ubiquitous in life sciences, the concept of information is harshly criticized. Uses of the concept other than those derived from Shannon's theory are denounced as pernicious metaphors. We perform a computational experiment to explore whether Shannon's information is adequate to describe the uses of said concept in commonplace scientific practice. Our results show that semantic sequences do not have unique complexity values different from the value of meaningless sequences. This result suggests that quantitative theoretical (...)
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  16.  55
    Modeling Inference of Mental States: As Simple as Possible, as Complex as Necessary.Ben Meijering, Niels A. Taatgen, Hedderik van Rijn & Rineke Verbrugge - 2014 - Interaction Studies: Social Behaviour and Communication in Biological and Artificial Systems 15 (3):455-477.
    Behavior oftentimes allows for many possible interpretations in terms of mental states, such as goals, beliefs, desires, and intentions. Reasoning about the relation between behavior and mental states is therefore considered to be an effortful process. We argue that people use simple strategies to deal with high cognitive demands of mental state inference. To test this hypothesis, we developed a computational cognitive model, which was able to simulate previous empirical findings: In two-player games, people apply simple strategies at first. (...)
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  17. Ontological Complexity and Human Culture.D. J. Saab & F. Fonseca - forthcoming - In R. Hagengruber (ed.), Proceedings of Philosophy's Relevance in Information Science.
    Ontologies are being used by information scientists in order to facilitate the sharing of meaningful information. However, computational ontologies are problematic in that they often decontextualize information. The semantic content of information is dependent upon the context in which it exists and the experience through which it emerges. For true semantic interoperability to occur among diverse information systems, within or across domains, information must remain contextualized. In order to bring more context to computational ontologies, we introduce culture as (...)
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  18. Supermachines and Superminds.Eric Steinhart - 2003 - Minds and Machines 13 (1):155-186.
    If the computational theory of mind is right, then minds are realized by machines. There is an ordered complexity hierarchy of machines. Some finite machines realize finitely complex minds; some Turing machines realize potentially infinitely complex minds. There are many logically possible machines whose powers exceed the Church–Turing limit (e.g. accelerating Turing machines). Some of these supermachines realize superminds. Superminds perform cognitive supertasks. Their thoughts are formed in infinitary languages. They perceive and manipulate the infinite detail of fractal (...)
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  19. On Testing the Simulation Theory.Tom Campbell, Houman Owhadi, Joe Savageau & David Watkinson - manuscript
    Can the theory that reality is a simulation be tested? We investigate this question based on the assumption that if the system performing the simulation is nite (i.e. has limited resources), then to achieve low computational complexity, such a system would, as in a video game, render content (reality) only at the moment that information becomes available for observation by a player and not at the moment of detection by a machine (that would be part of the simulation (...)
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  20.  31
    Repairing Ontologies Via Axiom Weakening.Daniele Porello & Oliver Kutz Nicolas Troquard, Roberto Confalonieri, Pietro Galliani, Rafael Peñaloza, Daniele Porello - 2018 - In Proceedings of the Thirty-Second {AAAI} Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th {AAAI} Symposium on Educational Advances in Artificial Intelligence (EAAI-18). pp. 1981--1988.
    Ontology engineering is a hard and error-prone task, in which small changes may lead to errors, or even produce an inconsistent ontology. As ontologies grow in size, the need for automated methods for repairing inconsistencies while preserving as much of the original knowledge as possible increases. Most previous approaches to this task are based on removing a few axioms from the ontology to regain consistency. We propose a new method based on weakening these axioms to make them less restrictive, employing (...)
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  21. Interactivity and Multimedia Interfaces.David Kirsh - 1997 - Instructional Science 25:79-96.
    Multimedia technology offers instructional designers an unprecedented opportunity to create richly interactive learning environments. With greater design freedom comes complexity. The standard answer to the problems of too much choice, disorientation, and complex navigation is thought to lie in the way we design interactivity in a system. Unfortunately, the theory of interactivity is at an early state of development. After critiquing the decision cycle model of interaction—the received theory in human computer interaction—I present arguments and observational data to show (...)
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  22.  48
    Digital Metaphysics.Eric Steinhart - 1998 - In Terrell Ward Bynum & James Moor (eds.), The Digital Phoenix: How Computers Are Changing Philosophy. Blackwell. pp. 117--134.
    I discuss the view, increasingly common in physics, that the foundational level of our physical reality is a network of computing machines (so that our universe is ultimately like a cellular automaton). I discuss finitely extended and divided (discrete) space-time and discrete causality. I examine reasons for thinking that the foundational computational complexity of our universe is finite. I discuss the emergence of an ordered complexity hierarchy of levels of objects over the foundational level and I show (...)
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  23. Seeing by Models: Vision as Adaptative Epistemology.Ignazio Licata - 2012 - In G. MInati (ed.), Methods, Models, Simulations and Approaches Towards a General Theory of Change. World Scientific.
    In this paper we suggest a clarification in relation to the notions of computational and intrinsic emergence, by showing how the latter is deeply connected to the new Logical Openness Theory, an original extension of Gödel theorems to the model theory. The epistemological scenario we are going to make use of is that of the theory of vision, a particularly instructive one. In order to reach our goal we introduce a dynamic theory of relationship between the observer and the (...)
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  24. Agent-Based Computational Economics: A Constructive Approach to Economic Theory.Leigh Tesfatsion - 2006 - In Leigh Tesfatsion & Kenneth L. Judd (eds.), Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics. Elsevier.
    Economies are complicated systems encompassing micro behaviors, interaction patterns, and global regularities. Whether partial or general in scope, studies of economic systems must consider how to handle difficult real-world aspects such as asymmetric information, imperfect competition, strategic interaction, collective learning, and the possibility of multiple equilibria. Recent advances in analytical and computational tools are permitting new approaches to the quantitative study of these aspects. One such approach is Agent-based Computational Economics (ACE), the computational study of economic processes (...)
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  25. Replicability or Reproducibility? On the Replication Crisis in Computational Neuroscience and Sharing Only Relevant Detail.Marcin Miłkowski, Witold M. Hensel & Mateusz Hohol - 2018 - Journal of Computational Neuroscience 3 (45):163-172.
    Replicability and reproducibility of computational models has been somewhat understudied by “the replication movement.” In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without (...)
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  26. Manufacturing Morality A General Theory of Moral Agency Grounding Computational Implementations: The ACTWith Model.Jeffrey White - 2013 - In Floares (ed.), Computational Intelligence. Nova Publications. pp. 1-65.
    The ultimate goal of research into computational intelligence is the construction of a fully embodied and fully autonomous artificial agent. This ultimate artificial agent must not only be able to act, but it must be able to act morally. In order to realize this goal, a number of challenges must be met, and a number of questions must be answered, the upshot being that, in doing so, the form of agency to which we must aim in developing artificial agents (...)
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  27. Being Emergence Vs. Pattern Emergence: Complexity, Control, and Goal-Directedness in Biological Systems.Jason Winning & William Bechtel - 2019 - In Sophie Gibb, Robin Hendry & Tom Lancaster (eds.), The Routledge Handbook of Philosophy of Emergence. London: pp. 134-144.
    Emergence is much discussed by both philosophers and scientists. But, as noted by Mitchell (2012), there is a significant gulf; philosophers and scientists talk past each other. We contend that this is because philosophers and scientists typically mean different things by emergence, leading us to distinguish being emergence and pattern emergence. While related to distinctions offered by others between, for example, strong/weak emergence or epistemic/ontological emergence (Clayton, 2004, pp. 9–11), we argue that the being vs. pattern distinction better captures what (...)
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  28. Computational Entrepreneurship: From Economic Complexities to Interdisciplinary Research.Quan-Hoang Vuong - 2019 - Problems and Perspectives in Management 17 (1):117-129.
    The development of technology is unbelievably rapid. From limited local networks to high speed Internet, from crude computing machines to powerful semi-conductors, the world had changed drastically compared to just a few decades ago. In the constantly renewing process of adapting to such an unnaturally high-entropy setting, innovations as well as entirely new concepts, were often born. In the business world, one such phenomenon was the creation of a new type of entrepreneurship. This paper proposes a new academic discipline of (...)
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  29. The Fragile World Hypothesis: Complexity, Fragility, and Systemic Existential Risk.David Manheim - forthcoming - Futures.
    The possibility of social and technological collapse has been the focus of science fiction tropes for decades, but more recent focus has been on specific sources of existential and global catastrophic risk. Because these scenarios are simple to understand and envision, they receive more attention than risks due to complex interplay of failures, or risks that cannot be clearly specified. In this paper, we discuss the possibility that complexity of a certain type leads to fragility which can function as (...)
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  30. Why Machine-Information Metaphors Are Bad for Science and Science Education.Massimo Pigliucci & Maarten Boudry - 2011 - Science & Education 20 (5-6):471.
    Genes are often described by biologists using metaphors derived from computa- tional science: they are thought of as carriers of information, as being the equivalent of ‘‘blueprints’’ for the construction of organisms. Likewise, cells are often characterized as ‘‘factories’’ and organisms themselves become analogous to machines. Accordingly, when the human genome project was initially announced, the promise was that we would soon know how a human being is made, just as we know how to make airplanes and buildings. Impor- tantly, (...)
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  31. Peeking Inside the Black Box: A New Kind of Scientific Visualization.Michael T. Stuart & Nancy J. Nersessian - 2018 - Minds and Machines 29 (1):87-107.
    Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization that was developed to address just this sort of epistemic (...)
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  32.  62
    Practicing Relativism in the Anthropocene: On Science, Belief, and the Humanities.Barbara Herrnstein Smith - 2018 - London UK: Open Humanities Press.
    The book addresses a set of contemporary issues involving knowledge and science from a constructivist-pragmatist perspective often labeled "relativism." As it demonstrates, what that perspective implies are neither absurd claims nor objectionable positions but an ongoing alertness to contingency, complexity, and multiplicity that is both intellectually and ethically valuable. In an extended examination of recent writings by Bruno Latour, I indicate the increasing centrality of theological investments in his work. Discussing computational methods in literary studies and efforts to (...)
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  33.  87
    From Computer Metaphor to Computational Modeling: The Evolution of Computationalism.Marcin Miłkowski - 2018 - Minds and Machines 28 (3):515-541.
    In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of (...)
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  34. An Improbable God Between Simplicity and Complexity: Thinking About Dawkins's Challenge.Philippe Gagnon - 2013 - International Philosophical Quarterly 53 (4):409-433.
    Richard Dawkins has popularized an argument that he thinks sound for showing that there is almost certainly no God. It rests on the assumptions (1) that complex and statistically improbable things are more difficult to explain than those that are not and (2) that an explanatory mechanism must show how this complexity can be built up from simpler means. But what justifies claims about the designer’s own complexity? One comes to a different understanding of order and of simplicity (...)
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  35. Components of Cultural Complexity Relating to Emotions: A Conceptual Framework.Radek Trnka, Iva Poláčková Šolcová & Peter Tavel - 2018 - New Ideas in Psychology 51:27-33.
    Many cultural variations in emotions have been documented in previous research, but a general theoretical framework involving cultural sources of these variations is still missing. The main goal of the present study was to determine what components of cultural complexity interact with the emotional experience and behavior of individuals. The proposed framework conceptually distinguishes five main components of cultural complexity relating to emotions: 1) emotion language, 2) conceptual knowledge about emotions, 3) emotion-related values, 4) feelings rules, i.e. norms (...)
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  36. Complexity Science: A "Gray" Science for the "Stuff in Between".Kurt A. Richardson, Paul Cilliers & Michael Lissack - 2001 - Emergence: Complexity and Organization 3 (2):6-18.
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  37. A Case Study on Computational Hermeneutics: E. J. Lowe’s Modal Ontological Argument.David Fuenmayor & Christoph Benzmueller - manuscript
    Computers may help us to better understand (not just verify) arguments. In this article we defend this claim by showcasing the application of a new, computer-assisted interpretive method to an exemplary natural-language ar- gument with strong ties to metaphysics and religion: E. J. Lowe’s modern variant of St. Anselm’s ontological argument for the existence of God. Our new method, which we call computational hermeneutics, has been particularly conceived for use in interactive-automated proof assistants. It aims at shedding light on (...)
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  38. A Computational Theory of Perspective and Reference in Narrative.Janyce M. Wiebe & William J. Rapaport - 1988 - In Proceedings of the 26th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. pp. 131-138.
    Narrative passages told from a character's perspective convey the character's thoughts and perceptions. We present a discourse process that recognizes characters'.
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  39. Complexity Biology-Based Information Structures Can Explain Subjectivity, Objective Reduction of Wave Packets, and Non-Computability.Alex Hankey - 2014 - Cosmos and History 10 (1):237-250.
    Background: how mind functions is subject to continuing scientific discussion. A simplistic approach says that, since no convincing way has been found to model subjective experience, mind cannot exist. A second holds that, since mind cannot be described by classical physics, it must be described by quantum physics. Another perspective concerns mind's hypothesized ability to interact with the world of quanta: it should be responsible for reduction of quantum wave packets; physics producing 'Objective Reduction' is postulated to form the basis (...)
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  40. How Do Narratives and Brains Mutually Influence Each Other? Taking Both the ‘Neuroscientific Turn’ and the ‘Narrative Turn’ in Explaining Bio-Political Orders.Machiel Keestra - manuscript
    Introduction: the neuroscientific turn in political science The observation that brains and political orders are interdependent is almost trivial. Obviously, political orders require brain processes in order to emerge and to remain in place, as these processes enable action and cognition. Conversely, every since Aristotle coined man as “by nature a political animal” (Aristotle, Pol.: 1252a 3; cf. Eth. Nic.: 1097b 11), this also suggests that the political engagements of this animal has likely consequences for its natural development, including the (...)
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  41.  73
    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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  42. Computational Mechanisms and Models of Computation.Marcin Miłkowski - 2014 - Philosophia Scientiæ 18:215-228.
    In most accounts of realization of computational processes by physical mechanisms, it is presupposed that there is one-to-one correspondence between the causally active states of the physical process and the states of the computation. Yet such proposals either stipulate that only one model of computation is implemented, or they do not reflect upon the variety of models that could be implemented physically. -/- In this paper, I claim that mechanistic accounts of computation should allow for a broad variation of (...)
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  43.  66
    Mechanistic Computational Individuation Without Biting the Bullet.Nir Fresco & Marcin Miłkowski - 2019 - British Journal for the Philosophy of Science:axz005.
    Is the mathematical function being computed by a given physical system determined by the system’s dynamics? This question is at the heart of the indeterminacy of computation phenomenon (Fresco et al. [unpublished]). A paradigmatic example is a conventional electrical AND-gate that is often said to compute conjunction, but it can just as well be used to compute disjunction. Despite the pervasiveness of this phenomenon in physical computational systems, it has been discussed in the philosophical literature only indirectly, mostly with (...)
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  44.  94
    The Motivations and Risks of Machine Ethics.Stephen Cave, Rune Nyrup, Karina Vold & Adrian Weller - 2019 - Proceedings of the IEEE 107 (3):562-574.
    Many authors have proposed constraining the behaviour of intelligent systems with ‘machine ethics’ to ensure positive social outcomes from the development of such systems. This paper critically analyses the prospects for machine ethics, identifying several inherent limitations. While machine ethics may increase the probability of ethical behaviour in some situations, it cannot guarantee it due to the nature of ethics, the computational limitations of computational agents and the complexity of the world. In addition, machine ethics, even if (...)
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  45.  86
    Computational Models (of Narrative) for Literary Studies.Antonio Lieto - 2015 - Semicerchio, Rivista di Poesia Comparata 2 (LIII):38-44.
    In the last decades a growing body of literature in Artificial Intelligence (AI) and Cognitive Science (CS) has approached the problem of narrative understanding by means of computational systems. Narrative, in fact, is an ubiquitous element in our everyday activity and the ability to generate and understand stories, and their structures, is a crucial cue of our intelligence. However, despite the fact that - from an historical standpoint - narrative (and narrative structures) have been an important topic of investigation (...)
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  46. Automata, Man-Machines and Embodiment: Deflating or Inflating Life?Charles T. Wolfe - forthcoming - In A. Radman & H. Sohn (eds.), Critical and Clinical Cartographies; Embodiment /Technology /Care /Design. 010.
    Early modern automata, understood as efforts to ‘model’ life, to grasp its singular properties and/or to unveil and demystify its seeming inaccessibility and mystery, are not just fascinating liminal, boundary, hybrid, crossover or go-between objects, while they are all of those of course. They also pose a direct challenge to some of our common conceptions about mechanism and embodiment. They challenge the simplicity of the distinction between a purported ‘mechanistic’ worldpicture, its ontology and its goals, and on the other hand (...)
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  47. Peirce on Complexity.Jaime Nubiola - 2001 - In Schmitz Walter (ed.), Proceedings of the 7th International Congress of the IASS-AIS.
    In a world of ever growing specialization, the issue of complexity attracts a good amount of attention from cross-disciplinary points of view as this Congress provides evidence. Charles S. Peirce's thought may help us not only to shoulder once again philosophical responsibility which has been largely abdicated by much of 20th century philosophy, but also to tackle some of the most stubborn contemporary problems. The founder of pragmatism identified one century ago most of these problems, and he also mapped (...)
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  48. The Nature of Computational Things.Franck Varenne - 2013 - In Frédéric Migayrou Brayer & Marie-Ange (eds.), Naturalizing Architecture. Orléans: HYX Editions. pp. 96-105.
    Architecture often relies on mathematical models, if only to anticipate the physical behavior of structures. Accordingly, mathematical modeling serves to find an optimal form given certain constraints, constraints themselves translated into a language which must be homogeneous to that of the model in order for resolution to be possible. Traditional modeling tied to design and architecture thus appears linked to a topdown vision of creation, of the modernist, voluntarist and uniformly normative type, because usually (mono)functionalist. One available instrument of calculation/representation/prescription (...)
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  49. The Formation of the Self. Nietzsche and Complexity.Paul Cilliers, Tanya de Villiers & Vasti Roodt - 2002 - South African Journal of Philosophy 21 (1):1-17.
    The purpose of this article is to examine the relationship between the formation of the self and the worldly horizon within which this self achieves its meaning. Our inquiry takes place from two perspectives: the first derived from the Nietzschean analysis of how one becomes what one is; the other from current developments in complexity theory. This two-angled approach opens up different, yet related dimensions of a non-essentialist understanding of the self that is none the less neither arbitrary nor (...)
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  50. The Central System as a Computational Engine.Susan Schneider - unknown
    The Language of Thought program has a suicidal edge. Jerry Fodor, of all people, has argued that although LOT will likely succeed in explaining modular processes, it will fail to explain the central system, a subsystem in the brain in which information from the different sense modalities is integrated, conscious deliberation occurs, and behavior is planned. A fundamental characteristic of the central system is that it is “informationally unencapsulated” -- its operations can draw from information from any cognitive domain. The (...)
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