Search results for 'Complexity' (try it on Scholar)

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  1. William Bechtel & Robert C. Richardson (1993). Discovering Complexity Decomposition and Localization as Strategies in Scientific Research. Princeton.
     
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  2.  79
    Peter Godfrey-Smith (1996). Complexity and the Function of Mind in Nature. Cambridge University Press.
    This book explains the relationship between intelligence and environmental complexity, and in so doing links philosophy of mind to more general issues about the relations between organisms and environments, and to the general pattern of 'externalist' explanations. The author provides a biological approach to the investigation of mind and cognition in nature. In particular he explores the idea that the function of cognition is to enable agents to deal with environmental complexity. The history of the idea in the (...)
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  3.  55
    Franck Varenne (2009). Models and Simulations in the Historical Emergence of the Science of Complexity. In Ma Aziz-Alaoui & C. Bertelle (eds.), From System Complexity to Emergent Properties. Springer 3--21.
    As brightly shown by Mainzer [24], the science of complexity has many distinct origins in many disciplines. Those various origins has led to “an interdisciplinary methodology to explain the emergence of certain macroscopic phenomena via the nonlinear interactions of microscopic elements” (ibid.). This paper suggests that the parallel and strong expansion of modeling and simulation - especially after the Second World War and the subsequent development of computers - is a rationale which also can be counted as an explanation (...)
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    Roger Lewin (1993). Complexity: Life at the Edge of Chaos. Maxwell Macmillan International.
    Examines the field of <span class='Hi'>complexity</span> science, with sections focusing on how the discipline works within computer simulations, natural ecosystems, and various social systems.
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  5.  21
    Melanie Mitchell (2009). Complexity: A Guided Tour. Oxford University Press.
    What enables individually simple insects like ants to act with such precision and purpose as a group? How do trillions of individual neurons produce something as extraordinarily complex as consciousness? What is it that guides self-organizing structures like the immune system, the World Wide Web, the global economy, and the human genome? These are just a few of the fascinating and elusive questions that the science of <span class='Hi'>complexity</span> seeks to answer. In this remarkably accessible and companionable book, leading (...)
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  6.  94
    Paul Cilliers (1998). Complexity and Postmodernism: Understanding Complex Systems. Routledge.
    Complexity and Postmodernism explores the notion of complexity in the light of contemporary perspectives from philosophy and science. The book integrates insights from complexity and computational theory with the philosophical position of thinkers including Derrida and Lyotard. Paul Cilliers takes a critical stance towards the use of the analytical method as a tool to cope with complexity, and he rejects Searle's superficial contribution to the debate.
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  7. Emil Jeřábek (2007). Complexity of Admissible Rules. Archive for Mathematical Logic 46 (2):73-92.
    We investigate the computational complexity of deciding whether a given inference rule is admissible for some modal and superintuitionistic logics. We state a broad condition under which the admissibility problem is coNEXP-hard. We also show that admissibility in several well-known systems (including GL, S4, and IPC) is in coNE, thus obtaining a sharp complexity estimate for admissibility in these systems.
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  8.  41
    Jakub Szymanik (2009). Quantifiers in TIME and SPACE. Computational Complexity of Generalized Quantifiers in Natural Language. Dissertation, University of Amsterdam
    In the dissertation we study the complexity of generalized quantifiers in natural language. Our perspective is interdisciplinary: we combine philosophical insights with theoretical computer science, experimental cognitive science and linguistic theories. -/- In Chapter 1 we argue for identifying a part of meaning, the so-called referential meaning (model-checking), with algorithms. Moreover, we discuss the influence of computational complexity theory on cognitive tasks. We give some arguments to treat as cognitively tractable only those problems which can be computed in (...)
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  9. Giulio Tononi, Gerald M. Edelman & Olaf Sporns (1998). Complexity and Coherency: Integrating Information in the Brain. Trends in Cognitive Sciences 2 (12):474-484.
    The brains of higher mammals are extraordinary integrative devices. Signals from large numbers of functionally specialized groups of neurons distributed over many brain regions are integrated to generate a coherent, multimodal scene. Signals from the environment are integrated with ongoing, patterned neural activity that provides them with a meaningful context. We review recent advances in neurophysiology and neuroimaging that are beginning to reveal the neural mechanisms of integration. In addition, we discuss concepts and measures derived from information theory that lend (...)
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  10.  33
    Graeme S. Halford, William H. Wilson & Steven Phillips (1998). Processing Capacity Defined by Relational Complexity: Implications for Comparative, Developmental, and Cognitive Psychology. Behavioral and Brain Sciences 21 (6):803-831.
    Working memory limits are best defined in terms of the complexity of the relations that can be processed in parallel. Complexity is defined as the number of related dimensions or sources of variation. A unary relation has one argument and one source of variation; its argument can be instantiated in only one way at a time. A binary relation has two arguments, two sources of variation, and two instantiations, and so on. Dimensionality is related to the number of (...)
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  11.  19
    Mark C. Taylor (2001). The Moment of Complexity: Emerging Network Culture. University of Chicago Press.
    " The Moment of Complexity is a profoundly original work. In remarkable and insightful ways, Mark Taylor traces an entirely new way to view the evolution of our culture, detailing how information theory and the scientific concept of complexity can be used to understand recent developments in the arts and humanities. This book will ultimately be seen as a classic."-John L. Casti, Santa Fe Institute, author of Godel: A Life of Logic, the Mind, and Mathematics The science of (...)
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  12.  37
    Mikhail Prokopenko, Fabio Boschetti & Alex J. Ryan (2009). An Information‐Theoretic Primer on Complexity, Self‐Organization, and Emergence. Complexity 15 (1):11-28.
  13.  46
    Jakub Szymanik (2010). Computational Complexity of Polyadic Lifts of Generalized Quantifiers in Natural Language. Linguistics and Philosophy 33 (3):215-250.
    We study the computational complexity of polyadic quantifiers in natural language. This type of quantification is widely used in formal semantics to model the meaning of multi-quantifier sentences. First, we show that the standard constructions that turn simple determiners into complex quantifiers, namely Boolean operations, iteration, cumulation, and resumption, are tractable. Then, we provide an insight into branching operation yielding intractable natural language multi-quantifier expressions. Next, we focus on a linguistic case study. We use computational complexity results to (...)
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  14. Kurt A. Richardson, Paul Cilliers & Michael Lissack (2001). Complexity Science: A "Gray" Science for the "Stuff in Between". Emergence: Complexity and Organization 3 (2):6-18.
  15.  27
    E. J. Chaisson (2011). Energy Rate Density as a Complexity Metric and Evolutionary Driver. Complexity 16 (3):27-40.
  16.  16
    Peter Lohmann & Heribert Vollmer (2013). Complexity Results for Modal Dependence Logic. Studia Logica 101 (2):343-366.
    Modal dependence logic was introduced recently by Väänänen. It enhances the basic modal language by an operator = (). For propositional variables p 1, . . . , p n , = (p 1, . . . , p n-1, p n ) intuitively states that the value of p n is determined by those of p 1, . . . , p n-1. Sevenster (J. Logic and Computation, 2009) showed that satisfiability for modal dependence logic is complete for nondeterministic (...)
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  17. Alex Hankey (2014). Complexity Biology-Based Information Structures Can Explain Subjectivity, Objective Reduction of Wave Packets, and Non-Computability. Cosmos and History: The Journal of Natural and Social Philosophy 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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  18.  8
    Ronnie Hawkins (forthcoming). Facing Up to Complexity: Implications for Our Social Experiments. Science and Engineering Ethics:1-40.
    Biological systems are highly complex, and for this reason there is a considerable degree of uncertainty as to the consequences of making significant interventions into their workings. Since a number of new technologies are already impinging on living systems, including our bodies, many of us have become participants in large-scale “social experiments”. I will discuss biological complexity and its relevance to the technologies that brought us BSE/vCJD and the controversy over GM foods. Then I will consider some of the (...)
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  19.  7
    Remi Fleurian, Tim Blackwell, Oded Ben‐Tal & Daniel Müllensiefen (2016). Information‐Theoretic Measures Predict the Human Judgment of Rhythm Complexity. Cognitive Science 40 (3):n/a-n/a.
    To formalize the human judgment of rhythm complexity, we used five measures from information theory and algorithmic complexity to measure the complexity of 48 artificially generated rhythmic sequences. We compared these measurements to human prediction accuracy and easiness judgments obtained from a listening experiment, in which 32 participants guessed the last beat of each sequence. We also investigated the modulating effects of musical expertise and general pattern identification ability. Entropy rate and Kolmogorov complexity were correlated with (...)
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  20. Deborah Osberg, Gert Biesta & Paul Cilliers (2008). From Representation to Emergence: Complexity's Challenge to the Epistemology of Schooling. Educational Philosophy and Theory 40 (1):213–227.
    In modern, Western societies the purpose of schooling is to ensure that school-goers acquire knowledge of pre-existing practices, events, entities and so on. The knowledge that is learned is then tested to see if the learner has acquired a correct or adequate understanding of it. For this reason, it can be argued that schooling is organised around a representational epistemology: one which holds that knowledge is an accurate representation of something that is separate from knowledge itself. Since the (...) of knowledge is assumed to exist separately from the knowledge itself, this epistemology can also be considered ‘spatial.’ In this paper we show how ideas from complexity have challenged the spatial epistemology’ of representation and we explore possibilities for an alternative ‘temporal’ understanding of knowledge in its relationship to reality. In addition to complexity, our alternative takes its inspiration from Deweyan ‘transactional realism’ and deconstruction. We suggest that ‘knowledge’ and ‘reality’ should not be understood as separate systems which somehow have to be brought into alignment with each other, but that they are part of the same emerging complex system which is never fully ‘present’ in any (discrete) moment in time. This not only introduces the notion of time into our understanding of the relationship between knowledge and reality, but also points to the importance of acknowledging the role of the ‘unrepresentable’ or ‘incalculable’. With this understanding knowledge reaches us not as something we receive but as a response, which brings forth new worlds because it necessarily adds something (which was not present anywhere before it appeared) to what came before. This understanding of knowledge suggests that the acquisition of curricular content should not be considered an end in itself. Rather, curricular content should be used to bring forth that which is incalculable from the perspective of the present. The epistemology of emergence therefore calls for a switch in focus for curricular thinking, away from questions about presentation and representation and towards questions about engagement and response. (shrink)
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  21. R. Brown, J. F. Glazebrook & I. C. Baianu (2007). A Conceptual Construction of Complexity Levels Theory in Spacetime Categorical Ontology: Non-Abelian Algebraic Topology, Many-Valued Logics and Dynamic Systems. [REVIEW] Axiomathes 17 (3-4):409-493.
    A novel conceptual framework is introduced for the Complexity Levels Theory in a Categorical Ontology of Space and Time. This conceptual and formal construction is intended for ontological studies of Emergent Biosystems, Super-complex Dynamics, Evolution and Human Consciousness. A claim is defended concerning the universal representation of an item’s essence in categorical terms. As an essential example, relational structures of living organisms are well represented by applying the important categorical concept of natural transformations to biomolecular reactions and relational (...)
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  22.  71
    Melissa R. Beck, Daniel T. Levin & Bonnie L. Angelone (2007). Change Blindness Blindness: Beliefs About the Roles of Intention and Scene Complexity in Change Detection. Consciousness and Cognition 16 (1):31-51.
    Observers have difficulty detecting visual changes. However, they are unaware of this inability, suggesting that people do not have an accurate understanding of visual processes. We explored whether this error is related to participants’ beliefs about the roles of intention and scene complexity in detecting changes. In Experiment 1 participants had a higher failure rate for detecting changes in an incidental change detection task than an intentional change detection task. This effect of intention was greatest for complex scenes. However, (...)
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  23.  51
    Alexander Clark & Shalom Lappin (2013). Complexity in Language Acquisition. Topics in Cognitive Science 5 (1):89-110.
    Learning theory has frequently been applied to language acquisition, but discussion has largely focused on information theoretic problems—in particular on the absence of direct negative evidence. Such arguments typically neglect the probabilistic nature of cognition and learning in general. We argue first that these arguments, and analyses based on them, suffer from a major flaw: they systematically conflate the hypothesis class and the learnable concept class. As a result, they do not allow one to draw significant conclusions about the learner. (...)
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  24.  11
    Jan Krajíček (1995). Bounded Arithmetic, Propositional Logic, and Complexity Theory. Cambridge University Press.
    This book presents an up-to-date, unified treatment of research in bounded arithmetic and complexity of propositional logic, with emphasis on independence proofs and lower bound proofs. The author discusses the deep connections between logic and complexity theory and lists a number of intriguing open problems. An introduction to the basics of logic and complexity theory is followed by discussion of important results in propositional proof systems and systems of bounded arithmetic. More advanced (...)
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  25.  57
    Matthias Scheutz (2001). Computational Vs. Causal Complexity. Minds and Machines 11 (4):543-566.
    The main claim of this paper is that notions of implementation based on an isomorphic correspondence between physical and computational states are not tenable. Rather, ``implementation'' has to be based on the notion of ``bisimulation'' in order to be able to block unwanted implementation results and incorporate intuitions from computational practice. A formal definition of implementation is suggested, which satisfies theoretical and practical requirements and may also be used to make the functionalist notion of ``physical realization'' precise. The upshot of (...)
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  26.  12
    Ryan J. Taft, Michael Pheasant & John S. Mattick (2007). The Relationship Between Non‐Protein‐Coding DNA and Eukaryotic Complexity. Bioessays 29 (3):288-299.
    There are two intriguing paradoxes in molecular biology-the inconsistent relationship between organismal complexity and (1) cellular DNA content and (2) the number of protein-coding genes-referred to as the C-value and G-value paradoxes, respectively. The C-value paradox may be largely explained by varying ploidy. The G-value paradox is more problematic, as the extent of protein coding sequence remains relatively static over a wide range of developmental complexity. We show by analysis of sequenced genomes that the relative amount of non-protein-coding (...)
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  27. R. Badii (1997). Complexity: Hierarchical Structures and Scaling in Physics. Cambridge University Press.
    This is a comprehensive discussion of complexity as it arises in physical, chemical, and biological systems, as well as in mathematical models of nature. Common features of these apparently unrelated fields are emphasised and incorporated into a uniform mathematical description, with the support of a large number of detailed examples and illustrations. The quantitative study of complexity is a rapidly developing subject with special impact in the fields of physics, mathematics, information science, and biology. Because of the variety (...)
     
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  28.  87
    Verónica Becher, Santiago Figueira, André Nies & Silvana Picchi (2005). Program Size Complexity for Possibly Infinite Computations. Notre Dame Journal of Formal Logic 46 (1):51-64.
    We define a program size complexity function $H^\infty$ as a variant of the prefix-free Kolmogorov complexity, based on Turing monotone machines performing possibly unending computations. We consider definitions of randomness and triviality for sequences in ${\{0,1\}}^\omega$ relative to the $H^\infty$ complexity. We prove that the classes of Martin-Löf random sequences and $H^\infty$-random sequences coincide and that the $H^\infty$-trivial sequences are exactly the recursive ones. We also study some properties of $H^\infty$ and compare it with other complexity (...)
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  29. Neil F. Johnson (2009). Simply Complexity: A Clear Guide to Complexity Theory. Oneworld.
    What exactly is complexity science? Two's company, three is complexity ; Disorder rules, OK? ; Chaos and all that jazz ; Mob mentality ; Getting connected -- What can complexity science do for me? Forecasting financial markets ; Tackling traffic networks and climbing the corporate ladder ; Looking for Mr./Mrs. Right ; Coping with conflict : next-generation wars and global terrorism -- Catching a cold, avoiding super-flu and curing cancer ; The mother of all complexities : our (...)
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  30.  12
    Michael Strevens (forthcoming). Complexity Theory. In Paul Humphreys (ed.), Oxford Handbook of the Philosophy of Science. Oxford UP
    Complexity theory attempts to explain, at the most general possible level, the interesting behaviors of complex systems. Two such behaviors are the emergence of simple or stable high-level behavior from relatively complex low-level behavior, and the emergence of sophisticated high-level behavior from relatively simple low-level behavior; they are often found nested in the same system. Concerning the emergence of simplicity, this essay examines Herbert Simon's explanation from near-decomposability and a stochastic explanation that generalizes the approach of statistical physics. A (...)
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  31.  11
    Hector Zenil, Jean‐Paul Delahaye & Cédric Gaucherel (2012). Image Characterization and Classification by Physical Complexity. Complexity 17 (3):26-42.
  32.  24
    H. P. P. Lotter (1999). The Complexity of Science. Koers 64 (4):499-520.
    In this article I present an alternative philosophy of science based on ideas drawn from the study of complex adaptive systems. As a result of the spectacular expansion in scientific disciplines, the number of scientists and scientific institutions in the twentieth century, I believe science can be characterised as a complex system. I want to interpret the processes of science through which scientists themselves determine what counts as good science. This characterisation of science as a complex system can give an (...)
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  33.  44
    Daniel W. McShea (1991). Complexity and Evolution: What Everybody Knows. Biology and Philosophy 6 (3):303-324.
    The consensus among evolutionists seems to be (and has been for at least a century) that the morphological <span class='Hi'>complexity</span> of organisms increases in evolution, although almost no empirical evidence for such a trend exists. Most studies of <span class='Hi'>complexity</span> have been theoretical, and the few empirical studies have not, with the exception of certain recent ones, been especially rigorous; reviews are presented of both the theoretical and empirical literature. The paucity of evidence raises the question of what (...)
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  34.  23
    Stefania Sitzia & Daniel John Zizzo (2011). Does Product Complexity Matter for Competition in Experimental Retail Markets? Theory and Decision 70 (1):65-82.
    We describe a first experiment on whether product complexity affects competition and consumers in retail markets. We are unable to detect a significant effect of product complexity on prices, except insofar as the demand elasticity for complex products is higher. However, there is qualified evidence that complex products have the potential to induce consumers to buy more than they would otherwise. In this sense, consumer exploitability in quantities cannot be ruled out. We also find evidence for shaping effects: (...)
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  35.  30
    Börje Ekstig (2010). Complexity and Evolution: A Study of the Growth of Complexity in Organic and Cultural Evolution. [REVIEW] Foundations of Science 15 (3):263-278.
    In the present paper I develop a model of the evolutionary process associated to the widespread although controversial notion of a prevailing trend of increasing complexity over time. The model builds on a coupling of evolution to individual developmental programs and introduces an integrated view of evolution implying that human culture and science form a continuous extension of organic evolution. It is formed as a mathematical model that has made possible a quantitative estimation in relative terms of the growth (...)
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  36.  10
    Minka Woermann (2012). The Ethics of Complexity and the Complexity of Ethics. South African Journal of Philosophy 31 (2):447-463.
    In this paper, we investigate the implications that a general view of complexity - i.e. the view that complex phenomena are irreducible - hold for our understanding of ethics. In this view, ethics should be conceived of as constitutive of knowledge and identity, rather than as a normative system that dictates right action. Using this understanding, we elaborate on the ethics of complexity and the complexity of ethics. Whilst the former concerns the nature and the status of (...)
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  37. Karen L. F. Houle (2013). Responsibility, Complexity, and Abortion: Toward a New Image of Ethical Thought. Lexington Books.
    Responsibility, Complexity, and Abortion: Toward a New Image of Ethical Thought draws from feminist theory, post-structuralist theory, and complexity theory to develop a new set of ethical concepts for broaching the thinking challenges that attend the experience of unwanted pregnancy. Author Karen Houle does not only argue for these concepts; she enacts a method for working with them, a method that brackets the tendency to take positions and to think that position-taking is what ethical analysis involves. This (...)
     
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  38.  18
    Daniel W. McShea (2000). Functional Complexity in Organisms: Parts as Proxies. [REVIEW] Biology and Philosophy 15 (5):641-668.
    The functional complexity, or the number of functions, of organisms hasfigured prominently in certain theoretical and empirical work inevolutionary biology. Large-scale trends in functional complexity andcorrelations between functional complexity and other variables, such assize, have been proposed. However, the notion of number of functions hasalso been operationally intractable, in that no method has been developedfor counting functions in an organism in a systematic and reliable way.Thus, studies have had to rely on the largely unsupported assumption thatnumber of (...)
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  39.  80
    Philippe Gagnon (2013). An Improbable God Between Simplicity and Complexity: Thinking About Dawkins's Challenge. 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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  40.  10
    Michael H. G. Hoffmann (2015). Changing Philosophy Through Technology: Complexity and Computer-Supported Collaborative Argument Mapping. Philosophy and Technology 28 (2):167-188.
    Technology is not only an object of philosophical reflection but also something that can change this reflection. This paper discusses the potential of computer-supported argument visualization tools for coping with the complexity of philosophical arguments. I will show, in particular, how the interactive and web-based argument mapping software “AGORA-net” can change the practice of philosophical reflection, communication, and collaboration. AGORA-net allows the graphical representation of complex argumentations in logical form and the synchronous and asynchronous collaboration on those “argument maps” (...)
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  41.  42
    Sandra D. Mitchell (2009). Unsimple Truths: Science, Complexity, and Policy. The University of Chicago Press Chicago and London.
    In Unsimple Truths, Sandra Mitchell argues that the long-standing scientific and philosophical deference to reductive explanations founded on simple universal ...
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  42.  35
    Olivier Finkel (2008). Topological Complexity of Locally Finite Ω-Languages. Archive for Mathematical Logic 47 (6):625-651.
    Locally finite omega languages were introduced by Ressayre [Formal languages defined by the underlying structure of their words. J Symb Log 53(4):1009–1026, 1988]. These languages are defined by local sentences and extend ω-languages accepted by Büchi automata or defined by monadic second order sentences. We investigate their topological complexity. All locally finite ω-languages are analytic sets, the class LOC ω of locally finite ω-languages meets all finite levels of the Borel hierarchy and there exist some locally finite ω-languages which (...)
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  43.  32
    E. J. Chaisson (2011). Energy Rate Density. II. Probing Further a New Complexity Metric. Complexity 17 (1):44-63.
  44.  21
    Jarmo Kontinen (2013). Coherence and Computational Complexity of Quantifier-Free Dependence Logic Formulas. Studia Logica 101 (2):267-291.
    We study the computational complexity of the model checking problem for quantifier-free dependence logic ${(\mathcal{D})}$ formulas. We characterize three thresholds in the complexity: logarithmic space (LOGSPACE), non-deterministic logarithmic space (NL) and non-deterministic polynomial time (NP).
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  45.  4
    Nikita A. Sakhanenko & David J. Galas (2011). Complexity of Networks I: The Set‐Complexity of Binary Graphs. Complexity 17 (2):51-64.
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  46.  12
    Jennifer Wells (2012). Complexity and Sustainability. Routledge.
    Introduction -- Elucidating complexity theories -- Complexity in the natural sciences -- Complexity in social theory -- Towards transdisciplinarity -- Complexity in philosophy: complexification and the limits to knowledge -- Complexity in ethics -- Earth in the anthropocene -- Complexity and climate change -- American dreams, ecological nightmares and new visions -- Complexity and sustainability: wicked problems, gordian knots and synergistic solutions -- Conclusion.
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  47.  12
    Carlos Gershenson & Nelson Fernandez (2012). Complexity and Information: Measuring Emergence, Self‐Organization, and Homeostasis at Multiple Scales. Complexity 18 (2):29-44.
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  48.  10
    Tomasz M. Ignac, Nikita A. Sakhanenko & David J. Galas (2012). Complexity of Networks II: The Set Complexity of Edge‐Colored Graphs. Complexity 17 (5):23-36.
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  49.  88
    Paul Cilliers, Tanya de Villiers & Vasti Roodt (2002). The Formation of the Self. Nietzsche and Complexity. 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.  51
    Ralph H. Abraham (2011). The Genesis of Complexity. World Futures 67 (4-5):380 - 394.
    The theories of complexity comprise a system of great breadth. But what is included under this umbrella? Here we attempt a portrait of complexity theory, seen through the lens of complexity theory itself. That is, we portray the subject as an evolving complex dynamical system, or social network, with bifurcations, emergent properties, and so on. This is a capsule history covering the twentieth century. Extensive background data may be seen at www.visual-chaos.org/complexity.
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