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  1. Jon Agar (1998). Computer: A History of the Information Machine. [REVIEW] British Journal for the History of Science 31 (3):361-375.
  2. Samuel Alexander (2011). A Paradox Related to the Turing Test. The Reasoner 5 (6):90-90.
  3. H. Beck (1986). Bio-Social Cybernetic Determination, or Responsible Freedom? In Philosophy and Technology II. Information Technology and Computers in Theory and Practice. Boston Studies in the Philosophy of Science 90:85-95.
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  4. Paul Richard Blum, Michael Polanyi: Can the Mind Be Represented by a Machine? Existence and Anthropology.
    On the 27th of October, 1949, the Department of Philosophy at the University of Manchester organized a symposium "Mind and Machine", as Michael Polanyi noted in his Personal Knowledge (1974, p. 261). This event is known, especially among scholars of Alan Turing, but it is scarcely documented. Wolfe Mays (2000) reported about the debate, which he personally had attended, and paraphrased a mimeographed document that is preserved at the Manchester University archive. He forwarded a copy to Andrew Hodges and (...)
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  5. Giuseppe Boccignone & Roberto Cordeschi, Bayesian Models and Simulations in Cognitive Science. Workshop Models and Simulations 2, Tillburg, NL.
    Bayesian models can be related to cognitive processes in a variety of ways that can be usefully understood in terms of Marr's distinction among three levels of explanation: computational, algorithmic and implementation. In this note, we discuss how an integrated probabilistic account of the different levels of explanation in cognitive science is resulting, at least for the current research practice, in a sort of unpredicted epistemological shift with respect to Marr's original proposal.
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  6. Thomas Boyer-Kassem (2014). Layers of Models in Computer Simulations. International Studies in the Philosophy of Science 28 (4):417-436.
    I discuss here the definition of computer simulations, and more specifically the views of Humphreys, who considers that an object is simulated when a computer provides a solution to a computational model, which in turn represents the object of interest. I argue that Humphreys's concepts are not able to analyse fully successfully a case of contemporary simulation in physics, which is more complex than the examples considered so far in the philosophical literature. I therefore modify Humphreys's definition of simulation. I (...)
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  7. J. Cabrera Calvo-Sotelo (1984). Prueba automatica de teoremas: un panorama. Teorema: International Journal of Philosophy 14 (3-4):475-496.
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  8. Robert C. Cummins (1986). Inexplicit Information. In Myles Brand & Robert M. Harnish (eds.), The Representation of Knowledge and Belief. University of Arizona Press
    A discussion of a number of ways that information can be present in a computer program without being explicitly represented.
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  9. Nuno David (2009). Validation and Verification in Social Simulation: Patterns and Clarification of Terminology. Epistemological Aspects of Computer Simulation in the Social Sciences, EPOS 2006, Revised Selected and Invited Papers, Lecture Notes in Artificial Intelligence, Squazzoni, Flaminio (Ed.) 5466:117-129.
    The terms ‘verification’ and ‘validation’ are widely used in science, both in the natural and the social sciences. They are extensively used in simulation, often associated with the need to evaluate models in different stages of the simulation development process. Frequently, terminological ambiguities arise when researchers conflate, along the simulation development process, the technical meanings of both terms with other meanings found in the philosophy of science and the social sciences. This article considers the problem of verification and validation in (...)
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  10. Nuno David, Maria Marietto, Jaime Sichman & Helder Coelho (2004). The Structure and Logic of Interdisciplinary Research in Agent-Based Social Simulation. Journal of Artificial Societies and Social Simulation 7 (3).
    This article reports an exploratory survey of the structure of interdisciplinary research in Agent-Based Social Simulation. One hundred and ninety six researchers participated in the survey completing an on-line questionnaire. The questionnaire had three distinct sections, a classification of research domains, a classification of models, and an inquiry into software requirements for designing simulation platforms. The survey results allowed us to disambiguate the variety of scientific goals and modus operandi of researchers with a reasonable level of detail, and to identify (...)
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  11. Nuno David, Jaime Sichman & Helder Coelho (2007). Simulation as Formal and Generative Social Science: The Very Idea. In Carlos Gershenson, Diederik Aerts & Bruce Edmonds (eds.), Worldviews, Science, and Us: Philosophy and Complexity. World Scientific 266--275.
    The formal and empirical-generative perspectives of computation are demonstrated to be inadequate to secure the goals of simulation in the social sciences. Simulation does not resemble formal demonstrations or generative mechanisms that deductively explain how certain models are sufficient to generate emergent macrostructures of interest. The description of scientific practice implies additional epistemic conceptions of scientific knowledge. Three kinds of knowledge that account for a comprehensive description of the discipline were identified: formal, empirical and intentional knowledge. The use of formal (...)
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  12. Nuno David, Jaime Sichman & Helder Coleho (2005). The Logic of the Method of Agent-Based Simulation in the Social Sciences: Empirical and Intentional Adequacy of Computer Programs. Journal of Artificial Societies and Social Simulation 8 (4).
    The classical theory of computation does not represent an adequate model of reality for simulation in the social sciences. The aim of this paper is to construct a methodological perspective that is able to conciliate the formal and empirical logic of program verification in computer science, with the interpretative and multiparadigmatic logic of the social sciences. We attempt to evaluate whether social simulation implies an additional perspective about the way one can understand the concepts of program and computation. We demonstrate (...)
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  13. Joe Dewhurst (2014). Mechanistic Miscomputation: A Reply to Fresco and Primiero. Philosophy and Technology 27 (3):495-498.
    Fresco and Primiero’s recent article, ‘Miscomputation’ , provides a useful framework with which to think about miscomputation, as well as an admirably broad taxonomy of different kinds of miscomputation. However, it also misconstrues the mechanistic approach to miscomputation, which I will argue should not recognise design errors as miscomputations per se. I argue that a computing mechanism, if it is functioning correctly in the physical sense, cannot miscompute on the basis of an error made by an external agent, such as (...)
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  14. Farzad Didehvar, A Contradiction and P=NP Problem.
    Here, by introducing a version of “Unexpected hanging paradox” first we try to open a new way and a new explanation for paradoxes, similar to liar paradox. Also, we will show that we have a semantic situation which no syntactical logical system could support it. Finally, we propose a claim in Theory of Computation about the consistency of this Theory. One of the major claim is:Theory of Computation and Classical Logic leads us to a contradiction.
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  15. Gordana Dodig-Crnkovic (2008). Knowledge Generation as Natural Computation. Journal of Systemics, Cybernetics and Informatics 6 (2).
    Knowledge generation can be naturalized by adopting computational model of cognition and evolutionary approach. In this framework knowledge is seen as a result of the structuring of input data (data → information → knowledge) by an interactive computational process going on in the agent during the adaptive interplay with the environment, which clearly presents developmental advantage by increasing agent’s ability to cope with the situation dynamics. This paper addresses the mechanism of knowledge generation, a process that may be modeled as (...)
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  16. Gordana Dodig-Crnkovic (2007). WHERE DO NEW IDEAS COME FROM? HOW DO THEY EMERGE? - EPISTEMOLOGY AS COMPUTATION. In Christian Calude (ed.), Randomness & Complexity, from Leibniz to Chaitin.
    This essay presents arguments for the claim that in the best of all possible worlds (Leibniz) there are sources of unpredictability and creativity for us humans, even given a pancomputational stance. A suggested answer to Chaitin’s questions: “Where do new mathematical and biological ideas come from? How do they emerge?” is that they come from the world and emerge from basic physical (computational) laws. For humans as a tiny subset of the universe, a part of the new ideas comes as (...)
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  17. Amnon H. Eden (2007). Three Paradigms of Computer Science. Minds and Machines 17 (2):135-167.
    We examine the philosophical disputes among computer scientists concerning methodological, ontological, and epistemological questions: Is computer science a branch of mathematics, an engineering discipline, or a natural science? Should knowledge about the behaviour of programs proceed deductively or empirically? Are computer programs on a par with mathematical objects, with mere data, or with mental processes? We conclude that distinct positions taken in regard to these questions emanate from distinct sets of received beliefs or paradigms within the discipline: – The rationalist (...)
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  18. Daniel Estrada & Jon Lawhead (2013). Gaming the Attention Economy. In Pietro Michelucci (ed.), The Handbook of Human Computation. Springer 961-978.
    The future of human computation (HC) benefits from examining tasks that agents already perform and designing environments to give those tasks computational significance. We call this natural human computation (NHC). We consider the possible future of NHC through the lens of Swarm!, an application under development for Google Glass. Swarm! motivates users to compute the solutions to a class of economic optimization problems by engaging the <span class='Hi'>attention</span> dynamics of crowds. We argue that anticipating and managing economies of <span class='Hi'>attention</span> (...)
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  19. Thomas Macaulay Ferguson (forthcoming). Faulty Belnap Computers and Subsystems of FDE. Journal of Logic and Computation.
    In this article, we consider variations of Nuel Belnap's "artificial reasoner". In particular, we examine cases in which the artificial reasoner is faulty, e.g. situations in which the reasoner is unable to calculate the value of a formula due to an inability to retrieve the values of its atoms. In the first half of the article, we consider two ways of modelling such circumstances and prove the deductive systems arising from these two types of models to be equivalent to Graham (...)
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  20. Alexander R. Galloway (2012). The Interface Effect. Polity.
    Introduction : the computer as a mode of mediation -- The unworkable interface -- Software and ideology -- Are some things unrepresentable? -- Disingenuous informatics -- Postscript : we are the gold farmers.
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  21. Helmut Hauser, Rudolf M. Füchslin & Rolf Pfeifer (eds.) (2014). Opinions and Outlooks on Morphological Computation. E-Book.
    Morphological Computation is based on the observation that biological systems seem to carry out relevant computations with their morphology (physical body) in order to successfully interact with their environments. This can be observed in a whole range of systems and at many different scales. It has been studied in animals – e.g., while running, the functionality of coping with impact and slight unevenness in the ground is "delivered" by the shape of the legs and the damped elasticity of the muscle-tendon (...)
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  22. Matej Hoffmann & Vincent C. Müller (2014). Trade-Offs in Exploiting Body Morphology for Control: From Simple Bodies and Model-Based Control to Complex Ones with Model-Free Distributed Control Schemes. In Helmut Hauser, Rudolf M. Füchslin & Rolf Pfeifer (eds.), Opinions and Outlooks on Morphological Computation. E-Book 185-194.
    Tailoring the design of robot bodies for control purposes is implicitly performed by engineers, however, a methodology or set of tools is largely absent and optimization of morphology (shape, material properties of robot bodies, etc.) is lag- ging behind the development of controllers. This has become even more prominent with the advent of compliant, deformable or "soft" bodies. These carry substantial potential regarding their exploitation for control – sometimes referred to as "mor- phological computation" in the sense of offloading computation (...)
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  23. Lucja Iwańska (1993). Logical Reasoning in Natural Language: It is All About Knowledge. [REVIEW] Minds and Machines 3 (4):475-510.
    A formal, computational, semantically clean representation of natural language is presented. This representation captures the fact that logical inferences in natural language crucially depend on the semantic relation of entailment between sentential constituents such as determiner, noun, adjective, adverb, preposition, and verb phrases.The representation parallels natural language in that it accounts for human intuition about entailment of sentences, it preserves its structure, it reflects the semantics of different syntactic categories, it simulates conjunction, disjunction, and negation in natural language by computable (...)
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  24. Robert Janusz (2012). Roberto Busa i humanistyczna informatyka. Rocznik Filozoficzny Ignatianum:91-106.
    Fr. Roberto Busa was an Italian Jesuit. In this article his biography will briefly be presented, and some issues raised by his philosophy analyzed. Busa was known as a pioneer of computerized research in the humanities. With the support of IBM he constructed the Index Thomisticus, containing all the works of St. Thomas Aquinas. He believed that expressions of the human can be mathematically modeled. He was the originator of a specific conception of hypertext, in which logically structured programs are (...)
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  25. Elisabeth A. Lloyd (2012). The Role of 'Complex' Empiricism in the Debates About Satellite Data and Climate Models. Studies in History and Philosophy of Science Part A 43 (2):390-401.
    climate scientists have been engaged in a decades-long debate over the standing of satellite measurements of the temperature trends of the atmosphere above the surface of the earth. This is especially significant because skeptics of global warming and the greenhouse effect have utilized this debate to spread doubt about global climate models used to predict future states of climate. I use this case from an under-studied science to illustrate two distinct philosophical approaches to the relation among <span class='Hi'>data</span>, scientists, measurement, (...)
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  26. Seh Lloyd (2007). Programming the Universe. Vintage.
  27. Aran Nayebi (2014). Practical Intractability: A Critique of the Hypercomputation Movement. [REVIEW] Minds and Machines 24 (3):275-305.
    For over a decade, the hypercomputation movement has produced computational models that in theory solve the algorithmically unsolvable, but they are not physically realizable according to currently accepted physical theories. While opponents to the hypercomputation movement provide arguments against the physical realizability of specific models in order to demonstrate this, these arguments lack the generality to be a satisfactory justification against the construction of any information-processing machine that computes beyond the universal Turing machine. To this end, I present a more (...)
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  28. Matthew W. Parker (2003). Three Concepts of Decidability for General Subsets of Uncountable Spaces. Theoretical Computer Science 351 (1):2-13.
    There is no uniquely standard concept of an effectively decidable set of real numbers or real n-tuples. Here we consider three notions: decidability up to measure zero [M.W. Parker, Undecidability in Rn: Riddled basins, the KAM tori, and the stability of the solar system, Phil. Sci. 70(2) (2003) 359–382], which we abbreviate d.m.z.; recursive approximability [or r.a.; K.-I. Ko, Complexity Theory of Real Functions, Birkhäuser, Boston, 1991]; and decidability ignoring boundaries [d.i.b.; W.C. Myrvold, The decision problem for entanglement, in: R.S. (...)
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  29. John Preston (2012). Unthinking Things. The Philosophers' Magazine 57 (57):79-83.
  30. John Preston (2012). What Are Computers (If They're Not Thinking Things)? In S. Barry Cooper (ed.), How the World Computes. 609--615.
  31. Giuseppe Primiero, Nir Fresco & Luciano Floridi (2015). On Malfunctioning Software. Synthese 192 (4):1199-1220.
    Artefacts do not always do what they are supposed to, due to a variety of reasons, including manufacturing problems, poor maintenance, and normal wear-and-tear. Since software is an artefact, it should be subject to malfunctioning in the same sense in which other artefacts can malfunction. Yet, whether software is on a par with other artefacts when it comes to malfunctioning crucially depends on the abstraction used in the analysis. We distinguish between “negative” and “positive” notions of malfunction. A negative malfunction, (...)
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  32. Varanasi Ramabrahmam, The Significance and Use of Absence.
    The significance and use of absence of a thing is highlighted taking examples from mathematics, physics, semi-conductor electronics, computer science and cognitive science. The profundity of absence is discussed.
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  33. William J. Rapaport (2005). Philosophy of Computer Science. Teaching Philosophy 28 (4):319-341.
    There are many branches of philosophy called “the philosophy of X,” where X = disciplines ranging from history to physics. The philosophy of artificial intelligence has a long history, and there are many courses and texts with that title. Surprisingly, the philosophy of computer science is not nearly as well-developed. This article proposes topics that might constitute the philosophy of computer science and describes a course covering those topics, along with suggested readings and assignments.
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  34. William J. Rapaport (2005). Philosophy of Computer Science : An Introductory Course Philosophy of Computer Science : An Introductory Course. Teaching Philosophy 28 (4):319-341.
    There are many branches of philosophy called "the philosophy of X," where X = disciplines ranging from history to physics. The philosophy of artificial intelligence has a long history, and there are many courses and texts with that title. Surprisingly, the philosophy of computer science is not nearly as well-developed. This article proposes topics that might constitute the philosophy of computer science and describes a course covering those topics, along with suggested readings and assignments.
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  35. William J. Rapaport (1999). Implementation Is Semantic Interpretation. The Monist 82 (1):109-130.
    What is the computational notion of "implementation"? It is not individuation, instantiation, reduction, or supervenience. It is, I suggest, semantic interpretation. The online version differs from the published version in being a bit longer and going into a bit more detail.
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  36. Emanuele Ratti (2014). Levels of Abstraction, Emergentism and Artificial Life. Journal of Experimental & Theoretical Artificial Intelligence:1-12.
    I diagnose the current debate between epistemological and ontological emergentism as a Kantian antinomy, which has reasonable but irreconcilable thesis and antithesis. Kantian antinomies have recently returned to contemporary philosophy in part through the work of Luciano Floridi, and the method of levels of abstraction. I use a thought experiment concerning a computer simulation to show how to resolve the epistemological/ontological antinomy about emergence. I also use emergentism and simulations in artificial life to illuminate both levels of abstraction (...)
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  37. Emilio Sanfilippo, Emanuele Ratti, Francesca Quattri, Aleksandra Sojic, Federico Boem, Gaoussou Camara & Eric Chuk (2013). Diverse Perspectives on Ontology. Applied Ontology 8 (8):59-71.
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  38. Joerg R. J. Schirra (2012). Sind Bilder ein Gegenstand der Informatik? Überlegungen zur Computervisualistik. In Dimitri Liebsch & Nicola Mößner (eds.), Visualisierung und Erkenntnis. Bildverstehen und Bildverwenden in Natur- und Geisteswissenschaften. Herbert von Halem Verlag. 329-359.
    Ausgehend von der provokanten These, Bilder seien gar kein genuiner Gegenstand der Informatik, geht es im Folgenden um die Beziehung zwischen Computervisualistik und bildwissenschaftlichen Begriffen. Denn trotz der These stellen Informatiker uns offensichtlich bereits seit Jahren Computer bereit, auf denen wir eine Vielfalt von Programmen zur Bildverarbeitung, Bilderzeugung und Bildanalyse nutzen können. Interaktive Bilder und immersive Systeme sind schließlich ohne die computervisualistischen Erzeugnisse gar nicht vorstellbar. Eine kleine Einführung in die Funktionsweisen beispielhaft gewählter bildverarbeitender und -erzeugender Algorithmen liefert das Anschauungsmaterial, (...)
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  39. Jeremy Seligman (2002). The Scope of Turing's Analysis of Effective Procedures. Minds and Machines 12 (2):203-220.
    Turing's (1936) analysis of effective symbolic procedures is a model of conceptual clarity that plays an essential role in the philosophy of mathematics. Yet appeal is often made to the effectiveness of human procedures in other areas of philosophy. This paper addresses the question of whether Turing's analysis can be applied to a broader class of effective human procedures. We use Sieg's (1994) presentation of Turing's Thesis to argue against Cleland's (1995) objections to Turing machines and we evaluate her proposal (...)
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  40. Yaroslav Sergeyev (2009). Evaluating the Exact Infinitesimal Values of Area of Sierpinski's Carpet and Volume of Menger's Sponge. Chaos, Solitons and Fractals 42: 3042–3046.
    Very often traditional approaches studying dynamics of self-similarity processes are not able to give their quantitative characteristics at infinity and, as a consequence, use limits to overcome this difficulty. For example, it is well know that the limit area of Sierpinski’s carpet and volume of Menger’s sponge are equal to zero. It is shown in this paper that recently introduced infinite and infinitesimal numbers allow us to use exact expressions instead of limits and to calculate exact infinitesimal values of areas (...)
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  41. Yaroslav Sergeyev & Alfredo Garro (2013). Single-Tape and Multi-Tape Turing Machines Through the Lens of the Grossone Methodology. Journal of Supercomputing 65 (2):645-663.
    The paper investigates how the mathematical languages used to describe and to observe automatic computations influence the accuracy of the obtained results. In particular, we focus our attention on Single and Multi-tape Turing machines which are described and observed through the lens of a new mathematical language which is strongly based on three methodological ideas borrowed from Physics and applied to Mathematics, namely: the distinction between the object (we speak here about a mathematical object) of an observation and the instrument (...)
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  42. P. Shor (1994). Algorithms for Quantum Computation: Discrete Logarithms and Factoring. Proceedings of the 35th Annual IEEE Symposium on Foundations of Computer Science:124-134.
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  43. Wilfried Sieg & John Byrnes, K-Graph Machines: Generalizing Turing's Machines and Arguments.
    Wilfred Sieg and John Byrnes. K-Graph Machines: Generalizing Turing's Machines and Arguments.
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  44. Jonathan Simon (2005). Formal Ontology for Natural Language Processing and the Integration of Biomedical Databases. International Journal of Medical Informatics 75:224-231.
    The central hypothesis of the collaboration between Language and Computing (L&C) and the Institute for Formal Ontology and Medical Information Science (IFOMIS) is that the methodology and conceptual rigor of a philosophically inspired formal ontology greatly benefits application ontologies. To this end r®, L&C’s ontology, which is designed to integrate and reason across various external databases simultaneously, has been submitted to the conceptual demands of IFOMIS’s Basic Formal Ontology (BFO). With this project we aim to move beyond the level of (...)
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  45. Beckett Sterner (2013). Well-Structured Biology: Numerical Taxonomy's Epistemic Vision for Systematics. In Andrew Hamilton (ed.), The Evolution of Phylogenetic Systematics. University of California Press 213-244.
    What does it look like when a group of scientists set out to re-envision an entire field of biology in symbolic and formal terms? I analyze the founding and articulation of Numerical Taxonomy between 1950 and 1970, the period when it set out a radical new approach to classification and founded a tradition of mathematics in systematic biology. I argue that introducing mathematics in a comprehensive way also requires re-organizing the daily work of scientists in the field. Numerical taxonomists sought (...)
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  46. Beckett Sterner (2009). Object Spaces: An Organizing Strategy for Biological Theorizing. Biological Theory 4 (3):280-286.
    A classic analytic approach to biological phenomena seeks to refine definitions until classes are sufficiently homogenous to support prediction and explanation, but this approach founders on cases where a single process produces objects with similar forms but heterogeneous behaviors. I introduce object spaces as a tool to tackle this challenging diversity of biological objects in terms of causal processes with well-defined formal properties. Object spaces have three primary components: (1) a combinatorial biological process such as protein synthesis that generates objects (...)
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