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  1. Peter Achinstein (ed.) (2004). Science Rules: A Historical Introduction to Scientific Methods. Johns Hopkins University Press.
    Is there a universal set of rules for discovering and testing scientific hypotheses? Since the birth of modern science, philosophers, scientists, and other thinkers have wrestled with this fundamental question of scientific practice. Efforts to devise rigorous methods for obtaining scientific knowledge include the twenty-one rules Descartes proposed in his Rules for the Direction of the Mind and the four rules of reasoning that begin the third book of Newton's Principia , and continue today in debates over the very possibility (...)
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  2. Alan Baker (2008). Complexity Unfavoured. Analysis 68 (297):85–88.
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  3. Greg Bamford (1999). What is the Problem of Ad Hoc Hypotheses? Science and Education 8 (4):375 - 86..
    The received view of an ad hochypothesis is that it accounts for only the observation(s) it was designed to account for, and so non-ad hocness is generally held to be necessary or important for an introduced hypothesis or modification to a theory. Attempts by Popper and several others to convincingly explicate this view, however, prove to be unsuccessful or of doubtful value, and familiar and firmer criteria for evaluating the hypotheses or modified theories so classified are characteristically available. These points (...)
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  4. Michael Baumgartner (forthcoming). Detecting Causal Chains in Small-N Data. Field Methods.
    The first part of this paper shows that Qualitative Comparative Analysis (QCA)--also in its most recent forms as presented in Ragin (2000, 2008)--, does not correctly analyze data generated by causal chains, which, after all, are very common among causal processes in the social sciences. The incorrect modeling of data originating from chains essentially stems from QCA’s reliance on Quine-McCluskey optimization to eliminate redundancies from sufficient and necessary conditions. Baumgartner (2009a,b) has introduced a Boolean methodology, termed Coincidence Analysis (CNA), that (...)
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  5. Michael Baumgartner (2009). Uncovering Deterministic Causal Structures: A Boolean Approach. Synthese 170 (1):71 - 96.
    While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are custom-built for (non-deterministic) probabilistic structures, this paper introduces a Boolean procedure that uncovers deterministic causal structures. Contrary to existing Boolean methodologies, the procedure advanced here successfully analyzes structures of arbitrary complexity. It roughly involves three parts: first, deterministic dependencies are identified in the data; second, these dependencies are suitably minimalized in order to eliminate redundancies; and third, one or—in case of ambiguities—more than one causal structure is assigned (...)
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  6. Bernadette Bensaude-Vincent, Sacha Loeve, Alfred Nordmann & Astrid Schwarz (2011). Matters of Interest: The Objects of Research in Science and Technoscience. [REVIEW] Journal for General Philosophy of Science 42 (2):365-383.
    This discussion paper proposes that a meaningful distinction between science and technoscience can be found at the level of the objects of research. Both notions intermingle in the attitudes, intentions, programs and projects of researchers and research institutions—that is, on the side of the subjects of research. But the difference between science and technoscience becomes more explicit when research results are presented in particular settings and when the objects of research are exhibited for the specific interest they hold. When an (...)
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  7. Gregor Betz (2010). Besprechung von ‘Zum methodologischen Wert von Vorhersagen’ von Cornelis Menke. [REVIEW] DZPhil 58:329-332.
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  8. Alex Broadbent (2011). Inferring Causation in Epidemiology: Mechanisms, Black Boxes, and Contrasts. In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press. 45--69.
    This chapter explores the idea that causal inference is warranted if and only if the mechanism underlying the inferred causal association is identified. This mechanistic stance is discernible in the epidemiological literature, and in the strategies adopted by epidemiologists seeking to establish causal hypotheses. But the exact opposite methodology is also discernible, the black box stance, which asserts that epidemiologists can and should make causal inferences on the basis of their evidence, without worrying about the mechanisms that might underlie their (...)
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  9. Peter Brössel (2013). Assessing Theories: The Coherentist Approach. Erkenntnis 79 (3):593-623.
    In this paper we show that the coherence measures of Olsson (J Philos 94:246–272, 2002), Shogenji (Log Anal 59:338–345, 1999), and Fitelson (Log Anal 63:194–199, 2003) satisfy the two most important adequacy requirements for the purpose of assessing theories. Following Hempel (Synthese 12:439–469, 1960), Levi (Gambling with truth, New York, A. A. Knopf, 1967), and recently Huber (Synthese 161:89–118, 2008) we require, as minimal or necessary conditions, that adequate assessment functions favor true theories over false theories and true and informative (...)
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  10. Daniel Cohnitz (2006). Poor Thought Experiments? A Comment on Peijnenburg and Atkinson. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 37 (2):373 - 392.
    In their paper, 'When are thought experiments poor ones?' (Peijnenburg and Atkinson, 2003, Journal of General Philosophy of Science 34, 305-322), Jeanne Peijnenburg and David Atkinson argue that most, if not all, philosophical thought experiments are "poor" ones with "disastrous consequences" and that they share the property of being poor with some (but not all) scientific thought experiments. Noting that unlike philosophy, the sciences have the resources to avoid the disastrous consequences, Peijnenburg and Atkinson come to the conclusion that the (...)
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  11. Richard Corry (2009). How is Scientific Analysis Possible? In Toby Handfield (ed.), Dispositions and Causes. Oxford University Press, Clarendon Press ;.
    One of the most powerful tools in science is the analytic method, whereby we seek to understand complex systems by studying simpler sub-systems from which the complex is composed. If this method is to be successful, something about the sub-systems must remain invariant as we move from the relatively isolated conditions in which we study them, to the complex conditions in which we want to put our knowledge to use. This paper asks what this invariant could be. The paper shows (...)
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  12. Tamás Demeter (2012). Hume's Experimental Method. British Journal for the History of Philosophy 20 (3):577-599.
    In this article I attempt to reconstruct David Hume's use of the label ?experimental? to characterise his method in the Treatise. Although its meaning may strike the present-day reader as unusual, such a reconstruction is possible from the background of eighteenth-century practices and concepts of natural inquiry. As I argue, Hume's inquiries into human nature are experimental not primarily because of the way the empirical data he uses are produced, but because of the way those data are theoretically processed. He (...)
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  13. Tamás Demeter & Gábor Á Zemplén (2010). Being Charitable to Scientific Controversies: On the Demonstrativity of Newton's Experimentum Crucis. The Monist 93 (4):640-656.
    Current philosophical reflections on science have departed from mainstream history of science with respect to both methodology and conclusions. The article investigates how different approaches to reconstructing commitments can explain these differences and facilitate a mutual understanding and communication of these two perspectives on science. Translating the differences into problems pertaining to principles of charity, the paper offers a platform for clarification and resolution of the differences between the two perspectives. The outlined contextual approach occupies a middle ground between mainstream (...)
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  14. Yiftach J. H. Fehige (2010). Quantum Physics and Theology. [REVIEW] American Catholic Philosophical Quarterly 84 (1):186-188.
  15. Yiftach J. H. Fehige (2007). Gedankenexperimente. Die Genese Einer Wissenschaftsphilosophischen Forschungstradition Nach Ulrich Kühne. Deutsche Zeitschrift für Philosophie (1):149-157.
    This is a review essay of what is probably the best contribution to the history of the philosophical investigation into thought experiments.
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  16. Yiftach J. H. Fehige & James R. Brown (2010). Thought Experiments. Stanford Encyclopedia of Philosophy 25 (1):135-142.
  17. Nicolas Fillion & Robert M. Corless (2014). On the Epistemological Analysis of Modeling and Computational Error in the Mathematical Sciences. Synthese 191 (7):1451-1467.
    Interest in the computational aspects of modeling has been steadily growing in philosophy of science. This paper aims to advance the discussion by articulating the way in which modeling and computational errors are related and by explaining the significance of error management strategies for the rational reconstruction of scientific practice. To this end, we first characterize the role and nature of modeling error in relation to a recipe for model construction known as Euler’s recipe. We then describe a general model (...)
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  18. Gabor Forrai (2002). Lakatos, Reason, and Rationality. In G. Kampis L. Kvasz & M. Stöltzner (eds.), Appraising Lakatos: Mathematics, Methodology, and the Man. Kluwer.
  19. Alexander Gebharter, Addendum to "A Formal Framework for Representing Mechanisms?&Quot;.
    In (Gebharter 2014) I suggested a framework for modeling the hierarchical organization of mechanisms. In this short addendum I want to highlight some connections of my approach to the statistics and machine learning literature and some of its limitations not mentioned in the paper.
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  20. Alexander Gebharter (2014). A Formal Framework for Representing Mechanisms? Philosophy of Science 81 (1):138-153.
    In this article I tackle the question of how the hierarchical order of mechanisms can be represented within a causal graph framework. I illustrate an answer to this question proposed by Casini, Illari, Russo, and Williamson and provide an example that their formalism does not support two important features of nested mechanisms: (i) a mechanism’s submechanisms are typically causally interacting with other parts of said mechanism, and (ii) intervening in some of a mechanism’s parts should have some influence on the (...)
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  21. Alexander Gebharter & Marie I. Kaiser (2014). Causal Graphs and Biological Mechanisms. In Marie I. Kaiser, Oliver Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the special sciences: The case of biology and history. Springer. 55-85.
    Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research practice (...)
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  22. Alexander Gebharter & Gerhard Schurz (2014). Explanation, Causality, and Unification. Theoria. An International Journal for Theory, History and Foundations of Science 29 (1):5-7.
  23. Pierre-Luc Germain, Emanuele Ratti & Federico Boem (2014). Junk or Functional DNA? ENCODE and the Function Controversy. Biology and Philosophy 29 (6):807-831.
    In its last round of publications in September 2012, the Encyclopedia Of DNA Elements (ENCODE) assigned a biochemical function to most of the human genome, which was taken up by the media as meaning the end of ‘Junk DNA’. This provoked a heated reaction from evolutionary biologists, who among other things claimed that ENCODE adopted a wrong and much too inclusive notion of function, making its dismissal of junk DNA merely rhetorical. We argue that this criticism rests on misunderstandings concerning (...)
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  24. Stephan Hartmann (1996). The World as a Process: Simulations in the Natural and Social Sciences. In Rainer Hegselmann (ed.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View.
    Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the "model-building era" (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the "simulation era". But what exactly are models? What is a simulation and what is the difference and the relation between a model and a simulation? (...)
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  25. Urs Hofmann & Michael Baumgartner (2011). Determinism and the Method of Difference. Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 26 (2):155-176.
    The first part of this paper reveals a conflict between the core principles of deterministic causation and the standard method of difference, which is widely seen (and used) as a correct method of causally analyzing deterministic structures. We show that applying the method of difference to deterministic structures can giverise to causal inferences that contradict the principles of deterministic causation. The second part then locates the source of this conflict in an inference rule implemented in the method of difference according (...)
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  26. Marcus Hutter (2010). A Complete Theory of Everything (Will Be Subjective). Algorithms 3 (4):329-350.
    Increasingly encompassing models have been suggested for our world. Theories range from generally accepted to increasingly speculative to apparently bogus. The progression of theories from ego- to geo- to helio-centric models to universe and multiverse theories and beyond was accompanied by a dramatic increase in the sizes of the postulated worlds, with humans being expelled from their center to ever more remote and random locations. Rather than leading to a true theory of everything, this trend faces a turning point after (...)
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  27. Rögnvaldur Ingthorsson (2013). The Natural Vs. The Human Sciences:: Myth, Methodology and Ontology. Discusiones Filosóficas 14 (22):25-41.
    I argue that the human sciences (i.e. humanities, social- and behavioural sciences) should not try to imitate the methodology of the natural sciences. The human sciences study meaningful phenomena whose nature is decisively different from the merely physical phenomena studied by the natural sciences, and whose study therefore require different methods; meaningful phenomena do not obviously obey natural laws while the merely physical necessarily does. This is not to say that the human sciences do not study an objective reality about (...)
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  28. Michael Jacovides (2002). The Epistemology Under Lockes Corpuscularianism. Archiv für Geschichte der Philosophie 84 (2):161-189.
    The intelligibility of our artifacts suggests to many seventeenth century thinkers that nature works along analogous lines, that the same principles that explain the operations of artifacts explain the operations of natural bodies.1 We may call this belief ‘corpuscularianism’ when conjoined with the premise that the details of the analogy depend upon the sub-microscopic textures of ordinary bodies and upon the rapidly moving, imperceptibly tiny corpuscles that surround these bodies.2 Locke’s sympathy for corpuscularianism comes out clearly where he describes the (...)
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  29. N. Jones (2011). Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science * Edited by Deborah G. Mayo and Aris Spanos. Analysis 71 (2):406-408.
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  30. Nicholaos Jones & Olaf Wolkenhauer (2012). Diagrams as Locality Aids for Explanation and Model Construction in Cell Biology. Biology and Philosophy 27 (5):705-721.
    Using as case studies two early diagrams that represent mechanisms of the cell division cycle, we aim to extend prior philosophical analyses of the roles of diagrams in scientific reasoning, and specifically their role in biological reasoning. The diagrams we discuss are, in practice, integral and indispensible elements of reasoning from experimental data about the cell division cycle to mathematical models of the cycle’s molecular mechanisms. In accordance with prior analyses, the diagrams provide functional explanations of the cell cycle and (...)
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  31. Pawel Kawalec (2012). Moderately Pluralistic Methodology. Roczniki Filozoficzne 60 (4):233-247.
    The paper outlines and discusses the major tenets of moderately pluralistic methodology. The latter is juxtaposed to J. Życiński’s principle of natural interdisciplinarity. It instantiates scientific pluralism as a domain-specific agenda for research. The symbolic and causal understanding are integrated in this methodological conception by means of a specific kind of counterfactual reasoning, which is coined the delimiting counterfactual. It makes the moderately pluralistic methodology applicable to non-experimental research. -/- Streszczenie Tytuł: “Umiarkowanie pluralistyczna metodologia” -/- Artykuł prezentuje i omawia zasadnicze (...)
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  32. Ansten Klev (2011). Dedekind and Hilbert on the Foundations of the Deductive Sciences. Review of Symbolic Logic 4 (4):645-681.
    We offer an interpretation of the words and works of Richard Dedekind and the David Hilbert of around 1900 on which they are held to entertain diverging views on the structure of a deductive science. Firstly, it is argued that Dedekind sees the beginnings of a science in concepts, whereas Hilbert sees such beginnings in axioms. Secondly, it is argued that for Dedekind, the primitive terms of a science are substantive terms whose sense is to be conveyed by elucidation, whereas (...)
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  33. Nicholas Maxwell (forthcoming). Can Scientific Method Help Us Create a Wiser World? In N. Dalal, A. Intezari & M. Heitz (eds.), Practical Wisdom in the Age of Technology: Insights, Issues and Questions for a New Millennium. Ashgate.
    Two great problems of learning confront humanity: (1) learning about the universe, and about ourselves as a part of the universe, and (2) learning how to make progress towards as good a world as possible. We solved the first problem when we created modern science in the 17th century, but we have not yet solved the second problem. This puts us in a situation of unprecedented danger. Modern science and technology enormously increase our power to act, but not our power (...)
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  34. Nicholas Maxwell (2013). Has Science Established That the Cosmos is Physically Comprehensible? In A. Travena & B. Soen (eds.), Recent Advances in Cosmology. Nova Science Publishers.
    Most scientists would hold that science has not established that the cosmos is physically comprehensible – i.e. such that there is some as-yet undiscovered true physical theory of everything that is unified. This is an empirically untestable, or metaphysical thesis. It thus lies beyond the scope of science. Only when physics has formulated a testable unified theory of everything which has been amply corroborated empirically will science be in a position to declare that it has established that the cosmos is (...)
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  35. Nicholas Maxwell (2012). How Universities Can Help Humanity Learn How to Resolve the Crises of Our Times - From Knowledge to Wisdom: The University College London Experience. In G. Heam, T. Katlelle & D. Rooney (eds.), Handbook on the Knowledge Economy, vol. 2. Edward Elgar Publishing Ltd.
    We are in a state of impending crisis. And the fault lies in part with academia. For two centuries or so, academia has been devoted to the pursuit of knowledge and technological know-how. This has enormously increased our power to act which has, in turn, brought us both all the great benefits of the modern world and the crises we now face. Modern science and technology have made possible modern industry and agriculture, the explosive growth of the world’s population, global (...)
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  36. Nicholas Maxwell (2011). Creating a Better World: Towards the University of Wisdom. In Ronald Barnett (ed.), The Future University: Ideas and Possibilities. Routledge.
    Universities need to change dramatically in order to help humanity make progress towards as good a world as possible.
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  37. Nicholas Maxwell (2010). Reply to Comments on Science and the Pursuit of Wisdom. Philosophia 38 (4):667-690.
    In this article I reply to comments made by Agustin Vicente and Giridhari Lal Pandit on Science and the Pursuit of Wisdom (McHenry 2009 ). I criticize analytic philosophy, go on to expound the argument for the need for a revolution in academic inquiry so that the basic aim becomes wisdom and not just knowledge, defend aim-oriented empiricism, outline my solution to the human world/physical universe problem, and defend the thesis that free will is compatible with physicalism.
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  38. Nicholas Maxwell (2010). The Urgent Need for an Academic Revolution: From Knowledge to Wisdom. In W. Karpiuk & K. Wisniewski (eds.), III International Interdisciplinary Technical Conference of Young Scientists: Proceedings.
    At present the basic intellectual aim of academic inquiry is to improve knowledge. Much of the structure, the whole character, of academic inquiry, in universities all over the world, is shaped by the adoption of this as the basic intellectual aim. But, judged from the standpoint of making a contribution to human welfare, academic inquiry of this type is damagingly irrational. Three of four of the most elementary rules of rational problem-solving are violated. A revolution in the aims and methods (...)
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  39. Nicholas Maxwell (2010). The Urgent Need for an Academic Revolution. In Mark Levene, Rob Johnson & Richard Maguire (eds.), History at the End of the World? History, Climate Change and the Possibility of Closure. Humanities-EBooks.
    Two great problems of learning confront humanity: first, learning about the nature of the universe and about ourselves as a part of the universe, and second, learning how to live wisely – learning how to make progress towards as good a world as possible. The first problem was solved, in essence, in the 17th century, with the creation of modern science. A method was discovered for progressively improving knowledge and understanding of the natural world, the famous empirical method of science. (...)
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  40. Nicholas Maxwell (2009). An Idea to Save the World. Sublime (17):90-93.
    Here is an idea that just might save the world. It is that science, properly understood, provides us with the methodological key to the salvation of humanity.
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  41. Nicholas Maxwell (2009). What’s Wrong With Science? Towards a People’s Rational Science of Delight and Compassion, Second Edition. Pentire Press.
    What ought to be the aims of science? How can science best serve humanity? What would an ideal science be like, a science that is sensitively and humanely responsive to the needs, problems and aspirations of people? How ought the institutional enterprise of science to be related to the rest of society? What ought to be the relationship between science and art, thought and feeling, reason and desire, mind and heart? Should the social sciences model themselves on the natural sciences: (...)
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  42. Nicholas Maxwell (2009). From Knowledge to Wisdom. In David Cayley (ed.), Ideas on the Nature of Science. Goose Lane Editions.
    There are these two absolutely basic problems: to learn about the universe and ourselves as a part of the universe, and to learn how to create a civilized world. Essentially, we have solved the first problem. We solved it when we created modern science. That is not to say that we know everything that is to be known, but we created a method for improving our knowledge about the world. But we haven't solved the second problem. And to solve the (...)
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  43. Nicholas Maxwell (2008). Are Philosophers Responsible for Global Warming? Philosophy Now 65 (65):12-13.
    The suggestion that philosophers are responsible for global warming seems, on the face of it, absurd. However, that we might cause global warming has been known for over a century. If we had had in existence a more rigorous kind of academic inquiry devoted to promoting human welfare, giving priority to problems of living, humanity might have become aware of the dangers of global warming long ago, and might have taken steps to meet these dangers decades ago. That we do (...)
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  44. Nicholas Maxwell, The Problem of Induction and Metaphysical Assumptions Concerning the Comprehensibility and Knowability of the Universe. PhilSci Archive.
    Even though evidence underdetermines theory, often in science one theory only is regarded as acceptable in the light of the evidence. This suggests there are additional unacknowledged assumptions which constrain what theories are to be accepted. In the case of physics, these additional assumptions are metaphysical theses concerning the comprehensibility and knowability of the universe. Rigour demands that these implicit assumptions be made explicit within science, so that they can be critically assessed and, we may hope improved. This leads to (...)
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  45. Nicholas Maxwell (2007). From Knowledge to Wisdom: A Revolution for Science and the Humanities (Second Edition). Pentire Press.
    From Knowledge to Wisdom argues that there is an urgent need, for both intellectual and humanitarian reasons, to bring about a revolution in science and the humanities. The outcome would be a kind of academic inquiry rationally devoted to helping humanity learn how to create a better world. Instead of giving priority to solving problems of knowledge, as at present, academia would devote itself to helping us solve our immense, current global problems – climate change, war, poverty, population growth, pollution (...)
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  46. Nicholas Maxwell (2007). From Knowledge to Wisdom: A Revolution for Science and the Humanities (Second Edition). Pentire Press.
    From Knowledge to Wisdom argues that there is an urgent need, for both intellectual and humanitarian reasons, to bring about a revolution in science and the humanities. The outcome would be a kind of academic inquiry rationally devoted to helping humanity learn how to create a better world. Instead of giving priority to solving problems of knowledge, as at present, academia would devote itself to helping us solve our immense, current global problems – climate change, war, poverty, population growth, pollution... (...)
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  47. Nicholas Maxwell, Aim-Oriented Empiricism: David Miller's Critique. PhilSci Archive.
    For three decades I have expounded and defended aim-oriented empiricism, a view of science which, l claim, solves a number of problems in the philosophy of science and has important implications for science itself and, when generalized, for the whole of academic inquiry, and for our capacity to solve our current global problems. Despite these claims, the view has received scant attention from philosophers of science. Recently, however, David Miller has criticized the view. Miller’s criticisms are, however, not valid.
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  48. Nicholas Maxwell (2006). The Enlightenment Programme and Karl Popper. In I. I. Jarvie, K. Milford & D. Miller (eds.), Karl Popper: A Centenary Assessment. Volume 1: Life and Times, Values in a World of Facts. Ashgate.
    Popper first developed his theory of scientific method – falsificationism – in his The Logic of Scientific Discovery, then generalized it to form critical rationalism, which he subsequently applied to social and political problems in The Open Society and Its Enemies. All this can be regarded as constituting a major development of the 18th century Enlightenment programme of learning from scientific progress how to achieve social progress towards a better world. Falsificationism is, however, defective. It misrepresents the real, problematic aims (...)
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  49. Nicholas Maxwell (2005). Science Under Attack. The Philosopher’s Magazine (31, 3rd Quarter 2005):37-41.
    Science has been under attack ever since William Blake and Romantic movement. In our time, criticisms of modern science have led to Alan Sokal's spoof, and the so-called science wars. Both sides missed the point. Science deserves to be criticized for seriously misrepresenting its highly problematic aims, which have metaphysical, value and political assumptions associated with them. Instead of repressing these problematic aims, science ought rather to make them explicit, so that they can be critically assessed and, we may hope, (...)
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  50. Nicholas Maxwell (2005). Popper, Kuhn, Lakatos and Aim-Oriented Empiricism. Philosophia 32 (1-4):181-239.
    In this paper I argue that aim-oriented empiricism (AOE), a conception of natural science that I have defended at some length elsewhere, is a kind of synthesis of the views of Popper, Kuhn and Lakatos, but is also an improvement over the views of all three. Whereas Popper's falsificationism protects metaphysical assumptions implicitly made by science from criticism, AOE exposes all such assumptions to sustained criticism, and furthermore focuses criticism on those assumptions most likely to need revision if science is (...)
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