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  1. William W. Agresti & Mark S. Mayzner (1978). Robustness of the Dynamic Visual Movement Effect. Bulletin of the Psychonomic Society 12 (2):147-148.
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  2. Luís Antunes, Andre Souto & Andreia Teixeira (2012). Robustness of Logical Depth. In S. Barry Cooper (ed.), How the World Computes. 29--34.
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  3. Cameron Buckner (2013). Morgan's Canon, Meet Hume's Dictum: Avoiding Anthropofabulation in Cross-Species Comparisons. Biology and Philosophy 28 (5):853-871.
    How should we determine the distribution of psychological traits—such as Theory of Mind, episodic memory, and metacognition—throughout the Animal kingdom? Researchers have long worried about the distorting effects of anthropomorphic bias on this comparative project. A purported corrective against this bias was offered as a cornerstone of comparative psychology by C. Lloyd Morgan in his famous “Canon”. Also dangerous, however, is a distinct bias that loads the deck against animal mentality: our tendency to tie the competence criteria for cognitive capacities (...)
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  4. H. G. Callaway (2014). Abduction, Competing Models and the Virtues of Hypotheses. In Lorenzo Magnani (ed.), (2014) Model-Based Reasoning in Science and Technology. Springer. 263-280.
    This paper focuses on abduction as explicit or readily formulatable inference to possible explanatory hypotheses--as contrasted with inference to conceptual innovations or abductive logic as a cycle of hypotheses, deduction of consequences and inductive testing. Inference to an explanation is often a matter of projection or extrapolation of elements of accepted theory for the solution of outstanding problems in particular domains of inquiry. I say "projections or extrapolation" of accepted theory, but I mean to point to something broader and suggest (...)
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  5. James D. Church & Edward L. Wike (1976). The Robustness of Homogeneity of Variance Tests for Asymmetric Distributions: A Monte Carlo Study. Bulletin of the Psychonomic Society 7 (5):417-420.
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  6. Klodian Coko & Jutta Schickore (2013). Robustness, Solidity, and Multiple Determinations. Metascience 22 (3):681-683.
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  7. Mark Colyvan (2008). The Ontological Commitments of Inconsistent Theories. Philosophical Studies 141 (1):115 - 123.
    In this paper I present an argument for belief in inconsistent objects. The argument relies on a particular, plausible version of scientific realism, and the fact that often our best scientific theories are inconsistent. It is not clear what to make of this argument. Is it a reductio of the version of scientific realism under consideration? If it is, what are the alternatives? Should we just accept the conclusion? I will argue (rather tentatively and suitably qualified) for a positive answer (...)
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  8. Richard Dawkins (1998). Unweaving the Rainbow: Science, Delusion, and the Appetite for Wonder. Houghton Mifflin.
    Did Newton "unweave the rainbow" by reducing it to its prismatic colors, as Keats contended? Did he, in other words, diminish beauty? Far from it, says Dawkins--Newton's unweaving is the key too much of modern astronomy and to the breathtaking poetry of modern cosmology. Mysteries don't lose their poetry because they are solved: the solution often is more beautiful than the puzzle, uncovering deeper mystery. (The Keats who spoke of "unweaving the rainbow" was a very young man, Dawkins reminds us.) (...)
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  9. Craig Dilworth (2012). Simplicity: A Meta-Metaphysics. Lexington Books.
    Simplicity provides a new logic with which to approach intellectual situations.
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  10. Heather Douglas (2014). The Value of Cognitive Values. Philosophy of Science 80 (5):796-806.
    Traditionally, cognitive values have been thought of as a collective pool of considerations in science that frequently trade against each other. I argue here that a finer-grained account of the value of cognitive values can help reduce such tensions. I separate the values into groups, minimal epistemic criteria, pragmatic considerations, and genuine epistemic assurance, based in part on the distinction between values that describe theories per se and values that describe theory-evidence relationships. This allows us to clarify why these values (...)
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  11. Malcolm Forster, Chapter 3: Simplicity and Unification in Model Selection.
    This chapter examines four solutions to the problem of many models, and finds some fault or limitation with all of them except the last. The first is the naïve empiricist view that best model is the one that best fits the data. The second is based on Popper’s falsificationism. The third approach is to compare models on the basis of some kind of trade off between fit and simplicity. The fourth is the most powerful: Cross validation testing.
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  12. Malcolm Forster, Discussion: Unification and Predictive Accuracy.
    Wayne Myrvold (2003) has captured an important feature of unified theories, and he has done so in Bayesian terms. What is not clear is whether the virtue of such unification is most clearly understood in terms of Bayesian confirmation. I argue that the virtue of such unification is better understood in terms of other truth-related virtues such as predictive accuracy.
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  13. Lars Peter Hansen & Thomas J. Sargent (2007). Robustness. Princeton University Press.
    Technical, rigorous, and self-contained, this book will be useful for macroeconomists who seek to improve the robustness of decision-making processes.
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  14. C. Held (2011). Truth Does Not Explain Predictive Success. Analysis 71 (2):232-234.
    Laudan famously argued that neither truth nor approximate truth can be part of an explanation of a scientific theory's predictive success because in the history of science there were theories that enjoyed some limited success but now are considered outright false. The power of his argument lay in the many historic examples he listed . Realists have disputed that all theories on Laudan's list can be regarded as predictively successful but let's suppose momentarily that at least some exist that support (...)
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  15. David Henderson, Rethinking the Connection Between Truth-Conducivity and Justification.
    Contents/Links I. The Referentialist's Objection and the Issues it Raises II. From Uses of Descriptions to Aspects of Concepts III. A Straightforward Understanding IV. A More Sophisticated Understanding V. What is Attributively Associated with "Justification"?
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  16. Dan Hicks (2014). A New Direction for Science and Values. Synthese 191 (14):3271-95.
    The controversy over the old ideal of “value-free science” has cooled significantly over the past decade. Many philosophers of science now agree that even ethical and political values may play a substantial role in all aspects of scientific inquiry. Consequently, in the last few years, work in science and values has become more specific: Which values may influence science, and in which ways? Or, how do we distinguish illegitimate from illegitimate kinds of influence? In this paper, I argue that this (...)
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  17. James Justus (2012). The Elusive Basis of Inferential Robustness. Philosophy of Science 79 (5):795-807.
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  18. Kevin T. Kelly, Simplicity, Truth, and Probability.
    Simplicity has long been recognized as an apparent mark of truth in science, but it is difficult to explain why simplicity should be accorded such weight. This chapter examines some standard, statistical explanations of the role of simplicity in scientific method and argues that none of them explains, without circularity, how a reliance on simplicity could be conducive to finding true models or theories. The discussion then turns to a less familiar approach that does explain, in a sense, the elusive (...)
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  19. Andre Kukla (1994). Non-Empirical Theoretical Virtues and the Argument From Underdetermination. Erkenntnis 41 (2):157 - 170.
    The antirealist argument from the underdetermination of theories by data relies on the premise that the empirical content of a theory is the only determinant of its belief-worthiness (premise NN). Several authors have claimed that the antirealist cannot endorse NN, on pain of internal inconsistency. I concede this point. Nevertheless, this refutation of the underdetermination argument fails because there are weaker substitutes for NN that will serve just as well as a premise to the argument. On the other hand, antirealists (...)
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  20. Jaakko Kuorikoski & Aki Lehtinen (2009). Incredible Worlds, Credible Results. Erkenntnis 70 (1):119 - 131.
    Robert Sugden argues that robustness analysis cannot play an epistemic role in grounding model-world relationships because the procedure is only a matter of comparing models with each other. We posit that this argument is based on a view of models as being surrogate systems in too literal a sense. In contrast, the epistemic importance of robustness analysis is easy to explicate if modelling is viewed as extended cognition, as inference from assumptions to conclusions. Robustness analysis is about assessing the reliability (...)
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  21. Chiara Lisciandra (forthcoming). Robustness Analysis Versus Reliable Process Reasoning. Metascience:1-5.
    Robert Hudson’s book is a contribution to the recent debate on robustness analysis in scientific practice, with a specific focus on the empirical sciences. In this context, robustness analysis is defined as a way to increase the probability of a certain hypothesis by showing that the same result is obtained from several, alternative methods. The rationale underlying this practice is that it would be highly unlikely if different, independent means of observation provided the same wrong outcome.We do not believe in (...)
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  22. Helen E. Longino (1995). Gender, Politics, and the Theoretical Virtues. Synthese 104 (3):383 - 397.
    Traits like simplicity and explanatory power have traditionally been treated as values internal to the sciences, constitutive rather than contextual. As such they are cognitive virtues. This essay contrasts a traditional set of such virtues with a set of alternative virtues drawn from feminist writings about the sciences. In certain theoretical contexts, the only reasons for preferring a traditional or an alternative virtue are socio-political. This undermines the notion that the traditional virtues can be considered purely cognitive.
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  23. Sebastian Lutz & Stephan Hartmann (2010). Conventional and Objective Invariance: Debs and Redhead on Symmetry. [REVIEW] Metascience 19:15-23.
    This review is a critical discussion of three main claims in Debs and Redhead’s thought-provoking book Objectivity, Invariance, and Convention. These claims are: (i) Social acts impinge upon formal aspects of scientific representation; (ii) symmetries introduce the need for conventional choice; (iii) perspectival symmetry is a necessary and sufficient condition for objectivity, while symmetry simpliciter fails to be necessary.
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  24. Adolfas Mackonis (2013). Inference to the Best Explanation, Coherence and Other Explanatory Virtues. Synthese 190 (6):975-995.
    This article generalizes the explanationist account of inference to the best explanation (IBE). It draws a clear distinction between IBE and abduction and presents abduction as the first step of IBE. The second step amounts to the evaluation of explanatory power, which consist in the degree of explanatory virtues that a hypothesis exhibits. Moreover, even though coherence is the most often cited explanatory virtue, on pain of circularity, it should not be treated as one of the explanatory virtues. Rather, coherence (...)
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  25. L. Taper Mark, F. Staples David & B. Shepard Bradley (2008). Model Structure Adequacy Analysis: Selecting Models on the Basis of Their Ability to Answer Scientific Questions. Synthese 163 (3).
    Models carry the meaning of science. This puts a tremendous burden on the process of model selection. In general practice, models are selected on the basis of their relative goodness of fit to data penalized by model complexity. However, this may not be the most effective approach for selecting models to answer a specific scientific question because model fit is sensitive to all aspects of a model, not just those relevant to the question. Model Structural Adequacy analysis is proposed as (...)
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  26. James W. McAllister (1996). Beauty & Revolution in Science. Cornell University Press.
  27. James W. Mcallister (1989). Truth and Beauty in Scientific Reason. Synthese 78 (1):25 - 51.
    A rationalist and realist model of scientific revolutions will be constructed by reference to two categories of criteria of theory-evaluation, denominated indicators of truth and of beauty. Whereas indicators of truth are formulateda priori and thus unite science in the pursuit of verisimilitude, aesthetic criteria are inductive constructs which lag behind the progression of theories in truthlikeness. Revolutions occur when the evaluative divergence between the two categories of criteria proves too wide to be recomposed or overlooked. This model of revolutions (...)
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  28. Kevin Meeker (2006). Pluralism, Exclusivism, and the Theoretical Virtues. Religious Studies 42 (2):193-206.
    This paper argues that John Hick's commitment to the moral principle of altruism undermines his pluralistic claim that all of the major world religions are equally efficacious from a soteriological perspective. This argument is placed in a context of a discussion evaluating the theoretical virtues of various hypotheses about religious diversity. (Published Online April 7 2006).
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  29. Michael Morreau (2013). Mr. Fit, Mr. Simplicity and Mr. Scope: From Social Choice to Theory Choice. Erkenntnis:1-16.
    An analogue of Arrow’s theorem has been thought to limit the possibilities for multi-criterial theory choice. Here, an example drawn from Toy Science, a model of theories and choice criteria, suggests that it does not. Arrow’s assumption that domains are unrestricted is inappropriate in connection with theory choice in Toy Science. There are, however, variants of Arrow’s theorem that do not require an unrestricted domain. They require instead that domains are, in a technical sense, ‘rich’. Since there are rich domains (...)
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  30. Wayne C. Myrvold & William L. Harper (2002). Model Selection, Simplicity, and Scientific Inference. Proceedings of the Philosophy of Science Association 2002 (3):S135-S149.
    The Akaike Information Criterion can be a valuable tool of scientific inference. This statistic, or any other statistical method for that matter, cannot, however, be the whole of scientific methodology. In this paper some of the limitations of Akaikean statistical methods are discussed. It is argued that the full import of empirical evidence is realized only by adopting a richer ideal of empirical success than predictive accuracy, and that the ability of a theory to turn phenomena into accurate, agreeing measurements (...)
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  31. Ilkka Niiniluoto (2008). Unification and Abductive Confirmation. Proceedings of the Xxii World Congress of Philosophy 43:151-156.
    According to the traditional requirement, formulated already by William Whewell in his account of the “consilience of inductions” in 1840, an explanatory scientific theory should be independently testable by new kinds of phenomena. A good theory should have unifying power in the sense that it explains and predicts several mutually independent phenomena. This paper studies the prospects of Bayesianism to motivate this kind of unification criterion for abductive confirmation.
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  32. Graham Oddie (2013). The Content, Consequence and Likeness Approaches to Verisimilitude: Compatibility, Trivialization, and Underdetermination. Synthese 190 (9):1647-1687.
    Theories of verisimilitude have routinely been classified into two rival camps—the content approach and the likeness approach—and these appear to be motivated by very different sets of data and principles. The question thus naturally arises as to whether these approaches can be fruitfully combined. Recently Zwart and Franssen (Synthese 158(1):75–92, 2007) have offered precise analyses of the content and likeness approaches, and shown that given these analyses any attempt to meld content and likeness orderings violates some basic desiderata. Unfortunately their (...)
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  33. Darrell P. Rowbottom (forthcoming). Extending the Argument From Unconceived Alternatives: Observations, Models, Predictions, Explanations, Methods, Instruments, Experiments, and Values. Synthese.
    Stanford’s argument against scientific realism focuses on theories, just as many earlier arguments from inconceivability have. However, there are possible arguments against scientific realism involving unconceived (or inconceivable) entities of different types: observations, models, predictions, explanations, methods, instruments, experiments, and values. This paper charts such arguments. In combination, they present the strongest challenge yet to scientific realism.
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  34. Alexander Rueger (1996). Risk and Diversification in Theory Choice. Synthese 109 (2):263 - 280.
    How can it be rational to work on a new theory that does not yet meet the standards for good or acceptable theories? If diversity of approaches is a condition for scientific progress, how can a scientific community achieve such progress when each member does what it is rational to do, namely work on the best theory? These two methodological problems, the problem of pursuit and the problem of diversity, can be solved by taking into account the cognitive risk that (...)
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  35. Sam Sanders (2013). On Algorithm and Robustness in a Non-Standard Sense. In Hanne Andersen, Dennis Dieks, Wenceslao González, Thomas Uebel & Gregory Wheeler (eds.), New Challenges to Philosophy of Science. Springer Verlag. 99--112.
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  36. Tevfik Aytekin–Erdinç Sayan (2010). Misrepresentation and Robustness of Meaning. Organon F 17 (1):21-38.
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  37. Stephen Scales (2002). Value-Ladenness, Theoretical Virtues, and Moral Wisdom. Teaching Ethics 2 (2):19-28.
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  38. Paul J. H. Schoemaker (2003). Huygens Versus Fermat: No Clear Winner. Behavioral and Brain Sciences 26 (6):781-782.
    How should we assess the appeal of multiple scientific theories when they can all explain a particular empirical phenomenon of interest? We contrast Huygens' and Fermat's explanations of the law of refraction of light and find that neither dominates the other when considering multiple criteria for assessing the overall appeal of a scientific theory. The absence of teleology in Huygens' account is a strong plus compared to Fermat's. But Huygens' wave theory scores less well with respect to other desiderata for (...)
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  39. Scott A. Shalkowski (1997). Theoretical Virtues and Theological Construction. International Journal for Philosophy of Religion 41 (2):71-89.
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  40. Jan Sprenger (2012). Environmental Risk Analysis: Robustness Is Essential for Precaution. Philosophy of Science 79 (5):881-892.
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  41. P. Thagard & C. P. Shelley (1997). Abductive Reasoning: Logic, Visual Thinking, and Coherence. In [Book Chapter].
    This paper discusses abductive reasoning---that is, reasoning in which explanatory hypotheses are formed and evaluated. First, it criticizes two recent formal logical models of abduction. An adequate formalization would have to take into account the following aspects of abduction: explanation is not deduction; hypotheses are layered; abduction is sometimes creative; hypotheses may be revolutionary; completeness is elusive; simplicity is complex; and abductive reasoning may be visual and non-sentential. Second, in order to illustrate visual aspects of hypothesis formation, the paper describes (...)
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  42. Valerie Tiberius (2012). Open-Mindedness And.. Normative Contingency. Oxford Studies in Metaethics 7:182.
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  43. Thierry Warin (2005). Popper's Falsifiability and Mises a-Priorism: Is Dogmatism Everywhere? Epistemologia 28 (1):121-138.
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  44. Renée Weber (ed.) (1986). Dialogues with Scientists and Sages: The Search for Unity. Routledge & Kegan Paul.
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  45. Claus O. Wilke (2006). Biology Through the Robustness Lens. BioScience 56 (8):695-696.
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  46. C. William (1976). William C. Wimsatt. In G. Gordon, Grover Maxwell & I. Savodnik (eds.), Consciousness and the Brain: A Scientific and Philosophical Inquiry. Plenum. 205.
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  47. Eyal Winter (1996). Mechanism Robustness in Multilateral Bargaining. Theory and Decision 40 (2):131-147.
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  48. Rasmus Grønfeldt Winther (2007). Estilos de Investigación Científica, Modelos E Insectos Sociales. In Edna Suárez Díaz (ed.), Variedad Infinita. Ciencia y representación. Un enfoque histórico y filosófico. UNAM and Editorial Limusa, Mexico.
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Aesthetic Virtues in Science
  1. 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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  2. Angela Breitenbach (2013). Aesthetics in Science: A Kantian Proposal. Proceedings of the Aristotelian Society 113 (1pt1):83-100.
    Can aesthetic judgements legitimately be linked to the success of scientific theories? I suggest that a satisfactory answer to this question should account for the persistent attraction that aesthetic considerations seem to have for scientists, while also explaining the apparent instability of the link between the beauty of a theory and its truth. I argue that two widespread tendencies in the literature, Pythagorean and subjectivist approaches, have difficulties meeting this twofold challenge. I propose a Kantian conception of aesthetic judgements as (...)
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