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  1. Logical reasoning and domain specificity: A critique of the social exchange theory of reasoning.Paul Sheldon Davies, James H. Fetzer & Thomas R. Foster - 1995 - Biology and Philosophy 10 (1):1-37.
    The social exchange theory of reasoning, which is championed by Leda Cosmides and John Tooby, falls under the general rubric “evolutionary psychology” and asserts that human reasoning is governed by content-dependent, domain-specific, evolutionarily-derived algorithms. According to Cosmides and Tooby, the presumptive existence of what they call “cheater-detection” algorithms disconfirms the claim that we reason via general-purpose mechanisms or via inductively acquired principles. We contend that the Cosmides/Tooby arguments in favor of domain-specific algorithms or evolutionarily-derived mechanisms fail and that the notion (...)
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  • The Propensity Interpretation of Probability: A Re-evaluation.Joseph Berkovitz - 2015 - Erkenntnis 80 (S3):629-711.
    Single-case and long-run propensity theories are among the main objective interpretations of probability. There have been various objections to these theories, e.g. that it is difficult to explain why propensities should satisfy the probability axioms and, worse, that propensities are at odds with these axioms, that the explication of propensities is circular and accordingly not informative, and that single-case propensities are metaphysical and accordingly non-scientific. We consider various propensity theories of probability and their prospects in light of these objections. We (...)
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  • On Probabilities in Biology and Physics.Joseph Berkovitz & Philippe Huneman - 2015 - Erkenntnis 80 (S3):433-456.
    This volume focuses on various questions concerning the interpretation of probability and probabilistic reasoning in biology and physics. It is inspired by the idea that philosophers of biology and philosophers of physics who work on the foundations of their disciplines encounter similar questions and problems concerning the role and application of probability, and that interaction between the two communities will be both interesting and fruitful. In this introduction we present the background to the main questions that the volume focuses on (...)
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  • Ontological aspects of information modeling.Robert L. Ashenhurst - 1996 - Minds and Machines 6 (3):287-394.
    Information modeling (also known as conceptual modeling or semantic data modeling) may be characterized as the formulation of a model in which information aspects of objective and subjective reality are presented (the application), independent of datasets and processes by which they may be realized (the system).A methodology for information modeling should incorporate a number of concepts which have appeared in the literature, but should also be formulated in terms of constructs which are understandable to and expressible by the system user (...)
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  • Probability and Manipulation: Evolution and Simulation in Applied Population Genetics.Marshall Abrams - 2015 - Erkenntnis 80 (S3):519-549.
    I define a concept of causal probability and apply it to questions about the role of probability in evolutionary processes. Causal probability is defined in terms of manipulation of patterns in empirical outcomes by manipulating properties that realize objective probabilities. The concept of causal probability allows us see how probabilities characterized by different interpretations of probability can share a similar causal character, and does so in such way as to allow new inferences about relationships between probabilities realized in different chance (...)
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  • Mechanistic probability.Marshall Abrams - 2012 - Synthese 187 (2):343-375.
    I describe a realist, ontologically objective interpretation of probability, "far-flung frequency (FFF) mechanistic probability". FFF mechanistic probability is defined in terms of facts about the causal structure of devices and certain sets of frequencies in the actual world. Though defined partly in terms of frequencies, FFF mechanistic probability avoids many drawbacks of well-known frequency theories and helps causally explain stable frequencies, which will usually be close to the values of mechanistic probabilities. I also argue that it's a virtue rather than (...)
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  • Infinite populations and counterfactual frequencies in evolutionary theory.Marshall Abrams - 2006 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 37 (2):256-268.
    One finds intertwined with ideas at the core of evolutionary theory claims about frequencies in counterfactual and infinitely large populations of organisms, as well as in sets of populations of organisms. One also finds claims about frequencies in counterfactual and infinitely large populations—of events—at the core of an answer to a question concerning the foundations of evolutionary theory. The question is this: To what do the numerical probabilities found throughout evolutionary theory correspond? The answer in question says that evolutionary probabilities (...)
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  • The causal and unification approaches to explanation unified—causally.Michael Strevens - 2004 - Noûs 38 (1):154–176.
    The two major modern accounts of explanation are the causal and unification accounts. My aim in this paper is to provide a kind of unification of the causal and the unification accounts, by using the central technical apparatus of the unification account to solve a central problem faced by the causal account, namely, the problem of determining which parts of a causal network are explanatorily relevant to the occurrence of an explanandum. The end product of my investigation is a causal (...)
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  • Bayesian confirmation of theories that incorporate idealizations.Michael J. Shaffer - 2001 - Philosophy of Science 68 (1):36-52.
    Following Nancy Cartwright and others, I suggest that most (if not all) theories incorporate, or depend on, one or more idealizing assumptions. I then argue that such theories ought to be regimented as counterfactuals, the antecedents of which are simplifying assumptions. If this account of the logic form of theories is granted, then a serious problem arises for Bayesians concerning the prior probabilities of theories that have counterfactual form. If no such probabilities can be assigned, the the posterior probabilities will (...)
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  • General causation.David Sapire - 1991 - Synthese 86 (3):321 - 347.
    This paper outlines a general theory of efficient causation, a theory that deals in a unified way with traditional or deterministic, indeterministic, probabilistic, and other causal concepts. Theorists like Lewis, Salmon, and Suppes have attempted to broaden our causal perspective by reductively analysing causal notions in other terms. By contrast, the present theory rests in the first place on a non-reductive analysis of traditional causal concepts — into formal or structural components, on the one hand, and a physical or metaphysical (...)
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  • A theory of objective chance.John F. Phillips - 2005 - Pacific Philosophical Quarterly 86 (2):267–283.
    Objective probability, or objective chance, is the probability of some event occurring in the future independent of what anyone thinks. This paper presents and defends a theory of objective chance. I develop an informal analysis of objective chance, taking the common sense picture underlying our talk about the likelihood of future events as our starting point. A formal semantics is introduced, and I argue that the theory presented satisfies certain criteria of adequacy for a theory of probability.
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  • Unification and Confirmation.Ilkka Niiniluoto - 2016 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 31 (1):107-123.
    According to the traditional requirement, formulated by William Whewell in his account of the “consilience of inductions” in 1840, a scientific hypothesis should have unifying power in the sense that it explains and predicts several mutually independent phenomena. Variants of this notion of consilience or unification include deductive, inductive, and approximate systematization. Inference from surprising phenomena to their theoretical explanations was called abduction by Charles Peirce. As a unifying theory is independently testable by new kinds of phenomena, it should also (...)
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  • Humphrey's paradox and the interpretation of inverse conditional propensities.Christopher S. I. Mccurdy - 1996 - Synthese 108 (1):105 - 125.
    The aim of this paper is to distinguish between, and examine, three issues surrounding Humphreys's paradox and interpretation of conditional propensities. The first issue involves the controversy over the interpretation of inverse conditional propensities — conditional propensities in which the conditioned event occurs before the conditioning event. The second issue is the consistency of the dispositional nature of the propensity interpretation and the inversion theorems of the probability calculus, where an inversion theorem is any theorem of probability that makes explicit (...)
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  • Severe testing as a basic concept in a neyman–pearson philosophy of induction.Deborah G. Mayo & Aris Spanos - 2006 - British Journal for the Philosophy of Science 57 (2):323-357.
    Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N–P tests stem from unclarity and confusion, even among N–P adherents, as to how a test's (pre-data) error probabilities are to be used for (post-data) inductive inference as opposed to inductive behavior. We argue that the relevance of error probabilities (...)
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  • Did Pearson reject the Neyman-Pearson philosophy of statistics?Deborah G. Mayo - 1992 - Synthese 90 (2):233 - 262.
    I document some of the main evidence showing that E. S. Pearson rejected the key features of the behavioral-decision philosophy that became associated with the Neyman-Pearson Theory of statistics (NPT). I argue that NPT principles arose not out of behavioral aims, where the concern is solely with behaving correctly sufficiently often in some long run, but out of the epistemological aim of learning about causes of experimental results (e.g., distinguishing genuine from spurious effects). The view Pearson did hold gives a (...)
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  • An objective theory of statistical testing.Deborah G. Mayo - 1983 - Synthese 57 (3):297 - 340.
    Theories of statistical testing may be seen as attempts to provide systematic means for evaluating scientific conjectures on the basis of incomplete or inaccurate observational data. The Neyman-Pearson Theory of Testing (NPT) has purported to provide an objective means for testing statistical hypotheses corresponding to scientific claims. Despite their widespread use in science, methods of NPT have themselves been accused of failing to be objective; and the purported objectivity of scientific claims based upon NPT has been called into question. The (...)
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  • Statistical explanation, probability, and counteracting conditions.Thomas R. Grimes - 1988 - British Journal for the Philosophy of Science 39 (4):495-503.
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  • Probability in GRW theory.Roman Frigg & Carl Hoefer - 2007 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 38 (2):371-389.
    GRW Theory postulates a stochastic mechanism assuring that every so often the wave function of a quantum system is `hit', which leaves it in a localised state. How are we to interpret the probabilities built into this mechanism? GRW theory is a firmly realist proposal and it is therefore clear that these probabilities are objective probabilities (i.e. chances). A discussion of the major theories of chance leads us to the conclusion that GRW probabilities can be understood only as either single (...)
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  • Philosophical aspects of program verification.James H. Fetzer - 1991 - Minds and Machines 1 (2):197-216.
    A debate over the theoretical capabilities of formal methods in computer science has raged for more than two years now. The function of this paper is to summarize the key elements of this debate and to respond to important criticisms others have advanced by placing these issues within a broader context of philosophical considerations about the nature of hardware and of software and about the kinds of knowledge that we have the capacity to acquire concerning their performance.
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  • Probability and objectivity in deterministic and indeterministic situations.James H. Fetzer - 1983 - Synthese 57 (3):367--86.
    This paper pursues the question, To what extent does the propensity approach to probability contribute to plausible solutions to various anomalies which occur in quantum mechanics? The position I shall defend is that of the three interpretations — the frequency, the subjective, and the propensity — only the third accommodates the possibility, in principle, of providing a realistic interpretation of ontic indeterminism. If these considerations are correct, then they lend support to Popper's contention that the propensity construction tends to remove (...)
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  • Evolution and atheism: Has Griffin reconciled science and religion?James H. Fetzer - 2011 - Synthese 178 (2):381 - 396.
    The distinguished theologian, David Ray Griffin, has advanced a set of thirteen theses intended to characterize (what he calls) "Neo-Darwinism" and which he contrasts with "Intelligent Design". Griffin maintains that Neo-Darwinism is "atheistic" in forgoing a creator but suggests that, by adopting a more modest scientific naturalism and embracing a more naturalistic theology, it is possible to find "a third way" that reconciles religion and science. The considerations adduced here suggest that Griffin has promised more than he can deliver. On (...)
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  • Evolution, rationality and testability.James H. Fetzer - 1990 - Synthese 82 (3):423-39.
    Cosmides, Wason, and Johnson-Laird, among others, have suggested evidence that reasoning abilities tend to be domain specific, insofar as humans do not appear to acquire capacities for logical reasoning that are applicable across different contexts. Unfortunately, the significance of these findings depends upon the specific variety of logical reasoning under consideration. Indeed, there seem to be at least three grounds for doubting such conclusions, since: (1) tests of reasoning involving the use of material conditionals may not be appropriate for representing (...)
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  • Objective probability theory theory.Ellery Eells - 1983 - Synthese 57 (3):387 - 442.
    I argue that to the extent to which philosophical theories of objective probability have offered theoretically adequateconceptions of objective probability (in connection with such desiderata as causal and explanatory significance, applicability to single cases, etc.), they have failed to satisfy amethodological standard — roughly, a requirement to the effect that the conception offered be specified with the precision appropriate for a physical interpretation of an abstract formal calculus and be fully explicated in terms of concepts, objects or phenomena understood independently (...)
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  • Objective Probability Theory Theory.Ellery Eells - 2010 - In Ellery Eells & James H. Fetzer (eds.), Synthese. Springer. pp. 3--44.
    I argue that to the extent to which philosophical theories of objective probability have offered theoretically adequate conceptions of objective probability , they have failed to satisfy a methodological standard -- roughly, a requirement to the effect that the conception offered be specified with the precision appropriate for a physical interpretation of an abstract formal calculus and be fully explicated in terms of concepts, objects or phenomena understood independently of the idea of physical probability. The significance of this, and of (...)
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  • Mahdollisuus.Ilkka Niiniluoto, Tuomas Tahko & Teemu Toppinen (eds.) - 2016 - Helsinki: Philosophical Society of Finland.
    Proceedings of the 2016 "one word" colloquium of the The Philosophical Society of Finland. The word was "Possibility".
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  • Scientific explanation.Michael Strevens - 2006 - In D. M. Borchert (ed.), Encyclopedia of Philosophy, second edition.
    The three cardinal aims of science are prediction, control, and explanation; but the greatest of these is explanation. Also the most inscrutable: prediction aims at truth, and control at happiness, and insofar as we have some independent grasp of these notions, we can evaluate science’s strategies of prediction and control from the outside. Explanation, by contrast, aims at scientific understanding, a good intrinsic to science and therefore something that it seems we can only look to science itself to explicate.
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