Results for 'Causal models'

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  1. Should Causal Models Always Be Markovian? The Case of Multi-Causal Forks in Medicine.Donald Gillies & Aidan Sudbury - 2013 - European Journal for Philosophy of Science 3 (3):275-308.
    The development of causal modelling since the 1950s has been accompanied by a number of controversies, the most striking of which concerns the Markov condition. Reichenbach's conjunctive forks did satisfy the Markov condition, while Salmon's interactive forks did not. Subsequently some experts in the field have argued that adequate causal models should always satisfy the Markov condition, while others have claimed that non-Markovian causal models are needed in some cases. This paper argues for the second (...)
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  2.  42
    Faithfulness, Coordination and Causal Coincidences.Naftali Weinberger - 2018 - Erkenntnis 83 (2):113-133.
    Within the causal modeling literature, debates about the Causal Faithfulness Condition have concerned whether it is probable that the parameters in causal models will have values such that distinct causal paths will cancel. As the parameters in a model are fixed by the probability distribution over its variables, it is initially puzzling what it means to assign probabilities to these parameters. I propose that to assign a probability to a parameter in a model is to (...)
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  3.  50
    A Critique of the Causal Theory of Memory.Marina Trakas - 2010 - Dissertation,
    In this Master's dissertation, I try to show that the causal theory of memory, which is the only theory developed so far that at first view seems more plausible and that could be integrated with psychological explanations and investigations of memory, shows some conceptual and ontological problems that go beyond the internal inconsistencies that each version can present. On one hand, the memory phenomenon analyzed is very limited: in general it is reduced to the conscious act of remembering expressed (...)
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  4. Causal Reasoning.Christoph Hoerl - 2011 - Philosophical Studies 152 (2):167-179.
    The main focus of this paper is the question as to what it is for an individual to think of her environment in terms of a concept of causation, or causal concepts, in contrast to some more primitive ways in which an individual might pick out or register what are in fact causal phenomena. I show how versions of this question arise in the context of two strands of work on causation, represented by Elizabeth Anscombe and Christopher Hitchcock, (...)
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  5. Of Miracles and Interventions.Luke Glynn - 2013 - Erkenntnis 78 (1):43-64.
    In Making Things Happen, James Woodward influentially combines a causal modeling analysis of actual causation with an interventionist semantics for the counterfactuals encoded in causal models. This leads to circularities, since interventions are defined in terms of both actual causation and interventionist counterfactuals. Circularity can be avoided by instead combining a causal modeling analysis with a semantics along the lines of that given by David Lewis, on which counterfactuals are to be evaluated with respect to worlds (...)
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  6. Where’s the Biff?Toby Handfield, Charles R. Twardy, Kevin B. Korb & Graham Oppy - 2008 - Erkenntnis 68 (2):149-68.
    This paper presents an attempt to integrate theories of causal processes—of the kind developed by Wesley Salmon and Phil Dowe—into a theory of causal models using Bayesian networks. We suggest that arcs in causal models must correspond to possible causal processes. Moreover, we suggest that when processes are rendered physically impossible by what occurs on distinct paths, the original model must be restricted by removing the relevant arc. These two techniques suffice to explain cases (...)
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  7. Causation, Physics, and Fit.Christian Loew - 2017 - Synthese 194 (6):1945–1965.
    Our ordinary causal concept seems to fit poorly with how our best physics describes the world. We think of causation as a time-asymmetric dependence relation between relatively local events. Yet fundamental physics describes the world in terms of dynamical laws that are, possible small exceptions aside, time symmetric and that relate global time slices. My goal in this paper is to show why we are successful at using local, time-asymmetric models in causal explanations despite this apparent mismatch (...)
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  8.  38
    Probabilistic Actual Causation.Fenton-Glynn Luke - manuscript
    Actual causes - e.g. Suzy's being exposed to asbestos - often bring about their effects - e.g. Suzy's suffering mesothelioma - probabilistically. I use probabilistic causal models to tackle one of the thornier difficulties for traditional accounts of probabilistic actual causation: namely probabilistic preemption.
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  9. Complements, Not Competitors: Causal and Mathematical Explanations.Holly Andersen - 2017 - British Journal for the Philosophy of Science:axw023.
    A finer-grained delineation of a given explanandum reveals a nexus of closely related causal and non- causal explanations, complementing one another in ways that yield further explanatory traction on the phenomenon in question. By taking a narrower construal of what counts as a causal explanation, a new class of distinctively mathematical explanations pops into focus; Lange’s characterization of distinctively mathematical explanations can be extended to cover these. This new class of distinctively mathematical explanations is illustrated with the (...)
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  10. An Internal Limit of the Structural Analysis of Causation.Alessandro Giordani - 2016 - Axiomathes 26 (4):429-450.
    Structural models of systems of causal connections have become a common tool in the analysis of the concept of causation. In the present paper I offer a general argument to show that one of the most powerful definitions of the concept of actual cause, provided within the structural models framework, is not sufficient to grant a full account of our intuitive judgements about actual causation, so that we are still waiting for a comprehensive definition. This is done (...)
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  11.  46
    Causal Modeling and the Efficacy of Action.Holly Andersen - forthcoming - In Michael Brent (ed.), Mental Action and the Conscious Mind. Routledge.
    This paper brings together Thompson's naive action explanation with interventionist modeling of causal structure to show how they work together to produce causal models that go beyond current modeling capabilities, when applied to specifically selected systems. By deploying well-justified assumptions about rationalization, we can strengthen existing causal modeling techniques' inferential power in cases where we take ourselves to be modeling causal systems that also involve actions. The internal connection between means and end exhibited in naive (...)
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  12. Model Robustness as a Confirmatory Virtue: The Case of Climate Science.Elisabeth A. Lloyd - 2015 - Studies in History and Philosophy of Science Part A 49:58-68.
    I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independentlysupported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. (...)
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  13. Causal Graphs and Biological Mechanisms.Alexander Gebharter & Marie I. Kaiser - 2014 - In Marie I. Kaiser, Oliver Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the special sciences: The case of biology and history. Dordrecht: Springer. pp. 55-86.
    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 (...)
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  14. The Good, the Bad, and the Timely: How Temporal Order and Moral Judgment Influence Causal Selection.Kevin Reuter, Lara Kirfel, Raphael van Riel & Luca Barlassina - 2014 - Frontiers in Psychology 5:1-10.
    Causal selection is the cognitive process through which one or more elements in a complex causal structure are singled out as actual causes of a certain effect. In this paper, we report on an experiment in which we investigated the role of moral and temporal factors in causal selection. Our results are as follows. First, when presented with a temporal chain in which two human agents perform the same action one after the other, subjects tend to judge (...)
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  15.  87
    A Proposed Probabilistic Extension of the Halpern and Pearl Definition of ‘Actual Cause’.Luke Fenton-Glynn - 2015 - British Journal for the Philosophy of Science 68 (4):1061-1164.
    In their article 'Causes and Explanations: A Structural-Model Approach. Part I: Causes', Joseph Halpern and Judea Pearl draw upon structural equation models to develop an attractive analysis of 'actual cause'. Their analysis is designed for the case of deterministic causation. I show that their account can be naturally extended to provide an elegant treatment of probabilistic causation.
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  16.  95
    A Model-Invariant Theory of Token Causation.J. Dmitri Gallow - manuscript
    I provide a theory of causation formulated within the causal modeling framework. In contrast to its predecessors, this theory is model-invariant in the following sense: if the theory says that C caused (didn't cause) E in a causal model, M, then it will continue to say that C caused (didn't cause) E once we've removed an inessential variable from M. I suggest that, if this theory is true, then we should understand a cause as something which transmits deviant (...)
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  17.  74
    An Informational Theory of Counterfactuals.Danilo Fraga Dantas - 2018 - Acta Analytica 33 (4):525-538.
    Backtracking counterfactuals are problem cases for the standard, similarity based, theories of counterfactuals e.g., Lewis. These theories usually need to employ extra-assumptions to deal with those cases. Hiddleston, 632–657, 2005) proposes a causal theory of counterfactuals that, supposedly, deals well with backtracking. The main advantage of the causal theory is that it provides a unified account for backtracking and non-backtracking counterfactuals. In this paper, I present a backtracking counterfactual that is a problem case for Hiddleston’s account. Then I (...)
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  18. The Proximate–Ultimate Distinction and Evolutionary Developmental Biology: Causal Irrelevance Versus Explanatory Abstraction.Massimo Pigliucci & Raphael Scholl - 2015 - Biology and Philosophy 30 (5):653-670.
    Mayr’s proximate–ultimate distinction has received renewed interest in recent years. Here we discuss its role in arguments about the relevance of developmental to evolutionary biology. We show that two recent critiques of the proximate–ultimate distinction fail to explain why developmental processes in particular should be of interest to evolutionary biologists. We trace these failures to a common problem: both critiques take the proximate–ultimate distinction to neglect specific causal interactions in nature. We argue that this is implausible, and that the (...)
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  19. Normality and Actual Causal Strength.Thomas F. Icard, Jonathan F. Kominsky & Joshua Knobe - 2017 - Cognition 161:80-93.
    Existing research suggests that people's judgments of actual causation can be influenced by the degree to which they regard certain events as normal. We develop an explanation for this phenomenon that draws on standard tools from the literature on graphical causal models and, in particular, on the idea of probabilistic sampling. Using these tools, we propose a new measure of actual causal strength. This measure accurately captures three effects of normality on causal judgment that have been (...)
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  20.  15
    When Are Purely Predictive Models Best?Robert Northcott - unknown
    Can purely predictive models be useful in investigating causal systems? I argue ‘yes’. Moreover, in many cases not only are they useful, they are essential. The alternative is to stick to models or mechanisms drawn from well-understood theory. But a necessary condition for explanation is empirical success, and in many cases in social and field sciences such success can only be achieved by purely predictive models, not by ones drawn from theory. Alas, the attempt to use (...)
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  21. On the Limits of Causal Modeling: Spatially-Structurally Complex Biological Phenomena.Marie I. Kaiser - 2016 - Philosophy of Science 83 (5):921-933.
    This paper examines the adequacy of causal graph theory as a tool for modeling biological phenomena and formalizing biological explanations. I point out that the causal graph approach reaches it limits when it comes to modeling biological phenomena that involve complex spatial and structural relations. Using a case study from molecular biology, DNA-binding and -recognition of proteins, I argue that causal graph models fail to adequately represent and explain causal phenomena in this field. The inadequacy (...)
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  22. Can Mental Representations Be Triggering Causes?Carrie Figdor - 2003 - Consciousness and Emotion 4 (1):43-61.
    Fred Dretske?s (1988) account of the causal role of intentional mental states was widely criticized for missing the target: he explained why a type of intentional state causes the type of bodily motion it does rather than some other type, when what we wanted was an account of how the intentional properties of these states play a causal role in each singular causal relation with a token bodily motion. I argue that the non-reductive metaphysics that Dretske defends (...)
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  23.  67
    The False Dichotomy Between Causal Realization and Semantic Computation.Marcin Miłkowski - 2017 - Hybris. Internetowy Magazyn Filozoficzny 38:1-21.
    In this paper, I show how semantic factors constrain the understanding of the computational phenomena to be explained so that they help build better mechanistic models. In particular, understanding what cognitive systems may refer to is important in building better models of cognitive processes. For that purpose, a recent study of some phenomena in rats that are capable of ‘entertaining’ future paths (Pfeiffer and Foster 2013) is analyzed. The case shows that the mechanistic account of physical computation may (...)
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  24. Verisimilitude: A Causal Approach.Robert Northcott - 2013 - Synthese 190 (9):1471-1488.
    I present a new definition of verisimilitude, framed in terms of causes. Roughly speaking, according to it a scientific model is approximately true if it captures accurately the strengths of the causes present in any given situation. Against much of the literature, I argue that any satisfactory account of verisimilitude must inevitably restrict its judgments to context-specific models rather than general theories. We may still endorse—and only need—a relativized notion of scientific progress, understood now not as global advance but (...)
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  25. Is There Room in Quantum Ontology for a Genuine Causal Role for Consciousness?Paavo Pylkkänen - 2017 - In E. Haven & A. Khrennikov (eds.), The Palgrave Handbook of Quantum Models in Social Science: Applications and Grand Challenges. London: Palgrave Macmillan. pp. 293-317.
    Western philosophy and science have a strongly dualistic tradition regarding the mental and physical aspects of reality, which makes it difficult to understand their possible causal relations. In recent debates in cognitive neuroscience it has been common to claim on the basis of neural experiments that conscious experiences are causally inefficacious. At the same time there is much evidence that consciousness does play an important role in guiding behavior. The author explores whether a new way of understanding the (...) role of mental states and consciousness could be provided by the ontological interpretation of the quantum theory (Bohm and Hiley, Phys. Rep. 144:323–348, 1987; Bohm and Hiley, The undivided universe: An ontological interpretation of quantum theory. Routledge: London, 1993). This interpretation radically changes our notion of matter by suggesting that a new type of active information plays a causal role at the quantum level of reality. The author thus considers to what extent the alleged causal powers of consciousness involve information, and then moves on to consider whether information in (conscious) mental states can be connected to the information at the level of quantum physics. In this way he sketches how quantum theory might help to throw light upon one of the grand challenges facing the social sciences and the humanities, namely the question of whether consciousness plays any genuine causal role in the physical world. (shrink)
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  26. Prisoner's Dilemma Doesn't Explain Much.Robert Northcott & Anna Alexandrova - 2015 - In Martin Peterson (ed.), The Prisoner’s Dilemma. Classic philosophical arguments. Cambridge: Cambridge University Press. pp. 64-84.
    We make the case that the Prisoner’s Dilemma, notwithstanding its fame and the quantity of intellectual resources devoted to it, has largely failed to explain any phenomena of social scientific or biological interest. In the heart of the paper we examine in detail a famous purported example of Prisoner’s Dilemma empirical success, namely Axelrod’s analysis of WWI trench warfare, and argue that this success is greatly overstated. Further, we explain why this negative verdict is likely true generally and not just (...)
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  27. Representations Gone Mental.Alex Morgan - 2014 - Synthese 191 (2):213-244.
    Many philosophers and psychologists have attempted to elucidate the nature of mental representation by appealing to notions like isomorphism or abstract structural resemblance. The ‘structural representations’ that these theorists champion are said to count as representations by virtue of functioning as internal models of distal systems. In his 2007 book, Representation Reconsidered, William Ramsey endorses the structural conception of mental representation, but uses it to develop a novel argument against representationalism, the widespread view that cognition essentially involves the manipulation (...)
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  28. Coherence and Confirmation Through Causation.Gregory Wheeler & Richard Scheines - 2013 - Mind 122 (485):135-170.
    Coherentism maintains that coherent beliefs are more likely to be true than incoherent beliefs, and that coherent evidence provides more confirmation of a hypothesis when the evidence is made coherent by the explanation provided by that hypothesis. Although probabilistic models of credence ought to be well-suited to justifying such claims, negative results from Bayesian epistemology have suggested otherwise. In this essay we argue that the connection between coherence and confirmation should be understood as a relation mediated by the (...) relationships among the evidence and a hypothesis, and we offer a framework for doing so by fitting together probabilistic models of coherence, confirmation, and causation. We show that the causal structure among the evidence and hypothesis is sometimes enough to determine whether the coherence of the evidence boosts confirmation of the hypothesis, makes no difference to it, or even reduces it. We also show that, ceteris paribus, it is not the coherence of the evidence that boosts confirmation, but rather the ratio of the coherence of the evidence to the coherence of the evidence conditional on a hypothesis. (shrink)
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  29. A Field Guide to Mechanisms: Part II.Holly Andersen - 2014 - Philosophy Compass 9 (4):284-293.
    In this field guide, I distinguish five separate senses with which the term ‘mechanism’ is used in contemporary philosophy of science. Many of these senses have overlapping areas of application but involve distinct philosophical claims and characterize the target mechanisms in relevantly different ways. This field guide will clarify the key features of each sense and introduce some main debates, distinguishing those that transpire within a given sense from those that are best understood as concerning two distinct senses. The ‘new (...)
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  30. Spoils to the Vector - How to Model Causes If You Are a Realist About Powers.Stephen Mumford & Rani Lill Anjum - 2011 - The Monist 94 (1):54-80.
    A standard way of representing causation is with neuron diagrams. This has become popular since the influential work of David Lewis. But it should not be assumed that such representations are metaphysically neutral and amenable to any theory of causation. On the contrary, this way of representing causation already makes several Humean assumptions about what causation is, and which suit Lewis’s programme of Humean Supervenience. An alternative of a vector diagram is better suited for a powers ontology. Causation should be (...)
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  31. Multiple Regression Is Not Multiple Regressions: The Meaning of Multiple Regression and the Non-Problem of Collinearity.Michael B. Morrissey & Graeme D. Ruxton - 2018 - Philosophy, Theory, and Practice in Biology 10 (3).
    Simple regression (regression analysis with a single explanatory variable), and multiple regression (regression models with multiple explanatory variables), typically correspond to very different biological questions. The former use regression lines to describe univariate associations. The latter describe the partial, or direct, effects of multiple variables, conditioned on one another. We suspect that the superficial similarity of simple and multiple regression leads to confusion in their interpretation. A clear understanding of these methods is essential, as they underlie a large range (...)
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  32. The Epistemology of Hedged Laws.Robert Kowalenko - 2011 - Studies in History and Philosophy of Science Part A 42 (3):445-452.
    Standard objections to the notion of a hedged, or ceteris paribus, law of nature usually boil down to the claim that such laws would be either 1) irredeemably vague, 2) untestable, 3) vacuous, 4) false, or a combination thereof. Using epidemiological studies in nutrition science as an example, I show that this is not true of the hedged law-like generalizations derived from data models used to interpret large and varied sets of empirical observations. Although it may be ‘in principle (...)
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  33.  42
    Modelling with Words: Narrative and Natural Selection.Dominic K. Dimech - 2017 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 62:20-24.
    I argue that verbal models should be included in a philosophical account of the scientific practice of modelling. Weisberg (2013) has directly opposed this thesis on the grounds that verbal structures, if they are used in science, only merely describe models. I look at examples from Darwin's On the Origin of Species (1859) of verbally constructed narratives that I claim model the general phenomenon of evolution by natural selection. In each of the cases I look at, a particular (...)
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  34.  70
    The Growth of Knowledge as a Problem of Philosophy of Science.Rinat M. Nugayev - 2006 - Filosofia Nauki (Philosophy of Science, Novosibirsk) 4 (31):3-19.
    The host of the growth of knowledge hallmarks, concocted by various philosophy of science models , is contemplated. It is enunciated that the most appropriate one is provided by methodology of scientific research programmes. Some salient drawbacks of the model, caused by the ambivalence of its basic notions, e.g. of the notions of ‘empirical content of a theory’, ‘progressive’ and ‘regressive’ ‘problemshifts’ can be mitigated by enriching the Lakatosian model with Nancy Cartwright’s results. To recapitulate: the genuine growth of (...)
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  35.  58
    Biological Explanation.Angela Potochnik - 2013 - In Kostas Kampourakis (ed.), The Philosophy of Biology: A Companion for Educators. Springer. pp. 49-65.
    One of the central aims of science is explanation: scientists seek to uncover why things happen the way they do. This chapter addresses what kinds of explanations are formulated in biology, how explanatory aims influence other features of the field of biology, and the implications of all of this for biology education. Philosophical treatments of scientific explanation have been both complicated and enriched by attention to explanatory strategies in biology. Most basically, whereas traditional philosophy of science based explanation on derivation (...)
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  36.  12
    Anchoring Causal Connections in Physical Concepts.Roland Poellinger & Mario Hubert - 2014 - In Maria Clara Galavotti, Dennis Dieks, W. J. Gonzalez, Stephan Hartmann, Thomas Uebel & Marcel Weber (eds.), New Directions in the Philosophy of Science. pp. 501-509.
    In their paper "How Fundamental Physics represents Causality", Andreas Bartels and Daniel Wohlfarth maintain that there is place for causality in General Relativity. Their argument contains two steps: First they show that there are time-asymmetric models in General Relativity, then they claim to derive that two events are causally connected if and only if there is a time-asymmetric energy flow from one event to the other. In our comment we first give a short summary of their paper followed by (...)
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  37. Explaining Schizophrenia: Auditory Verbal Hallucination and Self‐Monitoring.Wayne Wu - 2012 - Mind and Language 27 (1):86-107.
    Do self‐monitoring accounts, a dominant account of the positive symptoms of schizophrenia, explain auditory verbal hallucination? In this essay, I argue that the account fails to answer crucial questions any explanation of auditory verbal hallucination must address. Where the account provides a plausible answer, I make the case for an alternative explanation: auditory verbal hallucination is not the result of a failed control mechanism, namely failed self‐monitoring, but, rather, of the persistent automaticity of auditory experience of a voice. My argument (...)
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  38. Structural Equations and Beyond.Franz Huber - 2013 - Review of Symbolic Logic 6 (4):709-732.
    Recent accounts of actual causation are stated in terms of extended causal models. These extended causal models contain two elements representing two seemingly distinct modalities. The first element are structural equations which represent the or mechanisms of the model, just as ordinary causal models do. The second element are ranking functions which represent normality or typicality. The aim of this paper is to show that these two modalities can be unified. I do so by (...)
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  39. Commonsense Metaphysics and Lexical Semantics.Jerry R. Hobbs, William Croft, Todd Davies, Douglas Edwards & Kenneth Laws - 1987 - Computational Linguistics 13 (3&4):241-250.
    In the TACITUS project for using commonsense knowledge in the understanding of texts about mechanical devices and their failures, we have been developing various commonsense theories that are needed to mediate between the way we talk about the behavior of such devices and causal models of their operation. Of central importance in this effort is the axiomatization of what might be called commonsense metaphysics. This includes a number of areas that figure in virtually every domain of discourse, such (...)
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  40. Reducing the Dauer Larva: Molecular Models of Biological Phenomena in Caenorhabditis Elegans Research.Arciszewski Michal - manuscript
    One important aspect of biological explanation is detailed causal modeling of particular phenomena in limited experimental background conditions. Recognising this allows a new avenue for intertheoretic reduction to be seen. Reductions in biology are possible, when one fully recognises that a sufficient condition for a reduction in biology is a molecular model of 1) only the demonstrated causal parameters of a biological model and 2) only within a replicable experimental background. These intertheoretic identifications –which are ubiquitous in biology (...)
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  41.  91
    Why Simpler Computer Simulation Models Can Be Epistemically Better for Informing Decisions.Casey Helgeson, Vivek Srikrishnan, Klaus Keller & Nancy Tuana - manuscript
    For computer simulation models to usefully inform climate risk management decisions, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many times over, and because computing resources are finite, uncertainty assessment is more feasible using models that need less computer processor time. Such models are generally simpler in the sense of being more idealized, or less realistic. So modelers face a trade-off between realism and extent of uncertainty quantification. Seeing this (...)
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  42. Causal Selection Versus Causal Parity in Biology: Relevant Counterfactuals and Biologically Normal Interventions.Marcel Weber - forthcoming - In C. Kenneth Waters & James Woodward (eds.), Philosophical Perspectives on Causal Reasoning in Biology. Minnesota Studies in Philosophy of Science. Vol. XXI. Minneapolis: University of Minnesota Press.
    Causal selection is the task of picking out, from a field of known causally relevant factors, some factors as elements of an explanation. The Causal Parity Thesis in the philosophy of biology challenges the usual ways of making such selections among different causes operating in a developing organism. The main target of this thesis is usually gene centrism, the doctrine that genes play some special role in ontogeny, which is often described in terms of information-bearing or programming. This (...)
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  43. Causal Argument.Ulrike Hahn, Frank Zenker & Roland Bluhm - 2017 - In Michael R. Waldmann (ed.), The Oxford Handbook of Causal Reasoning. New York, NY: Oxford University Press. pp. 475-494.
    In this chapter, we outline the range of argument forms involving causation that can be found in everyday discourse. We also survey empirical work concerned with the generation and evaluation of such arguments. This survey makes clear that there is presently no unified body of research concerned with causal argument. We highlight the benefits of a unified treatment both for those interested in causal cognition and those interested in argumentation, and identify the key challenges that must be met (...)
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  44.  74
    Understanding From Machine Learning Models.Emily Sullivan - forthcoming - British Journal for the Philosophy of Science:axz035.
    Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal (...) provide understanding misguided? In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding. (shrink)
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  45.  80
    Tool Use and Causal Cognition: An Introduction.Teresa McCormack, Christoph Hoerl & Stephen Andrew Butterfill - 2011 - In Teresa McCormack, Christoph Hoerl & Stephen Andrew Butterfill (eds.), Tool Use and Causal Cognition. Oxford: Oxford University Press. pp. 1-17.
    This chapter begins with a discussion of the significance of studies of aspects of tool use in understanding causal cognition. It argues that tool use studies reveal the most basic type or causal understanding being put to use, in a way that studies that focus on learning statistical relationships between cause and effect or studies of perceptual causation do not. An overview of the subsequent chapters is also presented.
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  46. There Must Be A First: Why Thomas Aquinas Rejects Infinite, Essentially Ordered, Causal Series.Caleb Cohoe - 2013 - British Journal for the History of Philosophy 21 (5):838 - 856.
    Several of Thomas Aquinas's proofs for the existence of God rely on the claim that causal series cannot proceed in infinitum. I argue that Aquinas has good reason to hold this claim given his conception of causation. Because he holds that effects are ontologically dependent on their causes, he holds that the relevant causal series are wholly derivative: the later members of such series serve as causes only insofar as they have been caused by and are effects of (...)
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  47. Explaining Causal Closure.Justin Tiehen - 2015 - Philosophical Studies 172 (9):2405-2425.
    The physical realm is causally closed, according to physicalists like me. But why is it causally closed, what metaphysically explains causal closure? I argue that reductive physicalists are committed to one explanation of causal closure to the exclusion of any independent explanation, and that as a result, they must give up on using a causal argument to attack mind–body dualism. Reductive physicalists should view dualism in much the way that we view the hypothesis that unicorns exist, or (...)
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  48. Model Organisms Are Not Models.Arnon Levy & Adrian Currie - 2015 - British Journal for the Philosophy of Science 66 (2):327-348.
    Many biological investigations are organized around a small group of species, often referred to as ‘model organisms’, such as the fruit fly Drosophila melanogaster. The terms ‘model’ and ‘modelling’ also occur in biology in association with mathematical and mechanistic theorizing, as in the Lotka–Volterra model of predator-prey dynamics. What is the relation between theoretical models and model organisms? Are these models in the same sense? We offer an account on which the two practices are shown to have different (...)
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  49. Causal Superseding.Jonathan F. Kominsky, Jonathan Phillips, Tobias Gerstenberg, David Lagnado & Joshua Knobe - 2015 - Cognition 137:196-209.
    When agents violate norms, they are typically judged to be more of a cause of resulting outcomes. In this paper, we suggest that norm violations also affect the causality attributed to other agents, a phenomenon we refer to as "causal superseding." We propose and test a counterfactual reasoning model of this phenomenon in four experiments. Experiments 1 and 2 provide an initial demonstration of the causal superseding effect and distinguish it from previously studied effects. Experiment 3 shows that (...)
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  50. Confirmation and Robustness of Climate Models.Elisabeth A. Lloyd - 2010 - Philosophy of Science 77 (5):971–984.
    Recent philosophical attention to climate models has highlighted their weaknesses and uncertainties. Here I address the ways that models gain support through observational data. I review examples of model fit, variety of evidence, and independent support for aspects of the models, contrasting my analysis with that of other philosophers. I also investigate model robustness, which often emerges when comparing climate models simulating the same time period or set of conditions. Starting from Michael Weisberg’s analysis of robustness, (...)
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