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  1. Bayesian Belief Revision Based on Agent’s Criteria.Yongfeng Yuan - 2021 - Studia Logica 109 (6):1311-1346.
    In the literature of belief revision, it is widely accepted that: there is only one revision phase in belief revision which is well characterized by the Bayes’ Rule, Jeffrey’s Rule, etc.. However, as I argue in this article, there are at least four successive phases in belief revision, namely first/second order evaluation and first/second order revision. To characterize these phases, I propose mainly four rules of belief revision based on agent’s criteria, and make one composition rule to characterize belief revision (...)
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  • How to Count Biological Minds: Symbiosis, the Free Energy Principle, and Reciprocal Multiscale Integration.Matthew Sims - 2020 - Synthese 199 (1-2):2157-2179.
    The notion of a physiological individuals has been developed and applied in the philosophy of biology to understand symbiosis, an understanding of which is key to theorising about the major transition in evolution from multi-organismality to multi-cellularity. The paper begins by asking what such symbiotic individuals can help to reveal about a possible transition in the evolution of cognition. Such a transition marks the movement from cooperating individual biological cognizers to a functionally integrated cognizing unit. Somewhere along the way, did (...)
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  • On the Imprecision of Full Conditional Probabilities.Gregory Wheeler & Fabio G. Cozman - 2021 - Synthese 199 (1-2):3761-3782.
    The purpose of this paper is to show that if one adopts conditional probabilities as the primitive concept of probability, one must deal with the fact that even in very ordinary circumstances at least some probability values may be imprecise, and that some probability questions may fail to have numerically precise answers.
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  • Causality and Causal Modelling in the Social Sciences.Federica Russo - 2009 - Springer, Dordrecht.
    The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a bygone’ nor ‘another fetish of modern science’; it still occupies a large part of the current debate in philosophy and the sciences. This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, nor of regularity neither invariance, thus breaking down the dominant (...)
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  • Modelling Competing Legal Arguments Using Bayesian Model Comparison and Averaging.Martin Neil, Norman Fenton, David Lagnado & Richard David Gill - 2019 - Artificial Intelligence and Law 27 (4):403-430.
    Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent (...)
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  • The Oxford Handbook of Causal Reasoning.Michael R. Waldmann (ed.) - 2017 - Oxford, England: Oxford University Press.
    Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...)
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  • Subjective Causal Networks and Indeterminate Suppositional Credences.Jiji Zhang, Teddy Seidenfeld & Hailin Liu - 2019 - Synthese 198 (Suppl 27):6571-6597.
    This paper has two main parts. In the first part, we motivate a kind of indeterminate, suppositional credences by discussing the prospect for a subjective interpretation of a causal Bayesian network, an important tool for causal reasoning in artificial intelligence. A CBN consists of a causal graph and a collection of interventional probabilities. The subjective interpretation in question would take the causal graph in a CBN to represent the causal structure that is believed by an agent, and interventional probabilities in (...)
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  • Causal Concepts and Temporal Ordering.Reuben Stern - 2019 - Synthese 198 (Suppl 27):6505-6527.
    Though common sense says that causes must temporally precede their effects, the hugely influential interventionist account of causation makes no reference to temporal precedence. Does common sense lead us astray? In this paper, I evaluate the power of the commonsense assumption from within the interventionist approach to causal modeling. I first argue that if causes temporally precede their effects, then one need not consider the outcomes of interventions in order to infer causal relevance, and that one can instead use temporal (...)
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  • Formal Models of Source Reliability.Christoph Merdes, Momme von Sydow & Ulrike Hahn - 2020 - Synthese 198 (S23):5773-5801.
    The paper introduces, compares and contrasts formal models of source reliability proposed in the epistemology literature, in particular the prominent models of Bovens and Hartmann and Olsson :127–143, 2011). All are Bayesian models seeking to provide normative guidance, yet they differ subtly in assumptions and resulting behavior. Models are evaluated both on conceptual grounds and through simulations, and the relationship between models is clarified. The simulations both show surprising similarities and highlight relevant differences between these models. Most importantly, however, our (...)
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  • The Math is Not the Territory: Navigating the Free Energy Principle.Mel Andrews - 2021 - Biology and Philosophy 36 (3):1-19.
    Much has been written about the free energy principle, and much misunderstood. The principle has traditionally been put forth as a theory of brain function or biological self-organisation. Critiques of the framework have focused on its lack of empirical support and a failure to generate concrete, falsifiable predictions. I take both positive and negative evaluations of the FEP thus far to have been largely in error, and appeal to a robust literature on scientific modelling to rectify the situation. A prominent (...)
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  • Inference to the Best Explanation Made Incoherent.Nevin Climenhaga - 2017 - Journal of Philosophy 114 (5):251-273.
    Defenders of Inference to the Best Explanation claim that explanatory factors should play an important role in empirical inference. They disagree, however, about how exactly to formulate this role. In particular, they disagree about whether to formulate IBE as an inference rule for full beliefs or for degrees of belief, as well as how a rule for degrees of belief should relate to Bayesianism. In this essay I advance a new argument against non-Bayesian versions of IBE. My argument focuses on (...)
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  • Coherence, Truth, and the Development of Scientific Knowledge.Paul Thagard - 2007 - Philosophy of Science 74 (1):28-47.
    What is the relation between coherence and truth? This paper rejects numerous answers to this question, including the following: truth is coherence; coherence is irrelevant to truth; coherence always leads to truth; coherence leads to probability, which leads to truth. I will argue that coherence of the right kind leads to at least approximate truth. The right kind is explanatory coherence, where explanation consists in describing mechanisms. We can judge that a scientific theory is progressively approximating the truth if it (...)
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  • Generalised Reichenbachian Common Cause Systems.Claudio Mazzola - 2019 - Synthese 196 (10):4185-4209.
    The principle of the common cause claims that if an improbable coincidence has occurred, there must exist a common cause. This is generally taken to mean that positive correlations between non-causally related events should disappear when conditioning on the action of some underlying common cause. The extended interpretation of the principle, by contrast, urges that common causes should be called for in order to explain positive deviations between the estimated correlation of two events and the expected value of their correlation. (...)
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  • Cultural Blankets: Epistemological Pluralism in the Evolutionary Epistemology of Mechanisms.Pierre Poirier, Luc Faucher & Jean-Nicolas Bourdon - 2021 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 52 (2):335-350.
    In a recently published paper, we argued that theories of cultural evolution can gain explanatory power by being more pluralistic. In his reply to it, Dennett agreed that more pluralism is needed. Our paper’s main point was to urge cultural evolutionists to get their hands dirty by describing the fine details of cultural products and by striving to offer detailed and, when explanatory, varied algorithms or mechanisms to account for them. While Dennett’s latest work on cultural evolution does marvelously well (...)
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  • Coordinated Rational Choice.Luca Tummolini & Wynn C. Stirling - 2020 - Topoi 39 (2):317-327.
    When acting in social contexts, we are often able to voluntarily coordinate our choices with one another. It has been suggested that this ability relies on the adoption of preferences that transcend those of the individuals involved in the social interaction. Conditional game theory provides a formal framework that facilitates the study of coordinated rational choice in a way that disentangles the concepts of individual preference and group agency. We argue that these concepts are complementary: individual preferences are formed in (...)
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  • Review of Ernest Davis: Representations of Commonsense Knowledge. [REVIEW]Barry Smith - 1994 - Minds and Machines 4 (2):245-249.
    Review of a compendium of alternative formal representations of common-sense knowledge. The book is centered largely on formal representations drawn from first-order logic, and thus lies in the tradition of Kenneth Forbus, Patrick Hayes and Jerry Hobbs.
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  • In All But Finitely Many Possible Worlds: Model-Theoretic Investigations on ‘ Overwhelming Majority ’ Default Conditionals.Costas D. Koutras & Christos Rantsoudis - 2017 - Journal of Logic, Language and Information 26 (2):109-141.
    Defeasible conditionals are statements of the form ‘if A then normally B’. One plausible interpretation introduced in nonmonotonic reasoning dictates that ) is true iff B is true in ‘most’ A-worlds. In this paper, we investigate defeasible conditionals constructed upon a notion of ‘overwhelming majority’, defined as ‘truth in a cofinite subset of \’, the first infinite ordinal. One approach employs the modal logic of the frame \\), used in the temporal logic of discrete linear time. We introduce and investigate (...)
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  • Epistemic Justification: Its Subjective and its Objective Ways.Wolfgang Spohn - 2018 - Synthese 195 (9):3837-3856.
    Objective standards for justification or for being a reason would be desirable, but inductive skepticism tells us that they cannot be presupposed. Rather, we have to start from subjective-relative notions of justification and of being a reason. The paper lays out the strategic options we have given this dilemma. The paper explains the requirements for this subject-relative notion and how they may be satisfied. Then it discusses four quite heterogeneous ways of providing more objective standards, which combine without guaranteeing complete (...)
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  • The Mathematics of Causal Capacities.David Danks - unknown
    Models based on causal capacities, or independent causal influences/mechanisms, are widespread in the sciences. This paper develops a natural mathematical framework for representing such capacities by extending and generalizing previous results in cognitive psychology and machine learning, based on observations and arguments from prior philosophical debates. In addition to its substantial generality, the resulting framework provides a theoretical unification of the widely-used noisy-OR/AND and linear models, thereby showing how they are complementary rather than competing. This unification helps to explain many (...)
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  • Confirmation and the Generalized Nagel–Schaffner Model of Reduction: A Bayesian Analysis.Marko Tešić - 2019 - Synthese 196 (3):1097-1129.
    In their 2010 paper, Dizadji-Bahmani, Frigg, and Hartmann argue that the generalized version of the Nagel–Schaffner model that they have developed is the right one for intertheoretic reduction, i.e. the kind of reduction that involves theories with largely overlapping domains of application. Drawing on the GNS, DFH presented a Bayesian analysis of the confirmatory relation between the reducing theory and the reduced theory and argued that, post-reduction, evidence confirming the reducing theory also confirms the reduced theory and evidence confirming the (...)
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  • Discovering Binary Codes for Documents by Learning Deep Generative Models.Geoffrey Hinton & Ruslan Salakhutdinov - 2011 - Topics in Cognitive Science 3 (1):74-91.
    We describe a deep generative model in which the lowest layer represents the word-count vector of a document and the top layer represents a learned binary code for that document. The top two layers of the generative model form an undirected associative memory and the remaining layers form a belief net with directed, top-down connections. We present efficient learning and inference procedures for this type of generative model and show that it allows more accurate and much faster retrieval than latent (...)
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  • Uncertainty and Persistence: A Bayesian Update Semantics for Probabilistic Expressions.Deniz Rudin - 2018 - Journal of Philosophical Logic 47 (3):365-405.
    This paper presents a general-purpose update semantics for expressions of subjective uncertainty in natural language. First, a set of desiderata are established for how expressions of subjective uncertainty should behave in dynamic, update-based semantic systems; then extant implementations of expressions of subjective uncertainty in such models are evaluated and found wanting; finally, a new update semantics is proposed. The desiderata at the heart of this paper center around the contention that expressions of subjective uncertainty express beliefs which are not persistent, (...)
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  • Probability of Provability and Belief Functions.Philippe Smets - 1991 - Logique Et Analyse 133 (134):177-195.
  • Must, Knowledge, and (in)Directness.Daniel Lassiter - 2016 - Natural Language Semantics 24 (2):117-163.
    This paper presents corpus and experimental data that problematize the traditional analysis of must as a strong necessity modal, as recently revived and defended by von Fintel and Gillies :351–383, 2010). I provide naturalistic examples showing that must p can be used alongside an explicit denial of knowledge of p or certainty in p, and that it can be conjoined with an expression indicating that p is not certain or that not-p is possible. I also report the results of an (...)
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  • Peer Disagreement and Independence Preservation.Carl G. Wagner - 2011 - Erkenntnis 74 (2):277-288.
    It has often been recommended that the differing probability distributions of a group of experts should be reconciled in such a way as to preserve each instance of independence common to all of their distributions. When probability pooling is subject to a universal domain condition, along with state-wise aggregation, there are severe limitations on implementing this recommendation. In particular, when the individuals are epistemic peers whose probability assessments are to be accorded equal weight, universal preservation of independence is, with a (...)
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  • Believing More, Risking Less: On Coherence, Truth and Non-Trivial Extensions.Luc Bovens & Erik J. Olsson - 2002 - Erkenntnis 57 (2):137 - 150.
    If you believe more things you thereby run a greater risk of being in error than if you believe fewer things. From the point of view of avoiding error, it is best not to believe anything at all, or to have very uncommitted beliefs. But considering the fact that we all in fact do entertain many specific beliefs, this recommendation is obviously in flagrant dissonance with our actual epistemic practice. Let us call the problem raised by this apparent conflict the (...)
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  • Trust and the Value of Overconfidence: A Bayesian Perspective on Social Network Communication.Aron Vallinder & Erik J. Olsson - 2014 - Synthese 191 (9):1991-2007.
    The paper presents and defends a Bayesian theory of trust in social networks. In the first part of the paper, we provide justifications for the basic assumptions behind the model, and we give reasons for thinking that the model has plausible consequences for certain kinds of communication. In the second part of the paper we investigate the phenomenon of overconfidence. Many psychological studies have found that people think they are more reliable than they actually are. Using a simulation environment that (...)
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  • Normative Theories of Argumentation: Are Some Norms Better Than Others?Adam Corner & Ulrike Hahn - 2013 - Synthese 190 (16):3579-3610.
    Norms—that is, specifications of what we ought to do—play a critical role in the study of informal argumentation, as they do in studies of judgment, decision-making and reasoning more generally. Specifically, they guide a recurring theme: are people rational? Though rules and standards have been central to the study of reasoning, and behavior more generally, there has been little discussion within psychology about why (or indeed if) they should be considered normative despite the considerable philosophical literature that bears on this (...)
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  • From Similarity to Chance.Daniel Osherson - manuscript
    “In reality, all arguments from experience are founded on the similarity which we discover among natural objects, and by which we are induced to expect effects similar to those which we have found to follow from such objects. ... From causes which appear similar we expect similar effects.”.
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  • Aggregating Forecasts of Chance From Incoherent and Abstaining Experts.Daniel Osherson - manuscript
    Decision makers often rely on expert opinion when making forecasts under uncertainty. In doing so, they confront two methodological challenges: the elicitation problem, which requires them to extract meaningful information from experts; and the aggregation problem, which requires them to combine expert opinion by resolving disagreements. Linear averaging is a justifiably popular method for addressing aggregation, but its robust simplicity makes two requirements on elicitation. First, each expert must offer probabilistically coherent forecasts; second, each expert must respond to all our (...)
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  • The Frame Problem, the Relevance Problem, and a Package Solution to Both.Yingjin Xu & Pei Wang - 2012 - Synthese 187 (S1):43-72.
    As many philosophers agree, the frame problem is concerned with how an agent may efficiently filter out irrelevant information in the process of problem-solving. Hence, how to solve this problem hinges on how to properly handle semantic relevance in cognitive modeling, which is an area of cognitive science that deals with simulating human's cognitive processes in a computerized model. By "semantic relevance", we mean certain inferential relations among acquired beliefs which may facilitate information retrieval and practical reasoning under certain epistemic (...)
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  • Descriptive Assessment of Jeffrey's Rule.Jiaying Zhao & Daniel Osherson - unknown
    Jeffrey (1983) proposed a generalization of conditioning as a means of updating probability distributions when new evidence drives no event to certainty. His rule requires the stability of certain conditional probabilities through time. We tested this assumption (“invariance”) from the psychological point of view. In Experiment 1 participants offered probability estimates for events in Jeffrey’s candlelight example. Two further scenarios were investigated in Experiment 2, one in which invariance seems justified, the other in which it does not. Results were in (...)
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  • Constructing Variables That Support Causal Inference.Stephen E. Fancsali - unknown
  • Evidential Reasoning.Marcello Di Bello & Bart Verheij - 2018 - In G. Bongiovanni, G. Postema, A. Rotolo, G. Sartor, C. Valentini & D. Walton (eds.), Handbook in Legal Reasoning and Argumentation. Cham, Switzerland: Springer. pp. 447-493.
    The primary aim of this chapter is to explain the nature of evidential reasoning, the characteristic difficulties encountered, and the tools to address these difficulties. Our focus is on evidential reasoning in criminal cases. There is an extensive scholarly literature on these topics, and it is a secondary aim of the chapter to provide readers the means to find their way in historical and ongoing debates.
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  • A Primer on Probabilistic Inference.Thomas L. Griffiths & Alan Yuille - 2008 - In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press. pp. 33--57.
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  • A Brief Comparison of Pollock's Defeasible Reasoning and Ranking Functions.Wolfgang Spohn - 2002 - Synthese 131 (1):39-56.
    In this paper two theories of defeasible reasoning, Pollock's account and my theory of ranking functions, are compared, on a strategic level, since a strictly formal comparison would have been unfeasible. A brief summary of the accounts shows their basic difference: Pollock's is a strictly computational one, whereas ranking functions provide a regulative theory. Consequently, I argue that Pollock's theory is normatively defective, unable to provide a theoretical justification for its basic inference rules and thus an independent notion of admissible (...)
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  • Learning Causes: Psychological Explanations of Causal Explanation. [REVIEW]Clark Glymour - 1998 - Minds and Machines 8 (1):39-60.
    I argue that psychologists interested in human causal judgment should understand and adopt a representation of causal mechanisms by directed graphs that encode conditional independence (screening off) relations. I illustrate the benefits of that representation, now widely used in computer science and increasingly in statistics, by (i) showing that a dispute in psychology between ‘mechanist’ and ‘associationist’ psychological theories of causation rests on a false and confused dichotomy; (ii) showing that a recent, much-cited experiment, purporting to show that human subjects, (...)
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  • A Note on the Rational Closure of Knowledge Bases with Both Positive and Negative Knowledge.R. Booth & J. B. Paris - 1998 - Journal of Logic, Language and Information 7 (2):165-190.
    The notion of the rational closure of a positive knowledge base K of conditional assertions θ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$i$$ \end{document} |∼ φ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$i$$ \end{document} was first introduced by Lehmann and developed by Lehmann and Magidor. Following those authors we would also argue that the rational closure is, in a strong sense, the minimal information, or simplest, rational consequence relation satisfying K. In practice, however, one (...)
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  • Models in Systems Medicine.Jon Williamson - unknown
    Systems medicine is a promising new paradigm for discovering associations, causal relationships and mechanisms in medicine. But it faces some tough challenges that arise from the use of big data: in particular, the problem of how to integrate evidence and the problem of how to structure the development of models. I argue that objective Bayesian models offer one way of tackling the evidence integration problem. I also offer a general methodology for structuring the development of models, within which the objective (...)
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  • Detection of Unfaithfulness and Robust Causal Inference.Jiji Zhang & Peter Spirtes - 2008 - Minds and Machines 18 (2):239-271.
    Much of the recent work on the epistemology of causation has centered on two assumptions, known as the Causal Markov Condition and the Causal Faithfulness Condition. Philosophical discussions of the latter condition have exhibited situations in which it is likely to fail. This paper studies the Causal Faithfulness Condition as a conjunction of weaker conditions. We show that some of the weaker conjuncts can be empirically tested, and hence do not have to be assumed a priori. Our results lead to (...)
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  • Symmetries and Asymmetries in Evidential Support.Ellery Eells & Branden Fitelson - 2002 - Philosophical Studies 107 (2):129 - 142.
    Several forms of symmetry in degrees of evidential support areconsidered. Some of these symmetries are shown not to hold in general. This has implications for the adequacy of many measures of degree ofevidential support that have been proposed and defended in the philosophical literature.
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  • Bayesianism and Language Change.Jon Williamson - 2003 - Journal of Logic, Language and Information 12 (1):53-97.
    Bayesian probability is normally defined over a fixed language or eventspace. But in practice language is susceptible to change, and thequestion naturally arises as to how Bayesian degrees of belief shouldchange as language changes. I argue here that this question poses aserious challenge to Bayesianism. The Bayesian may be able to meet thischallenge however, and I outline a practical method for changing degreesof belief over changes in finite propositional languages.
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  • Explaining Disease: Correlations, Causes, and Mechanisms. [REVIEW]Paul Thagard - 1998 - Minds and Machines 8 (1):61-78.
    Why do people get sick? I argue that a disease explanation is best thought of as causal network instantiation, where a causal network describes the interrelations among multiple factors, and instantiation consists of observational or hypothetical assignment of factors to the patient whose disease is being explained. This paper first discusses inference from correlation to causation, integrating recent psychological discussions of causal reasoning with epidemiological approaches to understanding disease causation, particularly concerning ulcers and lung cancer. It then shows how causal (...)
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  • Quantum Physical Symbol Systems.Kathryn Blackmond Laskey - 2006 - Journal of Logic, Language and Information 15 (1-2):109-154.
    Because intelligent agents employ physically embodied cognitive systems to reason about the world, their cognitive abilities are constrained by the laws of physics. Scientists have used digital computers to develop and validate theories of physically embodied cognition. Computational theories of intelligence have advanced our understanding of the nature of intelligence and have yielded practically useful systems exhibiting some degree of intelligence. However, the view of cognition as algorithms running on digital computers rests on implicit assumptions about the physical world that (...)
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  • 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 position by considering the multi-causal (...)
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  • A Two-Level System of Knowledge Representation Based on Evidential Probability.Henry E. Kyburg - 1991 - Philosophical Studies 64 (1):105 - 114.
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  • A Model of Argumentation and its Application to Legal Reasoning.Kathleen Freeman & Arthur M. Farley - 1996 - Artificial Intelligence and Law 4 (3-4):163-197.
    We present a computational model of dialectical argumentation that could serve as a basis for legal reasoning. The legal domain is an instance of a domain in which knowledge is incomplete, uncertain, and inconsistent. Argumentation is well suited for reasoning in such weak theory domains. We model argument both as information structure, i.e., argument units connecting claims with supporting data, and as dialectical process, i.e., an alternating series of moves by opposing sides. Our model includes burden of proof as a (...)
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  • Intuitive Theories as Grammars for Causal Inference.Joshua B. Tenenbaum, Thomas L. Griffiths & Sourabh Niyogi - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. pp. 301--322.
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  • The Case for Psychologism in Default and Inheritance Reasoning.Francis Jeffry Pelletier & Renée Elio - 2005 - Synthese 146 (1-2):7-35.
    Default reasoning occurs whenever the truth of the evidence available to the reasoner does not guarantee the truth of the conclusion being drawn. Despite this, one is entitled to draw the conclusion “by default” on the grounds that we have no information which would make us doubt that the inference should be drawn. It is the type of conclusion we draw in the ordinary world and ordinary situations in which we find ourselves. Formally speaking, ‘nonmonotonic reasoning’ refers to argumentation in (...)
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  • Jon Williamson. Bayesian Nets and Causality: Philosophical and Computational Foundations.Kevin B. Korb - 2007 - Philosophia Mathematica 15 (3):389-396.
    Bayesian networks are computer programs which represent probabilitistic relationships graphically as directed acyclic graphs, and which can use those graphs to reason probabilistically , often at relatively low computational cost. Almost every expert system in the past tried to support probabilistic reasoning, but because of the computational difficulties they took approximating short-cuts, such as those afforded by MYCIN's certainty factors. That all changed with the publication of Judea Pearl's Probabilistic Reasoning in Intelligent Systems, in 1988, which synthesized a decade of (...)
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