Results for 'Probabilistic opinion pooling'

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  1. Probabilistic Opinion Pooling.Franz Dietrich & Christian List - 2016 - In Alan Hájek & Christopher Hitchcock (eds.), The Oxford Handbook of Probability and Philosophy. Oxford: Oxford University Press.
    Suppose several individuals (e.g., experts on a panel) each assign probabilities to some events. How can these individual probability assignments be aggregated into a single collective probability assignment? This article reviews several proposed solutions to this problem. We focus on three salient proposals: linear pooling (the weighted or unweighted linear averaging of probabilities), geometric pooling (the weighted or unweighted geometric averaging of probabilities), and multiplicative pooling (where probabilities are multiplied rather than averaged). We present axiomatic characterisations of (...)
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  2. Probabilistic Opinion Pooling Generalized. Part One: General Agendas.Franz Dietrich & Christian List - 2017 - Social Choice and Welfare 48 (4):747–786.
    How can different individuals' probability assignments to some events be aggregated into a collective probability assignment? Classic results on this problem assume that the set of relevant events -- the agenda -- is a sigma-algebra and is thus closed under disjunction (union) and conjunction (intersection). We drop this demanding assumption and explore probabilistic opinion pooling on general agendas. One might be interested in the probability of rain and that of an interest-rate increase, but not in the probability (...)
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  3. Probabilistic opinion pooling generalised. Part two: The premise-based approach.Franz Dietrich & Christian List - 2017 - Social Choice and Welfare 48 (4):787–814.
    How can different individuals' probability functions on a given sigma-algebra of events be aggregated into a collective probability function? Classic approaches to this problem often require 'event-wise independence': the collective probability for each event should depend only on the individuals' probabilities for that event. In practice, however, some events may be 'basic' and others 'derivative', so that it makes sense first to aggregate the probabilities for the former and then to let these constrain the probabilities for the latter. We formalize (...)
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  4. Aggregating agents with opinions about different propositions.Richard Pettigrew - 2022 - Synthese 200 (5):1-25.
    There are many reasons we might want to take the opinions of various individuals and pool them to give the opinions of the group they constitute. If all the individuals in the group have probabilistic opinions about the same propositions, there is a host of pooling functions we might deploy, such as linear or geometric pooling. However, there are also cases where different members of the group assign probabilities to different sets of propositions, which might overlap a (...)
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  5. Another Approach to Consensus and Maximally Informed Opinions with Increasing Evidence.Rush T. Stewart & Michael Nielsen - 2018 - Philosophy of Science (2):236-254.
    Merging of opinions results underwrite Bayesian rejoinders to complaints about the subjective nature of personal probability. Such results establish that sufficiently similar priors achieve consensus in the long run when fed the same increasing stream of evidence. Initial subjectivity, the line goes, is of mere transient significance, giving way to intersubjective agreement eventually. Here, we establish a merging result for sets of probability measures that are updated by Jeffrey conditioning. This generalizes a number of different merging results in the literature. (...)
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  6. The impossibility of non-manipulable probability aggregation.Franz Dietrich & Christian List - 2023
    A probability aggregation rule assigns to each profile of probability functions across a group of individuals (representing their individual probability assignments to some propositions) a collective probability function (representing the group's probability assignment). The rule is “non-manipulable” if no group member can manipulate the collective probability for any proposition in the direction of his or her own probability by misrepresenting his or her probability function (“strategic voting”). We show that, except in trivial cases, no probability aggregation rule satisfying two mild (...)
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  7. The aggregation of propositional attitudes: Towards a general theory.Franz Dietrich & Christian List - 2010 - Oxford Studies in Epistemology 3.
    How can the propositional attitudes of several individuals be aggregated into overall collective propositional attitudes? Although there are large bodies of work on the aggregation of various special kinds of propositional attitudes, such as preferences, judgments, probabilities and utilities, the aggregation of propositional attitudes is seldom studied in full generality. In this paper, we seek to contribute to filling this gap in the literature. We sketch the ingredients of a general theory of propositional attitude aggregation and prove two new theorems. (...)
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  8. A Theory of Bayesian Groups.Franz Dietrich - 2017 - Noûs 53 (3):708-736.
    A group is often construed as one agent with its own probabilistic beliefs (credences), which are obtained by aggregating those of the individuals, for instance through averaging. In their celebrated “Groupthink”, Russell et al. (2015) require group credences to undergo Bayesian revision whenever new information is learnt, i.e., whenever individual credences undergo Bayesian revision based on this information. To obtain a fully Bayesian group, one should often extend this requirement to non-public or even private information (learnt by not all (...)
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  9. Aggregating Causal Judgments.Richard Bradley, Franz Dietrich & Christian List - 2014 - Philosophy of Science 81 (4):491-515.
    Decision-making typically requires judgments about causal relations: we need to know the causal effects of our actions and the causal relevance of various environmental factors. We investigate how several individuals' causal judgments can be aggregated into collective causal judgments. First, we consider the aggregation of causal judgments via the aggregation of probabilistic judgments, and identify the limitations of this approach. We then explore the possibility of aggregating causal judgments independently of probabilistic ones. Formally, we introduce the problem of (...)
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  10. Introduction to the special issue “Beliefs in Groups” of Theory and Decision.Franz Dietrich & Wlodek Rabinowicz - 2018 - Theory and Decision 85 (1):1-4.
    This symposium in the overlap of philosophy and decision theory is described well by its title “Beliefs in Groups”. Each word in the title matters, with one intended ambiguity. The symposium is about beliefs rather than other attitudes such as preferences; these beliefs take the form of probabilities in the first three contributions, binary yes/no beliefs (‘judgments’) in the fourth contribution, and qualitative probabilities (‘probability grades’) in the fifth contribution. The beliefs occur in groups, which is ambiguous between beliefs of (...)
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  11. Pooling, Products, and Priors.Richard Pettigrew & Jonathan Weisberg -
    We often learn the opinions of others without hearing the evidence on which they're based. The orthodox Bayesian response is to treat the reported opinion as evidence itself and update on it by conditionalizing. But sometimes this isn't feasible. In these situations, a simpler way of combining one's existing opinion with opinions reported by others would be useful, especially if it yields the same results as conditionalization. We will show that one method---upco, also known as multiplicative pooling---is (...)
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  12. Geometric Pooling: A User's Guide.Richard Pettigrew & Jonathan Weisberg - forthcoming - British Journal for the Philosophy of Science.
    Much of our information comes to us indirectly, in the form of conclusions others have drawn from evidence they gathered. When we hear these conclusions, how can we modify our own opinions so as to gain the benefit of their evidence? In this paper we study the method known as geometric pooling. We consider two arguments in its favour, raising several objections to one, and proposing an amendment to the other.
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  13. Regret Averse Opinion Aggregation.Lee Elkin - 2021 - Ergo: An Open Access Journal of Philosophy 8 (16):473-495.
    It is often suggested that when opinions differ among individuals in a group, the opinions should be aggregated to form a compromise. This paper compares two approaches to aggregating opinions, linear pooling and what I call opinion agglomeration. In evaluating both strategies, I propose a pragmatic criterion, No Regrets, entailing that an aggregation strategy should prevent groups from buying and selling bets on events at prices regretted by their members. I show that only opinion agglomeration is able (...)
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  14. Support for Geometric Pooling.Jean Baccelli & Rush T. Stewart - 2023 - Review of Symbolic Logic 16 (1):298-337.
    Supra-Bayesianism is the Bayesian response to learning the opinions of others. Probability pooling constitutes an alternative response. One natural question is whether there are cases where probability pooling gives the supra-Bayesian result. This has been called the problem of Bayes-compatibility for pooling functions. It is known that in a common prior setting, under standard assumptions, linear pooling cannot be nontrivially Bayes-compatible. We show by contrast that geometric pooling can be nontrivially Bayes-compatible. Indeed, we show that, (...)
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  15. Independent Opinions? On the Causal Foundations of Belief Formation and Jury Theorems.Franz Dietrich & Kai Spiekermann - 2013 - Mind 122 (487):655-685.
    Democratic decision-making is often defended on grounds of the ‘wisdom of crowds’: decisions are more likely to be correct if they are based on many independent opinions, so a typical argument in social epistemology. But what does it mean to have independent opinions? Opinions can be probabilistically dependent even if individuals form their opinion in causal isolation from each other. We distinguish four probabilistic notions of opinion independence. Which of them holds depends on how individuals are causally (...)
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  16. Arguments from Expert Opinion – An Epistemological Approach.Christoph Lumer - 2020 - In Catarina Dutilh Novaes, Henrike Jansen, Jan Albert Van Laar & Bart Verheij (eds.), Reason to Dissent. Proceedings of the 3rd European Conference on Argumentation. College Publications. pp. 403-422.
    In times of populist mistrust towards experts, it is important and the aim of the paper to ascertain the rationality of arguments from expert opinion and to reconstruct their rational foundations as well as to determine their limits. The foundational approach chosen is probabilistic. However, there are at least three correct probabilistic reconstructions of such argumentations: statistical inferences, Bayesian updating, and interpretive arguments. To solve this competition problem, the paper proposes a recourse to the arguments' justification strengths (...)
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  17. On Accuracy and Coherence with Infinite Opinion Sets.Mikayla Kelley - 2023 - Philosophy of Science 90 (1):92-128.
    There is a well-known equivalence between avoiding accuracy dominance and having probabilistically coherent credences (see, e.g., de Finetti 1974, Joyce 2009, Predd et al. 2009, Pettigrew 2016). However, this equivalence has been established only when the set of propositions on which credence functions are defined is finite. In this paper, I establish connections between accuracy dominance and coherence when credence functions are defined on an infinite set of propositions. In particular, I establish the necessary results to extend the classic accuracy (...)
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  18. Mixing Expert Opinion.Brian Weatherson - manuscript
    This paper contributes to the project of articulating and defending the supra-Bayesian approach to judgment aggregation. I discuss three cases where a person is disposed to defer to two different experts, and ask how they should respond when they learn about the opinion of each. The guiding principles are that this learning should go by conditionalisation, and that they should aim to update on the evidence that the expert had updated on. But this doesn’t settle how the update on (...)
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  19. A Meta-Doomsday Argument: Uncertainty About the Validity of the Probabilistic Prediction of the End of the World.Alexey Turchin - manuscript
    Abstract: Four main forms of Doomsday Argument (DA) exist—Gott’s DA, Carter’s DA, Grace’s DA and Universal DA. All four forms use different probabilistic logic to predict that the end of the human civilization will happen unexpectedly soon based on our early location in human history. There are hundreds of publications about the validity of the Doomsday argument. Most of the attempts to disprove the Doomsday Argument have some weak points. As a result, we are uncertain about the validity of (...)
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  20. Aggregation for potentially infinite populations without continuity or completeness.David McCarthy, Kalle M. Mikkola & J. Teruji Thomas - 2019 - arXiv:1911.00872 [Econ.TH].
    We present an abstract social aggregation theorem. Society, and each individual, has a preorder that may be interpreted as expressing values or beliefs. The preorders are allowed to violate both completeness and continuity, and the population is allowed to be infinite. The preorders are only assumed to be represented by functions with values in partially ordered vector spaces, and whose product has convex range. This includes all preorders that satisfy strong independence. Any Pareto indifferent social preorder is then shown to (...)
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  21. On the Accuracy of Group Credences.Richard Pettigrew - 2019 - Oxford Studies in Epistemology 6.
    We often ask for the opinion of a group of individuals. How strongly does the scientific community believe that the rate at which sea levels are rising has increased over the last 200 years? How likely does the UK Treasury think it is that there will be a recession if the country leaves the European Union? What are these group credences that such questions request? And how do they relate to the individual credences assigned by the members of the (...)
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  22. Bayesian group belief.Franz Dietrich - 2010 - Social Choice and Welfare 35 (4):595-626.
    If a group is modelled as a single Bayesian agent, what should its beliefs be? I propose an axiomatic model that connects group beliefs to beliefs of group members, who are themselves modelled as Bayesian agents, possibly with different priors and different information. Group beliefs are proven to take a simple multiplicative form if people’s information is independent, and a more complex form if information overlaps arbitrarily. This shows that group beliefs can incorporate all information spread over the individuals without (...)
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  23. Fully Bayesian Aggregation.Franz Dietrich - 2021 - Journal of Economic Theory 194:105255.
    Can a group be an orthodox rational agent? This requires the group's aggregate preferences to follow expected utility (static rationality) and to evolve by Bayesian updating (dynamic rationality). Group rationality is possible, but the only preference aggregation rules which achieve it (and are minimally Paretian and continuous) are the linear-geometric rules, which combine individual values linearly and combine individual beliefs geometrically. Linear-geometric preference aggregation contrasts with classic linear-linear preference aggregation, which combines both values and beliefs linearly, but achieves only static (...)
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  24. An Epistemological Appraisal of Walton’s Argument Schemes.Christoph Lumer - 2022 - Informal Logic 43 (4):203-290.
    Abstract: The article presents and critically discusses Walton's (and Reed's and Macagno's) argument scheme approach to a theory of good argumentation. In particular, four characteristics of Walton's approach are presented: 1. It presents normative requirements for argumentation in the form of argument schemes, i.e. relatively concrete type descriptions. 2. These schemata are enthymematic, i.e. they omit some of the premises required by other approaches. 3. The actual argument schemes are usually supplemented by critical questions. 4. The method is inductive, bottom-up, (...)
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  25. Vorwort und Einführung.Sara Di Giulio & Alberto Frigo - 2020 - In Sara Di Giulio & Alberto Frigo (eds.), Kasuistik und Theorie des Gewissens. Von Pascal bis Kant. Berlino, Germania: pp. vii–viii; 1–15.
    Kant scholars have rarely addressed the centuries-old tradition of casuistry and the concept of conscience in Kant’s writings. This book offers a detailed exploration of the period from Pascal’s Provincial Letters to Kant’s critique of probabilism and discusses his proposal of a (new) casuistry as part of an moral education. *** -/- Die Debatte um Kasuistik und Probabilismus zählt zu den wichtigsten Themen der Moraltheologie und Moralphilosophie der frühen Neuzeit. In der enormen Verbreitung der Literatur über die Gewissensfälle (casus conscientiae) (...)
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  26. God, fine-tuning, and the problem of old evidence.Bradley Monton - 2006 - British Journal for the Philosophy of Science 57 (2):405-424.
    The fundamental constants that are involved in the laws of physics which describe our universe are finely-tuned for life, in the sense that if some of the constants had slightly different values life could not exist. Some people hold that this provides evidence for the existence of God. I will present a probabilistic version of this fine-tuning argument which is stronger than all other versions in the literature. Nevertheless, I will show that one can have reasonable opinions such that (...)
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  27. Four Approaches to Supposition.Benjamin Eva, Ted Shear & Branden Fitelson - 2022 - Ergo: An Open Access Journal of Philosophy 8 (26):58-98.
    Suppositions can be introduced in either the indicative or subjunctive mood. The introduction of either type of supposition initiates judgments that may be either qualitative, binary judgments about whether a given proposition is acceptable or quantitative, numerical ones about how acceptable it is. As such, accounts of qualitative/quantitative judgment under indicative/subjunctive supposition have been developed in the literature. We explore these four different types of theories by systematically explicating the relationships canonical representatives of each. Our representative qualitative accounts of indicative (...)
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  28. Survey-based naming conventions for use in OBO Foundry ontology development.Schober Daniel, Barry Smith, Lewis Suzanna, E. Kusnierczyk, Waclaw Lomax, Jane Mungall, Chris Taylor, F. Chris, Rocca-Serra Philippe & Sansone Susanna-Assunta - 2009 - BMC Bioinformatics 10 (1):125.
    A wide variety of ontologies relevant to the biological and medical domains are available through the OBO Foundry portal, and their number is growing rapidly. Integration of these ontologies, while requiring considerable effort, is extremely desirable. However, heterogeneities in format and style pose serious obstacles to such integration. In particular, inconsistencies in naming conventions can impair the readability and navigability of ontology class hierarchies, and hinder their alignment and integration. While other sources of diversity are tremendously complex and challenging, agreeing (...)
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  29. Higher-order uncertainty.Kevin Dorst - 2019 - In Mattias Skipper & Asbjørn Steglich-Petersen (eds.), Higher-Order Evidence: New Essays. Oxford, United Kingdom: Oxford University Press.
    You have higher-order uncertainty iff you are uncertain of what opinions you should have. I defend three claims about it. First, the higher-order evidence debate can be helpfully reframed in terms of higher-order uncertainty. The central question becomes how your first- and higher-order opinions should relate—a precise question that can be embedded within a general, tractable framework. Second, this question is nontrivial. Rational higher-order uncertainty is pervasive, and lies at the foundations of the epistemology of disagreement. Third, the answer is (...)
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  30. Be modest: you're living on the edge.Kevin Dorst - 2022 - Analysis 81 (4):611-621.
    Many have claimed that whenever an investigation might provide evidence for a claim, it might also provide evidence against it. Similarly, many have claimed that your credence should never be on the edge of the range of credences that you think might be rational. Surprisingly, both of these principles imply that you cannot rationally be modest: you cannot be uncertain what the rational opinions are.
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  31. When the (Bayesian) ideal is not ideal.Danilo Fraga Dantas - 2023 - Logos and Episteme 15 (3):271-298.
    Bayesian epistemologists support the norms of probabilism and conditionalization using Dutch book and accuracy arguments. These arguments assume that rationality requires agents to maximize practical or epistemic value in every doxastic state, which is evaluated from a subjective point of view (e.g., the agent’s expectancy of value). The accuracy arguments also presuppose that agents are opinionated. The goal of this paper is to discuss the assumptions of these arguments, including the measure of epistemic value. I have designed AI agents based (...)
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  32.  87
    Four questions about Quantum Bayesianism (QBism) and their answers by Ontology of Knowledge (OK) issue 20231208.Jean-Louis Boucon - manuscript
    The following article will attempt to highlight four questions which, in my opinion, are left unanswered (or overlooked) by QBism and to show the answers that the Ontology of Knowledge (OK) can provide. ● How does the subject come to exist for itself, individuated and persistent? ● From what common reality do world, mind, and meaning emerge? ● How does meaning emerge from the mathematical fact of probabilistic expectation? ● Is meaning animated by its own nature?
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  33. A Thought of Legal Research with Examples and Demonstrations.Kiyoung Kim - 2015 - SSRN.
    The policy makers or lawyers may face the need of legal research for reasons. The congressmen may plan to make new laws to address the challenges of their constituent or to the interest of nation. The lawyers may need to serve their clients who like to know the legal issues involved, the strategies to deal with their loss and recovery, and prospect for winning the case if the dispute has gotten worse. The lawyers may practice in a solo business or (...)
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  34. Deliberation and the Wisdom of Crowds.Franz Dietrich & Kai Spiekermann - manuscript
    Does pre-voting group deliberation increase majority competence? To address this question, we develop a probabilistic model of opinion formation and deliberation. Two new jury theorems, one pre-deliberation and one post-deliberation, suggest that deliberation is beneficial. Successful deliberation mitigates three voting failures: (1) overcounting widespread evidence, (2) neglecting evidential inequality, and (3) neglecting evidential complementarity. Formal results and simulations confirm this. But we identify four systematic exceptions where deliberation reduces majority competence, always by increasing Failure 1. Our analysis recommends (...)
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  35. Логические посылки научного метода галена.Andrey Shcheglov - 2018 - Schole 12 (1):147-166.
    The article is devoted to the analysis of the evident scientific method of Galen, which establishes the necessity of correct diagnosis of diseases, determination of true symptoms and causes of diseases, which results in the choice of the exact method of treatment. The article focuses on how Galen seeks to achieve reliable knowledge based on an undeniable logical necessity. Logical reliability is contrasted with “dialectical”, that is, probabilistic judgments, often leading to the opposite of what was originally asserted in (...)
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  36. Peirce, Pedigree, Probability.Rush T. Stewart & Tom F. Sterkenburg - 2022 - Transactions of the Charles S. Peirce Society 58 (2):138-166.
    An aspect of Peirce’s thought that may still be underappreciated is his resistance to what Levi calls _pedigree epistemology_, to the idea that a central focus in epistemology should be the justification of current beliefs. Somewhat more widely appreciated is his rejection of the subjective view of probability. We argue that Peirce’s criticisms of subjectivism, to the extent they grant such a conception of probability is viable at all, revert back to pedigree epistemology. A thoroughgoing rejection of pedigree in the (...)
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  37. Context Probabilism.Seth Yalcin - 2012 - In M. Aloni (ed.), 18th Amsterdam Colloquium. Springer. pp. 12-21.
    We investigate a basic probabilistic dynamic semantics for a fragment containing conditionals, probability operators, modals, and attitude verbs, with the aim of shedding light on the prospects for adding probabilistic structure to models of the conversational common ground.
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  38.  50
    Probabilistically coherent credences despite opacity.Christian List - forthcoming - Economics and Philosophy:1-10.
    Real human agents, even when they are rational by everyday standards, sometimes assign different credences to objectively equivalent statements, such as “George Orwell is a writer” and “Eric Arthur Blair is a writer”, or credences less than 1 to necessarily true statements, such as not-yet-proven theorems of arithmetic. Anna Mahtani calls this the phenomenon of “opacity” (a form of hyperintensionality). Opaque credences seem probabilistically incoherent, which goes against a key modelling assumption of probability theory. I sketch a modelling strategy for (...)
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  39. Are probabilism and special relativity incompatible?Nicholas Maxwell - 1985 - Philosophy of Science 52 (1):23-43.
    In this paper I expound an argument which seems to establish that probabilism and special relativity are incompatible. I examine the argument critically, and consider its implications for interpretative problems of quantum theory, and for theoretical physics as a whole.
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  40. Are probabilism and special relativity compatible?Nicholas Maxwell - 1988 - Philosophy of Science 55 (4):640-645.
    Are special relativity and probabilism compatible? Dieks argues that they are. But the possible universe he specifies, designed to exemplify both probabilism and special relativity, either incorporates a universal "now" (and is thus incompatible with special relativity), or amounts to a many world universe (which I have discussed, and rejected as too ad hoc to be taken seriously), or fails to have any one definite overall Minkowskian-type space-time structure (and thus differs drastically from special relativity as ordinarily understood). Probabilism and (...)
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  41. Are Probabilism and Special Relativity Compatible?Nicholas Maxwell - 1988 - Philosophy of Science 55 (4):640-645.
    Are probabilism and special relativity compatible? Dieks argues that they are. But the possible universe he specifies, designed to exemplify both probabilism and special relativity, either incorporates a universal “now”, or amounts to a many world universe, or fails to have any one definite overall Minkowskian-type space-time structure. Probabilism and special relativity appear to be incompatible after all. What is at issue is not whether “the flow of time” can be reconciled with special relativity, but rather whether explicitly probabilistic (...)
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  42. Probabilistic causation and the explanatory role of natural selection.Pablo Razeto-Barry & Ramiro Frick - 2011 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 42 (3):344-355.
    The explanatory role of natural selection is one of the long-term debates in evolutionary biology. Nevertheless, the consensus has been slippery because conceptual confusions and the absence of a unified, formal causal model that integrates different explanatory scopes of natural selection. In this study we attempt to examine two questions: (i) What can the theory of natural selection explain? and (ii) Is there a causal or explanatory model that integrates all natural selection explananda? For the first question, we argue that (...)
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  43. Pooled beneficence.Garrett Cullity - 2000 - In Michael Almeida (ed.), Imperceptible Harms and Benefits. Dordrecht: Kluwer. pp. 9-42.
    There can be situations in which, if I contribute to a pool of resources for helping a large number of people, the difference that my contribution makes to any of the people helped from the pool will be imperceptible at best, and maybe even non-existent. And this can be the case where it is also true that giving the same amount directly to one of the intended beneficiaries of the pool would have made a very large difference to her. Can (...)
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  44. Armstrong on Probabilistic Laws of Nature.Jonathan D. Jacobs & Robert J. Hartman - 2017 - Philosophical Papers 46 (3):373-387.
    D. M. Armstrong famously claims that deterministic laws of nature are contingent relations between universals and that his account can also be straightforwardly extended to irreducibly probabilistic laws of nature. For the most part, philosophers have neglected to scrutinize Armstrong’s account of probabilistic laws. This is surprising precisely because his own claims about probabilistic laws make it unclear just what he takes them to be. We offer three interpretations of what Armstrong-style probabilistic laws are, and argue (...)
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  45. Does probabilism solve the great quantum mystery?Nicholas Maxwell - 2010 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 19 (3):321-336.
    I put forward a micro realistic, probabilistic version of quantum theory, which specifies the precise nature of quantum entities thus solving the quantum wave/particle dilemma, and which both reproduces the empirical success of orthodox quantum theory, and yields predictions that differ from orthodox quantum theory for as yet unperformed experiments.
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  46. Probabilistic Arguments in the Epistemological Approach to Argumentation.Christoph Lumer - 2011 - In Frans H. Van Eemeren, Bart Garssen, David Godden & Gordon Mitchell (eds.), Proceedings of the 7th Conference of the International Society for the Study of Argumentation. Amsterdam, Netherlands: Rozenberg; Sic Sat. pp. 1141-1154.
    The aim of the paper is to develop general criteria of argumentative validity and adequacy for probabilistic arguments on the basis of the epistemological approach to argumentation. In this approach, as in most other approaches to argumentation, proabilistic arguments have been neglected somewhat. Nonetheless, criteria for several special types of probabilistic arguments have been developed, in particular by Richard Feldman and Christoph Lumer. In the first part (sects. 2-5) the epistemological basis of probabilistic arguments is discussed. With (...)
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  47. Is probabilistic evidence a source of knowledge?Ori Friedman & John Turri - 2015 - Cognitive Science 39 (5):1062-1080.
    We report a series of experiments examining whether people ascribe knowledge for true beliefs based on probabilistic evidence. Participants were less likely to ascribe knowledge for beliefs based on probabilistic evidence than for beliefs based on perceptual evidence or testimony providing causal information. Denial of knowledge for beliefs based on probabilistic evidence did not arise because participants viewed such beliefs as unjustified, nor because such beliefs leave open the possibility of error. These findings rule out traditional philosophical (...)
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  48. A probabilistic framework for analysing the compositionality of conceptual combinations.Peter Bruza, Kirsty Kitto, Brentyn Ramm & Laurianne Sitbon - 2015 - Journal of Mathematical Psychology 67:26-38.
    Conceptual combination performs a fundamental role in creating the broad range of compound phrases utilised in everyday language. This article provides a novel probabilistic framework for assessing whether the semantics of conceptual combinations are compositional, and so can be considered as a function of the semantics of the constituent concepts, or not. While the systematicity and productivity of language provide a strong argument in favor of assuming compositionality, this very assumption is still regularly questioned in both cognitive science and (...)
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  49. Probabilistic semantics for epistemic modals: Normality assumptions, conditional epistemic spaces and the strength of must and might.Guillermo Del Pinal - 2021 - Linguistics and Philosophy 45 (4):985-1026.
    The epistemic modal auxiliaries must and might are vehicles for expressing the force with which a proposition follows from some body of evidence or information. Standard approaches model these operators using quantificational modal logic, but probabilistic approaches are becoming increasingly influential. According to a traditional view, must is a maximally strong epistemic operator and might is a bare possibility one. A competing account—popular amongst proponents of a probabilisitic turn—says that, given a body of evidence, must \ entails that \\) (...)
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  50. Probabilistic promotion and ability.Luke Elson - 2019 - Ergo: An Open Access Journal of Philosophy 6.
    We often have some reason to do actions insofar as they promote outcomes or states of affairs, such as the satisfaction of a desire. But what is it to promote an outcome? I defend a new version of 'probabilism about promotion'. According to Minimal Probabilistic Promotion, we promote some outcome when we make that outcome more likely than it would have been if we had done something (anything) else. This makes promotion easy and reasons cheap.
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