Jon Williamson
- Published in print:
- 2004
- Published Online:
- September 2007
- ISBN:
- 9780198530794
- eISBN:
- 9780191712982
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198530794.001.0001
- Subject:
- Mathematics, Logic / Computer Science / Mathematical Philosophy
This book provides an introduction to, and analysis of, the use of Bayesian nets in causal modelling. It puts forward new conceptual foundations for causal network modelling: The book argues that ...
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This book provides an introduction to, and analysis of, the use of Bayesian nets in causal modelling. It puts forward new conceptual foundations for causal network modelling: The book argues that probability and causality need to be interpreted as epistemic notions in order for the key assumptions behind causal models to hold. Under the epistemic view, probability and causality are understood in terms of the beliefs an agent ought to adopt. The book develops an objective Bayesian notion of probability and a corresponding epistemic theory of causality. This yields a general framework for causal modelling, which is extended to cope with recursive causal relations, logically complex beliefs and changes in an agent's language.Less
This book provides an introduction to, and analysis of, the use of Bayesian nets in causal modelling. It puts forward new conceptual foundations for causal network modelling: The book argues that probability and causality need to be interpreted as epistemic notions in order for the key assumptions behind causal models to hold. Under the epistemic view, probability and causality are understood in terms of the beliefs an agent ought to adopt. The book develops an objective Bayesian notion of probability and a corresponding epistemic theory of causality. This yields a general framework for causal modelling, which is extended to cope with recursive causal relations, logically complex beliefs and changes in an agent's language.
Jon Williamson
- Published in print:
- 2010
- Published Online:
- September 2010
- ISBN:
- 9780199228003
- eISBN:
- 9780191711060
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199228003.001.0001
- Subject:
- Mathematics, Probability / Statistics, Logic / Computer Science / Mathematical Philosophy
Bayesian epistemology aims to answer the following question: How strongly should an agent believe the various propositions expressible in her language? Subjective Bayesians hold that.it is largely ...
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Bayesian epistemology aims to answer the following question: How strongly should an agent believe the various propositions expressible in her language? Subjective Bayesians hold that.it is largely (though not entirely) up to the agent as to which degrees of belief to adopt. Objective Bayesians, on the other hand, maintain that appropriate degrees of belief are largely (though not entirely) determined by the agent's evidence. This book states and defends a version of objective Bayesian epistemology. According to this version, objective Bayesianism is characterized by three norms: (i) Probability: degrees of belief should be probabilities; (ii) Calibration: they should be calibrated with evidence; and (iii) Equivocation: they should otherwise equivocate between basic outcomes. Objective Bayesianism has been challenged on a number of different fronts: for example, it has been accused of being poorly motivated, of failing to handle qualitative evidence, of yielding counter‐intuitive degrees of belief after updating, of suffering from a failure to learn from experience, of being computationally intractable, of being susceptible to paradox, of being language dependent, and of not being objective enough. The book argues that these criticisms can be met and that objective Bayesianism is a promising theory with an exciting agenda for further research.Less
Bayesian epistemology aims to answer the following question: How strongly should an agent believe the various propositions expressible in her language? Subjective Bayesians hold that.it is largely (though not entirely) up to the agent as to which degrees of belief to adopt. Objective Bayesians, on the other hand, maintain that appropriate degrees of belief are largely (though not entirely) determined by the agent's evidence. This book states and defends a version of objective Bayesian epistemology. According to this version, objective Bayesianism is characterized by three norms: (i) Probability: degrees of belief should be probabilities; (ii) Calibration: they should be calibrated with evidence; and (iii) Equivocation: they should otherwise equivocate between basic outcomes. Objective Bayesianism has been challenged on a number of different fronts: for example, it has been accused of being poorly motivated, of failing to handle qualitative evidence, of yielding counter‐intuitive degrees of belief after updating, of suffering from a failure to learn from experience, of being computationally intractable, of being susceptible to paradox, of being language dependent, and of not being objective enough. The book argues that these criticisms can be met and that objective Bayesianism is a promising theory with an exciting agenda for further research.
Jon Williamson
- Published in print:
- 2004
- Published Online:
- September 2007
- ISBN:
- 9780198530794
- eISBN:
- 9780191712982
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198530794.003.0001
- Subject:
- Mathematics, Logic / Computer Science / Mathematical Philosophy
This chapter describes the central claims of the book. From a philosophical point of view, the book argues for an objective Bayesian interpretation of probability and an epistemic interpretation of ...
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This chapter describes the central claims of the book. From a philosophical point of view, the book argues for an objective Bayesian interpretation of probability and an epistemic interpretation of causality, and claims that these offer a firm foundation for causal modelling. From the computational point of view, the book investigates the relationship between Bayesian nets and maximum entropy methods, and develops a general computational framework for probabilistic and causal reasoning.Less
This chapter describes the central claims of the book. From a philosophical point of view, the book argues for an objective Bayesian interpretation of probability and an epistemic interpretation of causality, and claims that these offer a firm foundation for causal modelling. From the computational point of view, the book investigates the relationship between Bayesian nets and maximum entropy methods, and develops a general computational framework for probabilistic and causal reasoning.
Jon Williamson
- Published in print:
- 2004
- Published Online:
- September 2007
- ISBN:
- 9780198530794
- eISBN:
- 9780191712982
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198530794.003.0006
- Subject:
- Mathematics, Logic / Computer Science / Mathematical Philosophy
Objective Bayesianism yields a justification of the causal Markov condition: in certain circumstances, the objective Bayesian net is just the causal net and so the causal net is an appropriate ...
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Objective Bayesianism yields a justification of the causal Markov condition: in certain circumstances, the objective Bayesian net is just the causal net and so the causal net is an appropriate representation of rational degrees of belief. However, the resulting Bayesian net may not yield accurate enough predictions. This motivates a two-stage methodology for using Bayesian nets: first construct a causal net, then refine this net to better represent physical probability.Less
Objective Bayesianism yields a justification of the causal Markov condition: in certain circumstances, the objective Bayesian net is just the causal net and so the causal net is an appropriate representation of rational degrees of belief. However, the resulting Bayesian net may not yield accurate enough predictions. This motivates a two-stage methodology for using Bayesian nets: first construct a causal net, then refine this net to better represent physical probability.
Robert E. Goodin
- Published in print:
- 2003
- Published Online:
- November 2003
- ISBN:
- 9780199256174
- eISBN:
- 9780191599354
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0199256179.003.0006
- Subject:
- Political Science, Political Theory
Shows how Bayesian thinking should make democratic outcomes so rationally compelling. Bayes's formula provides a mathematical expression for specifying exactly how we ought rationally to update our a ...
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Shows how Bayesian thinking should make democratic outcomes so rationally compelling. Bayes's formula provides a mathematical expression for specifying exactly how we ought rationally to update our a priori beliefs in light of subsequent evidence, and the proposal is that voters are modelled in like fashion: votes, let us suppose, constitute (among other things) ‘reports’ of the voter's experiences and perceptions; further suppose that voters accord ‘evidentiary value’ to the reports they receive from one another through those votes; and further suppose that voters are rational, and that part and parcel of their being rational is being prepared to revise their opinions in light of further evidence (including evidence emanating from one another's votes‐cum‐reports). In this process, each of us treats our own experiences and perceptions as one source of evidence, and regards our own report as right; in that sense, we are perfectly sincere when we vote in a particular way, although we also acknowledge that our own experiences and perspectives are particular and peculiar, and hence our own perceptions are themselves inconclusive; because of that, voters striving to behave rationally should sincerely want to adjust their a priori beliefs in the light of all other experiences and perceptions that are reported at an election. Bayesian updating of that sort may well lead people who started out believing (and voting) one way to end up believing (and genuinely wanting implemented) the opposite way, just so long as sufficiently many votes‐cum‐reports point in that different direction; in other words, Bayesian reasoning can, and in politically typical cases ought to, provide people with a compelling reason to accede to the majority verdict. In this way, Bayesianism ‘rationalizes’ majority rule in a pretty strong sense; indeed if anything, it underwrites majoritarianism too strongly.Less
Shows how Bayesian thinking should make democratic outcomes so rationally compelling. Bayes's formula provides a mathematical expression for specifying exactly how we ought rationally to update our a priori beliefs in light of subsequent evidence, and the proposal is that voters are modelled in like fashion: votes, let us suppose, constitute (among other things) ‘reports’ of the voter's experiences and perceptions; further suppose that voters accord ‘evidentiary value’ to the reports they receive from one another through those votes; and further suppose that voters are rational, and that part and parcel of their being rational is being prepared to revise their opinions in light of further evidence (including evidence emanating from one another's votes‐cum‐reports). In this process, each of us treats our own experiences and perceptions as one source of evidence, and regards our own report as right; in that sense, we are perfectly sincere when we vote in a particular way, although we also acknowledge that our own experiences and perspectives are particular and peculiar, and hence our own perceptions are themselves inconclusive; because of that, voters striving to behave rationally should sincerely want to adjust their a priori beliefs in the light of all other experiences and perceptions that are reported at an election. Bayesian updating of that sort may well lead people who started out believing (and voting) one way to end up believing (and genuinely wanting implemented) the opposite way, just so long as sufficiently many votes‐cum‐reports point in that different direction; in other words, Bayesian reasoning can, and in politically typical cases ought to, provide people with a compelling reason to accede to the majority verdict. In this way, Bayesianism ‘rationalizes’ majority rule in a pretty strong sense; indeed if anything, it underwrites majoritarianism too strongly.
Robert E. Goodin
- Published in print:
- 2003
- Published Online:
- November 2003
- ISBN:
- 9780199256174
- eISBN:
- 9780191599354
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0199256179.003.0007
- Subject:
- Political Science, Political Theory
This is the last of four chapters on belief democracy, and discusses how to rationalize persistent opposition in the light of Wollheim's paradox (how can you vote one way, and then immediately accept ...
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This is the last of four chapters on belief democracy, and discusses how to rationalize persistent opposition in the light of Wollheim's paradox (how can you vote one way, and then immediately accept the majority verdict in favour of the opposite view, when voting the first way is seen as asserting that it is true and acceding to the majority verdict is seen as accepting that the opposite is true), and Bayesian considerations that make it utterly irrational for anyone ever to continue disagreeing once everyone has voted. The first section demonstrates that none of the easy and obvious ways of extending (modifying) the Bayesian framework rationalize majoritarianism without derationalizing ongoing opposition. Next, various ways of moving beyond Bayes to overcome the paradox of persisting opposition are put forward; these include repudiating Bayesian reasoning altogether, assuming that disagreement betokens unreliability, assuming that smaller groups are inherently more reliable, and assuming that each election is different; none of these produce the required result. Finally, political arguments are put forward more explicitly to see whether they can rationalize ongoing opposition; these include the crucial role of opposition in democratic politics, proceduralism, denying propositional content, biased perception, strategic voting, segmented information pools, and different interests. In the light of what has been presented overall, the author puts forward his own explanation, which suggests that what is wrong with Bayesian models that ask us to update our beliefs in the light of others’ votes is that those votes mix facts and values, and only one of these (others’ assessments of the facts) can reasonably be taken into account in updating our beliefs; thus, it is the epistemic power of majorities when dealing with shared facts that underwrites the rationality of majority rule, but their lack of epistemic authority when it comes to matters of evaluations that underwrites the rationality of persisting opposition.Less
This is the last of four chapters on belief democracy, and discusses how to rationalize persistent opposition in the light of Wollheim's paradox (how can you vote one way, and then immediately accept the majority verdict in favour of the opposite view, when voting the first way is seen as asserting that it is true and acceding to the majority verdict is seen as accepting that the opposite is true), and Bayesian considerations that make it utterly irrational for anyone ever to continue disagreeing once everyone has voted. The first section demonstrates that none of the easy and obvious ways of extending (modifying) the Bayesian framework rationalize majoritarianism without derationalizing ongoing opposition. Next, various ways of moving beyond Bayes to overcome the paradox of persisting opposition are put forward; these include repudiating Bayesian reasoning altogether, assuming that disagreement betokens unreliability, assuming that smaller groups are inherently more reliable, and assuming that each election is different; none of these produce the required result. Finally, political arguments are put forward more explicitly to see whether they can rationalize ongoing opposition; these include the crucial role of opposition in democratic politics, proceduralism, denying propositional content, biased perception, strategic voting, segmented information pools, and different interests. In the light of what has been presented overall, the author puts forward his own explanation, which suggests that what is wrong with Bayesian models that ask us to update our beliefs in the light of others’ votes is that those votes mix facts and values, and only one of these (others’ assessments of the facts) can reasonably be taken into account in updating our beliefs; thus, it is the epistemic power of majorities when dealing with shared facts that underwrites the rationality of majority rule, but their lack of epistemic authority when it comes to matters of evaluations that underwrites the rationality of persisting opposition.
Jon Williamson
- Published in print:
- 2004
- Published Online:
- September 2007
- ISBN:
- 9780198530794
- eISBN:
- 9780191712982
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198530794.003.0002
- Subject:
- Mathematics, Logic / Computer Science / Mathematical Philosophy
Probability functions over domains of variables are introduced, as are the various interpretations of probability, including the frequency, propensity, chance, and Bayesian interpretations. The ...
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Probability functions over domains of variables are introduced, as are the various interpretations of probability, including the frequency, propensity, chance, and Bayesian interpretations. The Bayesian notion of chance as ultimate belief is also discussed.Less
Probability functions over domains of variables are introduced, as are the various interpretations of probability, including the frequency, propensity, chance, and Bayesian interpretations. The Bayesian notion of chance as ultimate belief is also discussed.
Elliott Sober
- Published in print:
- 2005
- Published Online:
- January 2012
- ISBN:
- 9780197263419
- eISBN:
- 9780191734175
- Item type:
- chapter
- Publisher:
- British Academy
- DOI:
- 10.5871/bacad/9780197263419.003.0002
- Subject:
- Philosophy, Logic/Philosophy of Mathematics
This chapter discusses the scope and limits of Bayesianism. The first topics discussed are the math and philosophy embedded in Bayesianism and the standard objection to Bayesianism. The concept of ...
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This chapter discusses the scope and limits of Bayesianism. The first topics discussed are the math and philosophy embedded in Bayesianism and the standard objection to Bayesianism. The concept of likelihood in Bayesianism is examined, and a problem of the best-case strategy of dealing with nuisance parameters is studied. Finally, the main weakness of strong Bayesianism is identified.Less
This chapter discusses the scope and limits of Bayesianism. The first topics discussed are the math and philosophy embedded in Bayesianism and the standard objection to Bayesianism. The concept of likelihood in Bayesianism is examined, and a problem of the best-case strategy of dealing with nuisance parameters is studied. Finally, the main weakness of strong Bayesianism is identified.
Paul F. A. Bartha
- Published in print:
- 2010
- Published Online:
- May 2010
- ISBN:
- 9780195325539
- eISBN:
- 9780199776313
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195325539.003.0008
- Subject:
- Philosophy, Logic/Philosophy of Mathematics
This chapter has two purposes. First, it explores the connection between analogical reasoning and symmetry to provide a second justification for the argument form, independent of the one offered in ...
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This chapter has two purposes. First, it explores the connection between analogical reasoning and symmetry to provide a second justification for the argument form, independent of the one offered in chapter 7. The chapter proposes that good analogical arguments are sanctioned by norms of symmetry and Reflection, as expounded by van Fraassen. This idea is elaborated first in a nonprobabilistic setting and then in a probabilistic framework. It is argued that a psychological construal of the relationship between symmetry and probability, as championed by de Finetti and others, is inadequate. The second objective of the chapter is to integrate analogical arguments into a Bayesian model of theoretical confirmation by refining Salmon's idea that analogical arguments contribute to establishing non‐negligible prior probability for hypotheses.Less
This chapter has two purposes. First, it explores the connection between analogical reasoning and symmetry to provide a second justification for the argument form, independent of the one offered in chapter 7. The chapter proposes that good analogical arguments are sanctioned by norms of symmetry and Reflection, as expounded by van Fraassen. This idea is elaborated first in a nonprobabilistic setting and then in a probabilistic framework. It is argued that a psychological construal of the relationship between symmetry and probability, as championed by de Finetti and others, is inadequate. The second objective of the chapter is to integrate analogical arguments into a Bayesian model of theoretical confirmation by refining Salmon's idea that analogical arguments contribute to establishing non‐negligible prior probability for hypotheses.
John T. Roberts
- Published in print:
- 2008
- Published Online:
- January 2009
- ISBN:
- 9780199557707
- eISBN:
- 9780191721052
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199557707.003.0004
- Subject:
- Philosophy, Metaphysics/Epistemology, Philosophy of Science
This chapter argues that if the universe is governed by laws of nature, then particular laws of nature cannot be discovered by empirical science unless the meta‐theoretic conception of laws is ...
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This chapter argues that if the universe is governed by laws of nature, then particular laws of nature cannot be discovered by empirical science unless the meta‐theoretic conception of laws is correct. This gives us a powerful reason to embrace the meta‐theoretic conception. The key argument appeals to underdetermination of theory by evidence, but it avoids the pitfalls of familiar underdetermination arguments against realism. Several objections to the argument are considered, including one based on Bayesianism, one based on inference to the best explanation (IBE), one based on contextualist epistemology, and one that alleges that the only way to avoid inductive skepticism is to adopt an epistemology of science that allows for the discovery of laws of nature in spite of radical underdetermination of the laws by the available evidence. All these objections are found wanting.Less
This chapter argues that if the universe is governed by laws of nature, then particular laws of nature cannot be discovered by empirical science unless the meta‐theoretic conception of laws is correct. This gives us a powerful reason to embrace the meta‐theoretic conception. The key argument appeals to underdetermination of theory by evidence, but it avoids the pitfalls of familiar underdetermination arguments against realism. Several objections to the argument are considered, including one based on Bayesianism, one based on inference to the best explanation (IBE), one based on contextualist epistemology, and one that alleges that the only way to avoid inductive skepticism is to adopt an epistemology of science that allows for the discovery of laws of nature in spite of radical underdetermination of the laws by the available evidence. All these objections are found wanting.
Joseph Heath
- Published in print:
- 2008
- Published Online:
- January 2009
- ISBN:
- 9780195370294
- eISBN:
- 9780199871230
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195370294.003.0004
- Subject:
- Philosophy, Logic/Philosophy of Mathematics
This chapter begins with a summary of the classical sociological solution to the problem of order advanced by Talcott Parsons. The central flaw in the solution is simply that it lacks deliberative ...
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This chapter begins with a summary of the classical sociological solution to the problem of order advanced by Talcott Parsons. The central flaw in the solution is simply that it lacks deliberative microfoundations; it is not clear how social norms interact with beliefs and desires, the two intentional states posited by standard decision theory. A solution is proposed, which involved positing “principles” as a set of intentional states associated direction with actions. A formal representation is developed, and the attractions of the model, from the standpoint of representing a variety of different social interaction types, are then touted.Less
This chapter begins with a summary of the classical sociological solution to the problem of order advanced by Talcott Parsons. The central flaw in the solution is simply that it lacks deliberative microfoundations; it is not clear how social norms interact with beliefs and desires, the two intentional states posited by standard decision theory. A solution is proposed, which involved positing “principles” as a set of intentional states associated direction with actions. A formal representation is developed, and the attractions of the model, from the standpoint of representing a variety of different social interaction types, are then touted.
Herman Philipse
- Published in print:
- 2012
- Published Online:
- May 2012
- ISBN:
- 9780199697533
- eISBN:
- 9780191738470
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199697533.001.0001
- Subject:
- Philosophy, Philosophy of Religion, Metaphysics/Epistemology
This book is a critical examination of the philosophical strategies for defending religious belief. The main strategies may be presented as conforming to the end nodes of a decision tree for a ...
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This book is a critical examination of the philosophical strategies for defending religious belief. The main strategies may be presented as conforming to the end nodes of a decision tree for a believer. The faithful can interpret a credal statement (e.g. ‘God exists’) either as a factual claim, or otherwise. If it is a factual claim, they can either be warranted to endorse it without evidence, etc., or not. Finally, should religious belief require evidential support, then ought that support be assessed by the same criteria that we use in evaluating evidence in science, or not? Each of these options has been defended by prominent analytic philosophers of religion. In Part I, Herman Philipse assesses the tenability of each of these strategies and argues that the most promising option for believers who want to be justified in accepting their creed in our scientific age is the Bayesian cumulative case strategy developed by Richard Swinburne. Parts II and III are devoted to an in-depth analysis of this case for theism. Using a ‘strategy of subsidiary arguments’, Philipse concludes (1) that theism cannot be stated meaningfully; (2) that if theism were meaningful, it would have no predictive power concerning existing evidence, so that Bayesian arguments cannot get started; and (3) that if the Bayesian cumulative case strategy did work, one should conclude that atheism is more probable than theism. According to a referee, the book is ‘full of careful, rigorous reasoning – much of it original’.Less
This book is a critical examination of the philosophical strategies for defending religious belief. The main strategies may be presented as conforming to the end nodes of a decision tree for a believer. The faithful can interpret a credal statement (e.g. ‘God exists’) either as a factual claim, or otherwise. If it is a factual claim, they can either be warranted to endorse it without evidence, etc., or not. Finally, should religious belief require evidential support, then ought that support be assessed by the same criteria that we use in evaluating evidence in science, or not? Each of these options has been defended by prominent analytic philosophers of religion. In Part I, Herman Philipse assesses the tenability of each of these strategies and argues that the most promising option for believers who want to be justified in accepting their creed in our scientific age is the Bayesian cumulative case strategy developed by Richard Swinburne. Parts II and III are devoted to an in-depth analysis of this case for theism. Using a ‘strategy of subsidiary arguments’, Philipse concludes (1) that theism cannot be stated meaningfully; (2) that if theism were meaningful, it would have no predictive power concerning existing evidence, so that Bayesian arguments cannot get started; and (3) that if the Bayesian cumulative case strategy did work, one should conclude that atheism is more probable than theism. According to a referee, the book is ‘full of careful, rigorous reasoning – much of it original’.
Richard Swinburne
- Published in print:
- 2005
- Published Online:
- January 2012
- ISBN:
- 9780197263419
- eISBN:
- 9780191734175
- Item type:
- chapter
- Publisher:
- British Academy
- DOI:
- 10.5871/bacad/9780197263419.003.0001
- Subject:
- Philosophy, Logic/Philosophy of Mathematics
This chapter introduces Bayes' Theorem, which is primarily concerned with probability. It discusses some of the different kinds of probability, such as statistical probability, before moving on to a ...
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This chapter introduces Bayes' Theorem, which is primarily concerned with probability. It discusses some of the different kinds of probability, such as statistical probability, before moving on to a discussion of probability axioms. Bayesianism, prior probability and simplicity, and countable additivity are also studied in the chapter.Less
This chapter introduces Bayes' Theorem, which is primarily concerned with probability. It discusses some of the different kinds of probability, such as statistical probability, before moving on to a discussion of probability axioms. Bayesianism, prior probability and simplicity, and countable additivity are also studied in the chapter.
Colin Howson
- Published in print:
- 2005
- Published Online:
- January 2012
- ISBN:
- 9780197263419
- eISBN:
- 9780191734175
- Item type:
- chapter
- Publisher:
- British Academy
- DOI:
- 10.5871/bacad/9780197263419.003.0003
- Subject:
- Philosophy, Logic/Philosophy of Mathematics
This chapter discusses Bayesianism in statistics. The first section of the chapter is devoted to the First Bayesian Theory, which is immediately followed by a discussion of significance tests and the ...
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This chapter discusses Bayesianism in statistics. The first section of the chapter is devoted to the First Bayesian Theory, which is immediately followed by a discussion of significance tests and the Second Bayesian Theory. Lindley's Paradox and the Neyman-Pearson Theory are examined in detail, along with the concept of priors and likelihood. The final portion of the chapter focuses on the Second Bayesian theory as logic.Less
This chapter discusses Bayesianism in statistics. The first section of the chapter is devoted to the First Bayesian Theory, which is immediately followed by a discussion of significance tests and the Second Bayesian Theory. Lindley's Paradox and the Neyman-Pearson Theory are examined in detail, along with the concept of priors and likelihood. The final portion of the chapter focuses on the Second Bayesian theory as logic.
Paul Horwich
- Published in print:
- 2005
- Published Online:
- April 2005
- ISBN:
- 9780199251261
- eISBN:
- 9780191602252
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0199251266.003.0007
- Subject:
- Philosophy, Metaphysics/Epistemology
This essay discusses the programme called ‘therapeutic Bayesianism’ from three abstract points of view: substantial, foundational, and meta-philosophical. It illustrates treatments of the ‘raven’ ...
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This essay discusses the programme called ‘therapeutic Bayesianism’ from three abstract points of view: substantial, foundational, and meta-philosophical. It illustrates treatments of the ‘raven’ paradox and the puzzle of diverse data, and defends the propriety of certain idealizations. It criticises a meta-philosophical perspective that does not properly distinguish science from the philosophy of science, and overvalues the use of symbolic apparatus.Less
This essay discusses the programme called ‘therapeutic Bayesianism’ from three abstract points of view: substantial, foundational, and meta-philosophical. It illustrates treatments of the ‘raven’ paradox and the puzzle of diverse data, and defends the propriety of certain idealizations. It criticises a meta-philosophical perspective that does not properly distinguish science from the philosophy of science, and overvalues the use of symbolic apparatus.
Alvin I. Goldman
- Published in print:
- 1999
- Published Online:
- November 2003
- ISBN:
- 9780198238201
- eISBN:
- 9780191597527
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198238207.001.0001
- Subject:
- Philosophy, Metaphysics/Epistemology
A certain conception of social epistemology is articulated and applied to numerous social arenas. This conception retains epistemology's traditional interest in truth and reliable inquiry, but ...
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A certain conception of social epistemology is articulated and applied to numerous social arenas. This conception retains epistemology's traditional interest in truth and reliable inquiry, but replaces its customary emphasis on solitary knowers with a focus on social institutions and interpersonal practices. Postmodernism, science studies, and pragmatism pose worries about the meaning and attainability of objective truth and knowledge. After laying these concerns to rest, “veritistic” social epistemology is advanced as a normative discipline seeking practices and institutions that would best foster knowledge. The book explores forms and methods of communication, including norms of argumentation, information technology, and institutional structures governing speech and the media. Social dimensions of knowledge quests are explored in science, law, democracy, and education. The book examines popular topics in contemporary epistemology such as testimony and Bayesianism, while breaking new ground by connecting epistemology with historically unrelated branches of philosophy such as political and legal theory. Democracy's success, it is argued, requires the attainment of certain epistemic desiderata, and substantive justice depends on well‐chosen procedures of legal evidence.Less
A certain conception of social epistemology is articulated and applied to numerous social arenas. This conception retains epistemology's traditional interest in truth and reliable inquiry, but replaces its customary emphasis on solitary knowers with a focus on social institutions and interpersonal practices. Postmodernism, science studies, and pragmatism pose worries about the meaning and attainability of objective truth and knowledge. After laying these concerns to rest, “veritistic” social epistemology is advanced as a normative discipline seeking practices and institutions that would best foster knowledge. The book explores forms and methods of communication, including norms of argumentation, information technology, and institutional structures governing speech and the media. Social dimensions of knowledge quests are explored in science, law, democracy, and education. The book examines popular topics in contemporary epistemology such as testimony and Bayesianism, while breaking new ground by connecting epistemology with historically unrelated branches of philosophy such as political and legal theory. Democracy's success, it is argued, requires the attainment of certain epistemic desiderata, and substantive justice depends on well‐chosen procedures of legal evidence.
Alvin Plantinga
- Published in print:
- 1993
- Published Online:
- November 2003
- ISBN:
- 9780195078640
- eISBN:
- 9780199872213
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0195078640.003.0008
- Subject:
- Philosophy, Metaphysics/Epistemology
In circumstances where one proposition A (or group of propositions G) is propositional evidence for another proposition B, my believing A (or G) can confer warrant (for me) upon B. I use the term ...
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In circumstances where one proposition A (or group of propositions G) is propositional evidence for another proposition B, my believing A (or G) can confer warrant (for me) upon B. I use the term “epistemic probability” to refer to the relationship between a pair of propositions A and B when A is propositional evidence for B; more precisely, in those cases, I shall say that the epistemic conditional probability of B on A is high. In this chapter and the next, I concern myself with an analysis of epistemic conditional probability. The first thing to see, in trying to get a general grasp of this topic, is to note the divide between epistemic probability and objective probability. In this chapter, I distinguish the former from the latter and point out some debilitating problems with the three main accounts of the former (Bayesianism, the logical theory of probability, and the account of Henry Kyburg); in the next chapter, I propose what I hope is a better substitute.Less
In circumstances where one proposition A (or group of propositions G) is propositional evidence for another proposition B, my believing A (or G) can confer warrant (for me) upon B. I use the term “epistemic probability” to refer to the relationship between a pair of propositions A and B when A is propositional evidence for B; more precisely, in those cases, I shall say that the epistemic conditional probability of B on A is high. In this chapter and the next, I concern myself with an analysis of epistemic conditional probability. The first thing to see, in trying to get a general grasp of this topic, is to note the divide between epistemic probability and objective probability. In this chapter, I distinguish the former from the latter and point out some debilitating problems with the three main accounts of the former (Bayesianism, the logical theory of probability, and the account of Henry Kyburg); in the next chapter, I propose what I hope is a better substitute.
Alvin Plantinga
- Published in print:
- 1993
- Published Online:
- November 2003
- ISBN:
- 9780195078626
- eISBN:
- 9780199833559
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0195078624.003.0006
- Subject:
- Philosophy, Metaphysics/Epistemology
In this chapter, I outline the essentials of Bayesianism (also known as Bayesian Coherentism) and ask whether it contributes to a satisfying account of warrant. From the perspective of my overall ...
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In this chapter, I outline the essentials of Bayesianism (also known as Bayesian Coherentism) and ask whether it contributes to a satisfying account of warrant. From the perspective of my overall project in Warrant: The Current Debate, Bayesianism can be seen as essentially suggesting conditions for a rational or reasonable set of partial beliefs, where a partial belief of an agent S is any belief that S accepts to some degree or another, no matter how small. Although Bayesians tend to speak not of warrant but of rationality, I consider in this chapter the relationship between Bayesianism and warrant. I conclude that the conditions for rationality proposed by Bayesians (e.g., coherence, strict coherence, changing belief by conditionalization or Jeffrey's “Probability Kinematics,” van Fraassen's Reflection) are neither severally necessary nor jointly sufficient conditions for warrant. Taken as a theory of warrant, Bayesianism is incomplete in that (1) it says nothing about the sort of relation between belief and experience required for warrant, and (2) it provides no account of evidence or evidential support.Less
In this chapter, I outline the essentials of Bayesianism (also known as Bayesian Coherentism) and ask whether it contributes to a satisfying account of warrant. From the perspective of my overall project in Warrant: The Current Debate, Bayesianism can be seen as essentially suggesting conditions for a rational or reasonable set of partial beliefs, where a partial belief of an agent S is any belief that S accepts to some degree or another, no matter how small. Although Bayesians tend to speak not of warrant but of rationality, I consider in this chapter the relationship between Bayesianism and warrant. I conclude that the conditions for rationality proposed by Bayesians (e.g., coherence, strict coherence, changing belief by conditionalization or Jeffrey's “Probability Kinematics,” van Fraassen's Reflection) are neither severally necessary nor jointly sufficient conditions for warrant. Taken as a theory of warrant, Bayesianism is incomplete in that (1) it says nothing about the sort of relation between belief and experience required for warrant, and (2) it provides no account of evidence or evidential support.
Alvin Plantinga
- Published in print:
- 1993
- Published Online:
- November 2003
- ISBN:
- 9780195078626
- eISBN:
- 9780199833559
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0195078624.003.0007
- Subject:
- Philosophy, Metaphysics/Epistemology
Rationality, although distinct from warrant, is a notion both interesting in its own right and important for a solid understanding of warrant. In this chapter, I first disambiguate at least five ...
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Rationality, although distinct from warrant, is a notion both interesting in its own right and important for a solid understanding of warrant. In this chapter, I first disambiguate at least five different forms of rationality, and, second, examine the relationship between Bayesianism and rationality (in its different forms or senses). Bayesians often claim that conformity to Bayesian constraints (such as coherence, changing belief by conditionalization or probability kinematics, or van Fraassen's Reflection) is necessary for rationality. Against this view, I argue that (1) none of the forms of rationality I distinguished requires coherence, and some of them in fact require incoherence, and that (2) changing belief by conditionalization (or by probability kinematics) is neither a sensible ideal for human cognizers nor a requirement for rationality. Finally, after a somewhat extended look at Reflection, I argue that (3) while van Fraassen surely has important and probably true things to say about what rational integrity requires with respect to one's commitments and intentions about belief change, it is nonetheless the case that rationality does not require that I conform to Reflection.Less
Rationality, although distinct from warrant, is a notion both interesting in its own right and important for a solid understanding of warrant. In this chapter, I first disambiguate at least five different forms of rationality, and, second, examine the relationship between Bayesianism and rationality (in its different forms or senses). Bayesians often claim that conformity to Bayesian constraints (such as coherence, changing belief by conditionalization or probability kinematics, or van Fraassen's Reflection) is necessary for rationality. Against this view, I argue that (1) none of the forms of rationality I distinguished requires coherence, and some of them in fact require incoherence, and that (2) changing belief by conditionalization (or by probability kinematics) is neither a sensible ideal for human cognizers nor a requirement for rationality. Finally, after a somewhat extended look at Reflection, I argue that (3) while van Fraassen surely has important and probably true things to say about what rational integrity requires with respect to one's commitments and intentions about belief change, it is nonetheless the case that rationality does not require that I conform to Reflection.
Philip Kitcher
- Published in print:
- 2002
- Published Online:
- November 2003
- ISBN:
- 9780195130058
- eISBN:
- 9780199833481
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0195130057.003.0014
- Subject:
- Philosophy, Metaphysics/Epistemology
In “Scientific Knowledge,” Philip Kitcher challenges arguments that deny the truth of the theoretical claims of science, and he attempts to discover reasons for endorsing the truth of such claims. He ...
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In “Scientific Knowledge,” Philip Kitcher challenges arguments that deny the truth of the theoretical claims of science, and he attempts to discover reasons for endorsing the truth of such claims. He suggests that the discovery of such reasons might succeed if we ask why anyone thinks that the theoretical claims we accept are true and then look for answers that reconstruct actual belief‐generating processes. To this end, Kitcher presents the “homely argument” for scientific truth, which claims that when a field of science is continually applied to yield precise predictions, then it is at least approximately true. He defends this approach and offers a supplementary account that gives more attention to detail. This account includes a historical aspect (a dependence on the previous conclusions of scientists) that must answer to skeptical challenges and a social aspect (the coordination of individuals in pursuit of specific knowledge‐related goals).Less
In “Scientific Knowledge,” Philip Kitcher challenges arguments that deny the truth of the theoretical claims of science, and he attempts to discover reasons for endorsing the truth of such claims. He suggests that the discovery of such reasons might succeed if we ask why anyone thinks that the theoretical claims we accept are true and then look for answers that reconstruct actual belief‐generating processes. To this end, Kitcher presents the “homely argument” for scientific truth, which claims that when a field of science is continually applied to yield precise predictions, then it is at least approximately true. He defends this approach and offers a supplementary account that gives more attention to detail. This account includes a historical aspect (a dependence on the previous conclusions of scientists) that must answer to skeptical challenges and a social aspect (the coordination of individuals in pursuit of specific knowledge‐related goals).
