Probabilistic Measures of Coherence and the Problem of Belief Individuation

Coherentism in epistemology has long suffered from lack of formal and quantitative explication of the notion of coherence. One might hope that probabilistic accounts of coherence such as those proposed by Lewis, Shogenji, Olsson, Fitelson, and Bovens and Hartmann will finally help solve this problem. This paper shows, however, that those accounts have a serious common problem: the problem of belief individuation. The coherence degree that each of the accounts assigns to an information set (or the verdict it gives as to whether the set is coherent tout court) depends on how beliefs (or propositions) that represent the set are individuated. Indeed, logically equivalent belief sets that represent the same information set can be given drastically different degrees of coherence. This feature clashes with our natural and reasonable expectation that the coherence degree of a belief set does not change unless the believer adds essentially new information to the set or drops old information from it; or, to put it simply, that the believer cannot raise or lower the degree of coherence by purely logical reasoning. None of the accounts in question can adequately deal with coherence once logical inferences get into the picture. Toward the end of the paper, another notion of coherence that takes into account not only the contents but also the origins (or sources) of the relevant beliefs is considered. It is argued that this notion of coherence is of dubious significance, and that it does not help solve the problem of belief individuation.     

A weak symmetry condition for probabilistic measures of confirmation

This paper presents a symmetry condition for probabilistic measures of confirmation which is weaker than commutativity symmetry, disconfirmation commutativity symmetry but also antisymmetry. It is based on the idea that for any value a probabilistic measure of confirmation can assign there is a corresponding case where degrees of confirmation are symmetric. It is shown that a number of prominent confirmation measures such as Carnap’s difference function, Rescher’s measure of confirmation, Gaifman’s confirmation rate and Mortimer’s inverted difference function do not satisfy this condition and instead exhibit a previously unnoticed and rather puzzling behavior in certain cases of disconfirmation. This behavior also carries over to probabilistic measures of information change, causal strength, explanatory power and coherence.     

Why There Cannot be a Single Probabilistic Measure of Coherence

Bayesian Coherence Theory of Justification or, for short, Bayesian Coherentism, is characterized by two theses, viz. (i) that our degree of confidence in the content of a set of propositions is positively affected by the coherence of the set, and (ii) that coherence can be characterized in probabilistic terms. There has been a longstanding question of how to construct a measure of coherence. We will show that Bayesian Coherentism cannot rest on a single measure of coherence, but requires a vector whose components exhaustively characterize the coherence properties of the set. Our degree of confidence in the content of the information set is a function of the reliability of the sources and the components of the coherence vector. The components of this coherence vector are weakly but not strongly separable, which blocks the construction of a single coherence measure.     

Probabilistic measures of coherence: from adequacy constraints towards pluralism

The debate on probabilistic measures of coherence flourishes for about 15 years now. Initiated by papers that have been published around the turn of the millennium, many different proposals have since then been put forward. This contribution is partly devoted to a reassessment of extant coherence measures. Focusing on a small number of reasonable adequacy constraints I show that (i) there can be no coherence measure that satisfies all constraints, and that (ii) subsets of these adequacy constraints motivate two different classes of coherence measures. These classes do not coincide with the common distinction between coherence as mutual support and coherence as relative set-theoretic overlap. Finally, I put forward arguments to the effect that for each such class of coherence measures there is an outstanding measure that outperforms all other extant proposals. One of these measures has recently been put forward in the literature, while the other one is based on a novel probabilistic measure of confirmation.     

Ways in which coherence is confirmation conducive

Recent works in epistemology show that the claim that coherence is truth conducive – in the sense that, given suitable ceteris paribus conditions, more coherent sets of statements are always more probable – is dubious and possibly false. From this, it does not follows that coherence is a useless notion in epistemology and philosophy of science. Dietrich and Moretti (Philosophy of science 72(3): 403–424, 2005) have proposed a formal of account of how coherence is confirmation conducive—that is, of how the coherence of a set of statements facilitates the confirmation of such statements. This account is grounded in two confirmation transmission properties that are satisfied by some of the measures of coherence recently proposed in the literature. These properties explicate everyday and scientific uses of coherence. In his paper, I review the main findings of Dietrich and Moretti (2005) and define two evidence-gathering properties that are satisfied by the same measures of coherence and constitute further ways in which coherence is confirmation conducive. At least one of these properties vindicates important applications of the notion of coherence in everyday life and in science.     

Problems with Priors in Probabilistic Measures of Coherence

Two of the probabilistic measures of coherence discussed in this paper take probabilistic dependence into account and so depend on prior probabilities in a fundamental way. An example is given which suggests that this prior-dependence can lead to potential problems. Another coherence measure is shown to be independent of prior probabilities in a clearly defined sense and consequently is able to avoid such problems. The issue of prior-dependence is linked to the fact that the first two measures can be understood as measures of coherence as striking agreement, while the third measure represents coherence as agreement. Thus, prior (in)dependence can be used to distinguish different conceptions of coherence.     

Justification by Coherence from Scratch

In this paper we make three points about justification of propositions by coherence “from scratch”, where pieces of evidence that are coherent have no individual credibility. First, we argue that no matter how many pieces of evidence are coherent, and no matter what relation we take coherence to be, coherence does not make independent pieces of evidence with no individual credibility credible. Second, we show that an intuitively plausible informal reasoning for justification by coherence from scratch is deficient since it relies on an understanding of “individual credibility” inappropriate for justification from scratch. Third, we show that coherence, when it is recurrent, can make independent sources of evidence with no individual credibility credible. We describe specifically a case in which the same group of independent witnesses with no individual credibility repeatedly produce reports that are in agreement with each other, and their reports become credible as a result.     

Against Probabilistic Measures of Coherence

It is shown that the probabilistic theories of coherence proposed up to now produce a number of counter-intuitive results. The last section provides some reasons for believing that no probabilistic measure will ever be able to adequately capture coherence. First, there can be no function whose arguments are nothing but tuples of probabilities, and which assigns different values to pairs of propositions {A, B} and {A, C} if A implies both B and C, or their negations, and if P(B)=P(C). But such sets may indeed differ in their degree of coherence. Second, coherence is sensitive to explanatory relations between the propositions in question. Explanation, however, can hardly be captured solely in terms of probability.     

Equivalent testimonies as a touchstone of coherence measures

Over the past years, a number of probabilistic measures of coherence have been proposed. As shown in the paper, however, many of them do not conform to the intuitition that equivalent testimonies are highly coherent, regardless of their prior probability.     

Pragmatic Inconsistency and Credibility

A critic may attack an arguer personally by pointing out that the arguer’s position is pragmatically inconsistent: the arguer does not practice what he preaches. A number of authors hold that such attacks can be part of a good argumentative discussion. However, there is a difficulty in accepting this kind of contribution as potentially legitimate, for the reason that there is nothing wrong for a protagonist to have an inconsistent position, in the sense of committing himself to mutually inconsistent propositions. If so, any such charge seems to be irrelevant. The questions to be answered in this essay are: what, if any, is the dialectical rationale for this type of criticism, and in what situations, if any, is this kind of charge dialectically legitimate? It will be shown that these attacks can be dialectically legitimate, in special circumstances, and that they can be seen as strategic?manoeuvres where a party attempts to reconcile his dialectical and his rhetorical objectives.     

Detection of coherence-disrupting and coherence-conferring alterations in text

In a study phase, subjects were presented with sentence pairs which were either coherent or anomalous. In a subsequent recognition test, each sentence pair was presented either in its original form or with one word changed. The changed word either disrupted the coherence of a previously coherent sentence pair or conferred coherence on a previously anomalous sentence pair. Alterations were more accurately detected if they were coherence-disrupting than if they were coherence-conferring. In a coherence-conferring alteration, the altered sentence can be assimilated to one of the abstract cognitive units activated by the studied sentence pair, whereas in a coherence-disrupting alteration, the altered sentence cannot be so assimilated. Analogous asymmetries have been observed for musical sequences that are coherent or anomalous with respect to tonality and rhythm. These parallels suggest that principles of cognitive organization that are responsible for our intuitions of coherence cut across propositional and nonpropositional domains.     

Coherence as an ideal of rationality

Probabilistic coherence is not an absolute requirement of rationality; nevertheless, it is an ideal of rationality with substantive normative import. An idealized rational agent who avoided making implicit logical errors in forming his preferences would be coherent. In response to the challenge, recently made by epistemologists such as Foley and Plantinga, that appeals to ideal rationality render probabilism either irrelevant or implausible, I argue that idealized requirements can be normatively relevant even when the ideals are unattainable, so long as they define a structure that links imperfect and perfect rationality in a way that enables us to make sense of the notion of better approximations to the ideal. I then analyze the notion of approximation to the ideal of coherence by developing a generalized theory of belief functions that allows for incoherence, and showing how such belief functions can be ordered with regard to greater or lesser coherence.Many people influenced the present version of this essay. Ban van Frassen, Richard Jeffrey, David Lewis, Mike Thau, and Alan Hájek provided extensive and invaluable written comments on the entire essay. Mark van Roojen provided helpful comments on Sections 3 and 4. Mike Than and John Barker provided essential aid when I was formulating the proofs in section 6. Finally, I am grateful for valuable discussion of the essay with Ned Hall, Fiona Cowie, Jim Woodward, David Hilbert, and Frank Arntzenius.     

Reliability conducive measures of coherence

A measure of coherence is said to be truth conducive if and only if a higher degree of coherence (as measured) results in a higher likelihood of truth. Recent impossibility results strongly indicate that there are no (non-trivial) probabilistic coherence measures that are truth conducive. Indeed, this holds even if truth conduciveness is understood in a weak ceteris paribus sense (Bovens & Hartmann, 2003, Bayesian epistemology. New York, Oxford: Oxford University Press; Olsson, 2005, Against coherence: Truth probability and justification. Oxford: Oxford University Press). This raises the problem of how coherence could nonetheless be an epistemically important property. Our proposal is that coherence may be linked in a certain way to reliability. We define a measure of coherence to be reliability conducive if and only if a higher degree of coherence (as measured) results in a higher probability that the information sources are reliable. Restricting ourselves to the most basic case, we investigate which coherence measures in the literature are reliability conducive. It turns out that, while a number of measures fail to be reliability conducive, except possibly in a trivial and uninteresting sense, Shogenji’s measure and several measures generated by Douven and Meijs’s recipe are notable exceptions to this rule.     

An Impossibility Result for Coherence Rankings

If we receive information from multiple independent and partially reliable information sources, then whether we are justified to believe these information items is affected by how reliable the sources are, by how well the information coheres with our background beliefs and by how internally coherent the information is. We consider the following question. Is coherence a separable determinant of our degree of belief, i.e. is it the case that the more coherent the new information is, the more justified we are in believing the new information, ceteris paribus? We show that if we consider sets of information items of any size (Holism), and if we assume that there exists a coherence Ordering over such sets and that coherence is a function of the probability distribution over the propositions in such sets (Probabilism), then Separability fails to hold.     

Statistical Data and Mathematical Propositions

Statistical tests of the primality of some numbers look similar to statistical tests of many nonmathematical, clearly empirical propositions. Yet interpretations of probability prima facie appear to preclude the possibility of statistical tests of mathematical propositions. For example, it is hard to understand how the statement that n is prime could have a frequentist probability other than 0 or 1. On the other hand, subjectivist approaches appear to be saddled with ‘coherence’ constraints on rational probabilities that require rational agents to assign extremal probabilities to logical and mathematical propositions. In the light of these problems, many philosophers have come to think that there must be some way to generalize a Bayesian statistical account. In this article I propose that a classical frequentist approach should be reconsidered. I conclude that we can give a conditional justification of statistical testing of at least some mathematical hypotheses: if statistical tests provide us with reasons to believe or bet on empirical hypotheses in the standard situations, then they also provide us with reasons to believe or bet on mathematical hypotheses in the structurally similar mathematical cases.     

The Role of Coherence of Evidence in the Non-Dynamic Model of Confirmation

This paper examines the role of coherence of evidence in what I call the non-dynamic model of confirmation. It appears that other things being equal, a higher degree of coherence among pieces of evidence raises to a higher degree the probability of the proposition they support. I argue against this view on the basis of three related observations. First, we should be able to assess the impact of coherence on any hypothesis of interest the evidence supports. Second, the impact of coherence among the pieces of evidence can be different on different hypotheses of interest they support. Third, when we assess the impact of coherence on a hypothesis of interest, other conditions that should be held equal for a fair assessment include the degrees of individual support which the propositions directly supported by the respective pieces of evidence provide for the hypothesis. Once we take these points into consideration, the impression that coherence of evidence plays a positive role in confirmation dissipates. In some cases it can be shown that other things being equal, a higher degree of coherence among the pieces of evidence reduces the degree of confirmation for the hypothesis they support.     

Measuring coherence

This paper aims to contribute to our understanding of the notion of coherence by explicating in probabilistic terms, step by step, what seem to be our most basic intuitions about that notion, to wit, that coherence is a matter of hanging or fitting together, and that coherence is a matter of degree. A qualitative theory of coherence will serve as a stepping stone to formulate a set of quantitative measures of coherence, each of which seems to capture well the aforementioned intuitions. Subsequently it will be argued that one of those measures does better than the others in light of some more specific intuitions about coherence. This measure will be defended against two seemingly obvious objections.     

The Story Gestalt: A Model Of Knowledge-Intensive Processes in Text Comprehension

How are knowledge-intensive, text-comprehension processes computed? Specifically, how are (1) explicit propositions remembered correctly, (2) pronouns resolved, (3) coherence and prediction inferences drawn, (4) on-going interpretations revised as more information becomes available, and (5) information learned in specific contexts generalized to novel texts? A constraint satisfaction model is presented that offers a number of advantages over previous models: Each of the previous processes can be seen as examples of the same process of constraint satisfaction, constraints can have strengths to represent the degrees of correlation among information, and the independence of constraints provides insight into generalization. In the model, propositions describing a simple event, such as going to the beach or a restaurant, are sequentially presented to a recurrent PDP network. The model is trained through practice processing a large number of example texts and answering questions. Questions are predicates from propositions explicit or inferable from the text, and the model has to answer with the proposition that fits that predicate. The model learns to perform well, though some processes require substantial training. A second simulation shows how the combinatorics in the training corpus can increase generalization. This effect is explained by introducing the concept of identity and associative constraints that are learned from a corpus. Overall, the model provides a number of insights into how a graded constraint-satisfaction model can compute knowledge-intensive processes in text comprehension.     

Probabilistic models of language processing and acquisition

Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical developments and online corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty of the stimulus, and suggesting fruitful links with probabilistic theories of categorization and ambiguity resolution in perception.