Why Epistemology Can ’ t be Operationalized

Epistemology, 2008

Operational epistemology is, to a first approximation, the attempt to provide cognitive rules such that one is in principle always in a position to know whether one is complying with them. In Knowledge and its Limits, I argue that the only such rules are trivial ones. In this paper, I generalize the argument in several ways to more thoroughly probabilistic settings, in order to show that it does not merely demonstrate some oddity of the folk epistemological conception of knowledge. Some of the generalizations involve a formal semantic framework for treating epistemic probabilities of epistemic probabilities and expectations of expectations. The upshot is that operational epistemology cannot work, and that knowledge-based epistemology has the right characteristics to avoid its problems.

International Journal of Game Theory, 1999

Springer eBooks, 2003

The aims of this paper are (i) to summarize the semantics of (the propositional part of) a unified epistemic/doxastic logic as it has been developed at greater length in Lenzen [1980] and (ii) to use some of these principles for the development of a semi-formal pragmatics of epistemic sentences. While a semantic investigation of epistemic attitudes has to elaborate the truth-conditions for, and the analytically true relations between, the fundamental notions of belief, knowledge, and conviction, a pragmatic investigation instead has to analyse the specific conditions of rational utterance or utterability of epistemic sentences. Some people might think that both tasks coincide. According to Wittgenstein, e.g., the meaning of a word or a phrase is nothing else but its use (say, within a certain community of speakers). Therefore the pragmatic conditions of utterance of words or sentences are assumed to determine the meaning of the corresponding expressions. One point I wish to make here, however, is that one may elaborate the meaning of epistemic expressions in a way that is largely independent of-and, indeed, even partly incompatible with-the pragmatic conditions of utterability. Furthermore, the crucial differences between the pragmatics and the semantics of epistemic expressions can satisfactorily be explained by means of some general principles of communication. In the first three sections of this paper the logic (or semantics) of the epistemic attitudes belief, knowledge, and conviction will be sketched. In the fourth section the basic idea of a general pragmatics will be developed which can then be applied to epistemic utterances in particular. 1 The Logic of Conviction Let 'C(a,p)' abbreviate the fact that person a is firmly convinced that p, i.e. that a considers the proposition p (or, equivalently, the state of affairs expressed by that proposition) as absolutely certain; in other words, p has maximal likelihood or probability for a. Using 'Prob' as a symbol for subjective probability functions, this idea can be formalized by the requirement: (PROB-C) C(a,p) ↔ Prob(a,p)=1. Within the framework of standard possible-worlds semantics <I,R,V>, C(a,p) would have to be interpreted by the following condition: (POSS-C) V(i,C(a,p))=t ↔ ∀j(iRj → V(j,p)=t). Here I is a non-empty set of (indices of) possible worlds; R is a binary relation on I such that iRj holds iff, in world i, a considers world j as possible; and V is a valuation-function assigning to each proposition p relative to each world i a truth-value V(i,p)∈{t,f}. Thus C(a,p) is true (in world i∈I) iff p itself is true in every possible world j which is considered by a as possible (relative to i). The probabilistic "definition" POSS-C together with some elementary theorems of the theory of subjective probability immediately entails the validity of the subsequent laws of conjunction and non-contradiction. If a is convinced both of p and of q, then a must also be convinced that p and q: (C1) C(a,p) ∧ C(a,q) → C(a,p∧q). For if both Prob(a,p) and Prob(a,q) are equal to 1, then it follows that Prob(a,p∧q)=1, too. Furthermore, if a is convinced that p (is true), a cannot be convinced that ¬p, i.e. that p is false: (C2) C(a,p) → ¬C(a,¬p). For if Prob(a,p)=1, then Prob(a,¬p)=0, and hence Prob(a,¬p)≠1. Just like the alethic modal operators of possibility, ◊, and necessity, , are linked by the relation ◊p ↔ ¬ ¬p, so also the doxastic modalities of thinking p to be possible-formally: P(a,p)-and of being convinced that p, C(a,p), satisfy the relation (Def. P) P(a,p) ↔ ¬C(a,¬p). Thus, from the probabilistic point of view, P(a,p) holds iff a assigns to the proposition p (or to the event expressed by that proposition) a likelihood greater than 0: (PROB-P) V(P(a,p))=t ↔ Prob(a,p)>0. Within the framework of possible-worlds semantics, one obtains the following condition: (POSS-P) V(i,P(a,p))=t ↔ ∃j(iRj ∧ V(j,p)=t), according to which P(a,p) is true in world i iff there is at least one possible world j-i.e. a world j accessible from i-in which p is true. 1 Cf., e.g., Hintikka [1970]. 2 Clearly, since C(a,p) ∨ ¬C(a,p) holds tautologically, C10 and C11 entail that C(a,C(a,p)) ∨ C(a,¬C(a,p)) is epistemic-logically true. So either way there exists a q such that C(a,q).

Causation in Decision, Belief Change, and Statistics, 1988

Synthese , 2013

The idea that knowledge can be extended by inference from what is known seems highly plausible. Yet, as shown by familiar preface paradox and lottery-type cases, the possibility of aggregating uncertainty casts doubt on its tenability. We show that these considerations go much further than previously recognized and significantly restrict the kinds of closure ordinary theories of knowledge can endorse. Meeting the challenge of uncertainty aggregation requires either the restriction of knowledge-extending inferences to single premises, or eliminating epistemic uncertainty in known premises. The first strategy, while effective, retains little of the original idea—conclusions even of modus ponens inferences from known premises are not always known. We then look at the second strategy, inspecting the most elaborate and promising attempt to secure the epistemic role of basic inferences, namely Timothy Williamson’s safety theory of knowledge. We argue that while it indeed has the merit of allowing basic inferences such as modus ponens to extend knowledge, Williamson’s theory faces formidable difficulties. These difficulties, moreover, arise from the very feature responsible for its virtue- the infallibilism of knowledge.

The British Journal for the Philosophy of Science, 1998

A theory of evidential probability is developed from two assumptions: (1) the evidential probability of a proposition is its probability conditional on the total evidence; (2) one's total evidence is one's total knowledge. Evidential probability is distinguished from both subjective and objective probability. Loss as well as gain of evidence is permitted. Evidential probability is embedded within epistemic logic by means of possible worlds semantics for modal logic; this allows a natural theory of higher-order probability to be developed. In particular, it is emphasized that it is sometimes uncertain which propositions are part of one's total evidence; some surprising implications of this fact are drawn out. 1 Evidential probability 2 Uncertain evidence 3 Evidence and knowledge 4 Epistemic accessibility 5 A puzzling'phenomenon Appendix I: Proofs Appendix II: A non-symmetric epistemic model

Philosophical Studies, 2012

Claims of the form 'I know P and it might be that not-P' tend to sound odd. One natural explanation of this oddity is that the conjuncts are semantically incompatible: in its core epistemic use, 'Might P' is true in a speaker's mouth only if the speaker does not know that not-P. In this paper I defend this view against an alternative proposal that has been advocated by Trent Dougherty and Patrick Rysiew and elaborated upon in Jeremy Fantl and Matthew McGrath's recent Knowledge in an Uncertain World.

The British Journal For the Philosophy of Science, 2000

This is a very selective survey of developments in epistemology, concentrating on work from the past twenty years which is of interest to philosophers of science. The selection is organized around interesting connections between distinct themes. I first connect issues about skepticism to issues about the reliability of belief-acquiring processes. Next I connect discussions of the defeasibility of reasons for belief with accounts of the theory-independence of evidence. Then I connect doubts about Bayesian epistemology to issues about the content of perception. The last detailed connection is between considerations of the finiteness of cognition and epistemic virtues. To connect the connections I end by briefly discussing the pressure that consideration of social roles in the transmission of belief puts on the purposes of epistemology.

Philosophical Issues, 2018

Probabilistic approaches to epistemology are promising in many ways. Among other things, they can give promising accounts of inference, including both deductive and non-deductive forms of inference. 1 One advantage of these probabilistic approaches, as we shall see, is that they can also give an account of how rational inferences can be defeasible-including an account of the distinction between so-called rebutting and undercutting defeaters. But how can these probabilistic approaches be married with a plausible account of the epistemology of perceptual belief? This is the problem that I shall discuss here. As I shall explain, there are three well-known models of how to account for perceptual belief within a probabilistic framework: (a) a Cartesian model; (b) a model advocated by Timothy Williamson (2000); and (c) a model advocated by Richard Jeffrey (2004). In Section 1, after first explaining the kind of probabilism that I am assuming here, I shall explain these three models of the epistemology of perceptual belief. In Section 2, I shall raise a problem that each of these models facesthe problem of accounting for the defeasibility of perceptual justification and perceptual knowledge. As we shall see, the Williamson-inspired theorists have to deny that defeasibility can be explained within the probabilistic framework at all; indeed, recently, some of these theorists have gone so far as to deny that knowledge is ever defeasible. If we are inclined to take both defeasibility and probabilism more seriously, we should search for a way of explaining defeasibility within the probabilistic framework. In Section 3, I shall focus on the version of this problem that Jonathan Weisberg (2009 and 2015) has raised against Jeffrey's model; in this section, I shall present a solution to this problem and defend the solution against Weisberg's objections. In Section 4, however, I shall argue that this solution is open to some further objections,

Analysis, 2020

According to a standard assumption in epistemology, if one only partially believes that p , then one cannot thereby have knowledge that p. For example, if one only partially believes that that it is raining outside, one cannot know that it is raining outside; and if one only partially believes that it is likely that it will rain outside, one cannot know that it is likely that it will rain outside. Many epistemologists will agree that epistemic agents are capable of partial beliefs in addition to full beliefs and that partial beliefs can be epistemically assessed along some dimensions. However, it has been generally assumed that such doxastic attitudes cannot possibly amount to knowledge. In Probabilistic Knowledge, Moss challenges this standard assumption and provides a formidable defense of the claim that probabilistic beliefs—a class of doxastic attitudes including credences and degrees of beliefs—can amount to knowledge too. Call this the probabilistic knowledge claim . Throughout the book, Moss goes to great lengths to show that probabilistic knowledge can be fruitfully applied to a variety of debates in epistemology and beyond. My goal in this essay is to explore a further application for probabilistic knowledge. I want to look at the role of probabilistic knowledge within a “knowledge-centered” psychology—a kind of psychology that assigns knowledge a central stage in explanations of intentional behavior. My suggestion is that Moss’s notion of probabilistic knowledge considerably helps further both a knowledge-centered psychology and a broadly intellectualist picture of action and know-how that naturally goes along with it. At the same time, though, it raises some interesting issues about the notion of explanation afforded by the resulting psychology.