Don Fallis

Accuracy-based arguments for conditionalization and probabilism appear to have a significant advantage over their Dutch Book rivals. They rely only on the plausible epistemic norm that one should try to decrease the inaccuracy of one's beliefs. Furthermore, it seems that conditionalization and probabilism follow from a wide range of measures of inaccuracy. However, we argue that among the measures in the literature, there are some from which one can prove conditionalization, others from which one can prove probabilism, and none from which one can prove both. Hence at present, the accuracy-based approach cannot underwrite both conditionalization and probabilism.

We want to keep hackers in the dark about our passwords and our credit card numbers. We want to keep potential eavesdroppers in the dark about our private communications with friends and business associates. This need for secrecy raises important questions in epistemology (how do we do it?) and in ethics (should we do it?). In order to answer these questions, it would be useful to have a good understanding of the concept of keeping someone in the dark. Several philosophers (e.g., Bok, 1983; Carson, 2010; Mahon, 2009; Scheppele, 1988) have analyzed this concept (or, equivalently, the concept of keeping secrets) in terms of concealing and/or withholding information. However, their analyses incorrectly exclude clear instances of keeping someone in the dark. And more important, they incorrectly focus on possible means of keeping someone in the dark rather than on what it is to keep someone in the dark. In this paper, I argue that you keep X in the dark about a proposition P if and only if you intentionally cause X not to have a true belief that P. In addition, I show how this analysis of keeping someone in the dark can be extended from a categorical belief model of epistemic states to a credence (or degree of belief) model.

Deception has long been an important topic in philosophy. However, the traditional analysis of the concept, which requires that a deceiver intentionally cause her victim to have a false belief, rules out the possibility of much deception in the animal kingdom. Cognitively unsophisticated species, such as fireflies and butterflies, have simply evolved to mislead potential predators and/or prey. To capture such cases of “functional deception,” several researchers Machiavellian intelligence II, Cambridge University Press, Cambridge, pp 112–143, 1997; Searcy and Nowicki, The evolution of animal communication, Princeton University Press, Princeton, 2005; Skyrms, Signals, Oxford University Press, Oxford 2010) have endorsed the broader view that deception only requires that a deceiver benefit from sending a misleading signal. Moreover, in order to facilitate game-theoretic study of deception in the context of Lewisian sender-receiver games, Brian Skyrms has proposed an influential formal analysis of this view. Such formal analyses have the potential to enhance our philosophical understanding of deception in humans as well as animals. However, as we argue in this paper, Skyrms’s analysis, as well as two recently proposed alternative analyses, are seriously flawed and can lead us to draw unwarranted conclusions about deception.

One of the primary goals of mathematicians is to establish new mathematical truths. Toward this end, mathematicians are almost invariably theorem provers. However, there are several methods other than writing down a proof which seem to achieve this epistemic goal of establishing mathematical truths. For instance, Michael Rabin describes a probabilistic test for primality which establishes to an arbitrarily high degree of certainty that a number is prime. Nevertheless, the vast majority of mathematicians are unwilling to employ such probabilistic methods in their attempts to establish new mathematical truths. I argue that mathematicians do not have good grounds for this rejection of probabilistic methods

In his recent book, Knowledge in a Social World, Alvin Goldman claims to have established that if a reasoner starts with accurate estimates of the reliability of new evidence and conditionalizes on this evidence, then this reasoner is objectively likely to end up closer to the truth. In this paper, I argue that Goldman's result is not nearly as philosophically significant as he would have us believe. First, accurately estimating the reliability of evidence – in the sense that Goldman requires – is not quite as easy as it might sound. Second, being objectively likely to end up closer to the truth – in the sense that Goldman establishes – is not quite as valuable as it might sound.

Two sorts of connections between privacy and knowledge (or lack thereof) have been suggested in the philosophical literature. First, Alvin Goldman has suggested that protecting privacy typically leads to less knowledge being acquired. Second, several other philosophers (e.g. Parent, Matheson, Blaauw and Peels) have claimed that lack of knowledge is definitive of having privacy. In other words, someone not knowing something is necessary and sufficient for someone else having privacy about that thing. Or equivalently, someone knowing something is necessary and sufficient for someone else losing privacy about that thing. In this paper, I argue that both of these suggestions are incorrect. I begin by arguing, contra Goldman, that protecting privacy often leads to more knowledge being acquired. I argue in the remainder of the paper, contra the defenders of the knowledge account of privacy, that someone knowing something is not necessary for someone else losing privacy about that thing

We all pursue epistemic goals as individuals. But we also pursue collective epistemic goals. In the case of many groups to which we belong, we want each member of the group - and sometimes even the group itself - to have as many true beliefs as possible and as few false beliefs as possible. In this paper, I respond to the main objections to the very idea of such collective epistemic goals. Furthermore, I describe the various ways that our collective epistemic goals can come into conflict with each other. And I argue that we must appeal to pragmatic considerations in order to resolve such conflicts.

Several philosophers have used the framework of means/ends reasoning to explain the methodological choices made by scientists and mathematicians (see, e.g., Goldman 1999, Levi 1962, Maddy 1997). In particular, they have tried to identify the epistemic objectives of scientists and mathematicians that will explain these choices. In this paper, the framework of means/ends reasoning is used to study an important methodological choice made by mathematicians. Namely, mathematicians will only use deductive proofs to establish the truth of mathematical claims. In this paper, I argue that none of the epistemic objectives of mathematicians that are currently on the table provide a satisfactory explanation of this rejection of probabilistic proofs.

An important issue for information ethics is how much control people should have over the dissemination of information that they have created. Since intellectual property policies have an impact on our welfare primarily because they have a huge impact on our ability to acquire knowledge, there is an important role for epistemology in resolving this issue. This paper discusses the various ways in which intellectual property policies can impact knowledge acquisition both positively and negatively. In particular, it looks at how intellectual property policies can affect the amount of information that people create, the quality of that information, the accessibility of that information, the diversity of that information, and the locatability of that information.

The doctrinal paradox shows that aggregating individual judgments by taking a majority vote does not always yield a consistent set of collective judgments. Philip Pettit, Luc Bovens, and Wlodek Rabinowicz have recently argued for the epistemic superiority of an aggregation procedure that always yields a consistent set of judgments. This paper identifies several additional epistemic advantages of their consistency maintaining procedure. However, this paper also shows that there are some circumstances where the majority vote procedure is epistemically superior. The epistemic value of maintaining consistency does not always outweigh the epistemic value of making true judgments.

According to the traditional philosophical definition, you lie if and only if you say something that you believe to be false and you intend to deceive someone into believing what you say. However, philosophers have recently noted the existence of bald-faced lies, lies which are not intended to deceive anyone into believing what is said. As a result, many philosophers have removed deception from their definitions of lying. According to Jennifer Lackey, this is ‘an unhappy divorce’ because it precludes an obvious explanation of the prima facie wrongness of lying. Moreover, Lackey claims that there is a sense of deception in which all lies are deceptive. In this paper, I argue that bald-faced lies are not deceptive on any plausible notion of deception. In addition, I argue that divorcing deception from lying may not be as unhappy a result as Lackey suggests

Donald Davidson once suggested that a liar ?must intend to represent himself as believing what he does not?. In this paper I argue that, while Davidson was mistaken about lying in a few important respects, his main insight yields a very attractive definition of lying. Namely, you lie if and only if you say something that you do not believe and you intend to represent yourself as believing what you say. Moreover, I show that this Davidsonian definition can handle counter-examples that undercut four prominent definitions of lying: viz., the traditional intend-to-deceive definition, Thomas Carson's definition, Don Fallis's definition, and Andreas Stokke's definition

In order to lie, you have to say something that you believe to be false. But lying is not simply saying what you believe to be false. Philosophers have made several suggestions for what the additional condition might be. For example, it has been suggested that the liar has to intend to deceive (Augustine 395, Bok 1978, Mahon 2006), that she has to believe that she will deceive (Chisholm and Feehan 1977), or that she has to warrant the truth of what she says (Carson 2006). In this paper, I argue that none of the existing definitions of lying identify a necessary condition on lying. I claim that lying is saying what you believe to be false when you believe that the following norm of conversation is in effect: "Do not say what you believe to be false" (Grice 1989, 27). And I argue that this definition handles all of the counter-examples to the existing definitions

In “How to Collaborate,” Paul Thagard tries to explain why there is so much collaboration in science, and so little collaboration in philosophy, by giving an epistemic cost-benefit analysis. In this paper, I argue that an adequate explanation requires a more fully developed epistemic value theory than Thagard utilizes. In addition, I offer an alternative to Thagard’s explanation of the lack of collaboration in philosophy. He appeals to its lack of a tradition of collaboration and to the a priori nature of much philosophical research. I claim that philosophers rarely collaborate simply because they can usually get the benefits without paying the costs of actually collaborating

Several different Bayesian models of epistemic utilities (see, e. g., [37], [24], [40], [46]) have been used to explain why it is rational for scientists to perform experiments. In this paper, I argue that a model-suggested independently by Patrick Maher [40] and Graham Oddie [46]-that assigns epistemic utility to degrees of belief in hypotheses provides the most comprehensive explanation. This is because this proper scoring rule (PSR) model captures a wider range of scientifically acceptable attitudes toward epistemic risk than the other Bayesian models that have been proposed. I also argue, however, that even the PSR model places unreasonably tight restrictions on a scientist's attitude toward epistemic risk. As a result, such Bayesian models of epistemic utilities fail as normative accounts-not just as descriptive accounts (see, e. g., [31], [14])-of scientific inquiry.

In the Groundwork, Immanuel Kant famously argued that it would be self-defeating for everyone to follow a maxim of lying whenever it is to his or her advantage. In his recent book Signals, Brian Skyrms claims that Kant was wrong about the impossibility of universal deception. Skyrms argues that there are Lewisian signaling games in which the sender always sends a signal that deceives the receiver. I show here that these purportedly deceptive signals simply fail to make the receiver as epistemically well off as she could have been. Since the receiver is not actually misled, Kant would not have considered these games to be examples of deception, much less universal deception. However, I argue that there is an important sense of deception, endorsed by Roderick Chisholm and Thomas Feehan in their seminal work on the topic, under which Skyrms has shown that universal deception is possible

According to the traditional philosophical definition, you lie if and only if you assert what you believe to be false with the intent to deceive. However, several philosophers (e.g., Carson 2006, Sorensen 2007, Fallis 2009) have pointed out that there are lies that are not intended to deceive and, thus, that the traditional definition fails. In 2009, I suggested an alternative definition: you lie if and only if you say what you believe to be false when you believe that one of Paul Grice's conversational norms (“Do not say what you believe to be false”) is in effect. Faulkner (forthcoming), Stokke (forthcoming), and Pruss (2012) have subsequently argued that my 2009 definition fails as well because it counts some statements that are clearly not lies as being lies. In this paper, I identify some additional counter-examples of this sort. But I argue that my 2009 definition can easily be revised to deal with such counter-examples once we clarify that the relevant norm is really against communicating something false rather than against merely saying it. Nevertheless, I show that even this revised version of my 2009 definition fails because it counts some statements that are lies as not being lies. Lies told by young children – which uncontroversially count as lies on the traditional philosophical definition – suggest that lying (as well as asserting in general) does not require believing that such a norm is in effect. Even so, I claim that, since all liars intend to do something that would violate this norm if it were in effect, there is a successful definition of lying that is at least in the spirit of my 2009 definition

Three of the major issues in information ethics – intellectual property, speech regulation, and privacy – concern the morality of restricting people’s access to certain information. Consequently, policies in these areas have a significant impact on the amount and types of knowledge that people acquire. As a result, epistemic considerations are critical to the ethics of information policy decisions (cf. Mill, 1978 [1859]). The fact that information ethics is a part of the philosophy of information highlights this important connection with epistemology. In this paper, I illustrate how a value-theoretic approach to epistemology can help to clarify these major issues in information ethics. However, I also identify several open questions about epistemic values that need to be answered before we will be able to evaluate the epistemic consequences of many information policies

If knowledge is the norm of practical reasoning, then we should be able to alter people's behavior by affecting their knowledge as well as by affecting their beliefs. Thus, as Roy Sorensen (2010) suggests, we should expect to find people telling lies that target knowledge rather than just lies that target beliefs. In this paper, however, I argue that Sorensen's discovery of “knowledge-lies” does not support the claim that knowledge is the norm of practical reasoning. First, I use a Bayesian framework to show that in each of Sorensen's examples, knowledge-lies alter people's behavior by affecting their beliefs. Second, I show that while we can imagine lies that target knowledge without targeting beliefs, they cannot alter people's behavior. In other words, knowledge-lies actually work (i.e., manipulate behavior) by targeting beliefs or they do not work at all