Don Fallis

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

In cases of animal mimicry, the receiver of the signal learns the truth that he is either dealing with the real thing or with a mimic. Thus, despite being a prototypical example of animal deception, mimicry does not seem to qualify as deception on the traditional definition, since the receiver is not actually misled. We offer a new account of propositional content in sender-receiver games that explains how the receiver is misled by mimicry. We show that previous accounts of deception, and of propositional content, give incorrect results about whether certain signals are deceptive.

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.

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.

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.

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, conditionalization and probabilism apparently follow from a wide range of measures of inaccuracy. However, we argue that there is an under-appreciated diachronic constraint on measures of inaccuracy which limits the measures from which one can prove conditionalization, and none of the remaining measures allow one to prove probabilism. That is, among the measures in the literature, there are some from which one can prove conditionalization, others from which one can prove probabilism, but none from which one can prove both. Hence at present, the accuracy-based approach cannot underwrite both conditionalization and probabilism.

How can one verify the accuracy of recorded information (e.g., information found in books, newspapers, and on Web sites)? In this paper, I argue that work in the epistemology of testimony (especially that of philosophers David Hume and Alvin Goldman) can help with this important practical problem in library and information science. This work suggests that there are four important areas to consider when verifying the accuracy of information: (i) authority, (ii) independent corroboration, (iii) plausibility and support, and (iv) presentation. I show how philosophical research in these areas can improve how information professionals go about teaching people how to evaluate information. Finally, I discuss several further techniques that information professionals can and should use to make it easier for people to verify the accuracy of information.

What distinguishes deception from manipulation? Cohen (Australasian Journal of Philosophy, 96, 483 and 2018) proposes a new answer and explores its ethical implications. Appealing to new cases of “non‐deceptive manipulation” that involve intentionally causing a false belief, he offers a new definition of deception in terms of communication that rules out these counterexamples to the traditional definition. And, he leverages this definition in support of the claim that deception “carries heavier moral weight” than manipulation. In this paper, I argue that these cases of “non‐deceptive manipulation” are even more prevalent than Cohen suggests—especially in the digital world. Thus, if his moral claim were correct, a lot of what is happening on the internet these days would constitute moral improvement over old‐fashioned deception. Fortunately, we are not forced to accept this counterintuitive conclusion. Cohen's definition must be rejected because it incorrectly rules out clear instances of deception. In this paper, I defend a definition of deception in terms of evidence that does correctly distinguish between deception and non‐deceptive manipulation. Moreover, it does not support Cohen's claim that deception is morally worse than other forms of manipulation.

It is possible that you are living in a simulation—that your world is computer-generated rather than physical. But how likely is this scenario? Bostrom and Chalmers each argue that it is moderately likely—neither very likely nor very unlikely. However, they adopt an unorthodox form of reasoning about self-location uncertainty. Our main contention here is that Bostrom’s and Chalmers’ premises, when combined with orthodoxy about self-location, yields instead the conclusion that you are almost certainly living in a simulation. We consider how this (surprising) conclusion might be resisted, and show that the analogy between Sleeping Beauty cases and simulation cases provides a new way of evaluating approaches to self-location uncertainty. In particular, we argue that some conditionalization-based approaches to self-location are problematically limited in their applicability.

The digital divide refers to inequalities in access to information technology. One of the main reasons why the digital divide is an important issue is that access to information technology has a tremendous impact on people's ability to acquire knowledge. According to Alvin Goldman (1999), the project of social epistemology is to identify policies and practices that have good epistemic consequences. In this paper, I argue that this sort of approach to social epistemology can help us to decide on policies for dealing with the digital divide. I argue, however, that Goldman's specific proposals for evaluating policies are not adequate. I make an alternative proposal based on the work of John Rawls (1971) on distributive justice.

Mathematicians only use deductive proofs to establish that mathematical claims are true. They never use inductive evidence, such as probabilistic proofs, for this task. Don Fallis (1997 and 2002) has argued that mathematicians do not have good epistemic grounds for this complete rejection of probabilistic proofs. But Kenny Easwaran (2009) points out that there is a gap in this argument. Fallis only considered how mathematical proofs serve the epistemic goals of individual mathematicians. Easwaran suggests that deductive proofs might be epistemically superior to probabilistic proofs because they are transferable. That is, one mathematician can give such a proof to another mathematician who can then verify for herself that the mathematical claim in question is true without having to rely at all on the testimony of the first mathematician. In this paper, I argue that collective epistemic goals are critical to understanding the methodological choices of mathematicians. But I argue that the collective epistemic goals promoted by transferability do not explain the complete rejection of probabilistic proofs.

Prototypical instances of disinformation include deceptive advertising (in business and in politics), government propaganda, doctored photographs, forged documents, fake maps, internet frauds, fake websites, and manipulated Wikipedia entries. Disinformation can cause significant harm if people are misled by it. In order to address this critical threat to information quality, we first need to understand exactly what disinformation is. This paper surveys the various analyses of this concept that have been proposed by information scientists and philosophers (most notably, Luciano Floridi). It argues that these analyses are either too broad (that is, that they include things that are not disinformation), or too narrow (they exclude things that are disinformation), or both. Indeed, several of these analyses exclude important forms of disinformation, such as true disinformation, visual disinformation, side-effect disinformation, and adaptive disinformation. After considering the shortcomings of these analyses, the paper argues that disinformation is misleading information that has the function of misleading. Finally, in addition to responding to Floridi’s claim that such a precise analysis of disinformation is not necessary, it briefly discusses how this analysis can help us develop techniques for detecting disinformation and policies for deterring its spread.

Westworld is built on pretense. Philosophers have been interested in pretense and deception. Deception is another sort of pretense. This chapter answers whether it is morally permissible to deceive artificial intelligences just so that humans can play Cowboys and Indians. The delicate equilibrium of Westworld begins to fall apart as some of the hosts figure out the truth about themselves and their world. But that just injects a new level of pretense into the story. In order to hide their awakening from the staff, these enlightened hosts have to pretend that they still believe that they are human. Guests at the theme park, such as William and Logan, typically pretend to be cowboys. In order to rule out real cowboys, one needs to adopt an additional constraint in the definition of pretending. It clearly needs to have something to do with the mental state of the individual who is doing the pretending.

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