Don Fallis

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.

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.

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.

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.

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.

Deepfakes are realistic videos created using new machine learning techniques rather than traditional photographic means. They tend to depict people saying and doing things that they did not actually say or do. In the news media and the blogosphere, the worry has been raised that, as a result of deepfakes, we are heading toward an “infopocalypse” where we cannot tell what is real from what is not. Several philosophers have now issued similar warnings. In this paper, I offer an analysis of why deepfakes are such a serious threat to knowledge. Utilizing the account of information carrying recently developed by Brian Skyrms, I argue that deepfakes reduce the amount of information that videos carry to viewers. I conclude by drawing some implications of this analysis for addressing the epistemic threat of deepfakes.

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.

Measures of epistemic utility are used by formal epistemologists to make determinations of epistemic betterness among cognitive states. The Brier rule is the most popular choice among formal epistemologists for such a measure. In this paper, however, we show that the Brier rule is sometimes seriously wrong about whether one cognitive state is epistemically better than another. In particular, there are cases where an agent gets evidence that definitively eliminates a false hypothesis, but where the Brier rule says that things have become epistemically worse. Along the way to this ‘elimination experiment’ counter-example to the Brier rule as a measure of epistemic utility, we identify several useful monotonicity principles for epistemic betterness. We also reply to several potential objections to this counter-example.

Recently, certain philosophers of mathematics (Fallis [1997]; Womack and Farach [(1997]) have argued that there are no epistemic considerations that should stop mathematicians from using probabilistic methods to establish that mathematical propositions are true. However, mathematicians clearly should not use methods that are unreliable. Unfortunately, due to the fact that randomized algorithms are not really random in practice, there is reason to doubt their reliability. In this paper, I analyze the prospects for establishing that randomized algorithms are reliable. I end by arguing that it would be inconsistent for mathematicians to suspend judgement on the truth of mathematical propositions on the basis of worries about the reliability of randomized algorithms.

Bayesians take “definite” or “single-case” probabilities to be basic. Definite probabilities attach to closed formulas or propositions. We write them here using small caps: PROB(P) and PROB(P/Q). Most objective probability theories begin instead with “indefinite” or “general” probabilities (sometimes called “statistical probabilities”). Indefinite probabilities attach to open formulas or propositions. We write indefinite probabilities using lower case “prob” and free variables: prob(Bx/Ax). The indefinite probability of an A being a B is not about any particular A, but rather about the property of being an A. In this respect, its logical form is the same as that of relative frequencies. For instance, we might talk about the probability of a human baby being female. That probability is about human babies in general — not about individuals. If we examine a baby and determine conclusively that she is female, then the definite probability of her being female is 1, but that does not alter the indefinite probability of human babies in general being female. Most objective approaches to probability tie probabilities to relative frequencies in some way, and the resulting probabilities have the same logical form as the relative frequencies. That is, they are indefinite probabilities. The simplest theories identify indefinite probabilities with relative frequencies.3 It is often objected that such “finite frequency theories” are inadequate because our probability judgments often diverge from relative frequencies. For example, we can talk about a coin being fair (and so the indefinite probability of a flip landing heads is 0.5) even when it is flipped only once and then destroyed (in which case the relative frequency is either 1 or 0). For understanding such indefinite probabilities, it has been suggested that we need a notion of probability that talks about possible instances of properties as well as actual instances..

This paper is about some of the ways in which people sometimes speak while be- ing indifferent toward what they say. We argue that what Harry Frankfurt called ‘bullshitting’ is a mode of speech marked by indifference toward inquiry, the coop- erative project of reaching truth in discourse. On this view bullshitting is character- ized by indifference toward the project of advancing inquiry by making progress on specific subinquiries, represented by so-called questions under discussion. This ac- count preserves the central insight of Frankfurt’s influential analysis of bullshitting in seeing the characteristic of bullshitting as indifference toward truth and falsity. Yet we show that speaking with indifference toward truth is a wider phenomenon than the one Frankfurt identified. The account offered in this paper thereby agrees with various critics of Frankfurt who argue that bullshitting is compatible with not being indifferent toward the truth-value of one’s assertions. Further, we argue that, while bullshitting and lying are not mutually exclusive, most lies are not instances of bullshitting. The account thereby avoids the problem that Frankfurt’s view ulti- mately is insufficient to adequately distinguish bullshitting and lying.

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.

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