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Profile: Don Fallis (University of Arizona)
  1. Don Fallis, Toward an Epistemology of Wikipedia.
    Wikipedia (the "free online encyclopedia that anyone can edit") is having a huge impact on how a great many people gather information about the world. So, it is important for epistemologists and information scientists to ask whether or not people are likely to acquire knowledge as a result of having access to this information source. In other words, is Wikipedia having good epistemic consequences? After surveying the various concerns that have been raised about the reliability of Wikipedia, this paper argues (...)
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  2. Don Fallis, What Do Mathematicians Want? Probabilistic Proofs and the Epistemic Goals of Mathematicians.
    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 (...)
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  3. Kay Mathiesen & Don Fallis, Information Ethics and the Library Profession.
    We consider the mission of the librarian as an information provider and the core value that gives this mission its social importance. Our focus here is on those issues that arise in relation to the role of the librarian as an information provider. In particular, we focus on questions of the selection and organization of information, which bring up issues of bias, neutrality, advocacy, and children's rights to access information.
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  4. Don Fallis (forthcoming). Skyrms on the Possibility of Universal Deception. Philosophical Studies:1-23.
    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 (...)
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  5. Don Fallis (2014). Are Bald‐Faced Lies Deceptive After All? Ratio 27 (4):n/a-n/a.
    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 (...)
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  6. Don Fallis (2014). What To Believe Now: Applying Epistemology to Contemporary Issues by Coady, David. Australasian Journal of Philosophy 92 (2):391-394.
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  7. Adam J. Arico & Don Fallis (2013). Lies, Damned Lies, and Statistics: An Empirical Investigation of the Concept of Lying. Philosophical Psychology 26 (6):790 - 816.
    There are many philosophical questions surrounding the notion of lying. Is it ever morally acceptable to lie? Can we acquire knowledge from people who might be lying to us? More fundamental, however, is the question of what, exactly, constitutes the concept of lying. According to one traditional definition, lying requires intending to deceive (Augustine. (1952). Lying (M. Muldowney, Trans.). In R. Deferrari (Ed.), Treatises on various subjects (pp. 53?120). New York, NY: Catholic University of America). More recently, Thomas Carson (2006. (...)
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  8. Don Fallis (2013). Davidson Was Almost Right About Lying. Australasian Journal of Philosophy 91 (2):337-353.
    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 (...)
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  9. Don Fallis (2013). Privacy and Lack of Knowledge. Episteme 10 (2):153-166.
    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 (...)
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  10. Don Fallis & Kay Mathiesen (2013). Veritistic Epistemology and the Epistemic Goals of Groups: A Reply to Vähämaa. Social Epistemology 27 (1):21 - 25.
    (2013). Veritistic Epistemology and the Epistemic Goals of Groups: A Reply to Vähämaa. Social Epistemology: Vol. 27, No. 1, pp. 21-25. doi: 10.1080/02691728.2012.760666.
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  11. D. Fallis (2012). Lying and Deception: Theory and Practice, by Thomas L. Carson. Mind 120 (480):1232-1237.
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  12. Don Fallis (2012). Lying as a Violation of Grice's First Maxim of Quality. Dialectica 66 (4):563-581.
    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 (...)
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  13. Don Fallis (2011). Floridi on Disinformation. Etica and Politica / Ethics and Politics (2):201-214.
  14. Don Fallis (2011). What Liars Can Tell Us About the Knowledge Norm of Practical Reasoning. Southern Journal of Philosophy 49 (4):347-367.
    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 (...)
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  15. Don Fallis (2010). Lying and Deception. Philosophers' Imprint 10 (11).
    According to the standard philosophical definition of lying, you lie if you say something that you believe to be false with the intent to deceive. Recently, several philosophers have argued that an intention to deceive is not a necessary condition on lying. But even if they are correct, it might still be suggested that the standard philosophical definition captures the type of lie that philosophers are primarily interested in (viz., lies that are intended to deceive). In this paper, I argue (...)
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  16. Don Fallis (2010). Wikipistemology. In Alvin I. Goldman & Dennis Whitcomb (eds.), Social Epistemology: Essential Readings. Oxford University Press.
     
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  17. Don Fallis (2009). Introduction: The Epistemology of Mass Collaboration. Episteme 6 (1):1-7.
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  18. Don Fallis (2009). Taking the Two Envelope Paradox to the Limit. Southwest Philosophy Review 25 (2):95-111.
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  19. Don Fallis (2009). What Is Lying? Journal of Philosophy 106 (1):29-56.
    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 (...)
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  20. Ian Evans, Don Fallis, Peter Gross, Terry Horgan, Jenann Ismael, John Pollock, Paul D. Thorn, Jacob N. Caton, Adam Arico, Daniel Sanderman, Orlin Vakerelov, Nathan Ballantyne, Matthew S. Bedke, Brian Fiala & Martin Fricke (2007). An Objectivist Argument for Thirdism. Analysis 68.
    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 (...)
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  21. Don Fallis (2007). Attitudes Toward Epistemic Risk and the Value of Experiments. Studia Logica 86 (2):215 - 246.
    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 (...)
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  22. Don Fallis (2007). Collective Epistemic Goals. Social Epistemology 21 (3):267 – 280.
    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 (...)
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  23. Don Fallis, On Verifying the Accuracy of Information: Philosophical Perspectives.
    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. (...)
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  24. Don Fallis (2007). Toward an Epistemology of Intellectual Property. Journal of Information Ethics 16 (2):34-51.
    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 (...)
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  25. Don Fallis (2006). The Epistemic Costs and Benefits of Collaboration. Southern Journal of Philosophy 44 (S1):197-208.
    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 (...)
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  26. Don Fallis (2005). Epistemic Value Theory and Social Epistemology. Episteme 2 (3):177-188.
    In order to guide the decisions of real people who want to bring about good epistemic outcomes for themselves and others, we need to understand our epistemic values. In Knowledge in a Social World, Alvin Goldman has proposed an epistemic value theory that allows us to say whether one outcome is epistemically better than another. However, it has been suggested that Goldman's theory is not really an epistemic value theory at all because whether one outcome is epistemically better than another (...)
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  27. Don Fallis (2005). Epistemic Value Theory and Judgment Aggregation. Episteme 2 (1):39-55.
    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 (...)
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  28. Don Fallis (2005). The Epistemic Costs and Benefits of Collaboration. Southern Journal of Philosophy 44 (Supplement):197-208.
    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 (...)
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  29. Don Fallis (2004). Epistemic Value Theory and Information Ethics. Minds and Machines 14 (1):101-117.
    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 (...)
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  30. Don Fallis (2003). Intentional Gaps in Mathematical Proofs. Synthese 134 (1-2):45 - 69.
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  31. Don Fallis (2002). Goldman on Probabilistic Inference. Philosophical Studies 109 (3):223 - 240.
    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 (...)
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  32. Don Fallis (2002). Introduction: Social Epistemology and Information Science. Social Epistemology 16 (1):1 – 4.
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  33. D. Fallis (2000). The Reliability of Randomized Algorithms. British Journal for the Philosophy of Science 51 (2):255-271.
    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 (...)
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  34. Don Fallis (2000). Veritistic Social Epistemology and Information Science. Social Epistemology 14 (4):305 – 316.
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  35. Don Fallis (1999). Review: Jon Barwise, John Etchemendy, The Language of First-Order Logic, Including the IBM-Compatible Windows Version of Tarski's World 4.0; Jon Barwise, John Etchemendy, Hyperproof. [REVIEW] Journal of Symbolic Logic 64 (2):916-918.
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  36. Don Fallis (1998). [Omnibus Review]. Journal of Symbolic Logic 63 (3):1196-1200.
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  37. Don Fallis (1997). The Epistemic Status of Probabilistic Proof. Journal of Philosophy 94 (4):165-186.
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  38. Don Fallis (1996). The Source of Chaitin's Incorrectness. Philosophia Mathematica 4 (3):261-269.