225 research outputs found
The simulation argument: Reply to Weatherson
No full textI reply to some recent comments by Brian Weatherson on my 'simulation argument'. I clarify some interpretational matters, and address issues relating to epistemological externalism, the difference from traditional brain-in-a-vat arguments, and a challenge based on 'grue'-like predicates. © The Editors of The Philosophical Quarterly, 2005
Conceptual room for ontic vagueness
No full textThis thesis is a systematic investigation of whether there might be conceptual room for the idea that the world itself might be vague, independently of how we describe it. This idea – the existence of so-called ontic vagueness – has generally been extremely unpopular in the literature; my thesis thus seeks to evaluate whether this ‘negative press’ is justified. I start by giving a working definition and semantics for ontic vagueness, and then attempt to show that there are no conclusive arguments that rule out vagueness of this kind. I subsequently establish what type of arguments I think would be most effective in establishing ontic vagueness and provide some arguments of this form. I then highlight a potential worry for this type of argument, but argue that it can be circumvented. Finally, I consider the main ways that the opponent of ontic vagueness would be likely resist the arguments I have offered, and argue that these strategies of response are methodologically problematic. I conclude by claiming that ontic vagueness is a perfectly plausible ontological commitment
Margins and Errors
No full textRecently, Timothy Williamson has argued that considerations about margins of errors can generate a new class of cases where agents have justified true beliefs without knowledge. I think this is a great argument, and it has a number of interesting philosophical conclusions. In this note I'm going to go over the assumptions of Williamson's argument. I'm going to argue that the assumptions which generate the justification without knowledge are true. I'm then going to go over some of the recent arguments in epistemology that are refuted by Williamson's work. And I'm going to end with an admittedly inconclusive discussion of what we can know when using an imperfect measuring device. Measurement, Justification and Knowledge Williamson's core example involves detecting the angle of a pointer on a wheel by eyesight. For various reasons, I find it easier to think about a slightly different example: measuring a quantity using a digital measurement device. This change has some costs relative to Williamson's version -for one thing, if we are measuring a quantity it might seem that the margin of error is related to the quantity measured. If I eyeball how many stories tall a building is, my margin of error is 0 if the building is 1-2 stories tall, and over 10 if the building is as tall as the World Trade Center. But this problem is not as pressing for digital devices, which are often very unreliable for small quantities. And, at least relative to my preferences, the familiarity of quantities makes up for the loss of symmetry properties involved in angular measurement. To make things explicit, I'll imagine the agent S is using a digital scale. The scale has a margin of error m. That means that if the reading, i.e., the apparent mass is a, then the agent is justified in believing that the mass is in [a − m, a + m]. We will assume that a and m are luminous; i.e., the agent knows their values, and knows she knows them, and so on. This is a relatively harmless idealisation for a; it is pretty clear what a digital scale reads. 1 It is a somewhat less plausible as- † Unpublished. These are some reflections on a paper Timothy Williamson gave at the 2012 CSMN/Arché epistemology conference. Thanks to Derek Ball, Herman Cappelen, Ishani Maitra, Sarah Moss and Robert Weatherson for helpful discussions, as well as audiences at Arché and Edinburgh. 1 This isn't always true. If a scale flickers between reading 832g and 833g, it takes a bit of skill to determine what the reading is. But we'll assume it is clear in this case. On an analogue scale, the luminosity assumption is rather implausible, since it is possible to eyeball with less than perfect accuracy how far between one marker and the next the pointer is. Margins and Errors 2 sumption for m. But we'll assume that S has been very diligent about calibrating her scale, and that the calibration has been recently and skillfully carried out, so in practice m can be assessed very accurately. We'll make three further assumptions about m that strike me as plausible, but which may I guess be challenged. I need to be a bit careful with terminology to set out the first one. I'll use V and v as variables that both pick out the true value of the mass. The difference is that v picks it out rigidly, while V picks out the value of the mass in any world under consideration. Think of V as shorthand for the mass of the object and v as shorthand for the actual mass of the object. (More carefully, V is a random variable, while v is a standard, rigid, variable.) Our first assumption then is that m is also related to what the agent can know. In particular, we'll assume that if the reading a equals v, then the agent can know that V ∈ [a − m, a + m], and can't know anything stronger than that. That is, the margin of error for justification equals, in the best case, the margin of error for knowledge. The second is that the scale has a readout that is finer than m. This is usually the case; the last digit on a digital scale is often not significant. The final assumption is that it is metaphysically possible that the scale has an error on an occasion that is greater than m. This is a kind of fallibilism assumption -saying that the margin of error is m does not mean there is anything incoherent about talking about cases where the error on an occasion is greater than m. This error term will do a lot of work in what follows, so I'll use e to be the error of the measurement, i.e., |a − v|. For ease of exposition, I'll assume that a ≥ v, i.e., that any error is on the high side. But this is entirely dispensible, and just lets me drop some disjunctions later on. Now we are in a position to state Williamson's argument. Assume that on a particular occasion, 0 < e < m. Perhaps v = 830, m = 10 and a = 832, so e = 2. Williamson appears to make the following two assumptions. 2 1. The agent is justified in believing what they would know if appearances matched reality, i.e., if V equalled a. 2. The agent cannot come to know something about V on the basis of a suboptimal measurement that they could not also know on the basis of an optimal measurement. I'm assuming here that the optimal measurement displays the correct mass. I don't assume the actual measurement is wrong. That would require saying something implausible about the semantic content of the display. It's not obvious that the display has a content that could be true or false, and if it does have such a 2 I'm not actually sure whether Williamson makes the first, or thinks it is the kind of thing anyone who thinks justification is prior to knowledge should make. Margins and Errors 3 content it might be true. (For instance, the content might be that the object on the scale has a mass near to a, or that with a high probability it has a mass near to a, and both of those things are true.) But the optimal measurement would be to have a = v, and in this sense the measurement is suboptimal. The argument then is pretty quick. From the first assumption, we get that the agent is justified in believing that V ∈ [a − m, a + m]. Assume then that the agent forms this justified belief. This belief is incompatible with V ∈ [v − m, a − m). But if a equalled v, then the agent wouldn't be in a position to rule out that V ∈ [v − m, a − m). So by premise 2 she can't knowledgeably rule it out on the basis of a mismeasurement. So her belief that V ≥ a − m cannot be knowledge. So this justified true belief is not knowledge. If you prefer doing this with numbers, here's the way the example works using the numbers above. The mass of the object is 830. So if the reading was correct, the agent would know just that the mass is between 820 and 840. The reading is 832. So she's justified in believing, and we'll assume she does believe, that the mass is between 822 and 842. That belief is incompatible with the mass being 821. But by premise 2 she can't know the mass is greater than 821. So the belief doesn't amount to knowledge, despite being justified and, crucially, true. After all, 830 is between 822 and 842, so her belief that the mass is in this range is true. So simple reflections on the workings on measuring devices let us generate cases of justified true beliefs that are not knowledge. I'll end this section with a couple of objections and replies
Knowledge
No full textIn this book the author argues for a groundbreaking perspective that knowledge is inherently interest-relative. This means that what one knows is influenced not just by belief, evidence, and truth, but crucially by the purposes those beliefs serve. Drawing from classical Nyāya epistemologies, the book asserts that knowledge rationalizes action: if you know something, it is sensible to act on it—and the best way to square this with an anti-sceptical epistemology is to say that knowledge is interest-relative. While versions of this view have been debated, they haven’t gained wide acceptance. The author addresses common objections with a refined formulation and explores how this perspective elucidates the role of knowledge in inquiry, daily life, and the history of thought. Key distinctions include the impact of “long odds” situations on knowledge, the distinctive role knowledge has a starting point for inquiry, and the importance of using non-ideal models in theorising about knowledge. Building on decades of scholarship, the author offers a cohesive theory that integrates and clarifies previous works, demonstrating that not only knowledge but also belief, rational belief, and evidence are interest-relative. This book is essential for those seeking a deeper understanding of the intricate relationship between knowledge and practical interests
Should We Respond to Evil with Indifference
No full textIn a recent article, Adam Elga outlines a strategy for “Defeating Dr Evil with Self-Locating Belief”. The strategy relies on an indifference princi-ple that is not up to the task. In general, there are two things to dislike about indifference principles: adopting one normally means confusing risk for uncertainty, and they tend to lead to incoherent views in some ‘paradoxical ’ situations. I argue that both kinds of objection can be lev-elled against Elga’s indifference principle. There are also some difficulties with the concept of evidence that Elga uses, and these create further dif-ficulties for the principle. In a recent article, Adam Elga outlines a strategy for “Defeating Dr Evil with Self-Locating Belief”. The strategy relies on an indifference principle that is not up to the task. In general, there are two things to dislike about indifference principles: adopting one normally means confusing risk for uncertainty, and they tend to lead to incoherent views in some ‘paradoxical ’ situations. Each kind of objection can b
For Bayesians, rational modesty requires imprecision
No full textGordon Belot (2013) has recently developed a novel argument against Bayesianism. He shows that there is an interesting class of problems that, intuitively, no rational belief forming method is likely to get right. But a Bayesian agent’s credence, before the problem starts, that she will get the problem right has to be 1. This is an implausible kind of immodesty on the part of Bayesians. My aim is to show that while this is a good argument against traditional, precise Bayesians, the argument doesn’t neatly extend to imprecise Bayesians. As such, Belot’s argument is a reason to prefer imprecise Bayesianism to precise Bayesianism.Peer reviewe
Humean Supervenience
No full textHumean supervenience is the conjunction of three theses: Truth supervenes on being, Anti‐haecceitism, and Spatiotemporalism. The first clause is a core part of Lewis's metaphysics. The second clause is related to Lewis's counterpart theory. The third clause says there are no fundamental relations beyond the spatiotemporal, or fundamental properties of extended objects. Supervenience is classified into strong modal Humean supervenience, local modal Humean supervenience and familiar modal Humean supervenience which states that: for any two "worlds like ours", if the spatiotemporal distribution of fundamental qualities is the same at each world, the contingent facts are also the same. The fact that quantum mechanics raises problems for Humean supervenience does not undercut the philosophical significance of Lewis's defense of Humean supervenience. Humean supervenience says that in a world like ours, the fundamental properties are local qualities: perfectly natural intrinsic properties of points, or of point‐sized occupants of points
The Role of Naturalness in Lewis's Theory of Meaning
No full textMany writers have held that in his later work, David Lewis adopted a theory of predicate meaning such that the meaning of a predicate is the most natural property that is (mostly) consistent with the way the predicate is used. That orthodox interpretation is shared by both supporters and critics of Lewis's theory of meaning, but it has recently been strongly criticised by Wolfgang Schwarz. In this paper, I accept many of Schwarze's criticisms of the orthodox interpretation, and add some more. But I also argue that the orthodox interpretation has a grain of truth in it, and seeing that helps us appreciate the strength of Lewis's late theory of meaning
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