claim that his thought experiment shows that a currently living person is not a random sample is refuted. His thought experiment is reduced to a probability model, and is shown to be identical to one previously developed by Dieks. The status of the Doomsday Argument is left unresolved, since Dieks's refutation attempt is disputed in the literature.

Carter and Leslie (1996) have argued, using Bayes's theorem, that our being alive now supports the hypothesis of an early 'Doomsday'. Unlike some critics (Eckhardt 1997), we accept their argument in part: given that we exist, our existence now indeed favors 'Doom sooner' over 'Doom later'. The very fact of our existence, however, favors 'Doom later'. In simple cases, a hypothetical approach to the problem of 'old evidence' shows that these two effects cancel out: our existence now yields no information (...) about the coming of Doom. More complex cases suggest a move from countably additive to non-standard probability measures. (shrink)

We provide a solution to the well-known “Shooting-Room” paradox, developed by John Leslie in connection with his Doomsday Argument. In the “Shooting-Room” paradox, the death of an individual is contingent upon an event that has a 1/36 chance of occurring, yet the relative frequency of death in the relevant population is 0.9. There are two intuitively plausible arguments, one concluding that the appropriate subjective probability of death is 1/36, the other that this probability is 0.9. How are these two values (...) to be reconciled? We show that only the first argument is valid for a standard, countably additive probability distribution. However, both lines of reasoning are legitimate if probabilities are non-standard. The subjective probability of death rises from 1/36 to 0.9 by conditionalizing on an event that is not measurable, or whose probability is zero. Thus we can sometimes meaningfully ascribe conditional probabilities even when the event conditionalized upon is not of positive finite (or even infinitesimal) measure. (shrink)

A recent paper by Korb and Oliver in this journal attempts to refute the Carter-Leslie Doomsday argument. I organize their remarks into five objections and show that they all fail. Further efforts are thus called upon to find out what, if anything, is wrong with Carter and Leslie's disturbing reasoning. While ultimately unsuccessful, Korb and Oliver's objections do however in some instances force us to become clearer about what the Doomsday argument does and doesn't imply.

Rarely does philosophy produce empirical predictions. The Doomsday argument is an important exception. From seemingly trivial premises it seeks to show that the risk that humankind will go extinct soon has been systematically underestimated. Nearly everybody's first reaction is that there must be something wrong with such an argument. Yet despite being subjected to intense scrutiny by a growing number of philosophers, no simple flaw in the argument has been identified.

George Sowers tries to refute the Doomsday argument on grounds that true random sampling requires all possible samples to be equally probable the time when the sample is taken. Yet the Doomsday argument does not rely on true random sampling. It presupposes random sampling only in a metaphorical sense. After arguing that Sowers’ critique fails, I outline my own view on the matter, which is that the Doomsday argument is inconclusive and that by developing a theory of observation selection effects (...) one can show why that is so. (shrink)

The Doomsday argument purports to show that the risk of the human species going extinct soon has been systematically underestimated. This argument has something in common with controversial forms of reasoning in other areas, including: game theoretic problems with imperfect recall, the methodology of cosmology, the epistemology of indexical belief, and the debate over so-called fine-tuning arguments for the design hypothesis. The common denominator is a certain premiss: the Self-Sampling Assumption. We present two strands of argument in favor of this (...) assumption. Through a series of thought experiments we then investigate some bizarre prima facie consequences – backward causation, psychic powers, and an apparent conflict with the Principal Principle. (shrink)

John Leslie presents a thought experiment to show that chances are sometimes observer-relative in a paradoxical way. The pivotal assumption in his argument – a version of the weak anthropic principle – is the same as the one used to get the disturbing Doomsday argument off the ground. I show that Leslie's thought experiment trades on the sense/reference ambiguity and is fallacious. I then describe a related case where chances are observer-relative in an interesting way. But not in a paradoxical (...) way. The result can be generalized: At least for a very wide range of cases, the weak anthropic principle does not giverise to paradoxical observer-relative chances. This finding could be taken to give new indirect support to the doomsday argument. (shrink)

In a recent paper in this journal, Ken Olum attempts to refute the Doomsday argument by appealing to the self-indication assumption (SIA), the idea that your very existence gives you reason to think that there are many observers. In contrast to earlier refutation attempts that use this strategy, Olum confronts and try to counter some of the objections that have been made against SIA. We argue that his defense of SIA is unsuccessful. This does not, however, mean that one has (...) to accept the Doomsday argument (or the other counterintuitive results that flow from related thought experiments). A developed theory of observation selection effects shows why the Doomsday argument is inconclusive and how one can consistently reject both it and SIA. (shrink)

I argue that four problems that appear to be very different have the same structure. I give a unified treatment of the Doomsday Argument, Sleeping Beauty, the Fine-tuning Argument and confirmation in the Everett interpretation of quantum mechanics. All these cases involve self-locating evidence. However, the confusing feature of all these cases is not self-location, but observation selection effects. I explain how observation selection effects operate, why they affect the four problem cases, and how they can be incorporated into confirmation (...) theory. I will defend the Doomsday Argument, the halfer position in Sleeping Beauty, the Fine-tuning Argument and the applicability of confirmation theory to the Everett interpretation of quantum mechanics. (shrink)

The Doomsday Argument says we should increase our subjective probability that Doomsday will occur once we take into account how many humans have lived before us. One objection to this conclusion is that we should accept the Self-Indication Assumption (SIA): Given the fact that you exist, you should (other things equal) favor hypotheses according to which many observers exist over hypotheses on which few observers exist. Nick Bostrom argues that we should not accept the SIA, because it can be used (...) without knowledge of birth rank. Bradley Monton tries to construct a Doomsday Argument without knowledge of birth rank. I argue that Monton fails. The argument he constructs has implicit knowledge of birth rank and it is this knowledge that does the work. Furthermore, I argue that provided we dont have certain specific information about the future, the Doomsday Argument requires knowledge of birth rank. (shrink)

In sum, then, Chalmers’s attempt to argue against physicalism based on the conceivability of zombies misses the mark. His version of conceivability does indeed imply possibility, but at the cost of making it unclear whether zombies are indeed conceivable.

In a recent article, John Leslie has defended the intriguing Carter-Leslie ‘Doomsday Argument’ (Philosophy, January 2000). I argue that an essential presupposition of the argument—that ‘the case of one's name coming out of [an] urn is sufficiently similar to the case of being born into the world’—engenders, in turn, a parallel ‘Ussherian Corollary’. The dubiousness of this Corollary, coupled with independent considerations, casts doubt upon the Carter-Leslie presupposition, and hence, dooms the Doomsday argument.

According to the Doomsday Argument we have to rethink the probabilities we assign to a soon or not so soon extinction of mankind when we realize that we are living now, rather early in the history of mankind. Sleeping Beauty finds herself in a similar predicament: on learning the date of her first awakening, she is asked to re-evaluate the probabilities of her two possible future scenarios. In connection with Doom, I argue that it is wrong to assume that our (...) ordinary probability judgements do not already reflect our place in history: we justify the predictive use we make of the probabilities yielded by science (or other sources of information) by our knowledge of the fact that we live now, a certain time before the possible occurrence of the events the probabilities refer to. Our degrees of belief should change drastically when we forget the date—importantly, this follows without invoking the “Self Indication Assumption”. Subsequent conditionalization on information about which year it is cancels this probability shift again. The Doomsday Argument is about such probability shifts, but tells us nothing about the concrete values of the probabilities—for these, experience provides the only basis. Essentially the same analysis applies to the Sleeping Beauty problem. I argue that Sleeping Beauty “thirders” should be committed to thinking that the Doomsday Argument is ineffective; whereas “halfers” should agree that doom is imminent—but they are wrong. (shrink)

According to the Doomsday Argument we have to rethink the probabilities we assign to a soon or not so soon extinction of mankind when we realize that we are living now, rather early in the history of mankind. Sleeping Beauty finds herself in a similar predicament: on learning the date of her first awakening, she is asked to re-evaluate the probabilities of her two possible future scenarios. In connection with Doom, I argue that it is wrong to assume that our (...) ordinary probability judgements do not already reflect our place in history: we justify the predictive use we make of the probabilities yielded by science (or other sources of information) by our knowledge of the fact that we live now, a certain time before the possible occurrence of the events the probabilities refer to. Our degrees of belief should change drastically when we forget the date—importantly, this follows without invoking the "Self Indication Assumption". Subsequent conditionalization on information about which year it is cancels this probability shift again. The Doomsday Argument is about such probability shifts, but tells us nothing about the concrete values of the probabilities—for these, experience provides the only basis. Essentially the same analysis applies to the Sleeping Beauty problem. I argue that Sleeping Beauty "thirders" should be committed to thinking that the Doomsday Argument is ineffective; whereas "halfers" should agree that doom is imminent—but they are wrong. (shrink)

According to the Doomsday Argument the probability of an impending extinction of mankind is much higher than we think. The adduced reason is that in our assignment of probabilities to soon or not so soon doom we have not fully taken into account that we live in the specific year 2001. This is relevant information, because if I consider myself as an arbitrary member of the human race I have a much higher probability of finding myself living in 2001 on (...) the hypothesis of a soon extinction, Doom Soon, than on the hypothesis of Doom Late---according to the latter hypothesis there are so many more years I could have found myself living in. Accordingly, Bayesian reasoning leads to a posterior probability of the Doom Soon hypothesis, after I have taken the evidence of my birth date fully into account, that is much higher than the prior probability. I show that the Argument is nothing but a rather trivial mathematical exercise in the calculation of posterior from prior probabilities; it is only about the relation between these probabilities and is silent about the concrete values these probabilities should have. Nothing in the Argument supports the conclusion its proponents think it supports, namely that Doom Soon is much more probable than we ordinarily think. The Argument is formally valid, but ineffective. (shrink)

In this paper, I present a solution to the Doomsday argument based on a third type of solution, by contrast to, on the one hand, the Carter-Leslie view and, on the other hand, the Eckhardt et al. analysis. I begin by strengthening both competing models by highlighting some variations of their original models, which renders them less vulnerable to several objections. I then describe a third line of solution, which incorporates insights from both Leslie and Eckhardt’s models and fits more (...) adequately with the human situation corresponding to DA. I argue then that this two-sided analogy casts new light on the reference class problem. This leads finally to a novel formulation of the argument that could well be more plausible than the original one. (shrink)

Several philosophical problems are based on an analogy between a real situation and a probabilistic model. Such problems are based on urn analogies. The present dissertation aims to describe and implement a methodology oriented towards the resolution of philosophical problems based on an urn analogy. This methodology is based on the use of the n-universes. To this end, I describe first the n-universes in a detailed way. I also discuss the difficulties of the theory of n-universes related to the demultiplication (...) of the criteria and to the relation one/many between the objects and a given criterion. On the one hand, I present an application of the framework of n-universes to the Doomsday argument and to the problems recently appeared in the literature in keeping with the Doomsday argument. My concern is also with showing how the application of the framework of n-universes to several problems and thought experiments related to the Doomsday argument helps clarifying the problem data and making disappear the associated ambiguity. I present then an analysis of the following problems related to the Doomsday argument: the two urn case, God's Coin Toss, the Sleeping Beauty Problem, the Presumptuous Philosopher, Lazy Adam, and the Shooting-Room Paradox. I present lastly a solution to the Doomsday argument, based on a third route, by contrast to two types of solutions classically described. On the other hand, I present an application of the framework of n-universes to Goodman's paradox. I replace first Goodman's statement in the framework of n-universes. I propose then a solution to the paradox, based on a distinction between two different modelizations of Goodman's statement in two structurally different n-universes. (shrink)

English translation of a paper originally pupblished in French in the Canadian Journal of Philosophy under the title 'Comment l'urne de Carter et Leslie se déverse dans celle de Hempel'. In this paper, I present firstly a solution to Hempel's Problem. I recall secondly the solution to the Doomsday Argument described in my previous Une Solution pour l'Argument de l'Apocalypse (Canadian Journal of Philosophy 1998-2) and remark that both solutions are based on a similar line of reasoning. I show thirdly (...) that the Doomsday Argument can be reduced to the core of Hempel's Problem. (shrink)

Attribué à Brandon Carter, l' argument de l'Apocalypse Doomsday Argument , soit DA, dans ce qui suit) a été décrit par John Leslie (1992). On peut formuler ainsi cet argument. Soit A l'événement: l'Apocalypse se produira avant l'an 2150 ; et B l'événement: l'Apocalypse ne se produira pas avant 2150 . Soit également Z l'événement: j'ai connu les années 1990 . On peut par ailleurs estimer à 40 milliards le nombre d'humains ayant existé depuis la naissance de l'humanité, jusqu'à notre (...) époque: soit H1996 un tel nombre. On peut ainsi admettre, de manière raisonnable, qu'un humain sur dix, dans le cas de l'événement.. (shrink)

The Doomsday Argument is alive and kicking, and since its formulation in the beginning of the Eighties by the astrophysicist Brandon Carter it has gained wide attention, been strongly criticized and has been described in many different, and sometimes non-interchangeable analogies. I will briefly present the argument here, and departing from Nick Bostrom's interpretation, I will defend that doom may be sooner than we think if we start building conscious machines soon in the future.

A refutation of the doomsday argument is offered. Through a simple thought experiment analysed in Bayesian terms the fallacy is shown to be the assumption that a currently living person represents a random sample from the population of all persons who will ever have existed. A more general version of the counter argument is then given. Previous arguments that purport to answer this concern are also addressed. One result is determining criteria for the applicability of time sampling arguments, i.e., under (...) what conditions can a specific instant in time be regarded as a random sample from a time span. Given this new understanding, the incredible consequences of the doomsday and related arguments evaporate. (shrink)

Recent developments in cosmology indicate that every history having a non-zero probability is realized in infinitely many distinct regions of spacetime. Thus, it appears that the universe contains infinitely many civilizations exactly like our own, as well as infinitely many civilizations that differ from our own in any way permitted by physical laws. We explore the implications of this conclusion for ethical theory and for the doomsday argument. In the infinite universe, we find that the doomsday argument applies only to (...) effects which change the average lifetime of all civilizations, and not those which affect our civilization alone. (shrink)

Carter and Leslie's Doomsday Argument maintains that reflection upon the number of humans born thus far, when that number is viewed as having been uniformly randomly selected from amongst all humans, past, present and future, leads to a dramatic rise in the probability of an early end to the human experiment. We examine the Bayesian structure of the Argument and find that the drama is largely due to its oversimplification.

Suppose various observers are divided randomly into two groups, a large and a small. Not knowing into which group anyone has been sent, each can have strong grounds for believing in being in the large group, although recognizing that every observer in the other group has equally powerful reasons for thinking of this other group as the large one. Justified belief can therefore be observer-relative in a rather paradoxical way. Appreciating this allows one to reject an intriguing new objection against (...) Brandon Carter's 'doomsday argument'. Carter encourages us to doubt that we are among only the first hundredth, say, or first millionth, of all humans who will ever have existed. He thereby reinforces whatever reasons we may have for suspecting that, unless we take great care, the human race will not survive long. Admittedly his argument is weakened if our world is indeterministic, so that there is no suitably guaranteed 'fact of the matter' of how many humans will ever have existed. But even then, it can caution us against believing that a lengthy future for humankind 'is as good as determined'. Of all the objections the argument has yet faced, the new one is the most interesting. (shrink)

The Simulation Argument and the Doomsday Argument share certain structural similarities, and hence are often discussed together (Bostrom 2003, Aranyosi 2004, Richmond 2008, Bostrom and Kulczycki 2011). Both are cases where reflecting on one’s location among a set of possibilities yields a counter-intuitive conclusion—in one case that the end of humankind is closer than you initially thought, and in the second case that it is more likely than you initially thought that you are living in a computer simulation. Indeed, the (...) two arguments do share strong structural similarities. But there are also some disanalogies between the two arguments, and I argue that these disanalogies mean that the Simulation Argument succeeds and the Doomsday Argument fails. (shrink)

I argue that the Doomsday argument fails because it fails to take into account the lesson of the Sleeping Beauty puzzle.

The Carter-Leslie Doomsday argument, as standardly presented, relies on the assumption that you have knowledge of your approximate birth rank. I demonstrate that the Doomsday argument can still be given in a situation where you have no knowledge of your birth rank. This allows one to reply to Bostrom's defense of the Doomsday argument against the refutation based on the idea that your existence makes it more likely that many observers exist.

Physicist J. Richard Gott uses the Copernican principle that “we are not special” to make predictions about the future lifetime of the human race, based on how long the human race has been in existence so far. We show that the predictions which can be derived from Gott’s argument are less strong than one might be inclined to believe, that Gott’s argument illegitimately assumes that the human race will not last forever, that certain versions of Gott’s argument are incompatible with (...) Bayesian conditionalization, and that Gott’s argument is self-refuting. (shrink)

Bayesian probabilistic explication of inductive inference conflates neutrality of supporting evidence for some hypothesis H (“not supporting H”) with disfavoring evidence (“supporting not-H”). This expressive inadequacy leads to spurious results that are artifacts of a poor choice of inductive logic. I illustrate how such artifacts have arisen in simple inductive inferences in cosmology. In the inductive disjunctive fallacy, neutral support for many possibilities is spuriously converted into strong support for their disjunction. The Bayesian “doomsday argument” is shown to rely entirely (...) on a similar artifact, for the result disappears in a reanalysis that employs fragments of inductive logic able to represent evidential neutrality. Finally, the mere supposition of a multiverse is not yet enough to warrant the introduction of probabilities without some factual analog of a randomizer over the multiverses. (shrink)

If the human race comes to an end relatively shortly, then we have been born at a fairly typical time in the history of humanity; if trillions of people eventually exist, then we have been born in the first surprisingly tiny fraction of all people. According to the 'doomsday argument' of Carter, Leslie, Gott and Nielsen, this means that the chance of a disaster which would obliterate humanity is much larger than usually thought. But treating possible observers in the same (...) way as those who actually exist avoids this conclusion: our existence is more likely in a race which is long-lived, and this cancels out the doomsday argument, so that the chance of a disaster is only what one would ordinarily estimate. (shrink)

J. Richard Gott III (1993) has used the “Copernican principle” to derive a probability density function for the total longevity of any phenomenon, based solely on the phenomenon’s past longevity. John Leslie (1996) and others have used an apparently similar probabilistic argument, the “Doomsday Argument,” to claim that conventional predictions of longevity must be adjusted, based on Bayes’ Theorem, in favor of shorter longevities. Here I show that Gott’s arguments are flawed and contradictory, but that one of his conclusions—his delta (...) t formula—is mathematically equivalent to Laplace’s famous (and notorious) ‘rule of succession’; moreover, Gott’s delta t formula is a plausible worst-case (if one favors greater longevity) bound in some contexts. On the other hand, the Doomsday Argument is fallacious: the argument’s Bayesian formalism is stated in terms of total duration, but all attempted real-life applications of the argument—with one exception, an application by Gott 1994—actually plug in prior probabilities for future duration; moreover, the Self-Sampling Assumption, an essential premise of the Doomsday Argument, is contradicted by the prior information in all known real-life cases. But rejecting the Doomsday Argument does not entail rejecting the possibility of learning about the future from the past. Applying the work of Bruce M. Hill (1968, 1988, 1993) and Frank P.A. Coolen (1998, 2006) in the field of non-parametric predictive inference, I propose and defend an alternative methodology for quantifying how past longevity of any phenomenon does provide evidence for future longevity. In so doing, I identify an objective standard by which to choose among counting time intervals, counting population, or counting any other measure of past longevity in predicting future longevity. This methodology forms the basis of a calculus of induction. (shrink)

Gott ( 1993 ) has used the ‘Copernican principle’ to derive a probability distribution for the total longevity of any phenomenon, based solely on the phenomenon’s past longevity. Leslie ( 1996 ) and others have used an apparently similar probabilistic argument, the ‘Doomsday Argument’, to claim that conventional predictions of longevity must be adjusted, based on Bayes’s Theorem, in favor of shorter longevities. Here I show that Gott’s arguments are flawed and contradictory, but that one of his conclusions is plausible (...) and mathematically equivalent to Laplace’s famous—and notorious—‘rule of succession’. On the other hand, the Doomsday Argument, though it appears consistent with some common‐sense grains of truth, is fallacious; the argument’s key error is to conflate future longevity and total longevity. Applying the work of Hill ( 1968 ) and Coolen ( 1998 , 2006 ) in the field of nonparametric predictive inference, I propose an alternative argument for quantifying how past longevity of a phenomenon does provide evidence for future longevity. In so doing, I identify an objective standard by which to choose among counting time intervals, counting population, or counting any other measure of past longevity in predicting future longevity. *Received May 2007; revised October 2008. †To contact the author, please e‐mail: pos@ronpisaturo.com. (shrink)

Bayesian conceptions of evidence have been invoked in recent arguments regarding the existence of God, the hypothesis of multiple physical universes, and the Doomsday Argument. Philosophers writing on these topics often claim that, given a Bayesian account of evidence, our existence or something entailed by our existence (perhaps in conjunction with some background knowledge or assumption) may serve as evidence for each of us. In this paper, I argue that this widespread view is mistaken. The mere fact of one's existence (...) qua conscious creature cannot serve as evidence on the standard Bayesian conception of evidence because knowledge of one's existence is a necessary part of the background knowledge relative to which all epistemic probabilities are defined. It follows that some formulations of the fine-tuning argument (for theism or a multiverse), the argument from consciousness (for theism) and a rejoinder to the Doomsday argument are mistaken. (shrink)

This paper attempts three tasks in relation to Carter and Leslie's Doomsday Argument. First, it criticises Timothy Chambers' 'Ussherian Corollary', a striking but unsuccessful objection to standard Doomsday arguments. Second, it reformulates the Ussherian Corollary as an objection to Bradley Monton's variant Doomsday and Nick Bostrom's Simulation Argument. Finally, it tries to diagnose the epistemic/metaphysical problems facing Doomsday-related arguments.1.

I discuss two versions of the doomsday argument. According to ``Gott's Line'',the fact that the human race has existed for 200,000 years licences the predictionthat it will last between 5100 and 7.8 million more years. According to ``Leslie'sWedge'', the fact that I currently exist is evidence that increases the plausibilityof the hypothesis that the human race will come to an end sooner rather than later.Both arguments rest on substantive assumptions about the sampling process thatunderlies our observations. These sampling assumptions have (...) testable consequences,and so the sampling assumptions themselves must be regarded as empirical claims.The result of testing some of these consequences is that both doomsday argumentsare empirically disconfirmed. (shrink)

A refutation of the doomsday argument is offered. Through a simple thought experiment analysed in Bayesian terms the fallacy is shown to be the assumption that a currently living person represents a random sample from the population of all persons who will ever have existed. A more general version of the counter argument is then given. Previous arguments that purport to answer this concern are also addressed. One result is determining criteria for the applicability of time sampling arguments, i.e., under (...) what conditions can a specific instant in time be regarded as a random sample from a time span. Given this new understanding, the incredible consequences of the doomsday and related arguments evaporate. (shrink)

John Leslie's Doomsday argument uses the frequency interpretation of probability to argue that the end of the universe is closer than we might have thought. Oh well - all the worse for the frequency interpretation.

Leslie’s doomsday argument purports to show that the likelihood of the human race perishing soon is greater than we think. The probability we attach to it, based on our estimate of the chance of various calamities which might bring extinction about (a nuclear holocaust, an ecological disaster, etc.), should be adjusted as follows. If the human race were to survive for a long time, we, livingnow, would be atypical. So our living now increases the probability that the human race will (...) end shortly. In this paper, I criticize some attempts to rebut the argument, and present my own. To facilitate the analysis, I consider a structurally similar problem, the “ShootingRoom.”. (shrink)

When somebody, human or not, performs an observation, this action itself implies that there be an actor: the observer. Moreover, such observation is carried out from the particular vantage point of the observer, i.e. the observer’s unique spacetime location. The standpoint of the viewer and indeed her mere existence result in observational biases which may be relevant in the appraisal of the gained data. These effects are called observation selection effects (OSE). In his Anthropic Bias: Observation Selection Effects in Science (...) and Philosophy, Nick Bostrom attempts to analyze these effects both in scientific contexts as well as in philosophical debates and to construct a theory of these selection effects. His aim is to first develop a methodology of how to deal with OSE and second, to apply the theory to scientific and philosophical problems where such effects are pertinent. Among others, observation selection biases have applications to the fine-tuning problem in modern cosmology, the issue of time’s arrow in thermodynamics, the debate on the likelihood of the evolution of intelligent life on Earth in evolutionary biology, and why you tend to end up in the slowest lane when driving home. Bostrom illustrates beautifully why, how, and to what extent OSE have implications for all these and many more problems. In his ingenious analysis of extant arguments pertaining to these issues or to the infamous Doomsday argument, he operates within a strictly Bayesian framework of belief revision. (shrink)

In Baumann (American Philosophical Quarterly 42: 71–79, 2005) I argued that reflections on a variation of the Monty Hall problem throws a very general skeptical light on the idea of single-case probabilities. Levy (Synthese, forthcoming, 2007) puts forward some interesting objections which I answer here.

Bovens (2010) points out that there is a structural analogy between the Judy Benjamin problem (JB) and the Sleeping Beauty problem (SB). On grounds of this structural analogy, he argues that both should receive the same solution, viz. the posterior probability of the eastern region of the matrix in Table 1 should equal 1/3. Hence, P*(Red) = 1/3 in the JB and P*(Heads) = 1/3 in the SB. Bovens’s argument rests on a standard error in implementing Bayesian updating, which is (...) spelled out in Shafer 1985. When we are informed of some proposition, we do not only learn the proposition in question, but also that we have learned the proposition as one of the many propositions that we might have learned. The information is generated by a protocol, which determines the various propositions that we might learn. We should then update not on the proposition in question, but rather on the fact that we learned this proposition as one of the many propositions that we might have learned. A well-known application of this insight is the Monty Hall problem (MH) as Speed (1985: 276) points out in a discussion of Shafer 1985. As an illustration, let us apply Shafer’s insight to the MH. In the MH, the contestant in a game show learns that there is a goat behind two of three doors X, Y and Z and a car behind one door. She is asked to pick one of the three doors. The contestant picks door X. Monty will then open one of the remaining doors, which he knows to have a goat behind it. Suppose Monty opens door Y. The contestant is then asked whether she wants to …. (shrink)

In sum, then, Chalmers’s attempt to argue against physicalism based on the conceivability of zombies misses the mark. His version of conceivability does indeed imply possibility, but at the cost of making it unclear whether zombies are indeed conceivable.

Despite the results of David Lewis, Peter Gärdenfors, and others, showing that imaging and classical conditionalization coincide only in the most trivial probabilistic models of belief revision, it turns out that imaging on a proposition A can always be described via Popper function conditionalization on a proposition that entails A. This result generalizes to any method of belief revision meeting certain minimal requirements. The proof is illustrated by an application of imaging in the context of the Monty Hall Problem.

Likelihoodists and Bayesians seem to have a fundamental disagreement about the proper probabilistic explication of relational (or contrastive) conceptions of evidential support (or confirmation). In this paper, I will survey some recent arguments and results in this area, with an eye toward pinpointing the nexus of the dispute. This will lead, first, to an important shift in the way the debate has been couched, and, second, to an alternative explication of relational support, which is in some sense a "middle way" (...) between Likelihoodism and Bayesianism. In the process, I will propose some new work for an old probability puzzle: the "Monty Hall" problem. (shrink)

This research examined choice behaviour and probability judgement in a counterintuitive reasoning problem called the Monty Hall problem (MHP). In Experiments 1 and 2 we examined whether learning from a simulated card game similar to the MHP affected how people solved the MHP. Results indicated that the experience with the card game affected participants' choice behaviour, in that participants selected to switch in the MHP. However, it did not affect their understanding of the objective probabilities. This suggests that there is (...) dissociation between implicit knowledge gained from the task and the explicit understanding as to why switching was the best strategy. In Experiment 3, the number of prizes and doors were manipulated to examine how participants construed the problem space of the MHP. Results revealed that participants partition the probability judgement to reflect the number of prizes over the number of unopened doors. (shrink)

The Monty Hall Problem (MHP), a process of two-stage decision making, was presented in atypical form via a custom software game. Differing from the normal three-box MHP, the game added one additional box on-screen for each game—culminating on game 23 with 25 on-screen boxes to initially choose from. A total of 108 participants played 23 games (trials) in one of four conditions; (1) “Vanish” condition—all non-winning boxes totally removed from the screen; (2) “Empty” condition—all non-winning boxes remain on-screen, but with (...) an “empty” label on them; (3) “Steroids” condition—all non-winning boxes removed from the screen, with initially chosen box becoming 25% larger; (4) “Steroids2” condition—all non-winning boxes removed from the screen, box not currently chosen becomes 25% larger. Results indicate second-stage on-screen presence of boxes influences switching; with their absence having the opposite effect. Size manipulation appears to elicit demand characteristics resulting in indeterminate influence. (shrink)

Peter Baumann uses the Monty Hall game to demonstrate that probabilities cannot be meaningfully applied to individual games. Baumann draws from this first conclusion a second: in a single game, it is not necessarily rational to switch from the door that I have initially chosen to the door that Monty Hall did not open. After challenging Baumann’s particular arguments for these conclusions, I argue that there is a deeper problem with his position: it rests on the false assumption that what (...) justifies the switching strategy is its leading me to win a greater percentage of the time. In fact, what justifies the switching strategy is not any statistical result over the long run but rather the “causal structure” intrinsic to each individual game itself. Finally, I argue that an argument by Hilary Putnam will not help to save Baumann’s second conclusion above. (shrink)

The application of probabilistic arguments to rational decisions in a single case is a contentious philosophical issue which arises in various contexts. Some authors (e.g. Horgan, Philos Pap 24:209–222, 1995; Levy, Synthese 158:139–151, 2007) affirm the normative force of probabilistic arguments in single cases while others (Baumann, Am Philos Q 42:71–79, 2005; Synthese 162:265–273, 2008) deny it. I demonstrate that both sides do not give convincing arguments for their case and propose a new account of the relationship between probabilistic reasoning (...) and rational decisions. In particular, I elaborate a flaw in Baumann’s reductio of rational single-case decisions in a modified Monty Hall Problem. (shrink)

How can self-locating propositions be integrated into normal patterns of belief revision? Puzzles such as Sleeping Beauty seem to show that such propositions lead to violation of ordinary principles for reasoning with subjective probability, such as Conditionalization and Reflection. I show that sophisticated forms of Conditionalization and Reflection are not only compatible with self-locating propositions, but also indispensable in understanding how they can function as evidence in Sleeping Beauty and similar cases.

The Sleeping Beauty problem is test stone for theories about self- locating belief, i.e. theories about how we should reason when data or theories contain indexical information. Opinion on this problem is split between two camps, those who defend the “1/2 view” and those who advocate the “1/3 view”. I argue that both these positions are mistaken. Instead, I propose a new “hybrid” model, which avoids the faults of the standard views while retaining their attractive properties. This model appears to (...) violate Bayesian conditionalization, but I argue that this is not the case. By paying close attention to the details of conditionalization in contexts where indexical information is relevant, we discover that the hybrid model is in fact consistent with Bayesian kinematics. If the proposed model is correct, there are important lessons for the study of self-location, observation selection theory, and anthropic reasoning. (shrink)

Bovens (2010) points out that there is a structural analogy between the Judy Benjamin problem (JB) and the Sleeping Beauty problem (SB). On grounds of this structural analogy, he argues that both should receive the same solution, viz. the posterior probability of the eastern region of the matrix in Table 1 should equal 1/3. Hence, P*(Red) = 1/3 in the JB and P*(Heads) = 1/3 in the SB. Bovens’s argument rests on a standard error in implementing Bayesian updating, which is (...) spelled out in Shafer 1985. When we are informed of some proposition, we do not only learn the proposition in question, but also that we have learned the proposition as one of the many propositions that we might have learned. The information is generated by a protocol, which determines the various propositions that we might learn. We should then update not on the proposition in question, but rather on the fact that we learned this proposition as one of the many propositions that we might have learned. A well-known application of this insight is the Monty Hall problem (MH) as Speed (1985: 276) points out in a discussion of Shafer 1985. As an illustration, let us apply Shafer’s insight to the MH. In the MH, the contestant in a game show learns that there is a goat behind two of three doors X, Y and Z and a car behind one door. She is asked to pick one of the three doors. The contestant picks door X. Monty will then open one of the remaining doors, which he knows to have a goat behind it. Suppose Monty opens door Y. The contestant is then asked whether she wants to …. (shrink)

(No abstract is available for this citation).

The Puzzle of the Hats is a betting arrangement which seems to show that a Dutch book can be made against a group of rational players with common priors who act in the common interest and have full trust in the other players’ rationality. But we show that appearances are misleading—no such Dutch book can be made. There are four morals. First, what can be learned from the puzzle is that there is a class of situations in which credences and (...) betting rates diverge. Second, there is an analogy between ways of dealing with situations of this kind and different policies for sequential choice. Third, there is an analogy with strategic voting, showing that the common interest is not always served by expressing how things seem to you in social decision-making. And fourth, our analysis of the Puzzle of the Hats casts light on a recent controversy about the Dutch book argument for the Sleeping Beauty. (shrink)

I argue that four problems that appear to be very different have the same structure. I give a unified treatment of the Doomsday Argument, Sleeping Beauty, the Fine-tuning Argument and confirmation in the Everett interpretation of quantum mechanics. All these cases involve self-locating evidence. However, the confusing feature of all these cases is not self-location, but observation selection effects. I explain how observation selection effects operate, why they affect the four problem cases, and how they can be incorporated into confirmation (...) theory. I will defend the Doomsday Argument, the halfer position in Sleeping Beauty, the Fine-tuning Argument and the applicability of confirmation theory to the Everett interpretation of quantum mechanics. (shrink)

How do temporal and eternal beliefs interact? I argue that acquiring a temporal belief should have no effect on eternal beliefs for an important range of cases. Thus, I oppose the popular view that new norms of belief change must be introduced for cases where the only change is the passing of time. I defend this position from the purported counter-examples of the Prisoner and Sleeping Beauty. I distinguish two importantly different ways in which temporal beliefs can be acquired and (...) draw some general conclusions about their impact on eternal beliefs. (shrink)

There has been much recent discussion about how to model agents who learn selflocating beliefs. I argue that there are two different ways self-locating beliefs can be learnt. One of these ways – which I call belief mutation – is unique to selflocating beliefs, and presents a challenge to conditionalization. I defend conditionalization from purported violations in the Prisoner and Sleeping Beauty thought experiments, and argue that belief mutation should never change an agent’s degree of belief in any non-self-locating proposition.

Sometimes we learn what the world is like, and sometimes we learn where in the world we are. Are there any interesting differences between the two kinds of cases? The main aim of this article is to argue that learning where we are in the world brings into view the same kind of observation selection effects that operate when sampling from a population. I will first explain what observation selection effects are ( Section 1 ) and how they are relevant (...) to learning where we are in the world ( Section 2 ). I will show how measurements in the Many Worlds Interpretation of quantum mechanics can be understood as learning where you are in the world via some observation selection effect ( Section 3 ). I will apply a similar argument to the Sleeping Beauty Problem ( Section 4 ) and explain what I take the significance of the analogy to be ( Section 5 ). Finally, I will defend the Restricted Principle of Indifference on which some of my arguments depend ( Section 6 ). (shrink)

Beauty is about to be drugged, rendering her unconscious for a long time. During that time she will be awakened briefly, either once (on Monday) or twice (on Monday and Tuesday). The number of awakenings depends on the toss of a fair coin: if the result is Tails, she is awakened twice: if Heads, once. The nature of the drug is that she will not remember being awake. In particular, when she is awakened, she will not know whether it is (...) Monday or Tuesday. Upon awakening on Monday, what should her degree of belief be that the coin landed Heads? (shrink)

If an agent believes that the probability of E being true is 1/2, should she accept a bet on E at even odds or better? Yes, but only given certain conditions. This paper is about what those conditions are. In particular, we think that there is a condition that has been overlooked so far in the literature. We discovered it in response to a paper by Hitchcock (2004) in which he argues for the 1/3 answer to the Sleeping Beauty problem. (...) Hitchcock argues that this credence follows from calculating her fair betting odds, plus the assumption that Sleeping Beauty’s credences should track her fair betting odds. We will show that this last assumption is false. Sleeping Beauty’s credences should not follow her fair betting odds due to a peculiar feature of her epistemic situation. (shrink)

According to the Doomsday Argument we have to rethink the probabilities we assign to a soon or not so soon extinction of mankind when we realize that we are living now, rather early in the history of mankind. Sleeping Beauty finds herself in a similar predicament: on learning the date of her first awakening, she is asked to re-evaluate the probabilities of her two possible future scenarios. In connection with Doom, I argue that it is wrong to assume that our (...) ordinary probability judgements do not already reflect our place in history: we justify the predictive use we make of the probabilities yielded by science (or other sources of information) by our knowledge of the fact that we live now, a certain time before the possible occurrence of the events the probabilities refer to. Our degrees of belief should change drastically when we forget the date—importantly, this follows without invoking the “Self Indication Assumption”. Subsequent conditionalization on information about which year it is cancels this probability shift again. The Doomsday Argument is about such probability shifts, but tells us nothing about the concrete values of the probabilities—for these, experience provides the only basis. Essentially the same analysis applies to the Sleeping Beauty problem. I argue that Sleeping Beauty “thirders” should be committed to thinking that the Doomsday Argument is ineffective; whereas “halfers” should agree that doom is imminent—but they are wrong. (shrink)

Let me regale you with yet another variant of the story of Sleeping Beauty. In this one, the experiment takes place in a room with a skylight, so that Beauty can see what the weather is like outside as soon as she wakes up. The weather can be in any one of n different states on any given day. Beauty regards each of these states as equiprobable; moreover, she takes there to be no correlation between the weather on Monday and (...) the weather on Tuesday, or between the weather on either day and the coin-toss. The rest of the story works as usual: Beauty will be awoken on Monday, after which a coin will be tossed; if it lands Tails, she will be woken again on Tuesday having had her memories of the Monday awakening erased; otherwise, she will stay asleep until Wednesday.1 The weather is the only source of variation in her wakings, so if it should happen that the weather is the same on Monday and Tuesday, her total evidence will be exactly the same on both wakings. (shrink)

Argues for the "thirder" solution to the Sleeping Beauty puzzle. The argument turns on an analogy with a variant case, in which a coin-toss on Monday night determines whether one's memories of Monday are permanently erased, or merely suspended in such a way that they will return some time after one wakes up on Tuesday.

Philosophical interest in the role of self-locating information in the confirmation of hypotheses has intensified in virtue of the Sleeping Beauty problem. If the correct solution to that problem is 1/3, various attractive views on confirmation and probabilistic reasoning appear to be undermined; and some writers have used the problem as a basis for rejecting some of those views. My interest here is in two such views. One of them is the thesis that self-locating information cannot be evidentially relevant to (...) a non-self-locating hypothesis. The other, a basic tenet of Bayesian confirmation theory, is the thesis that an ideally rational agent updates her credence in a non-self-locating hypothesis in response to new information only by conditionalization. I argue that we can disprove these two theses by way of cases that are much less puzzling than Sleeping Beauty. I present two such cases in this paper. (shrink)

The probability puzzle known as "Sleeping Beauty" raises interesting and difficult ques tions about the nature of evidence. It appears that the puzzle itself has already been solved, for there is a near consensus in the relevant philosophical literature that 1/3 is the correct answer.' Be that as it may, no new argument for that result is offered here. Instead, an at tempt is made to clarify the nature of certain problems that an answer of 1/3 raises for theories of (...) evidential reasoning. Various approaches to solving the most important of these problems are then considered and rejected. (shrink)

Hitchcock advances a diachronic Dutch Book argument (DDB) for a 1/3 answer to the Sleeping Beauty problem. Bradley and Leitgeb argue that Hitchcock’s DDB argument fails. We demonstrate the following: (a) Bradley and Leitgeb’s criticism of Hitchcock is unconvincing; (b) nonetheless, there are serious reasons to worry about the success of Hitchcock’s argument; (c) however, it is possible to construct a new DDB for 1/3 about which such worries cannot be raised.

Dr. Evil learns that a duplicate of Dr. Evil has been created. Upon learning this, how seriously should he take the hypothesis that he himself is that duplicate? I answer: very seriously. I defend a principle of indifference for self-locating belief which entails that after Dr. Evil learns that a duplicate has been created, he ought to have exactly the same degree of belief that he is Dr. Evil as that he is the duplicate. More generally, the principle shows that (...) there is a sharp distinction between ordinary skeptical hypotheses, and self-locating skeptical hypotheses. (shrink)

In addition to being uncertain about what the world is like, one can also be uncertain about one’s own spatial or temporal location in the world. My aim is to pose a problem arising from the interaction between these two sorts of uncertainty, solve the problem, and draw two lessons from the solution.

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.. (shrink)

Six variations on the Sleeping beauty theme are presented.

I describe in this paper a solution to the Sleeping Beauty problem. I begin with the consensual emerald case and discuss then Bostrom's Incubator gedanken. I address then the Sleeping Beauty problem. I argue that the root cause of the flaw in the argument for 1/3 is an erroneous assimilation with a repeated experiment. I show that the same type of analysis also applies to Elga's version of the argument for 1/3. Lastly, I show that the core of the Sleeping (...) Beauty problem is related to the problem of world reduction. (shrink)

I describe in this paper an ontological solution to the Sleeping Beauty problem. I begin with describing the Entanglement urn experiment. I restate first the Sleeping Beauty problem from a wider perspective than the usual opposition between halfers and thirders. I also argue that the Sleeping Beauty experiment is best modelled with the Entanglement urn. I draw then the consequences of considering that some balls in the Entanglement urn have ontologically different properties form normal ones. The upshot is that I (...) endorse the halfer conclusion on the probability of Heads once beauty is awaken and the thirder conclusion on conditional probabilities, and that original conclusions ensue on the probability of waking on Monday. (shrink)

I present a solution to the Sleeping Beauty problem. I begin with the consensual emerald case and describe then a set of relevant urn analogies and situations. These latter experiments make it easier to diagnose the flaw in the thirder's line of reasoning. I discuss in detail the root cause of the flaw in the argument for 1/3 which is an erroneous assimilation with a repeated experiment. Lastly, I discuss an informative variant of the original Sleeping Beauty experiment that casts (...) light on the diagnosis of the fallacy in the argument for 1/3. (shrink)

The way a rational agent changes her belief in certain propositions/hypotheses in the light of new evidence lies at the heart of Bayesian inference. The basic natural assumption, as summarized in van Fraassen's Reflection Principle ([1984]), would be that in the absence of new evidence the belief should not change. Yet, there are examples that are claimed to violate this assumption. The apparent paradox presented by such examples, if not settled, would demonstrate the inconsistency and/or incompleteness of the Bayesian approach, (...) and without eliminating this inconsistency, the approach cannot be regarded as scientific. The Sleeping Beauty Problem is just such an example. The existing attempts to solve the problem fall into three <span class='Hi'>categories</span>. The first two share the view that new evidence is absent, but differ about the conclusion of whether Sleeping Beauty should change her belief or not, and why. The third category is characterized by the view that, after all, new evidence (although hidden from the initial view) is involved. My solution is radically different and does not fall into either of these <span class='Hi'>categories</span>. I deflate the paradox by arguing that the two different degrees of belief presented in the Sleeping Beauty Problem are in fact beliefs in two different propositions, i.e., there is no need to explain the (un)change of belief. The Sleeping Beauty Problem The Problem Deflated 2.1 From contradiction to consistency 2.2 The inanimate version 2.3 Back to SB Summary CiteULike Connotea Del.icio.us What's this? (shrink)

The best arguments for the 1/3 answer to the Sleeping Beauty problem all require that when Beauty awakes on Monday she should be uncertain what day it is. I argue that this claim should be rejected, thereby clearing the way to accept the 1/2 solution.

In the Sleeping Beauty problem, Beauty is uncertain whether the outcome of a certain coin toss was heads or tails. One argument suggests that her degree of belief in heads should be 1/3, while a second suggests that it should be 1/2. Prima facie, the argument for 1/2 appears to be stronger. I offer a diachronic Dutch Book argument in favor of 1/3. Even for those who are not routinely persuaded by diachronic Dutch Book arguments, this one has some important (...) morals. (shrink)

I maintain, in defending “thirdism,” that Sleeping Beauty should do Bayesian updating after assigning the “preliminary probability” 1/4 to the statement S: “Today is Tuesday and the coin flip is heads.” (This preliminary probability obtains relative to a specific proper subset I of her available information.) Pust objects that her preliminary probability for S is really zero, because she could not be in an epistemic situation in which S is true. I reply that the impossibility of being in such an (...) epistemic situation is irrelevant, because relative to I, statement S nonetheless has degree of evidential support 1/4. (shrink)

1. The story of Sleeping Beauty is set forth as follows by Dorr (2002): Sleeping Beauty is a paradigm of rationality. On Sunday she learns for certain that she is to be the subject of an experiment. The experimenters will wake her up on Monday morning, and tell her some time later that it is Monday. When she goes back to sleep, they will toss a fair coin. If the outcome of the toss is Heads, they will do nothing. If (...) the outcome is Tails, they will administer a drug whose effect is to destroy all memories from the previous day, so that when she wakes up on Tuesday, she will be unable to tell [1] that it is not Monday. (2002: 292) Let HEADS be the hypothesis that the coin lands heads, and let TAILS be the hypothesis that it lands tails. The Sleeping Beauty Problem is this. When Sleeping Beauty finds herself awakened by the experimenters, with no memory of a prior awakening and with no ability to tell whether or not it is Monday, what probabilities should she assign to HEADS and TAILS respectively? Elga (2000) maintains that when she is awakened, P(HEADS) = 1/3 and P(TAILS) = 2/3. He offers the following intuitively plausible argument (2000: 143 4). If the experiment were performed many times, then over the long run about 1/3 of the awakenings would happen on trials in which the coin lands heads, and about 2/3 on trials in which it lands tails. So in the present circumstance in which the experiment is performed just once, P(HEADS) = 1/3 and.. (shrink)

We present a new argument for the claim that in the Sleeping Beauty problem, the probability that the coin comes up heads is 1/3. Our argument depends on a principle for the updating of probabilities that we call ‘generalized conditionalization’, and on a species of generalized conditionalization we call ‘synchronic conditionalization on old information’. We set forth a rationale for the legitimacy of generalized conditionalization, and we explain why our new argument for thirdism is immune to two attacks that Pust (...) (Synthese 160:97–101, 2008) has leveled at other arguments for thirdism. (shrink)