How should you take into account the opinions of an advisor? When you completely defer to the advisor's judgment, then you should treat the advisor as a guru. Roughly, that means you should believe what you expect she would believe, if supplied with your extra evidence. When the advisor is your own future self, the resulting principle amounts to a version of the Reflection Principle---a version amended to handle cases of information loss. When you count an advisor as an epistemic (...) peer, you should give her conclusions the same weight as your own. Denying that view---call it the ``equal weight view''---leads to absurdity: the absurdity that you could reasonably come to believe yourself to be an epistemic superior to an advisor simply by noting cases of disagreement with her, and taking it that she made most of the mistakes. Accepting the view seems to lead to another absurdity: that one should suspend judgment about everything that one's smart and well-informed friends disagree on, which means suspending judgment about almost everything interesting. But despite appearances, the equal weight view does not have this absurd consequence. Furthermore, the view can be generalized to handle cases involving not just epistemic peers, but also epistemic superiors and inferiors. (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.

Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue that their purported confirmation largely relies on a methodology that depends on premises that are inconsistent with the claim that people are Bayesian about learning and inference. Bayesian models in cognitive science derive their appeal from their normative claim that the modeled inference is in some sense rational. Standard accounts of the rationality of Bayesian inference imply predictions that an agent selects the option that maximizes the (...) posterior expected utility. Experimental confirmation of the models, however, has been claimed because of groups of agents that probability match the posterior. Probability matching only constitutes support for the Bayesian claim if additional unobvious and untested (but testable) assumptions are invoked. The alternative strategy of weakening the underlying notion of rationality no longer distinguishes the Bayesian model uniquely. A new account of rationality—either for inference or for decision-making—is required to successfully confirm Bayesian models in cognitive science. (shrink)

Expected accuracy arguments have been used by several authors (Leitgeb and Pettigrew, and Greaves and Wallace) to support the diachronic principle of conditionalization, in updates where there are only finitely many possible propositions to learn. I show that these arguments can be extended to infinite cases, giving an argument not just for conditionalization but also for principles known as ‘conglomerability’ and ‘reflection’. This shows that the expected accuracy approach is stronger than has been realized. I also argue that we should (...) be careful to distinguish diachronic update principles from related synchronic principles for conditional probability. (shrink)

Bayesianism is a collection of positions in several related fields, centered on the interpretation of probability as something like degree of belief, as contrasted with relative frequency, or objective chance. However, Bayesianism is far from a unified movement. Bayesians are divided about the nature of the probability functions they discuss; about the normative force of this probability function for ordinary and scientific reasoning and decision making; and about what relation (if any) holds between Bayesian and non-Bayesian concepts.

We explore the question of whether cost-free uncertain evidence is worth waiting for in advance of making a decision. A classical result in Bayesian decision theory, known as the value of evidence theorem, says that, under certain conditions, when you update your credences by conditionalizing on some cost-free and certain evidence, the subjective expected utility of obtaining this evidence is never less than the subjective expected utility of not obtaining it. We extend this result to a type of update method, (...) a variant of Judea Pearl’s virtual conditionalization, where uncertain evidence is represented as a set of likelihood ratios. Moreover, we argue that focusing on this method rather than on the widely accepted Jeffrey conditionalization enables us to show that, under a fairly plausible assumption, gathering uncertain evidence not only maximizes expected pragmatic utility, but also minimizes expected epistemic disutility. (shrink)

I formulate and tentatively defend the view that we cannot be rationally required to have one type of doxastic attitude (e.g., beliefs, credences, imprecise credences, etc.) because we have another type; in other words, we can only be required to have, say, given credences because we have some other credences already. I explore an argument that appeals to the idea that there is no good reasoning from one type to the other type. I consider some important possible responses, and conclude (...) by sketching a different argument for the same conclusion, appealing to the nature of inquiry and belief-formation. (shrink)

Many have the intuition that the right response to the Lottery Paradox is to deny that one can justifiably believe of even a single lottery ticket that it will lose. The paper shows that from any theory of justification that solves the paradox in accordance with this intuition, a theory not of that kind can be derived that also solves the paradox but is more conducive to our epistemic goal than the former. It is argued that currently there is no (...) valid reason not to give preference to the derived accounts over the accounts from which they come. (shrink)

Van Fraassen (1989) argues that Inference to the Best Explanation is incoherent in the sense that adopting it as a rule for belief change will make one susceptible to a dynamic Dutch book. The present paper argues against this. A strategy is described that allows us to infer to the best explanation free of charge.

The Eternal Coin is a fair coin has existed forever, and will exist forever, in a region causally isolated from you. It is tossed every day. How confident should you be that the Coin lands heads today, conditional on (i) the hypothesis that it has landed Heads on every past day, or (ii) the hypothesis that it will land Heads on every future day? I argue for the extremely counterintuitive claim that the correct answer to both questions is 1.

Many have claimed that whenever an investigation might provide evidence for a claim, it might also provide evidence against it. Similarly, many have claimed that your credence should never be on the edge of the range of credences that you think might be rational. Surprisingly, both of these principles imply that you cannot rationally be modest: you cannot be uncertain what the rational opinions are.

Assume that it is your evidence that determines what opinions you should have. I argue that since you should take peer disagreement seriously, evidence must have two features. (1) It must sometimes warrant being modest: uncertain what your evidence warrants, and (thus) uncertain whether you’re rational. (2) But it must always warrant being guided: disposed to treat your evidence as a guide. Surprisingly, it is very difficult to vindicate both (1) and (2). But diagnosing why this is so leads to (...) a proposal—Trust—that is weak enough to allow modesty but strong enough to yield many guiding features. In fact, I claim that Trust is the Goldilocks principle—for it is necessary and sufficient to vindicate the claim that you should always prefer to use free evidence. Upshot: Trust lays the foundations for a theory of disagreement and, more generally, an epistemology that permits self-doubt—a modest epistemology. (shrink)

There are many things—call them ‘experts’—that you should defer to in forming your opinions. The trouble is, many experts are modest: they’re less than certain that they are worthy of deference. When this happens, the standard theories of deference break down: the most popular (“Reflection”-style) principles collapse to inconsistency, while their most popular (“New-Reflection”-style) variants allow you to defer to someone while regarding them as an anti-expert. We propose a middle way: deferring to someone involves preferring to make any decision (...) using their opinions instead of your own. In a slogan, deferring opinions is deferring decisions. Generalizing the proposal of Dorst (2020a), we first formulate a new principle that shows exactly how your opinions must relate to an expert’s for this to be so. We then build off the results of Levinstein (2019) and Campbell-Moore (2020) to show that this principle is also equivalent to the constraint that you must always expect the expert’s estimates to be more accurate than your own. Finally, we characterize the conditions an expert’s opinions must meet to be worthy of deference in this sense, showing how they sit naturally between the too-strong constraints of Reflection and the too-weak constraints of New Reflection. (shrink)

The main question of this paper is: how do we manage to know what our own degrees of belief are? Section 1 briefly reviews and criticizes the traditional functionalist view, a view notably associated with David Lewis and sometimes called the theory-theory. I use this criticism to motivate the approach I want to promote. Section 2, the bulk of the paper, examines and begins to develop the view that we have a special kind of introspective access to our degrees of (...) belief. I give an initial assessment of the view by examining its compatibility with leading theories of introspection. And I identify a challenge for the view, and explain why I'm optimistic that the view can overcome it. (shrink)

Przedmowa Problematyka związana z zależnościami przyczynowymi, ich modelowaniem i odkrywa¬niem, po długiej nieobecności w filozofii i metodologii nauk, budzi współcześnie duże zainteresowanie. Wiąże się to przede wszystkim z dynamicznym rozwojem, zwłaszcza od lat 1990., technik obli¬czeniowych. Wypracowane w tym czasie sieci bayesowskie uznaje się za matematyczny język przyczynowości. Pozwalają one na daleko idącą auto¬matyzację wnioskowań, co jest także zachętą do podjęcia prób algorytmiza¬cji odkrywania przyczyn. Na potrzeby badań naukowych, które pozwalają na przeprowadzenie eksperymentu z randomizacją, standardowe metody ustalania zależności przyczynowych (...) opracowano na początku XX wieku. Zupełnie inaczej sprawa przedstawia się w przypadku badań nieeksperymentalnych, gdzie podobne rozwiązania pozostają kwestią przyszłości. Zadaniem tej książki jest podanie warunków, które powinny być spełnione przez te rozwiązania, oraz sformułowanie proceduralnego kryterium zależności przy¬czynowych jako szczegółowej realizacji tych warunków. Pociąga ono waż¬kie konsekwencje dla filozofii i metodologii nauk, które ujawnia – podany w Części II – zarys me-todolo¬gii proceduralnej. W literaturze przedmiotu brakuje w miarę wszechstronnego i systema¬tycznego omówie¬nia najnowszych filozoficznych i metodologicznych dys¬kusji na temat przy¬czynowości, co niech będzie wytłumaczeniem, dlaczego w niektórych punktach obecnej książki szczegółowo referuję trudno dos¬tępne teksty źró¬dłowe. Przymiotnik „proceduralny” używam tu w znaczeniu węższym niż Huw Price (w którego pracach właściwszy byłby termin „kryterialny”) dla podkre¬ślenia – zgodnie z łacińskim źródłosłowem procedo – że dla ustalenia przy¬czyny niezbędne jest podjęcie przez uczonych określonych interakcji z ba¬daną rzeczywistością. Zalążki zamysłu prezentowanego w tej książce przedstawiłem podczas warsztatów filozoficznych „Philosophy and Probability” w roku 2002, zor¬ganizowanych przez Instytut Filozofii Uniwersytetu w Konstancji. Wdzięczny jestem uczestnikom tych warsztatów za uwagi, a przede wszyst¬kim następującym osobom: Luc Bovens, Brandon Fitelson, Alan Hájek, Stephan Hartmann oraz Jon Williamson. Podczas międzynarodowej konferencji „Analytical Pragmatism”, zorgani¬zowanej w Lublinie w roku 2003 przez Wydział Filo¬zofii Katolickiego Uniwersytetu Lubelskiego, odniosłem swoją koncepcję do prac Nancy Cartwright. Szczególnie inspirujący okazał się komentarz Huw Price’a do mojego referatu i przeprowadzona z nim dysku¬sja. Ujęcie koncepcji metodologii proceduralnej na szerszym tle współ-czes¬nego nurtu empirystycznego w filozofii nauki przedstawiłem w roku 2004 podczas konferencji „5th Quadrennial Fellows Conference”, zorgani¬zowanej przez Instytut Filozofii Uniwersytetu Jagiellońskiego oraz Centrum Filozofii Nauki w Pittsburghu. Szczególnie pomocne w dalszych moich pra¬cach były uwagi Jamesa Bogena, Janet Kourany, Jamesa Lennoxa, Johna Nortona, Thomasa Bonka, Jana Woleńskiego i Johna Worralla, za które wyra¬żam swoją wdzięczność. Korpus książki powstał podczas mojego stażu w Centrum Filozofii Nauki w Pittsburghu, który odbyłem jako stypendysta Fundacji na Rzecz Nauki Polskiej w roku akademickim 2004-2005. Uczestniczyłem w tym czasie w życiu naukowym Centrum i w pracach badawczych zespołu z Instytutu Filozofii Uniwersytetu Carnegie-Mellon w Pittsburghu kierowanego przez Clarka Glymoura. Na jego ręce składam podziękowanie za wiele po¬mocnych uwag do moich wy¬stąpień oraz tekstów i za dyskusje przede wszystkim z nim samym i z jego najbliższymi współpracownikami: Peterem Spirtesem oraz Richardem Scheinesem, a także pozostałymi członkami tego zes¬połu, doktorantami i uczestnikami seminarium badawczego „Causality in the Social Sciences”. Za wieloletnie wsparcie, wielopłaszczyznowe inspiracje towarzyszące pi¬saniu tej książki, a także liczne pomocne uwagi do jej wcześniejszych wersji dziękuję przede wszystkim Księdzu Profesorowi Andrzejowi Bronkowi oraz Księdzu Profesorowi Józefowi Herbutowi, współprowadzącemu seminarium doktorskie w Katedrze Metodologii Nauk Katolickiego Uniwersytetu Lubel¬skiego im. Jana Pawła II, jak również pozostałym uczestnikom tego semina¬rium. Dziękuję mojej Żonie, dr Annie Kawalec za wiele wysiłku włożonego w ulepszenie redakcji – językowej i merytorycznej – obecnej książki. Książkę tę można czytać na kilka sposobów. Czytelnikom zainteresowa¬nym przede wszystkim prowadzeniem badań empirycznych polecałbym rozpoczęcie od Rozdziału 2. i kontynuację pozostałych rozdziałów Części I, a następnie Dodatków. Czytelnikom zainteresowanym problemami filozo¬fii i metodologii nauk polecałbym rozpoczęcie lektury książki od Części II i uzupełniającą lekturę Rozdziału 2., a następnie Wprowadzenia i Zakończenia. Czytelnikom mniej zainteresowanym zagadnieniami teoretycznymi pole¬całbym zapoznanie się z fascynującymi dziejami odkrycia przyczyn cholery przez Johna Snowa, które rekonstruuję w Rozdziale 1. W dalszej części nato¬miast polecałbym przejście do Wprowadzenia i Zakończenia, które w mniej specjalistyczny sposób przybliżają proponowane tu rozstrzygnięcia. Tekst książki nie był dotąd publikowany. Wyjątkiem są pewne fragmenty Rozdziału 8. oraz 9., które w zmienionej postaci ukazały się w Rocznikach Filozoficznych (Kawalec 2004). -/- Lublin, luty 2006 r. (shrink)

To clarify and illuminate the place of probability in science Ellery Eells and James H. Fetzer have brought together some of the most distinguished philosophers ...

The Institute Vienna Circle held a conference in Vienna in 2003, Cambridge and Vienna a?

Principles of expert deference say that you should align your credences with those of an expert. This expert could be your doctor, the objective chances, or your future self, after you've learnt something new. These kinds of principles face difficulties in cases in which you are uncertain of the truth-conditions of the thoughts in which you invest credence, as well as cases in which the thoughts have different truth-conditions for you and the expert. For instance, you shouldn't defer to your (...) doctor by aligning your credence in the de se thought 'I am sick' with the doctor's credence in that same de se thought. Here, I generalise principles of expert deference to handle these kinds of problem cases. (shrink)

An overview of what constitutes as lying and the epistemic and moral consequences lying incurs.

This thesis is about making decisions when we are uncertain about what will happen, how valuable it will be, and even how to make decisions. Even the most sure-footed amongst us are sometimes uncertain about all three, but surprisingly little attention has been given to the latter two. The three essays that constitute my thesis hope to do a small part in rectifying this problem. The first essay is about the value of finding out how to make decisions. Society spends (...) considerable resources funding people (like me) to research decision-making, so it is natural to wonder whether society is getting a good deal. This question is so shockingly underresearched that bedrock facts are readily discoverable, such as when this kind of information is valuable. My second essay concerns whether we can compare value when we are uncertain about value. Many people are in fact uncertain about value, and how we deal with this uncertainty hinges on these comparisons. I argue that value comparisons are only sometimes possible; I call this weak comparability. This essay is largely a synthesis of the literature, but I also present an argument which begins with a peculiar view of the self: it is as if each of us is a crowd of different people separated by time (but connected by continuity of experience). I’m not the first to endorse this peculiar view of the self, but I am the first to show how it supports the benign view that value is sometimes comparable. We may be uncertain of any decision rules, even those that would tell us how to act when we face uncertainty in decision rules. We may be uncertain of how to decide how to decide how to... And so on. If so, we might have to accept infinitely many decision rules just to make any mundane decision, such as whether to pick up a five-cent piece from the gutter. My third essay addresses this problem of regress. I think all of our decisions are forced: we must decide now or continue to deliberate. Surprisingly, this allows us to avoid the original problem. I call this solution “when forced, do your best”. (shrink)

This paper aims to open a dialogue between philosophers working in decision theory and operations researchers and engineers whose research addresses the topic of decision making under deep uncertainty. Specifically, we assess the recommendation to follow a norm of robust satisficing when making decisions under deep uncertainty in the context of decision analyses that rely on the tools of Robust Decision Making developed by Robert Lempert and colleagues at RAND. We discuss decision-theoretic and voting-theoretic motivations for robust satisficing, then use (...) these motivations to select among candidate formulations of the robust satisficing norm. We also discuss two challenges for robust satisficing: whether the norm might in fact derive its plausibility from an implicit appeal to probabilistic representations of uncertainty of the kind that deep uncertainty is supposed to preclude; and whether there is adequate justification for adopting a satisficing norm, as opposed to an optimizing norm that is sensitive to considerations of robustness. GPI Working Paper No. 15-2020. (shrink)

The probabilities a Bayesian agent assigns to a set of events typically change with time, for instance when the agent updates them in the light of new data. In this paper we address the question of how an agent's probabilities at different times are constrained by Dutch-book coherence. We review and attempt to clarify the argument that, although an agent is not forced by coherence to use the usual Bayesian conditioning rule to update his probabilities, coherence does require the agent's (...) probabilities to satisfy van Fraassen's [1984] reflection principle (which entails a related constraint pointed out by Goldstein [1983]). We then exhibit the specialized assumption needed to recover Bayesian conditioning from an analogous reflection-style consideration. Bringing the argument to the context of quantum measurement theory, we show that "quantum decoherence" can be understood in purely personalist terms---quantum decoherence (as supposed in a von Neumann chain) is not a physical process at all, but an application of the reflection principle. From this point of view, the decoherence theory of Zeh, Zurek, and others as a story of quantum measurement has the plot turned exactly backward. (shrink)

RÉSUMÉ: Bas van Fraassen soutient que le «Principe de Réflexion» constitue une contrainte sur la rationalité et il rattache ce principe à une sorte de volontarisme épistémologique, c’est-à-dire la doctrine selon laquelle la croyance est affaire de volonté. Je soutiens que la version du volontarisme qui est compatible à la fois avec le Principe de Réflexion et avec l’idée de van Fraassen que les jugements épistémiques constituent une forme d’engagement, est tout à fait différente du volontarisme qu’envisageaient William James ou (...) Blaise Pascal. En fait, la prétention de van Fraassen d’avoir établi une théorie libératrice et pragmatique de la connaissance est mise en question par sa propre thèse selon laquelle le Principe de Réflexion sert d’appui à son volontarisme. Van Fraassen ne peut sauver le Principe de Réflexion que dans le cadre d’une théorie de la connaissance plus restrictive que celle qu’il envisage. (shrink)

Modern theory of rationality has truly grown into a science of its own. Still, the general topic remained a genuinely philosophical one. This essay is concerned with giving a brief overview. Section 2 explains the fundamental scheme of all rationality assessments. With its help, a schematic order of the main questions concerning the theory of rationality can be given; the questions turn out to be quite unevenly addressed in the literature. Section 3 discusses the fundamental issue that the theory of (...) rationality seems to be both a normative and an empirical theory. Section 4, finally, shows how the unity of the theory of rationality can nevertheless be maintained. (shrink)

This paper revisits Richard Hare's classical and much discussed argument for preference utilitarianism, which relies on the conception of moral deliberation as a process of thought experimentation, with concomitant preference change. The paper focuses on an apparent gap in Hare's reasoning, the so-called No-Conflict Problem. A solution to this difficulty which was proposed in is re-examined and shown to lead to a number of difficulties. The paper therefore also considers an alternative idea, due to Daniel Elstein. This new proposal may (...) well turn out to be the best way of filling the gap in Hare's argument. The paper also examines whether the gap is there to begin with: The problem should perhaps be dissolved rather than solved. This suggestion goes back to an idea of Zeno Vendler. Unfortunately, it turns out that Vendler's move does not save Hare from criticism: It does dissolve the No-Conflict Problem, but at the same time it gives rise to another, potentially more serious difficulty. (shrink)

The Snow White problem is introduced to demonstrate how learning something of which one could not have learnt the opposite (due to observer selection bias) can change an agent’s probability assignment. This helps us to analyse the Sleeping Beauty problem, which is deconstructed as a combinatorial engine and a subjective wrapper. The combinatorial engine of the problem is analogous to Bertrand’s boxes paradox and can be solved with standard probability theory. The subjective wrapper is clarified using the Snow White problem. (...) Sample spaces for all three problems are presented. The conclusion is that subjectivity plays no irreducible role in solving the Sleeping Beauty problem and that no reference to centered worlds is required to provide the answer. (shrink)

My dissertation explores the ways in which Rudolf Carnap sought to make philosophy scientific by further developing recent interpretive efforts to explain Carnap’s mature philosophical work as a form of engineering. It does this by looking in detail at his philosophical practice in his most sustained mature project, his work on pure and applied inductive logic. I, first, specify the sort of engineering Carnap is engaged in as involving an engineering design problem and then draw out the complications of design (...) problems from current work in history of engineering and technology studies. I then model Carnap’s practice based on those lessons and uncover ways in which Carnap’s technical work in inductive logic takes some of these lessons on board. This shows ways in which Carnap’s philosophical project subtly changes right through his late work on induction, providing an important corrective to interpretations that ignore the work on inductive logic. Specifically, I show that paying attention to the historical details of Carnap’s attempt to apply his work in inductive logic to decision theory and theoretical statistics in the 1950s and 1960s helps us understand how Carnap develops and rearticulates the philosophical point of the practical/theoretical distinction in his late work, offering thus a new interpretation of Carnap’s technical work within the broader context of philosophy of science and analytical philosophy in general. (shrink)

The paper is essentially a short version Spohn "Strategic Rationality" which emphasizes in particular how the ideas developed there may be used to shed new light on the iterated prisoner's dilemma (and on iterated Newcomb's problem).

The paper argues that the standard decision theoretic account of strategies and their rationality or optimality is much too narrow, that strategies should rather condition future action to future decision situations (a point of view already developed in my Grundlagen der Entscheidungstheorie, sect. 4.4), that practical deliberation must therefore essentially rely on a relation of superiority and inferiority between possible future decision situations, that all this allows to substantially broaden the theory of practical rationality, that a long list of points (...) attended to in the literature can be subsumed under the broadened perspective (including a novel view on the iterated prisoner's dilemma and on iterated Newcomb's problem, which, however, is revised in Spohn (2003) "Dependency Equilibria and the Causal Structure of Decision and Game Situation"), and that the task to complete and systematize this list indeed forms a fruitful research programme. (shrink)

Bayesian Epistemology is a general framework for thinking about agents who have beliefs that come in degrees. Theories in this framework give accounts of rational belief and rational belief change, which share two key features: (i) rational belief states are represented with probability functions, and (ii) rational belief change results from the acquisition of evidence. This dissertation focuses specifically on the second feature. I pose the Evidence Question: What is it to have evidence? Before addressing this question we must have (...) an understanding of Bayesian Epistemology. The first chapter argues that we should understand Bayesian Epistemology as giving us theories that are evaluative and not action-guiding. I reach this verdict after considering the popular ‘ought’-implies-‘can’ objection to Bayesian Epistemology. The second chapter argues that it is important for theories in Bayesian Epistemology to answer the Evidence Question, and distinguishes between internalist and externalist answers. The third and fourth chapters present and defend a specific answer to the Evidence Question. The account is inspired by reliabilist accounts of justification, and attempts to understand what it is to have evidence by appealing solely to considerations of reliability. Chapter 3 explains how to understand reliability, and how the account fits with Bayesian Epistemology, in particular, the requirement that an agent’s evidence receive probability 1. Chapter 4 responds to objections, which maintain that the account gives the wrong verdict in a variety of situations including skeptical scenarios, lottery cases, scientific cases, and cases involving inference. After slight modifications, I argue that my account has the resources to answer the objections. The fifth chapter considers the possibility of losing evidence. I show how my account can model these cases. To do so, however, we require a modification to Conditionalization, the orthodox principle governing belief change. I present such a modification. The sixth and seventh chapters propose a new understanding of Dutch Book Arguments, historically important arguments for Bayesian principles. The proposal shows that the Dutch Book Arguments for implausible principles are defective, while the ones for plausible principles are not. The final chapter is a conclusion. (shrink)

About a decade ago, Adam Elga introduced philosophers to an intriguing puzzle. In it, Sleeping Beauty, a perfectly rational agent, undergoes an experiment in which she becomes ignorant of what time it is. This situation is puzzling for two reasons: First, because there are two equally plausible views about how she will change her degree of belief given her situation and, second, because the traditional rules for updating degrees of belief don't seem to apply to this case. In this dissertation, (...) my goals are to settle the debate concerning this puzzle and to offer a new rule for updating some types of degrees of belief. Regarding the puzzle, I will defend a view called "the Lesser view," a view largely favorable to the Thirders' position in the traditional debate on the puzzle. Regarding the general rule for updating, I will present and defend a rule called "Shifted Jeffrey Conditionalization." My discussions of the above view and rule will complement each other: On the one hand, I defend the Lesser view by making use of Shifted Jeffrey Conditionalization. On the other hand, I test Shifted Jeffrey Conditionalization by applying it to various credal transitions in the Sleeping Beauty problem and revise that rule in accordance with the results of the test application. In the end, I will present and defend an updating rule called "General Shifted Jeffrey Conditionalization," which I suspect is the general rule for updating one's degrees of belief in so-called tensed propositions. (shrink)

We introduce a family of rules for adjusting one's credences in response to learning the credences of others. These rules have a number of desirable features. 1. They yield the posterior credences that would result from updating by standard Bayesian conditionalization on one's peers' reported credences if one's likelihood function takes a particular simple form. 2. In the simplest form, they are symmetric among the agents in the group. 3. They map neatly onto the familiar Condorcet voting results. 4. They (...) preserve shared agreement about independence in a wide range of cases. 5. They commute with conditionalization and with multiple peer updates. Importantly, these rules have a surprising property that we call synergy - peer testimony of credences can provide mutually supporting evidence raising an individual's credence higher than any peer's initial prior report. At first, this may seem to be a strike against them. We argue, however, that synergy is actually a desirable feature and the failure of other updating rules to yield synergy is a strike against them. (shrink)

This paper discusses the possibility of autonomy in our epistemic lives, and the importance of the concept of the first person in weathering fluctuations in our epistemic perspective over time.

What kind of thing do you believe when you believe that you are in a certain place, that it is a certain time, and that you are a certain individual? What happens if you get lost, or lose track of the time? Can you ever be unsure of your own identity? These are the kind of questions considered in my thesis. Beliefs about where, when and who you are are what are called in the literature de se, or self-locating beliefs. (...) This thesis examines how we can represent de se beliefs, and how we can reason about de se uncertainty. In the first part of the thesis, I present and motivate a specific account of the content of de se belief, based on the one given by David Lewis. On this account, the content of de se beliefs are centred propositions. I defend this view against a rival account, put forward by Robert Stalnaker, according to whom the content of de se beliefs are ordinary propositions. In the second part of the thesis, I explore how we can reason probabilistically about de se uncertainty. I start by defining probabilities over centred propositions, and investigate what probabilities mean in this context. As it turns out, all the main interpretations of probability can be extended to centred propositions. The only trouble seems to arise for the Bayesian principle of updating via conditionalization. After giving a diagnosis of the problem, I offer a solution by formulating a natural extension of conditionalization, which I argue preserves the essential features of Bayesian reasoning. In the final chapter, I apply my view and show that it leads to a natural resolution of a puzzle that is generally taken to be a test case for any account of centred updating. (shrink)

An extended analysis is given of the program, originally suggested by Deutsch, of solving the probability problem in the Everett interpretation by means of decision theory. Deutsch's own proof is discussed, and alternatives are presented which are based upon different decision theories and upon Gleason's Theorem. It is argued that decision theory gives Everettians most or all of what they need from `probability'. Contact is made with Lewis's Principal Principle linking subjective credence with objective chance: an Everettian Principal Principle is (...) formulated, and shown to be at least as defensible as the usual Principle. Some consequences of (Everettian) quantum mechanics for decision theory itself are also discussed. -/- [NB: this this long (70 pages) and occasionally rambling online-only paper has been almost entirely superseded by material in subsequent; if something is not included in them it usually means that I have had second thoughts. I include it for completeness only. (shrink)

It is well known that de se (or ‘self-locating’) propositions complicate the standard picture of how we should respond to evidence. This has given rise to a substantial literature centered around puzzles like Sleeping Beauty, Dr. Evil, and Doomsday—and it has also sparked controversy over a style of argument that has recently been adopted by theoretical cosmologists. These discussions often dwell on intuitions about a single kind of case, but it’s worth seeking a rule that can unify our treatment of (...) all evidence, whether de dicto or de se. -/- This paper is about three candidates for such a rule, presented as replacements for the standard updating rule. Each rule stems from the idea that we should treat ourselves as a random sample, a heuristic that underlies many of the intuitions that have been pumped in treatments of the standard puzzles. But each rule also yields some strange results when applied across the board. This leaves us with some difficult options. We can seek another way to refine the random-sample heuristic, e.g. by restricting one of our rules. We can try to live with the strange results, perhaps granting that useful principles can fail at the margins. Or we can reject the random-sample heuristic as fatally flawed—which means rethinking its influence in even the simplest cases. (shrink)

It is important to have an adequate model of uncertainty, since decisions must be made before the uncertainty can be resolved. For instance, flood defenses must be designed before we know the future distribution of flood events. It is standardly assumed that probability theory offers the best model of uncertain information. I think there are reasons to be sceptical of this claim. I criticise some arguments for the claim that probability theory is the only adequate model of uncertainty. In particular (...) I critique Dutch book arguments, representation theorems, and accuracy based arguments. Then I put forward my preferred model: imprecise probabilities. These are sets of probability measures. I offer several motivations for this model of uncertain belief, and suggest a number of interpretations of the framework. I also defend the model against some criticisms, including the so-called problem of dilation. I apply this framework to decision problems in the abstract. I discuss some decision rules from the literature including Levi’s E-admissibility and the more permissive rule favoured by Walley, among others. I then point towards some applications to climate decisions. My conclusions are largely negative: decision making under such severe uncertainty is inevitably difficult. I finish with a case study of scientific uncertainty. Climate modellers attempt to offer probabilistic forecasts of future climate change. There is reason to be sceptical that the model probabilities offered really do reflect the chances of future climate change, at least at regional scales and long lead times. Indeed, scientific uncertainty is multi-dimensional, and difficult to quantify. I argue that probability theory is not an adequate representation of the kinds of severe uncertainty that arise in some areas in science. I claim that this requires that we look for a better framework for modelling uncertainty. (shrink)

This chapter presents a new semantics for inductive empirical knowledge. The epistemic agent is represented concretely as a learner who processes new inputs through time and who forms new beliefs from those inputs by means of a concrete, computable learning program. The agent’s belief state is represented hyper-intensionally as a set of time-indexed sentences. Knowledge is interpreted as avoidance of error in the limit and as having converged to true belief from the present time onward. Familiar topics are re-examined within (...) the semantics, such as inductive skepticism, the logic of discovery, Duhem’s problem, the articulation of theories by auxiliary hypotheses, the role of serendipity in scientific knowledge, Fitch’s paradox, deductive closure of knowability, whether one can know inductively that one knows inductively, whether one can know inductively that one does not know inductively, and whether expert instruction can spread common inductive knowledge—as opposed to mere, true belief—through a community of gullible pupils. (shrink)

According to the traditional Bayesian view of credence, its structure is that of precise probability, its objects are descriptive propositions about the empirical world, and its dynamics are given by conditionalization. Each of the three essays that make up this thesis deals with a different variation on this traditional picture. The first variation replaces precise probability with sets of probabilities. The resulting imprecise Bayesianism is sometimes motivated on the grounds that our beliefs should not be more precise than the evidence (...) calls for. One known problem for this evidentially motivated imprecise view is that in certain cases, our imprecise credence in a particular proposition will remain the same no matter how much evidence we receive. In the first essay I argue that the problem is much more general than has been appreciated so far, and that it’s difficult to avoid without compromising the initial evidentialist motivation. The second variation replaces descriptive claims with moral claims as the objects of credence. I consider three standard arguments for probabilism with respect to descriptive uncertainty—representation theorem arguments, Dutch book arguments, and accuracy arguments—in order to examine whether such arguments can also be used to establish probabilism with respect to moral uncertainty. In the second essay, I argue that by and large they can, with some caveats. First, I don’t examine whether these arguments can be given sound non-cognitivist readings, and any conclusions therefore only hold conditional on cognitivism. Second, decision-theoretic representation theorems are found to be less convincing in the moral case, because there they implausibly commit us to thinking that intertheoretic comparisons of value are always possible. Third and finally, certain considerations may lead one to think that imprecise probabilism provides a more plausible model of moral epistemology. The third variation considers whether, in addition to conditionalization, agents may also change their minds by becoming aware of propositions they had not previously entertained, and therefore not previously assigned any probability. More specifically, I argue that if we wish to make room for reflective equilibrium in a probabilistic moral epistemology, we must allow for awareness growth. In the third essay, I sketch the outline of such a Bayesian account of reflective equilibrium. Given that this account gives a central place to awareness growth, and that the rationality constraints on belief change by awareness growth are much weaker than those on belief change by conditionalization, it follows that the rationality constraints on the credences of agents who are seeking reflective equilibrium are correspondingly weaker. (shrink)

There is a growing interest in the foundations as well as the application of imprecise probability in contemporary epistemology. This dissertation is concerned with the application. In particular, the research presented concerns ways in which imprecise probability, i.e. sets of probability measures, may helpfully address certain philosophical problems pertaining to rational belief. The issues I consider are disagreement among epistemic peers, complete ignorance, and inductive reasoning with imprecise priors. For each of these topics, it is assumed that belief can be (...) modeled with imprecise probability, and thus there is a non-classical solution to be given to each problem. I argue that this is the case for peer disagreement and complete ignorance. However, I discovered that the approach has its shortcomings, too, specifically in regard to inductive reasoning with imprecise priors. Nevertheless, the dissertation ultimately illustrates that imprecise probability as a model of rational belief has a lot of promise, but one should be aware of its limitations also. (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.

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. -/- *****Note: For Bradley Monton's up-to-date thoughts on Gott's argument, see his _Philosophical Quarterly_ paper co-authored with Brian Kierland, "How To Predict Future Duration from Present Age".*****. (shrink)

I restate the Sleeping Beauty probabilistic paradox and offer an overview of the ongoing discussions that aim at resolving the problem. I summarize and eventually criticize briefly the various views: neutral position, Bayesian thirdism, non-Bayesian thirdism, traditional halfism, as well as the new halfism of credence conservation, a somewhat neglected but interesting point of view. I try at the same time to clarify some essential notions, to introduce the main actors of the debate, and to anticipate its evolution.

This is a defense of the doctrine of scientific realism. SR is defined through the following two claims: Most essential unobservables posited by the well-established current scientific theories exist independently of our minds. We know our well-established scientific theories to be approximately true. I first offer positive argumentation for SR. I begin with the so-called 'success arguments' for SR: 1) scientific theories most of the times entail successful predictions; 2) science is methodologically successful in generating empirically successful theories. SR explains (...) these facts via inference to the best explanation. I combine Hacking's experimental argument for entity realism with Salmon's common-cause principle. I take entity realism to be foundational to SR: one may believe in the existence of some theoretical entities without believing in any particular theory in which these are embedded. Its motivation comes from experimental practice, where the manipulation of these entities often relies on incompatible theoretical accounts. The underdetermination topic is thereafter discussed. Several attempts to distinguish between an observable and an unobservable realm are critically discussed, as well as the possibility that for any given theory, there are empirically equivalents generated by means of algorithms. I present extensive argumentation to the effect that such algorithmic rivals are not to be taken seriously. Social constructivism is being critically treated. I proceed by distinguishing between a metaphysical, a semantic, and an epistemic variant of SC. I conclude that only a moderate metaphysical constructivism can stand on its own feet. Its claim is merely that some facts about the world are socially constructed. I finish with a plea for a selective SR, able to do justice to the presence of both instrumentalism and modest constructivism in scientific practice. (shrink)