The thesis of instrumental convergence holds that a wide range of ends have common means: for instance, self preservation, desire preservation, self improvement, and resource acquisition. Bostrom contends that instrumental convergence gives us reason to think that "the default outcome of the creation of machine superintelligence is existential catastrophe". I use the tools of decision theory to investigate whether this thesis is true. I find that, even if intrinsic desires are randomly selected, instrumental rationality induces biases towards certain kinds of (...) choices. Firstly, a bias towards choices which leave less up to chance. Secondly, a bias towards desire preservation, in line with Bostrom's conjecture. And thirdly, a bias towards choices which afford more choices later on. I do not find biases towards any other of the convergent instrumental means on Bostrom's list. I conclude that the biases induced by instrumental rationality at best weakly support Bostrom's conclusion that machine superintelligence is likely to lead to existential catastrophe. (shrink)

This paper argues that expected utility theory for actions in chancy environments should be formulated in terms of centered chances. The subjective expected utility of an option A may be seen as a weighted sum of the utilities of A in different possible worlds, with weights being the credences that the agent assigns to these worlds. The utility of A in a given world is then definable as a weighted sum of the values of A’s different possible outcomes, with weights (...) being the conditional chances of these outcomes if A were performed. On the centered-chance view, the chances to be used as weights in the definition of utility are centered. Unlike ordinary chances, centered chances depend not only on what happens prior to the agent’s choice but also on the events that occur after the choice. Thus, to give an example, suppose that the action under consideration results in a bad outcome due to some event whose ordinary chance of occurring was very low at the time of choice. Then the utility of that action in the actual world could be high on the non-centered view, but on the centered view that utility is negative, since the centered chance of the event in question given the action was one, given that it did actually take place. A precise definition of centered chances is not easy to frame, but the concept can be made intuitively clear. The resulting decision theory is, in my opinion, philosophically more satisfactory than the extant proposals, even though it doesn’t differ much in its practical recommendations, with the exception of some rather peculiar cases. (shrink)

El texto que se presenta aquí es la segunda parte de la traducción de New Theory of Universalsy se corresponde con las últimas secciones del artículo original. En el anterior número de Ideas y Valores (157 de abril de 2015) se publicó la primera parte, que corresponde a la introducción y las dos primeras secciones del original.

Advocates of Causal Decision Theory (CDT) argue that Evidential Decision Theory (EDT) is inadequate because it gives the wrong result in Newcomb problems. Egan (2007) provides a recipe for converting Newcomb problems to counterexamples to CDT, arguing that CDT is inadequate too. Proposed by Edgington (2011), the Conditional Causal Decision Theory (CCDT) is widely taken uncritically in the recent literature as a version of CDT that conforms to the supposedly correct pre-theoretic judgments about the rationality of acts in Newcomb problems (...) and Egan cases. While the renewed attention to the debate between EDT and CDT is mostly focused on how various theories treat Egan cases, this paper scrutinizes Edgington’s treatment of Newcomb problems and argues that, despite Edgington’s promise, CCDT actually fails in Newcomb problems. This, I assume, shows that CCDT is superior neither to the classic visions of CDT nor to EDT. The paper concludes by arguing, in general, that CCDT is systematically bound to deliver the same results as does EDT in any decision problem. Despite being formulated in terms of causal notions, therefore, it collapses back into the classic EDT. (shrink)

The classic formulation of causal decision theory (CDT) appeals to counterfactuals. It says that you should aim to choose an option that would have a good outcome, were you to choose it. However, this version of CDT faces trouble if the laws of nature are deterministic. After all, the standard theory of counterfactuals says that, if the laws are deterministic, then if anything—including the choice you make—were different in the present, either the laws would be violated or the distant past (...) would be changed. And as several authors have shown, it's easy to transform this upshot of the standard theory of counterfactuals into full-blown counterexamples to CDT. In response to these counterexamples, I argue here that the problem lies, not so much with CDT's guiding idea—that it's the expected causal consequences of your actions that matter for rational decision-making—but with the fact that the classic formulation of CDT doesn't pay sufficient attention to the context-sensitivity of counterfactuals. I develop a contextualist version of CDT which better accounts for this context-sensitivity. And I show that my theory avoids the problems faced by the classic formulation of CDT in determinstic worlds. (shrink)

This volume concerns Rational Agents - humans, players in a game, software or institutions - which must decide the proper next action in an atmosphere of partial information and uncertainty. The book collects formal accounts of Uncertainty, Rationality and Agency, and also of their interaction. It will benefit researchers in artificial systems which must gather information, reason about it and then make a rational decision on which action to take.

This volume contains fifteen papers by Paul Humphreys, who has made important contributions to the philosophy of computer simulations, emergence, the philosophy of probability, probabilistic causality, and scientific explanation. It includes detailed postscripts to each section and a philosophical introduction. One of the papers is previously unpublished.

The role of causation and counterfactuals in causal decision theory is vexed and disputed. Recently, Brian Hedden (2023) argues that we should abandon causal decision theory in favour of an alternative: counterfactual decision theory. I argue that, pace Hedden, counterfactual decision theory is not a competitor to, but rather a version of, causal decision theory – the most popular version by far. I provide textual evidence that the founding fathers of causal decision theory (Stalnaker, Gibbard, Harper, Lewis, Skyrms, Sobel, and (...) Joyce) all endorse counterfactual decision theories. I additionally discuss why these theories came to be called ‘causal’, rather than ‘counterfactual’. And I argue that, properly understood, causal decision theory escapes Hedden's objections. (shrink)

Judging an act’s causal efficacy plays a crucial role in causal decision theory. A recent development appeals to the causal modeling framework with an emphasis on the analysis of intervention based on the causal Bayes net for clarifying what causally depends on our acts. However, few writers have focused on exploring the usefulness of extending structural causal models to decision problems that are not ideal for intervention analysis. The thesis concludes that structural models provide a more general framework for rational (...) decision makers. (shrink)

In Newcomb’s paradox you can choose to receive either the contents of a particular closed box, or the contents of both that closed box and another one. Before you choose though, an antagonist uses a prediction algorithm to accurately deduce your choice, and uses that deduction to fill the two boxes. The way they do this guarantees that you made the wrong choice. Newcomb’s paradox is that game theory’s expected utility and dominance principles appear to provide conflicting recommendations for what (...) you should choose. Here we show that the conflicting recommendations assume different probabilistic structures relating your choice and the algorithm’s prediction. This resolves the paradox: the reason there appears to be two conflicting recommendations is that the probabilistic structure relating the problem’s random variables is open to two, conflicting interpretations. We then show that the accuracy of the prediction algorithm in Newcomb’s paradox, the focus of much previous work, is irrelevant. We end by showing that Newcomb’s paradox is time-reversal invariant; both the paradox and its resolution are unchanged if the algorithm makes its ‘prediction’ after you make your choice rather than before. (shrink)

Until recently, many philosophers took Causal Decision Theory to be more successful than its rival, Evidential Decision Theory. Things have changed, however, with a renewed concern that cases involving an extreme form of decision instability are counterexamples to CDT :392–403, 1984; Egan in Philos Rev 116:93–114, 2007). Most prominent among those cases of extreme decision instability is the Psychopath Button, due to Andy Egan; in that case, CDT recommends a seemingly absurd act that almost certainly results in your death. This (...) renewed attention to decision instability has spurned an array of modifications to and rejections of CDT. I argue, however, that the Psychopath Button and its ilk are no counterexamples to CDT. That is, given the causalist’s commitments in Newcomb Problems, they already have the tools to justify CDT’s verdict in Egan-style cases of extreme decision instability. I first argue that there is no reason to think the Psychopath Button is a counterexample to CDT; in particular, many philosophers have placed too much weight on pre-theoretic intuition in Egan-cases, and apart from pre-theoretic intuition, arguments against CDT in cases of extreme decision instability are flawed. My second claim is that the causalist can provide good reasons for following CDT in cases of extreme decision instability. I present a new case, the Two Button Defense, that highlights precisely why the causalist can reasonably follow CDT in even the Psychopath Button. CDT therefore stands as a viable decision theory, without need for modification, restriction, or rejection. (shrink)

In October 1993, a survey of health care agency administrators was undertaken shortly after they had experienced two sudden reductions in public funding. The purpose of this investigation was to gain insight into the role of ethics in health administrator decision making. A mail questionnaire was designed for this purpose. Descriptive statistics and content analysis were used to summarize the data. Staff reductions and bed closures were the two most frequently reported mechanisms for addressing the funding reductions. Most administrators did (...) not believe that these changes would have a negative public impact. In contrast, the majority indicated that future changes in reaction to additional funding reductions would have a negative public impact. Approximately one-third of the administrators reported ethics to be an element of recent administrative decision making, and one-half could foresee that ethics would be important in the future if reductions continued. These findings are discussed in relation to ethics. Issues for additional research are outlined. (shrink)

The 'Why ain'cha rich?' argument for one-boxing in Newcomb's problem allegedly vindicates evidential decision theory and undermines causal decision theory. But there is a good response to the argument on behalf of causal decision theory. I develop this response. Then I pose a new problem and use it to give a new 'Why ain'cha rich?' argument. Unlike the old argument, the new argument targets evidential decision theory. And unlike the old argument, the new argument is sound.

Suppose that you have to take a test tomorrow but you do not want to study. Unfortunately you should study, since you care about passing and you expect to pass only if you study. Is there anything you can do to make it the case that you should not study? Is there any way for you to ‘rationalize’ slacking off? I suggest that such rationalization is impossible. Then I show that if evidential decision theory is true, rationalization is not only (...) possible but sometimes advisable. (shrink)

Expected-utility theory advances representation theorems that do not take the risk an act generates as a consequence of the act. However, a principle of expected-utility maximization that explains the rationality of preferences among acts must, for normative accuracy, take the act’s risk as a consequence of the act if the agent cares about the risk. I defend this conclusion against the charge that taking an act’s consequences to comprehend all the agent cares about trivializes the principle of expected-utility maximization.

There are arguments which purport to rebut psychological determinism by appealing to its alleged incompatibility with rationality. I argue that they all fail. Against Davidson, I argue that rationality does not preclude the existence of psychological laws. Against Popper, I argue that rationality is compatible with the possibility of predicting human actions. Against Schlesinger, I claim that Newcomb's problem cannot be invoked to show that human actions are unpredictable. Having vindicated the possibility of a rationally-based theory of action, I consider (...) the form it might take. (shrink)

Causal decision theory produces decision instability in cases such as Death in Damascus where a decision itself provides evidence concerning the utility of options. Several authors have proposed ways of handling this instability. William Harper (1985 and 1986) advances one of the most elegant proposals. He recommends maximizing causal expected utility among the options that are causally ratifiable. Unfortunately, Harper's proposal imposes certain restrictions; for instance, the restriction that mixed strategies are freely available. To obtain a completely general method of (...) handling decision instability, I step outside the confines of pure causal decision theory. I introduce a new kind of backtracking expected utility and propose maximizing it among the options that are causally ratifiable. In other words, I propose a hierarchical maximization of (1) conditional causal expected utility and (2) the new backtracking expected utility. I support this proposal with some intuitive considerations concerning the distinction between optimality and conditional optimality. And I prove that the proposal yields a solution in every finite decision problem. (shrink)

The expected utility of an option for a decision maker is defined with respect to probability and utility functions that represent the decision maker’s beliefs and desires. Therefore, as the decision maker’s beliefs and desires change, the expected utility of an opinion may change. Some options are such that their realizations change beliefs and desires in ways that change the expected utilities of the options. If a decision is made among options that include one or more of these special options, (...) I call it a decision made in a dynamic setting.The rule to maximize expected utility, MEU, is insufficient for decisions in dynamic settings. It does not say how to take account of information about the way in which the realization of an option would change the expected utility of the option. (shrink)

In some decision problems adoption of an option furnishes evidence about the option's consequences. Rational decisions take account of that evidence, although it makes an option's adoption changes the option's expected utility.

A common type of change in science occurs as theorists generalize a model of a phenomenon by removing some idealizations of the model. This type of change occurs in the decision sciences and also in the normative branch of the decision sciences that treats rational choice. After presenting a general ac-count of model generalization, the paper illustrates generalization of models in normative decision theory. The principal illustration generalizes a standard model of rational choice by removing the idealization that deliberation has (...) no cost. (shrink)

This article proposes a new theory of rational decision, distinct from both causal decision theory (CDT) and evidential decision theory (EDT). First, some intuitive counterexamples to CDT and EDT are presented. Then the motivation for the new theory is given: the correct theory of rational decision will resemble CDT in that it will not be sensitive to any comparisons of absolute levels of value across different states of nature, but only to comparisons of the differences in value between the available (...) options within states of nature; however, the correct theory will also resemble EDT in that it will rely on conditional probabilities (not unconditional probabilities). The new theory gives a prominent role to the notion of a “benchmark” for each state of nature, by comparison with which the value of the available options in that state of nature are measured, and so it has been called the Benchmark Theory (BT). It is argued that BT gives the right verdict on the cases that seem to be counterexamples to CDT and EDT. Finally, some objections to BT are considered and answered. (shrink)

It is commonly thought, in line with the position defended in an influential paper by David Lewis, that the decision problems faced in the prisoner’s dilemma and the Newcomb situation are essentially the same problem. José Luis Bermúdez has recently attacked the case Lewis makes for this claim. While I think the claim is false, I contend that Bermúdez’s reason for rejecting Lewis’s argument is inadequate, and then outline what I take to be a better reason for doing so.

In this paper I explore a version of standard (expected utility) decision theory in which the probability parameter is interpreted as an objective chance believed by agents to obtain and values of this parameter are fixed by indicative conditionals linking possible actions with possible outcomes. After reviewing some recent developments centering on the common-cause counterexamples to the standard approach, I introduce and briefly discuss the key notions in my own approach. (This approach has essentially the same results as the causal (...) approach in common-cause cases.) I then discuss the Rule of Dominance and find, in the context of the present proposal, that it cannot serve as an independent source of action justification. Turning next to Newcomb''s Problem, I argue that the much discussed issue of back-tracking counterfactuals is something of a red herring for decision theory. Once the twin distractions of back-tracking counterfactuals and Dominance Reasoning are set aside the 1-box solution emerges as a natural consequence of the present proposal. It is of interest that this proposal agrees with the causal approach in the standard common-cause examples and the expected-utility approach in the Newcomb case: one can be smart and rich and keep on smoking. (shrink)

Causal eliminativists maintain that all causal talk is false. The prospects for such a view seem to be stymied by an indispensability argument, charging that any agent must distinguish between effective and ineffective strategies, and that such a distinction must commit that agent to causal notions. However, this argument has been under-explored. The contributions of this paper are twofold: first, I provide a thorough explication of the indispensability argument and the various ways it might be defended. Second, I point to (...) an important limitation in the argument and suggest that it does not give us sufficient reason to reject eliminativism. In support of this last claim, I show that the distinction between effective and ineffective strategies could perfectly well be grounded in a counterfactual rather than a causal decision theory and argue that there are fully adequate explanations of how we could come to make the requisite counterfactual judgments that need not invoke causal concepts. (shrink)

Quine and others have recommended principles of charity which discourage judgments of irrationality. Such principles have been proposed to govern translation, psychology, and economics. After comparing principles of charity of different degrees of severity, we argue that the stronger principles are likely to block understanding of human behavior and impede progress toward improving it. We support a moderate principle of charity which leaves room for empirically justified judgments of irrationality.

Examples involving common causes — most prominently, examples involving genetically influenced choices — are analytically equivalent not to standard Newcomb Problems — in which the Predictor genuinely predicts the agent's decision — but to non-standard Newcomb Problems — in which the Predictor guarantees the truth of her predictions by interfering with the agent's decision to make the agent choose as it was predicted she would. When properly qualified, causal and epistemic decision theories diverge only on standard — not on non-standard (...) — Newcomb Problems, and thus not on examples involving common causes. (shrink)

Jim Joyce has argued that David Lewis’s formulation of causal decision theory is inadequate because it fails to apply to the “small world” decisions that people face in real life. Meanwhile, several authors have argued that causal decision theory should be developed such that it integrates the interventionist approach to causal modeling because of the expressive power afforded by the language of causal models, but, as of now, there has been little work towards this end. In this paper, I propose (...) a variant of Lewis’s causal decision theory that is intended to meet both of these demands. Specifically, I argue that Lewis’s causal decision theory can be rendered applicable to small world decisions if one analyzes his dependency hypotheses as causal hypotheses that depend on the interventionist causal modeling framework for their semantics. I then argue that this interventionist variant of Lewis’s causal decision theory is preferable to interventionist causal decision theories that purportedly generalize Lewis’s through the use of conditional probabilities. This is because Lewisian interventionist decision theory captures the causal decision theorist’s conviction that any correlation between what the agent does and cannot cause should be irrelevant to the agent’s choice, while purported generalizations do not. (shrink)

Meek and Glymour use the graphical approach to causal modeling to argue that one and the same norm of rational choice can be used to deliver both causal-decision-theoretic verdicts and evidential-decision-theoretic verdicts. Specifically, they argue that if an agent maximizes conditional expected utility, then the agent will follow the causal decision theorist’s advice when she represents herself as intervening, and will follow the evidential decision theorist’s advice when she represents herself as not intervening. Since Meek and Glymour take no stand (...) on whether agents should represent themselves as intervening, they provide more general advice than standard causal decision theorists and evidential decision theorists. But I argue here that even Meek and Glymour’s advice is not sufficiently general. This is because their advice is not sensitive to the distinct ways in which agents can fail to intervene, and there are decision-making contexts in which agents can reasonably have non-extreme confidence that they are intervening. I then show that the most natural extension of Meek and Glymour’s framework fails, but offer a generalization of my “Interventionist Decision Theory” that does not suffer from the same problems. (shrink)

In this paper, I use interventionist causal models to identify some novel Newcomb problems, and subsequently use these problems to refine existing interventionist treatments of causal decision theory. The new Newcomb problems that make trouble for existing interventionist treatments involve so-called ‘exotic choice’—that is, decision-making contexts where the agent has evidence about the outcome of her choice. I argue that when choice is exotic, the interventionist can adequately capture causal decision-theoretic reasoning by introducing a new interventionist approach to updating on (...) exotic evidence. But I also argue that this new updating procedure is principled only if the interventionist trades in the typical interventionist conception of choice for an alternative Ramseyan conception. I end by arguing that the guide to exotic choice developed here may, despite its name, be useful in some everyday contexts. (shrink)

The paper will show how one may rationalize one-boxing in Newcomb's problem and drinking the toxin in the Toxin puzzle within the confines of causal decision theory by ascending to so-called reflexive decision models which reflect how actions are caused by decision situations (beliefs, desires, and intentions) represented by ordinary unreflexive decision models.

The crucial premise of the standard argument for two-boxing in Newcomb's problem, a causal dominance principle, is false. We present some counterexamples. We then offer a metaethical explanation for why the counterexamples arise. Our explanation reveals a new and superior argument for two-boxing, one that eschews the causal dominance principle in favor of a principle linking rational choice to guidance and actual value maximization.

Consequentialists often assume rational monism: the thesis that options are always made rationally permissible by the maximization of the selfsame quantity. This essay argues that consequentialists should reject rational monism and instead accept rational pluralism: the thesis that, on different occasions, options are made rationally permissible by the maximization of different quantities. The essay then develops a systematic form of rational pluralism which, unlike its rivals, is capable of handling both the Newcomb problems that challenge evidential decision theory and the (...) unstable problems that challenge causal decision theory. (shrink)

I have maintained that some but not all prisoners' dilemmas are side-by-side Necomb problems. The present paper argues that, similarly, some but not all versions of Newcomb's Problem are prisoners' dilemmas in which Taking Two and Predicting Two make an equilibrium that is dispreferred by both the box-chooser and predictor to the outcome in which only one box is taken and this is predicted. I comment on what kinds of prisoner's dilemmas Newcomb's Problem can be, and on opportunities that results (...) reached may open for kinds of cooperative reasoning in versions of Newcomb's Problem. (shrink)

Responses to Newcomb-like challenges to evidential decision theories such as Jeffrey’s “logic of decision” range from allegations of incoherence and irrelevance; through stonewalling - “Just one box for me, thank you.“; to arguments that maintain that when properly applied by an ideal agent such theories get the right answers and, for example, prescribe the taking of both boxes, not just one; on to conservative revisions of evidential decision theories that are held to get these supposedly right answers while remaining true (...) to its evidential, Humean spirit; and finally to responses of radical revisionists who claim that only causal decision theories can get all Newcomb-like problem cases right. I discuss responses of the first sort in another paper - “Newcomb-like Problems for Jeffrey’s Logic of Decision.”. (shrink)

The papers collected here are, with three exceptions, those presented at a conference on probability and causation held at the University of California at Irvine on July 15-19, 1985. The exceptions are that David Freedman and Abner Shimony were not able to contribute the papers that they presented to this volume, and that Clark Glymour who was not able to attend the conference did contribute a paper. We would like to thank the National Science Foundation and the School of Humanities (...) of the University of California at Irvine for generous support. WILLIAM HARPER University of Western Ontario BRIAN SKYRMS University of California at Irvine VII INTRODUCTION TO CAUSATION, CHANCE, AND CREDENCE The search for causes is so central to science that it has sometimes been taken as the defining attribute of the scientific enterprise. Yet even after twenty-five centuries of philosophical analysis the meaning of "cause" is still a matter of controversy, among scientists as well as philosophers. Part of the problem is that the servicable concepts of causation built out of Necessity, Sufficiency, Locality, and Temporal Precedence were constructed for a deterministic world-view which has been obsolete since the advent of quantum theory. A physically credible theory of causation must be, at basis, statistical. And statistical analyses of caus­ ation may be of interest even when an underlying deterministic theory is assumed, as in classical statistical mechanics. (shrink)

I want in this paper to do two things. First, I want to respond to some studies that argue that people are often not rational: that people regularly and systematically depart from rationality. The conclusion itself does not worry me. I pressed for the same in a recent book. But the arguments seem to me wrong, and wrong in an interesting way. There may be something to be learned from seeing how and why they fail.

I present a game-theoretic way to understand the situation describing Newcomb’s Problem (NP) which helps to explain the intuition of both one-boxers and two-boxers. David Lewis has shown that the NP may be modelled as a Prisoners Dilemma game (PD) in which ‘cooperating’ corresponds to ‘taking one box’. Adopting relevant results from game theory, this means that one should take just one box if the NP is repeated an indefinite number of times, but both boxes if it is a one-shot (...) game. Causal decision theorists thus give the right answer for the one-shot situation, whereas the one-boxers’ solution applies to the indefinitely iterated case. Because Nozick’s set-up of the NP is ambiguous between a one-shot and a repeated game, both of these solutions may appear plausible – depending on whether one conceives of the situation as one-off or repeated. If the players’ aim is to maximize their payoffs, the symmetric structure of the PD implies that the two players will behave alike both when the game is one-shot and when it is played repeatedly. Therefore neither the observed outcome of both players selecting the same strategy (in the PD) nor, correspondingly, the predictor’s accurate prediction of this outcome (in the NP) is at all surprising. There is no need for a supernatural predictor to explain the NP phenomena. (shrink)

The puzzle of the absentminded driver combines an unstable decision problem with a version of the Sleeping Beauty problem. Its analysis depends on the choice between “halfing” and “thirding” as well as that between “evidential” and “causal” decision theory. I show that all four combinations lead to interestingly different solutions, and draw some general lessons about the formulation of causal decision theory, the interpretation of mixed strategies and the connection between rational credence and objective chance.

In Necessary Existence, Pruss and Rasmussen give an argument for a necessary being employing a modest causal principle. Here I note that, when applied to highly general and fundamental matters, the principle may well be false (or at least not so obvious).

Arif Ahmed has recently argued that causal decision theory is dynamically inconsistent and that we should therefore prefer evidential decision theory. However, the principal formulation of the evidential theory, Richard Jeffrey’s Logic of Decision, has a mixed record of its own when it comes to evaluating plans consistently across time. This note probes that neglected record, establishing the dynamic consistency of evidential decision theory within a restricted class of problems but then illustrating how evidentialists can fall into sequential incoherence outside (...) of this class. Uncovering the evidentialist’s own dynamic inconsistencies reveals, contra Ahmed, that sequential choice considerations do not significantly favor the evidentialist’s theory over the causalist’s. (shrink)

In ‘An argument against causal decision theory’, Jack Spencer shows that standard formulations of causal decision theory run afoul of his Guaranteed Principle. In the sequential choice problem he employs to make this case, the transgression stems from an awkward discrepancy between how causalists typically value present vs future acts. This note suggests a version of causal decision theory that avoids this incongruity and so respects the Guaranteed Principle in Spencer’s problem. However, this formulation, and hence symmetric appraisal of present (...) and future acts, is also shown to be insufficient to secure causalist satisfaction of the Guaranteed Principle in general. (shrink)

Instability occurs when the very fact of choosing one particular possible option rather than another affects the expected values of those possible options. In decision theory: An act is stable iff given that it is actually performed, its expected utility is maximal. When there is no stable choice available, the resulting instability can seem to pose a dilemma of practical rationality. A structurally very similar kind of instability, which occurs in cases of anti-expertise, can likewise seem to create dilemmas of (...) epistemic rationality. One possible line of response to such cases of instability, suggested by both Jeffrey (1983) and Sorensen (1987), is to insist that a rational agent can simply refuse to accept that such instability applies to herself in the first place. According to this line of thought it can be rational for a subject to discount even very strong empirical evidence that the anti-expertise condition obtains. I present a new variety of anti-expertise condition where no particular empirical stage-setting is required, since the subject can deduce a priori that an anti-expertise condition obtains. This kind of anti-expertise case is therefore not amenable to the line of response that Jeffrey and Sorensen recommend. (shrink)