Evidential Decision Theory is a radical theory of rational decision-making. It recommends that instead of thinking about what your decisions *cause*, you should think about what they *reveal*. This Element explains in simple terms why thinking in this way makes a big difference, and argues that doing so makes for *better* decisions. An appendix gives an intuitive explanation of the measure-theoretic foundations of Evidential Decision Theory.

has offered evidential decision theorists a defence against the charge that they make unintuitive recommendations for cases like Newcomb's Problem. He says that when conditional probabilities are assessed from the agent's point of view, evidential decision theory makes the same recommendation as intuition. I argue that calculating the probabilities in Price's way leads to no recommendation. It condemns the agent to perpetual oscillation between different options. Price's Argument Instability Objections Conclusion.

Andy Egan has presented a dilemma for decision theory. As is well known, Newcomb cases appear to undermine the case for evidential decision theory. However, Egan has come up with a new scenario which poses difficulties for causal decision theory. I offer a simple solution to this dilemma in terms of a modified EDT. I propose an epistemological test: take some feature which is relevant to your evaluation of the scenarios under consideration, evidentially (...) correlated with the actions under consideration albeit, causally independent of them. Hold this feature fixed as a hypothesis. The test shows that, in Newcomb cases, EDT would mislead the agent. Where the test shows EDT to be misleading, I propose to use fictive conditional credences in the EDT-formula under the constraint that they are set to equal values. I then discuss Huw Price’s defence of EDT as an alternative to my diagnosis. I argue that my solution also applies if one accepts the main premisses of Price’s argument. I close with applying my solution to Nozick’s original Newcomb problem. (shrink)

This paper argues against evidential decision-theory, by showing that the newest responses to its biggest current problem – the medical Newcomb problems – don’t work. The latest approach is described, and the arguments of two main proponents of it – Huw Price and CR Hitchcock – clearly distinguished and examined. It is argued that since neither new defence is successful, causation remains essential to understanding means-end agency.

I defend evidential decision theory and the theory of deliberation-probability dynamics from a recent criticism advanced by Jordan Howard Sobel. I argue that his alleged counterexample to the theories, called the Popcorn Problem is not a genuine counterexample.

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)

It is plausible that Newcomb problems in which causal maximizers and evidential maximizers would do different things would not be possible for ideal maximizers who are attentive to metatickles. An objection to Eells’s first argument for this makes welcome a second. Against it I argue that even ideal evidential and causal maximizers would do different things in some non-dominance Newcomb problems; and that they would hope for different things in some third-person and non-action problems, which is relevant if (...) a good theory of rational choices of acts should fit smoothly into a good theory of rational desires for facts. (shrink)

In this paper I study dynamical models of rational deliberation within the context of Newcomb's problem. Such models have been used to argue against the soundness of the "tickle'" defense of evidential decision theory, which is based on the idea that sophisticated decision makers can break correlations between states and acts by introspecting their own beliefs and desires. If correct, this would show that evidential decision theory agrees with the recommendations of causal (...) class='Hi'>decision theory. I argue that an adequate understanding of rational deliberation leads to deliberative processes for which this idea works. My proposal is modest, though. Evidential decision theory cannot always be reconciled with causal decision theory along the lines suggested here, but the agreement between the two theories is broader than previously thought. (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)

A review of some major topics of debate in normative decision theory from circa 2007 to 2019. Topics discussed include the ongoing debate between causal and evidential decision theory, decision instability, risk-weighted expected utility theory, decision-making with incomplete preferences, and decision-making with imprecise credences.

Should we act only for the sake of what we might bring about (causal decision theory); or is it enough for a decent motive that our action is highly correlated with something desirable (evidential decision theory)? The conflict between these points of view is embodied in Newcomb's problem. It is argued here that intuitive evidence from familiar decision contexts does not enable us to settle the issue, since the two theories dictate the same results (...) in normal circumstances. Nevertheless, there are several reasons to reject the causal approach: (1) its relative complexity; (2) its commitment to the existence of situations in which every possible act would be irrational; (3) its incorporation of an arbitrary time bias; and (4) its implicit distinction between what ought to be done and what ought to be hoped for. (shrink)

After a brief presentation of evidential decision theory, causal decision theory, and Newcomb type prima facie counterexamples to the evidential theory, three kinds of "metatickle" defenses of the evidential theory are discussed. Each has its weaknesses, but one of them seems stronger than the other two. The weaknesses of the best of the three, and the intricacy of metatickle analysis, does not constitute an advantage of causal decision theory over (...) the evidential theory, however. It is argued, by way of an example, that causal decision theory also stands in need of a metatickle defense. (shrink)

I explore the prospects for modelling epistemic rationality (in the probabilist setting) via an epistemic decision theory, in a consequentialist spirit. Previous work has focused on cases in which the truth-values of the propositions in which the agent is selecting credences do not depend, either causally or merely evidentially, on the agent’s choice of credences. Relaxing that restriction leads to a proliferation of puzzle cases and theories to deal with them, including epistemic analogues of evidential and causal (...) decision theory, and of the Newcomb Problem and ‘Psychopath Button’ Problem. A variant of causal epistemic decision theory deals well with most cases. However, there is a recalcitrant class of problem cases for which no epistemic decision theory seems able to match our intuitive judgements of epistemic rationality. This lends both precision and credence to the view that there is a fundamental mismatch between epistemic consequentialism and the intuitive notion of epistemic rationality; the implications for understanding the latter are briefly discussed. (shrink)

This paper argues that the types of intention can be modeled both as modal operators and via a multi-hyperintensional semantics. I delineate the semantic profiles of the types of intention, and provide a precise account of how the types of intention are unified in virtue of both their operations in a single, encompassing, epistemic space, and their role in practical reasoning. I endeavor to provide reasons adducing against the proposal that the types of intention are reducible to the mental states (...) of belief and desire, where the former state is codified by subjective probability measures and the latter is codified by a utility function. I argue, instead, that each of the types of intention -- i.e., intention-in-action, intention-as-explanation, and intention-for-the-future -- has as its aim the value of an outcome of the agent's action, as derived by her partial beliefs and assignments of utility, and as codified by the value of expected utility in evidential decision theory. (shrink)

In the context of the Sleeping Beauty problem, it has been argued that so-called “halfers” can avoid Dutch book arguments by adopting evidential decision theory. I introduce a Dutch book for a variant of the Sleeping Beauty problem and argue that evidential decision theorists fall prey to it, whether they are halfers or thirders. The argument crucially requires that an action can provide evidence for what the agent would do not only at other decision (...) points where she has exactly the same information, but also at decision points where she has different but “symmetric” information. (shrink)

This up-to-date introduction to decision theory offers comprehensive and accessible discussions of decision-making under ignorance and risk, the foundations of utility theory, the debate over subjective and objective probability, Bayesianism, causal decision theory, game theory, and social choice theory. No mathematical skills are assumed, and all concepts and results are explained in non-technical and intuitive as well as more formal ways. There are over 100 exercises with solutions, and a glossary of key (...) terms and concepts. An emphasis on foundational aspects of normative decision theory (rather than descriptive decision theory) makes the book particularly useful for philosophy students, but it will appeal to readers in a range of disciplines including economics, psychology, political science and computer science. • Has over 100 end of chapter review questions and exercises with solutions • Includes a chapter on how to draw a decision matrix • Explains the link between individual decision making, game theory and social choice theory Contents Preface; 1. Introduction; 2. The decision matrix; 3. Decisions under ignorance; 4. Decisions under risk; 5. Utility; 6. The mathematics of probability; 7. The philosophy of probability; 8. Why should we accept the preference axioms; 9. Causal vs. evidential decision theory; 10. Bayesian vs. non-Bayesian decision theory; 11. Game theory I: basic concepts and zero sum games; 12. Game theory II: nonzero sum and co-operative games; 13. Social choice theory; 14. Overview of descriptive decision theory; Appendix A. Glossary; Appendix B. Proof of the von Neumann-Morgenstern theorem; Further reading; Index. (shrink)

Defenders of sophisticated evidential decision theory (EDT) have argued (1) that its failure to provide correct recommendations in problems where the agent believes himself asymmetrically fallible in executing his choices is no flaw of the theory, and (2) that causal decision theory gives incorrect recommendations in certain examples unless it is supplemented with an additional metatickle or ratifiability deliberation mechanism. In the first part of this paper, I argue that both positions are incorrect. In (...) the second part of the paper, I show how the agent's preferences involved in standard counterexamples to EDT, such as Newcomb's problem, violate the Jeffrey/Bolker preference axioms, specifically the Impartiality axiom. (shrink)

The paper argues that on three out of eight possible hypotheses about the EPR experiment we can construct novel and realistic decision problems on which (a) Causal Decision Theory and Evidential Decision Theory conflict (b) Causal Decision Theory and the EPR statistics conflict. We infer that anyone who fully accepts any of these three hypotheses has strong reasons to reject Causal Decision Theory. Finally, we extend the original construction to show (...) that anyone who gives any of the three hypotheses any non-zero credence has strong reasons to reject Causal Decision Theory. However, we concede that no version of the Many Worlds Interpretation (Vaidman, in Zalta, E.N. (ed.), Stanford Encyclopaedia of Philosophy 2014) gives rise to the conflicts that we point out. (shrink)

The standard formulation of Newcomb's problem compares evidential and causal conceptions of expected utility, with those maximizing evidential expected utility tending to end up far richer. Thus, in a world in which agents face Newcomb problems, the evidential decision theorist might ask the causal decision theorist: "if you're so smart, why ain’cha rich?” Ultimately, however, the expected riches of evidential decision theorists in Newcomb problems do not vindicate their theory, because their success (...) does not generalize. Consider a theory that allows the agents who employ it to end up rich in worlds containing Newcomb problems and continues to outperform in other cases. This type of theory, which I call a “success-first” decision theory, is motivated by the desire to draw a tighter connection between rationality and success, rather than to support any particular account of expected utility. The primary aim of this paper is to provide a comprehensive justification of success-first decision theories as accounts of rational decision. I locate this justification in an experimental approach to decision theory supported by the aims of methodological naturalism. (shrink)

The ontology of decision theory has been subject to considerable debate in the past, and discussion of just how we ought to view decision problems has revealed more than one interesting problem, as well as suggested some novel modifications of classical decision theory. In this paper it will be argued that Bayesian, or evidential, decision-theoretic characterizations of decision situations fail to adequately account for knowledge concerning the causal connections between acts, states, and (...) outcomes in decision situations, and so they are incomplete. Second, it will be argues that when we attempt to incorporate the knowledge of such causal connections into Bayesian decision theory, a substantial technical problem arises for which there is no currently available solution that does not suffer from some damning objection or other. From a broader perspective, this then throws into question the use of decision theory as a model of human or machine planning. (shrink)

Decision theory is founded on the principle that we ought to take the action that has the maximum expected value from among actions we are in a position to take. But prior to the notion of expected value is the notion of the actual value of that action: roughly, a measure of the good outcomes you would in fact procure if you were to take it. Surprisingly many decision theories operate without an analysis of actual value. I (...) offer a definition of actual value, and show that a form of decision theory due to Stalnaker can be reformulated so as to be in line with the edict to maximize expected value. By contrast, I show that there is no quantity — given by my definition or otherwise — that plays the role of actual value in Jeffrey's decision theory. (shrink)

실천적 결정이론은 크게 증거적 실천적 결정이론(Evidential Practical Decision Theory)과 인과적 실천적 결정이론(Causal Practical Decision Theory)으로 나뉜다. 그리고 잘 알려진 것처럼 전통적 뉴컴 문제는 우리가 왜 인과적 실천적 결정이론을 받아들여야 하는지에 대한 좋은 이유를 제공하는 것으로 이해된다. 그런데 최근 인식론에서는 실천적 결정 이론의 핵심적 아이디어가 합리적 믿음과 관련된 다양한 인식적 문제들에 적용되고 있다. 이러한 시도를 하는 이론을 ‘인식적 결정이론 (Epistemic Decision Theory)’이라고 부른다. 인식적 결정이론도 실천적 결정이론과 유사하게 증거적 인식적 결정이론(Evidential Epistemic Decision (...) Theory)과 인과적 인식적 결정이론(Causal Epistemic Decision Theory)으로 나뉜다. 그런데 흥미롭게도 힐러리 그리브스 (Greaves, 2013)는 인식적 뉴컴 문제를 제시하고, 전통적 뉴컴 문제와 유사하게, 인식적 뉴컴 문제도 우리가 왜 증거적 인식적 결정이론보다 인과적 인식적 결정이론을 받아들여야 하는지에 대한 이유를 제공하는 것으로 이해될 수 있다고 주장한다. 이 논문의 목적은 과연 인식적 뉴컴 문제를 통해 인과적 인식적 결정이론이 증거적 인식적 결정이론보다 합리적 믿음의 본성을 더 잘 포착하는지 여부를 탐구하는 것이다. (shrink)

Many philosophers (myself included) have been converted to causal decision theory by something like the following line of argument: Evidential decision theory endorses irrational courses of action in a range of examples, and endorses “an irrational policy of managing the news”. These are fatal problems for evidential decision theory. Causal decision theory delivers the right results in the troublesome examples, and does not endorse this kind of irrational news-managing. So we (...) should give up evidential decision theory, and be causal decision theorists instead. Unfortunately, causal decision theory has its own family of problematic examples for which it endorses irrational courses of action, and its own irrational policy that it is committed to endorsing. These are, I think, fatal problems for causal decision theory. I wish that I had another theory to offer in its place. (shrink)

I argue that standard decision theories, namely causal decision theory and evidential decision theory, both are unsatisfactory. I devise a new decision theory, from which, under certain conditions, standard game theory can be derived.

One of us (Eells 1982) has defended traditional evidential decision theory against prima facie Newcomb counterexamples by assuming that a common cause forms a conjunctive fork with its joint effects. In this paper, the evidential theory is defended without this assumption. The suggested rationale shows that the theory's assumptions are not about the nature of causality, but about the nature of rational deliberation. These presuppositions are weak enough for the argument to count as a (...) strong justification of the evidential theory. (shrink)

This paper empirically investigates whether people’s implicit decision theory is more like causal decision theory or more like a non-causal decision theory (such as evidential decision theory). We also aim to determine whether implicit causalists, without prompting and without prior education, make a distinction that is crucial to causal decision theorists: preferring something _as a news item_ and preferring it _as an object of choice_. Finally, we investigate whether differences in (...) people’s implicit decision theory correlate with differences in their level of _future bias_: the preference for positively valenced events to be located in the future and not the past, and negatively valenced events to be located in the past and not the future. We find that people are overwhelmingly non-causalists. We also find that implicit causalists _do not_ make the distinction between news items and objects of choice, and that there is little to no correlation between people’s future bias and their implicit decision theory. We end by discussing the philosophical upshots of this work. (shrink)

Causal decision theory (CDT) is the best theory of rational choice now available.2 I intend to provide some support for that claim in part I of this paper by responding to two criticisms of CDT. The first criticism says that CDT is superfluous, since it does no better in the problems that matter than does evidential decision theory (EDT) at recommending correct choices. A second criticism says that CDT by itself is flawed: according to (...) this view, there are problems in which CDT makes bad recommendations, unless it is supplemented with an additional deliberation mechanism, either involving metatickles or screening for ratifiable choices.3 I will argue in response to the first criticism that CDT is genuinely better than any EDT of the most sophisticated sort: there are problems where CDT gives better recommendations, and they are problems that do matter. (shrink)

Decision theory cannot be a purely formal theory, free of all metaphysical assumptions and ascertainments. It must instead rely upon the end user for the wisdom it takes to prime the decision formalism – with principles and assumptions about the metaphysics of the application context – so that the formalism in its turn can generate good advice. Appreciating this idea is fundamental to understanding the true rivalry between evidential decision theory (EDT) and causal (...) decision theory (CDT) in specific cases. I shall argue that no decision theory can deliver a verdict unless assumptions are made about the degrees of freedom in the context of decision, that EDT and CDT disagree fundamentally about how to diagnose the degrees of freedom in any given situation, and that from this fundamental disagreement flow their surface disagreements in iconic cases. (shrink)

It is often thought that the main significant difference between evidential decision theory and causal decision theory is that they recommend different acts in Newcomb-style examples (broadly construed) where acts and states are correlated in peculiar ways. However, this paper presents a class of non-Newcombian examples that evidential decision theory cannot adequately model whereas causal decision theory can. Briefly, the examples involve situations where it is clearly best to perform an (...) act that will not influence the desired outcome. On evidential decision theory—but not causal decision theory—this situation turns out to be impossible: acts that an agent does not think influence the desired outcome are never optimal. Typically, sophisticated versions of evidential decision theory emulate causal decision theoretic reasoning by (implicitly) conditioning on causal confounders, but in the kind of example considered here, this trick does not work. The upshot is that there is more to causal reasoning than has so far been appreciated. (shrink)

Calibration inductive logics are based on accepting estimates of relative frequencies, which are used to generate imprecise probabilities. In turn, these imprecise probabilities are intended to guide beliefs and decisions — a process called “calibration”. Two prominent examples are Henry E. Kyburg's system of Evidential Probability and Jon Williamson's version of Objective Bayesianism. There are many unexplored questions about these logics. How well do they perform in the short-run? Under what circumstances do they do better or worse? What is (...) their performance relative to traditional Bayesianism? -/- In this article, we develop an agent-based model of a classic binomial decision problem, including players based on variations of Evidential Probability and Objective Bayesianism. We compare the performances of these players, including against a benchmark player who uses standard Bayesian inductive logic. We find that the calibrated players can match the performance of the Bayesian player, but only with particular acceptance thresholds and decision rules. Among other points, our discussion raises some challenges for characterising “cautious” reasoning using imprecise probabilities. Thus, we demonstrate a new way of systematically comparing imprecise probability systems, and we conclude that calibration inductive logics are surprisingly promising for making decisions. (shrink)

We argue that causal decision theory is no worse off than evidential decision theory in handling entanglement, regardless of one’s preferred interpretation of quantum mechanics. In recent works, Ahmed and Ahmed and Caulton : 4315–4352, 2014) have claimed the opposite; we argue that they are mistaken. Bell-type experiments are not instances of Newcomb problems, so CDT and EDT do not diverge in their recommendations. We highlight the fact that a Causal Decision Theorist should take (...) all lawlike correlations into account, including potentially acausal entanglement correlations. This paper also provides a brief introduction to CDT with a motivating “small” Newcomb problem. The main point of our argument is that quantum theory does not provide grounds for favouring EDT over CDT. (shrink)

A "symptomatic act" is an act that is evidence for a state that it has no tendency to cause. In this paper I show that when the evidential value of a symptomatic act might influence subsequent choices, causal decision theory may initially recommend against its own use for those subsequent choices. And if one knows that one will nevertheless use causal decision theory to make those subsequent choices, causal decision theory may favor the (...) one-box solution in Newcomb's problem, and may recommend against making cost-free observations. But if one can control one's future choices, then causal decision theory never recommends against cost-free observation. (shrink)

We argue that causal decision theory is no worse off than evidential decision theory in handling entanglement, regardless of one’s preferred interpretation of quantum mechanics. In recent works, Ahmed and Ahmed and Caulton : 4315–4352, 2014) have claimed the opposite; we argue that they are mistaken. Bell-type experiments are not instances of Newcomb problems, so CDT and EDT do not diverge in their recommendations. We highlight the fact that a Causal Decision Theorist should take (...) all lawlike correlations into account, including potentially acausal entanglement correlations. This paper also provides a brief introduction to CDT with a motivating “small” Newcomb problem. The main point of our argument is that quantum theory does not provide grounds for favouring EDT over CDT. (shrink)

Decision theorists disagree about how instrumentally rational agents, i.e., agents trying to achieve some goal, should behave in so-called Newcomb-like problems, with the main contenders being causal and evidential decision theory. Since the main goal of artificial intelligence research is to create machines that make instrumentally rational decisions, the disagreement pertains to this field. In addition to the more philosophical question of what the right decision theory is, the goal of AI poses the question (...) of how to implement any given decision theory in an AI. For example, how would one go about building an AI whose behavior matches evidential decision theory’s recommendations? Conversely, we can ask which decision theories describe the behavior of any existing AI design. In this paper, we study what decision theory an approval-directed agent, i.e., an agent whose goal it is to maximize the score it receives from an overseer, implements. If we assume that the overseer rewards the agent based on the expected value of some von Neumann–Morgenstern utility function, then such an approval-directed agent is guided by two decision theories: the one used by the agent to decide which action to choose in order to maximize the reward and the one used by the overseer to compute the expected utility of a chosen action. We show which of these two decision theories describes the agent’s behavior in which situations. (shrink)

Evidential decision theory (EDT) says that the choiceworthiness of an option depends on its evidential connections to possible outcomes. Causal decision theory (CDT) holds that it depends on your beliefs about its causal connections. While Newcomb cases support CDT, Arif Ahmed has described examples that support EDT. A new account is needed to get all cases right. I argue that an option A's choiceworthiness is determined by the probability that a good outcome ensues at (...) possible A‐worlds that match actuality in the facts causally unaffected by your decision (the “unaffected facts”). Moreover, you should evaluate A on the assumption that A is compossible with the unaffected facts. This view entails that you should use EDT when evaluating A on the assumption that the unaffected facts determine your action, but use CDT when assessing A on the opposite assumption. A's choiceworthiness equals a weighted average of these conditional assessments. The weights are determined by your beliefs about whether the unaffected fact determine your action. This account gets both Newcomb and Ahmed cases right. According to an influential view, whether you take the unaffected facts to determine your action can make a difference to whether you can regard yourself as free and the action as being under your control. While my account is neutral on this issue, it entails that whether you take the unaffected facts to determine your action is important in a different way: it matters to whether you should follow EDT or CDT. (shrink)

A decision theory can be useful not only as a tool for determining which action, given your desires and beliefs, is most preferable, but also as a means for analyzing the nature of rational deliberation. In this paper, I turn to two classic proposals for a causal decision theory, that of Lewis and that of Sobel :407–437, 1986. doi: 10.1080/00048408612342621). As Rabinowicz revealed, Lewis’ proposal is unable to be applied to as broad a set of (...) class='Hi'>decision problems as a version of CDT offered by Sobel, an account of decision theory that Lewis thought was equivalent to his own. Rabinowicz argued by offering a counterexample to Lewis’ account. In this essay, I build on that approach, offering a novel counterexample, the “Faulty Signal Problem,” which proves that Lewis’ theory fails to provide a recommendation in an even broader class decision problems than Rabinowicz recognized, particularly those that exhibit what I refer to as counterfactual asymmetries. The problem for Lewis, however, is not just a technicality. Lewis and Sobel’s theories, respectively, conceptualize rational deliberation in two different ways. Lewis’ proposal, which utilized conditionalization as its form of belief revision, shares with evidential decision theory the same underlying attitude toward the agent as evidence-maker. In contrast, Sobel’s theory, based on imaging, captures the idea that deliberation requires a distinctly suppositional attitude that respects the agent’s causal views. The Faulty Signal Problem not only gives reason to favor Sobel’s proposal over Lewis’; it also provides justification for seeing imaging as necessary for a genuinely causal decision theory. (shrink)

Enjoying great popularity in decision theory, epistemology, and philosophy of science, Bayesianism as understood here is fundamentally concerned with epistemically ideal rationality. It assumes a tight connection between evidential probability and ideally rational credence, and usually interprets evidential probability in terms of such credence. Timothy Williamson challenges Bayesianism by arguing that evidential probabilities cannot be adequately interpreted as the credences of an ideal agent. From this and his assumption that evidential probabilities cannot be interpreted (...) as the actual credences of human agents either, he concludes that no interpretation of evidential probabilities in terms of credence is adequate. I argue to the contrary. My overarching aim is to show on behalf of Bayesians how one can still interpret evidential probabilities in terms of ideally rational credence and how one can maintain a tight connection between evidential probabilities and ideally rational credence even if the former cannot be interpreted in terms of the latter. By achieving this aim I illuminate the limits and prospects of Bayesianism. (shrink)

Most philosophers agree that causal knowledge is essential to decision-making: agents should choose from the available options those that probably cause the outcomes that they want. This book argues against this theory and in favour of evidential or Bayesian decision theory, which emphasises the symptomatic value of options over their causal role. It examines a variety of settings, including economic theory, quantum mechanics and philosophical thought-experiments, where causal knowledge seems to make a practical difference. (...) The arguments make novel use of machinery from other areas of philosophical inquiry, including first-person epistemology and the free will debate. The book also illustrates the applicability of decision theory itself to questions about the direction of time and the special epistemic status of agents. (shrink)

Benchmark theory , introduced by Ralph Wedgwood, departs from decision theories of pure expectation maximization like evidential decision theory and causal decision theory and instead ranks actions according to the desirability of an outcome they produce in some state of affairs compared to a standard—a benchmark—for that state of affairs. Wedgwood motivates BT through what he terms Gandalf’s principle, that the merits of an action in a given state should be evaluated relative only (...) to the performances of other actions in that state, and not to their performances in other states. Although BT succeeds in selecting intuitively rational actions in a number of cases—including some in which EDT or CDT seem to go wrong—it places constraints on rational decision-making that either lack motivation or are untenable. Specifically, I argue that as it stands BT is committed both to endorsing and rejecting the independence of irrelevant alternatives. Furthermore its requirement that weakly dominated actions be excluded from consideration of rational action lacks motivation and threatens to collide with traditional game theory. In the final section of the paper, I construct a counterexample to BT. (shrink)

The conclusions derived by Keynes in his Treatise on Probability (1921) concerning induction, analogical reasoning, expectations formation and decision making, mirror and foreshadow the main conclusions of cognitive science and psychology.The problem of weight is studied within an economic context by examining the role it played in Keynes' applied philosophy work, The General Theory (1936). Keynes' approach is then reformulated as an optimal control approach to dealing with changes in information evaluation over time. Based on this analysis the (...) problem of inductive justification, from a societal perspective, is not, What can we rationally believe will occur in the economic future, given our past experiences? but Can we make the future so as to attain specific economic goals with practical certainty? An answer requires that restrictions be placed on the methodological individualist approach and the acceptance of a restricted holistic approach. (shrink)

It is a platitude among decision theorists that agents should choose their actions so as to maximize expected value. But exactly how to define expected value is contentious. Evidential decision theory (henceforth EDT), causal decision theory (henceforth CDT), and a theory proposed by Ralph Wedgwood that this essay will call benchmark theory (BT) all advise agents to maximize different types of expected value. Consequently, their verdicts sometimes conflict. In certain famous cases of (...) conflict—medical Newcomb problems—CDT and BT seem to get things right, while EDT seems to get things wrong. In other cases of conflict, including some recent examples suggested by Andy Egan, EDT and BT seem to get things right, while CDT seems to get things wrong. In still other cases, EDT and CDT seems to get things right, while BT gets things wrong. It's no accident, this essay claims, that all three decision theories are subject to counterexamples. Decision rules can be reinterpreted as voting rules, where the voters are the agent's possible future selves. The problematic examples have the structure of voting paradoxes. Just as voting paradoxes show that no voting rule can do everything we want, decision-theoretic paradoxes show that no decision rule can do everything we want. Luckily, the so-called “tickle defense” establishes that EDT, CDT, and BT will do everything we want in a wide range of situations. Most decision situations, this essay argues, are analogues of voting situations in which the voters unanimously adopt the same set of preferences. In such situations, all plausible voting rules and all plausible decision rules agree. (shrink)

A prominent philosophical debate concerns whether we should accept causal decision theory or evidential decision theory as our best theory of rational choice. However, instead of accepting one of these theories at the expense of the other, an alternative would be to accept that both theories play a partial role in the true account of rational choice. In this paper, I defend a pluralist account of this sort. In particular, I argue that rational permissibility (...) is an indeterminate notion, with EDT and CDT each corresponding to one sharpening of this notion. (shrink)

In this paper I propose an approach to decision theory that I call metanormativism, where the key idea is that decision theory should take into account decision-theoretic uncertainty. I don’t attempt to argue in favor of this view, though I briefly offer some motivation for it. Instead, I argue that if the view is correct, it has important implications for the causal versus evidential decision-theory debate. First, it allows us to make rational (...) sense of our seemingly divergent intuitions across the Smoking Lesion and The Psychopath Button cases. Second, it generates strong new arguments for preferring the causal approach to decision-theory over the evidential approach. (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.

Richard Jeffrey long held that decision theory should be formulated without recourse to explicitly causal notions. Newcomb problems stand out as putative counterexamples to this ‘evidential’ decision theory. Jeffrey initially sought to defuse Newcomb problems via recourse to the doctrine of ratificationism, but later came to see this as problematic. We will see that Jeffrey’s worries about ratificationism were not compelling, but that valid ratificationist arguments implicitly presuppose causal decision theory. In later work, (...) Jeffrey argued that Newcomb problems are not decisions at all because agents who face them possess so much evidence about correlations between their actions and states of the world that they are unable to regard their deliberate choices as causes of outcomes, and so cannot see themselves as making free choices. Jeffrey’s reasoning goes wrong because it fails to recognize that an agent’s beliefs about her immediately available acts are so closely tied to the immediate causes of these actions that she can create evidence that outweighs any antecedent correlations between acts and states. Once we recognize that deliberating agents are free to believe what they want about their own actions, it will be clear that Newcomb problems are indeed counterexamples to evidential decision theory. (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)

Two key questions of normative decision theory are: 1) whether the probabilities relevant to decision theory are evidential or causal; and 2) whether agents should be risk-neutral, and so maximise the expected value of the outcome, or instead risk-averse (or otherwise sensitive to risk). These questions are typically thought to be independent---that our answer to one bears little on our answer to the other. But there is a surprising argument that they are not. In this (...) paper, I show that evidential decision theory implies risk neutrality, at least in moral decision-making and at least on plausible empirical assumptions. Take any risk-aversion-accommodating decision theory, apply it using the probabilities prescribed by evidential decision theory, and every verdict of moral betterness you reach will match those of expected value theory. (shrink)

This paper focuses on the normative analysis—in the sense of the classic decision-theoretic formulation—of decision problems that arise in connection with forensic expert reporting. We distinguish this analytical account from other common types of decision analyses, such as descriptive approaches. While decision theory is, since several decades, an extensively discussed topic in legal literature, its use in forensic science is more recent, and with an emphasis on goals such as the analysis of the logical structure (...) of forensic expert conclusions regarding, for example, propositions of common source of evidential and known materials. Typical examples are so-called identification decisions, especially categorical conclusions according to which fingermarks come from a particular a person of interest. We will present and compare ways of stating forensic identification decisions in decision-theoretic terms and explain their underlying rationale. In particular, we will emphasize the importance of viewing this analysis as normative in the sense of providing a reflective rather than a prescriptive reference point against which people in charge of forensic identification decisions may compare their otherwise intuitive and informal reasoning, before acting. Normative decision analysis in forensic science thus provides a vector through which current practice can be articulated, scrutinized and rethought. (shrink)

Sophisticated ‘tickle’-style defences of Evidential Decision Theory take your motivational state to screen off your act from any state that is causally independent of it, thus ensuring that EDT and CDT converge. That leads to unacceptable instability in cases in which the correct action is obvious. We need a more liberal conception of what the agent controls. It follows that an ordinary deliberator should sometimes consider the past and not only the future to be subject to her (...) present choice. (shrink)