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In collective action problems, large numbers of contributions together produce a good outcome, but any one contribution often makes no difference. Many philosophers think that act consequentialism implies that individuals should not contribute in these cases, given that their contributions cannot be expected to affect the outcome. Nearly everyone has assumed that the relevant expected effects of an action are those effects that are counterfactually dependent on what a given agent does. This assumption is at the heart of causal decision theory. In contrast, evidential decision theory evaluates actions on the basis of whatever can be expected to happen, given that the agent performs some action, where this includes events that are simply correlated with the action. I show that causal and evidentialist versions of act consequentialism can diverge in collective action problems. Evidential decision theory can require us to do our part even when causal decision theory does not. [ABSTRACT FROM AUTHOR]

This paper develops a hybridization of Newcomb's Problem and the Frustrater (Spencer and Wells's 2019 paper 'Why take both boxes?'), underscoring how difficult it is to reconcile the rationality of taking both boxes in Newcomb's Problem and the rationality of taking the envelope in the Frustrater. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The possibility of predetermined choices raises a challenge for Causal Decision Theory [Ahmed 2014b]. Sandgren and Williamson [2021] have recently proposed a response—Selective Causal Decision Theory—that they hope will avoid Ahmed's counterexamples, maintain (a particular kind of) compatibilism, and endorse universal Two-boxing in Newcomb's Problem—CDT's raison d'être. Their proposal does an admirable job of satisfying the first two desiderata. However, in this reply I raise several worries about whether it can satisfy the third. [ABSTRACT FROM AUTHOR]

I give a Dutch book argument for CDT thirders in the context of a generalized Sleeping Beauty scenario. In combination with the Briggs (2010) Dutch book for EDT thirders, this amounts to a purely decision-theoretic argument for halfing in Sleeping Beauty. In combination with the Hitchcock (2004) Dutch book for CDT halfers, this amounts to a Dutch book argument against CDT. The combined Dutch book against CDT invites a plausible diagnosis of the reasons for CDT's failure: CDT is incompatible with the Dutch book requirement because it allows the values of epistemically impossible outcomes influence one's decision. [ABSTRACT FROM AUTHOR]

Though common sense says that causes must temporally precede their effects, the hugely influential interventionist account of causation makes no reference to temporal precedence. Does common sense lead us astray? In this paper, I evaluate the power of the commonsense assumption from within the interventionist approach to causal modeling. I first argue that if causes temporally precede their effects, then one need not consider the outcomes of interventions in order to infer causal relevance, and that one can instead use temporal and probabilistic information to infer exactly when X is causally relevant to Y in each of the senses captured by Woodward's interventionist treatment. Then, I consider the upshot of these findings for causal decision theory, and argue that the commonsense assumption is especially powerful when an agent seeks to determine whether so-called "dominance reasoning" is applicable. [ABSTRACT FROM AUTHOR]

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 (if any) 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. [ABSTRACT FROM AUTHOR]

In this paper, I present a puzzle about the connection between an agent's knowledge and her rationality and a way to solve it. The puzzle is that, intuitively, many of us want to accept both that it is rational for an agent to act on what she knows and that it is irrational for an agent to take what she knows for granting in her practical reasoning. These two claims about rationality present us with a puzzle because, holding fixed our interpretation of rationality, we cannot accept them both. According to my view, the most compelling way of solving this puzzle is to distinguish between our primary and dispositional evaluations of actions. By making this distinction, we not only gain a unique perspective on the relationship between knowledge and rationality, we also see how doing what we know is best might still manifest an undesirable habit. [ABSTRACT FROM AUTHOR]

Newcomb's problem has spawned a debate about which variant of expected utility maximisation (if any) should guide rational choice. In this paper, we provide a new argument against what is probably the most popular variant: causal decision theory (CDT). In particular, we provide two scenarios in which CDT voluntarily loses money. In the first, an agent faces a single choice and following CDT's recommendation yields a loss of money in expectation. The second scenario extends the first to a diachronic Dutch book against CDT. [ABSTRACT FROM AUTHOR]

Richard Bradley's landmark book Decision Theory with a Human Face makes seminal contributions to nearly every major area of decision theory, as well as most areas of formal epistemology and many areas of semantics. In addition to sketching Bradley's distinctive semantics for conditional beliefs and desires, I will explain his theory of conditional desire, focusing particularly on his claim that we should not desire events, either positively or negatively, under the supposition that they will occur. I shall argue, to the contrary, that permitting non-trivial desirabilities for events whose occurrence is known or assumed is both more intuitively plausible and more theoretically fruitful than Bradley's approach. In the course of the discussion I will contrast Bradley's broadly evidentialist picture of decision theory with my own more orthodox causal approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Rational agents face choices, even when taking seriously the possibility of determinism. Rational agents also follow the advice of Causal Decision Theory (CDT). Although many take these claims to be well-motivated, there is growing pressure to reject one of them, as CDT seems to go badly wrong in some deterministic cases. We argue that deterministic cases do not undermine a counterfactual model of rational deliberation, which is characteristic of CDT. Rather, they force us to distinguish between counterfactuals that are relevant and ones that are irrelevant for the purposes of deliberation. We incorporate this distinction into decision theory to develop 'Selective Causal Decision Theory', which delivers the correct recommendations in deterministic cases while respecting the key motivations behind CDT. [ABSTRACT FROM AUTHOR]

It has often been noted that there is some tension between engaging in decision-making and believing that one's choices might be predetermined. The possibility that our choices are predetermined forces us to consider, in our decisions, act-state pairs which are inconsistent, and hence to which we cannot assign sensible utilities. But the reasoning which justifies two-boxing in Newcomb's problem also justifies associating a non-zero causal probability with these inconsistent act-state pairs. Put together these undefined utilities and non-zero probabilities entail that expected utilities are undefined whenever it is a possibility that our choices are predetermined. There are three ways to solve the problem, but all of them suffer serious costs: always assume that, contrary to our evidence, the outcome of our present decision-making is not predetermined; give up the reasoning that justifies unconditional two-boxing in Newcomb's problem; or allow epistemically impossible outcomes to contribute to expected utility, leading to the wrong results in a series of cases introduced by Ahmed (Br J Philos Sci 65(4):665–685, 2014a, Evidence, decision and causality, Cambridge University Press, Cambridge, 2014b). However they choose to respond, causal decision theorists cannot remain silent: the intuitive tension between decision-making and the possibility of predetermination can be made precise, and resolving it will require giving up something. Causal decision theorists have a predetermination problem. [ABSTRACT FROM AUTHOR]

A one-boxer, Erica, and a two-boxer, Chloe, engage in a familiar debate. The debate begins with Erica asking Chloe, 'If you're so smart, then why ain'cha rich?' As the debate progresses, Chloe is led to endorse a novel causalist theory of rational choice. This new theory allows her to forge a connection between rational choice and long-run riches. In brief, Chloe concludes that it is not long-run wealth but rather long-run wealth creation that is symptomatic of rationality. [ABSTRACT FROM AUTHOR]

Newcomb dilemmas show a discrepancy in our rational reasoning, as made clear by comparing Evidential Decision Theory with Causal Decision Theory. In this paper, I look at three versions of the dilemma: the original, highly technical and abstract one plus two more mundane cases. I also account for the general schema of the dilemma possibly appearing in macroeconomic situations. Ahmed (2014) aims to provide a solution for macroeconomic cases that opens room for forming a development management Newcomb dilemma – an imaginary case of electric motor competition between Toyota and Tesla. I argue that Ahmed’s solution may solve the macroeconomic Newcomb dilemma, but it cannot be applied to the development management dilemma. If I am right, similar Newcomb situations could be cropping up regularly in development management, leading to seemingly insoluble strategic decisions having to be made. This may create an inevitable pitfall for development management. [ABSTRACT FROM AUTHOR]

Counter-intuitive consequences of both causal decision theory and evidential decision theory are dramatized. Each of those theories is thereby put under some pressure to supply an error theory to explain away intuitions that seem to favour the other. Because trouble is stirred up for both sides, complacency about Newcomb's problem is discouraged. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Andy Egan has recently presented a prominent objection to causal decision theory (CDT). However, in this paper, I argue that this objection fails if CDT's proponent accepts the plausible view that decision-theoretic options are intentions. This result both provides a defence of CDT against a prominent objection and highlights the importance of resolving the nature of decision-theoretic options. [ABSTRACT FROM AUTHOR]

We argue that causal decision theory (CDT) is no worse off than evidential decision theory (EDT) in handling entanglement, regardless of one's preferred interpretation of quantum mechanics. In recent works, Ahmed (Evidence, decision, and causality, Cambridge University Press, Cambridge, 2014) and Ahmed and Caulton (Synthese, 191(18): 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. [ABSTRACT FROM AUTHOR]

A prominent philosophical debate concerns whether we should accept causal decision theory (CDT) or evidential decision theory (EDT) 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. [ABSTRACT FROM AUTHOR]

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 (Australas J Philos 591:5-30, 1981a. doi:10.1080/00048408112340011) and that of Sobel (Australas J Philos 64(4):407-437, 1986. doi:10.1080/00048408612342621). As Rabinowicz (Philosophical essays dedicated to Lennart Åqvist on His Fiftieth Birthday, Department of Philosophy, Uppsala, 1982) revealed, Lewis’ proposal is unable to be applied to as broad a set of 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. [ABSTRACT FROM AUTHOR]

Causal decision theory (CDT) is one of our most prominent theories of rational choice and the 'why ain'cha rich?' argument (WAR) is one of the most prominent objections to this theory. According to WAR, CDT is not an adequate theory of rational choice because it leads agents to make decisions that foreseeably leave them less well off than agents that decide in some other manner. Some philosophers take WAR to decisively undermine CDT. On the other hand, others (including David Lewis and Terry Horgan) take WAR to fail to resolve the debate over CDT's adequacy. In this paper, I will defend this second view: WAR does not resolve the debate at hand but instead leads to deadlock. Then, in the second half of this paper, I will show that this deadlock is not broken by a recent variant on WAR due to Caspar Hare and Brian Hedden. Not only does this result have implications for the debate over CDT's adequacy but this discussion also casts light on the broader success/rationality link. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Normative causal decision theories argue that people should use their causal knowledge in decision making. Based on these ideas, we argue that causal knowledge and reasoning may support and thereby potentially improve decision making based on expected outcomes, narratives, and even cues. We will summarize findings from empirical research, which indicate that decision makers use their causal knowledge and reasoning when making a choice. Focusing on decision making in mental health, we will also show that at least sometimes the outcomes resulting from a chosen action can be improved by taking causal knowledge into account. In the second part of the chapter, we propose a structured process describing when and how decision makers should use causal knowledge and reasoning in decision making: the Causal-explanation-based Decision Making framework. Mental health will again be our exemplary domain. We will end by arguing that our model may serve as a blueprint for a rational model for clinical decision making, which may also guide future research. [ABSTRACT FROM AUTHOR]