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)

The problem of the man who met death in Damascus appeared in the infancy of the theory of rational choice known as causal decision theory. A straightforward, unadorned version of causal decision theory is presented here and applied, along with Brian Skyrms’ deliberation dynamics, to Death in Damascus and similar problems. Decision instability is a fascinating topic, but not a source of difficulty for causal decision theory. Andy Egan’s purported (...) counterexample to causal decision theory, Murder Lesion, is considered; a simple response shows how Murder Lesion and similar examples fail to be counterexamples, and clarifies the use of the unadorned theory in problems of decision instability. I compare unadorned causal decision theory to previous treatments by Frank Arntzenius and by Jim Joyce, and recommend a well-founded heuristic that all three accounts can endorse. Whatever course deliberation takes, causal decision theory is consistently a good guide to rational action. (shrink)

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)

The essay presents a novel counterexample to Causal Decision Theory (CDT). Its interest is that it generates a case in which CDT violates the very principles that motivated it in the first place. The essay argues that the objection applies to all extant formulations of CDT and that the only way out for that theory is a modification of it that entails incompatibilism. The essay invites the reader to find this consequence of CDT a reason to (...) reject it. (shrink)

In this paper we discuss how Causal Decision Theory should be modified to handle a class of problematic cases involving deterministic laws. Causal Decision Theory, as it stands, is problematically biased against your endorsing deterministic propositions (for example it tells you to deny Newtonian physics, regardless of how confident you are of its truth). Our response is that this is not a problem for Causal Decision Theory per se, but arises because (...) of the standard method for assessing the truth of certain counterfactuals. The truth of deterministic laws is `modally fragile' on the standard semantics for counterfactuals: if determinism is true and you were to do otherwise, the laws would be different. We provide two ways of avoiding this problem: 1) supplement the standard semantics for counterfactuals with impossible worlds, or 2) introduce rigid designators into the description of problematic decision situations. We argue that both of these approaches are well-motivated and can be readily incorporated into Lewisian Causal Decision Theory. (shrink)

This paper develops an argument against causal decision theory. I formulate a principle of preference, which I call the Guaranteed Principle. I argue that the preferences of rational agents satisfy the Guaranteed Principle, that the preferences of agents who embody causal decision theory do not, and hence that causal decision theory is false.

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 primary aim of this paper is the presentation of a foundation for causal decision theory. This is worth doing because causal decision theory (CDT) is philosophically the most adequate rational decision theory now available. I will not defend that claim here by elaborate comparison of the theory with all its competitors, but by providing the foundation. This puts the theory on an equal footing with competitors for which foundations have (...) already been given. It turns out that it will also produce a reply to the most serious objections made so far against CDT and against the particular version of CDT I will defend. (shrink)

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)

Causal decision theory defines a rational action as the one that tends to cause the best outcomes. If we adopt counterfactual or probabilistic theories of causation, then we may face problems in overdetermination cases. Do such problems affect Causal decision theory? The aim of this work is to show that the concept of causation that has been fundamental in all versions of causal decision theory is not the most intuitive one. Since (...) overdetermination poses problems for a counterfactual theory of causation, one can think that a version of causal decision theory based on counterfactual dependence may also be vulnerable to such scenarios. However, only when an intuitive, not analyzed notion of causation is presupposed as a ground for a more plausible version of causal decision theory, overdetermination turns problematic. The first interesting consequence of this is that there are more reasons to dismiss traditional theories of causation (and to accept others). The second is to confirm that traditional causal decision theory is not based on our intuitive concept of the causal relation. (shrink)

This paper explores the possibility that causal decision theory can be formulated in terms of probabilities of conditionals. It is argued that a generalized Stalnaker semantics in combination with an underlying branching time structure not only provides the basis for a plausible account of the semantics of indicative conditionals, but also that the resulting conditionals have properties that make them well-suited as a basis for formulating causal decision theory. Decision theory (at least (...) if we omit the frills) is not an esoteric science, however unfamiliar it may seem to an outsider. Rather it is a systematic exposition of the consequences of certain well-chosen platitudes about belief, desire, preference and choice. It is the very core of our common-sense theory of persons, dissected out and elegantly systematized. (David Lewis, Synthese 23:331–344, 1974, p. 337). A small distortion in the analysis of the conditional may create spurious problems with the analysis of other concepts. So if the facts about usage favor one among a number of subtly different theories, it may be important to determine which one it is. (Robert Stalnaker, A Defense of Conditional Excluded Middle, pp. 87–104, 1980, p. 87). (shrink)

According to orthodox causal decision theory, performing an action can give you information about factors outside of your control, but you should not take this information into account when deciding what to do. Causal decision theorists caution against an irrational policy of 'managing the news'. But, by providing information about factors outside of your control, performing an act can give you two, importantly different, kinds of good news. It can tell you that the world in (...) which you find yourself is good in ways you can't control, and it can also tell you that the act itself is in a position to make the world better. While the first kind of news does not speak in favor of performing an act, I believe that the second kind of news does. I present a revision of causal decision theory which advises you to manage the news about the good you stand to promote, while ignoring news about the good the world has provided for you. (shrink)

This paper has as its topic two recent philosophical disputes. One of these disputes is internal to the project known as decision theory, and while by now familiar to many, may well seem to be of pressing concern only to specialists. It has been carried on over the last twenty years or so, but by now the two opposing camps are pretty well entrenched in their respective positions, and the situation appears to many observers (as well as to (...) some of the parties involved) to have reached a sort of stalemate. The second of these two disputes is, on the other hand, very much alive. While it has been framed in decision theoretic terms, it is definitely not a dispute internal to that enterprise. It is, rather, a debate about the very coherence of the notion of objective value, and as such touches on issues of central importance to, for example, meta–ethics and moral psychology. (shrink)

Why should we be means-end rational? Why care whether someone’s mental states exhibit certain formal patterns, like the ones formalized in causal decision theory? This paper establishes a dominance argument for these constraints in a finite setting. If you violate the norms of causal decision theory, then your desires will be aptness dominated. That is, there will be some alternative set of desires that you could have had, which would be more apt (closer to (...) the actual values fixed by your sensibility/ what you intrinsically care about) no matter which world is actual. If we care about having apt desires, then, we should never let ourselves violate these norms of means-end rationality. This argument shares a form with now-standard accuracy arguments for probabilism, opening up a new terrain for that discussion. I show that the foundational assumptions about the shape of accuracy and of aptness required to run the arguments for means-end rationality and for probabilism run closely parallel. I show the argument is robust under different theories of subjective actual value, including nonclassical treatments of indeterminacy in actual value; I show the argument may (but need not!) be generalized to objective theories of value. The upshot is that aptness-domination arguments for decision theories should be added to the philosophical playbook. (shrink)

I defend counterfactual decision theory, which says that you should evaluate an action in terms of which outcomes would likely obtain were you to perform it. Counterfactual decision theory has traditionally been subsumed under causal decision theory as a particular formulation of the latter. This is a mistake. Counterfactual decision theory is importantly different from, and superior to, causal decision theory, properly so called. Causation and counterfactuals come apart (...) in three kinds of cases. In cases of overdetermination, an action can cause a good outcome without the latter counterfactually depending on the former. In cases of constitution, an action can constitute a good outcome rather than causing it. And in cases of determinism, either the laws or the past counterfactually depend on your action, even though your action cannot cause the laws or the past to be different. In each of these cases, it is counterfactual decision theory which gives the right verdict, and for the right reasons. (shrink)

The dispute in philosophical decision theory between causalists and evidentialists remains unsettled. Many are attracted to the causal view’s endorsement of a species of dominance reasoning, and to the intuitive verdicts it gets on a range of cases with the structure of the infamous Newcomb’s Problem. But it also faces a rising wave of purported counterexamples and theoretical challenges. In this paper I will describe a novel decision theory which saves what is appealing about the (...) causal view while avoiding its most worrying objections, and which promises to generalize to solve a set of related problems in other normative domains. (shrink)

1Department of Philosophy, University of Nottingham, University Park, Nottingham NG7 2RD, UK. [email protected] of Philosophy, University of Nottingham, University Park, Nottingham NG7 2RD, UK. [email protected].

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.

(1) Suppose that you care only about speaking the truth, and are confident that some particular deterministic theory is true. If someone asks you whether that theory is true, are you rationally required to answer "yes"? -/- (2) Suppose that you face a problem in which (as in Newcomb's problem) one of your options---call it "taking two boxes"---causally dominates your only other option. Are you rationally required to take two boxes? -/- Those of us attracted to causal (...) decision theory are under pressure to answer "yes" to both questions. However, it has been shown that many existing decision theories are inconsistent with doing so (Ahmed 2014). A simple proof shows that the same goes for an even wider class of theories: all "suppositional" decision theories. The moral is that causal decision theorists must either answer "no" to one of the above questions, or else abandon suppositional decision theories. (shrink)

I respond to a dilemma for Causal Decision Theory (CDT) under determinism, posed in Adam Elga's paper “Confessions of a Causal Decision Theorist”. The treatment I present highlights (i) the status of laws as predictors, and (ii) the consequences of decision dependence, which arises natively out of Jeffrey Conditioning and CDT's characteristic equation. My argument leverages decision dependence to work around a key assumption of Elga's proof: to wit, that in the two problems (...) he presents, the CDTer must employ subjunctive‐suppositional (rather than evidential) transformations of a shared prior. (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)

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)

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)

Decision theory has had a long-standing history in the behavioural and social sciences as a tool for constructing good approximations of human behaviour. Yet as artificially intelligent systems (AIs) grow in intellectual capacity and eventually outpace humans, decision theory becomes evermore important as a model of AI behaviour. What sort of decision procedure might an AI employ? In this work, I propose that policy-based causal decision theory (PCDT), which places a primacy on (...) the decision-relevance of predictors and simulations of agent behaviour, may be such a procedure. I compare this account to the recently-developed functional decision theory (FDT), which is motivated by similar concerns. I also address potentially counterintuitive features of PCDT, such as its refusal to condition on observations made at certain times. (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)

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)

My topic is how to make decisions when you possess foreknowledge of the consequences of your choice. Many have thought that these kinds of decisions pose a distinctive and novel problem for causal decision theory (CDT). My thesis is that foreknowledge poses no new problems for CDT. Some of the purported problems are not problems. Others are problems, but they are not problems for CDT. Rather, they are problems for our theories of subjunctive supposition. Others are problems, (...) but they are not new problems. They are old problems transposed into a new key. Nonetheless, decisions made with foreknowledge illustrate important lessons about the instrumental value of our choices. Once we've appreciated these lessons, we are left with a version of CDT which faces no novel threats from foreknowledge. (shrink)

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. (shrink)

The nature of evidence is a problem for epistemology, but I argue that this problem intersects with normative decision theory in a way that I think is underappreciated. Among some decision theorists, there is a presumption that one can always ignore the nature of evidence while theorizing about principles of rational choice. In slogan form: decision theory only cares about the credences agents actually have, not the credences they should have. I argue against this presumption. (...) In particular, I argue that if evidence can be unspecific, then an alleged counterexample to causal decision theory fails. This implies that what theory of decision we think is true may depend on our opinions regarding the nature of evidence. Even when we are theorizing about subjective theories of rationality, we cannot set aside questions about the objective nature of evidence. (shrink)

I describe epistemic versions of the contrast between causal and conventionally probabilistic decision theory, including an epistemic version of Newcomb's paradox.

Causal Decision Theory reckons the choice-worthiness of an option to be completely independent of its evidential bearing on its non-effects. But after one has made a choice this bearing is relevant to future decisions. Therefore it is possible to construct problems of sequential choice in which Causal Decision Theory makes a guaranteed loss. So Causal Decision Theory is wrong. The source of the problem is the idea that agents have a special (...) perspective on their own contemplated actions, from which evidential connections that observers can see are either irrelevant or invisible. (shrink)

Orthodox causal decision theory is unstable. Its advice changes as you make up your mind about what you will do. Several have objected to this kind of instability and explored stable alternatives. Here, I'll show that explorers in search of stability must part with a vestige of their homeland. There is no plausible stable decision theory which satisfies Savage's Sure Thing Principle. So those in search of stability must learn to live without it.

Andy Egan recently drew attention to a class of decision situations that provide a certain kind of informational feedback, which he claims constitute a counterexample to causal decision theory. Arntzenius and Wallace have sought to vindicate a form of CDT by describing a dynamic process of deliberation that culminates in a “mixed” decision. I show that, for many of the cases in question, this proposal depends on an incorrect way of calculating expected utilities, and argue (...) that it is therefore unsuccessful. I then tentatively defend an alternative proposal by Joyce, which produces a similar process of dynamic deliberation but for a different reason. (shrink)

If the laws are deterministic, then standard theories of counterfactuals are forced to reject at least one of the following conditionals: 1) had you chosen differently, there would not have been a violation of the laws of nature; and 2) had you chosen differently, the initial conditions of the universe would not have been different. On the relevant readings—where we hold fixed factors causally independent of your choice—both of these conditionals appear true. And rejecting either one leads to trouble for (...) philosophical theories which rely upon counterfactual conditionals—like, for instance, causal decision theory. Here, I outline a semantics for counterfactual conditionals which allows us to accept both (1) and (2). And I discuss how this semantics deals with objections to causal decision theory from Arif Ahmed. (shrink)

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. (shrink)

I present a decision problem in which causal decision theory appears to violate the independence of irrelevant alternatives (IIA) and normal-form extensive-form equivalence (NEE). I show that these violations lead to exploitable behavior and long-run poverty. These consequences appear damning, but I urge caution. This decision should lead causalists to a better understanding of what it takes for a decision between some collection of options to count as a subdecision of a decision between (...) a larger collection of options. And with this better understanding of subdecisions in hand, causalists will not violate the IIA or the NEE. This decision will also teach causalists that, in sequential decisions, a rational agent may be led to make a series of choices which are causally dominated by some other sequence of choices they could have made instead. I will encourage causalists to recognise this as an intrapersonal tragedy of the commons. (shrink)

A common argument for favouring Evidential Decision Theory (EDT) over Causal Decision Theory (CDT) is that EDT has predictably higher expected returns in Newcomb Problems. But this doesn’t show much. For almost any pair of theories you can come up with cases where one does, on average, better than the other. Here I describe a case involving dynamic choice where EDT predictably does worse than CDT.

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. (shrink)

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)

Andy Egan's Smoking Lesion and Psycho Button cases are supposed to be counterexamples to Causal Decision Theory. This paper argues that they are not: more precisely, it argues that if CDT makes the right call in Newcomb's problem then it makes the right call in Egan cases too.

In this Master's dissertation, I try to show that the causal theory of memory, which is the only theory developed so far that at first view seems more plausible and that could be integrated with psychological explanations and investigations of memory, shows some conceptual and ontological problems that go beyond the internal inconsistencies that each version can present. On one hand, the memory phenomenon analyzed is very limited: in general it is reduced to the conscious act of (...) remembering expressed in a propositional format: the idea of an agent who can control his memory reports, in contrast to a passive subject who merely retrieve his encoded memories, is not even considered. Furthermore, the representational function of memory is the only function taken into account; and all the diachronic changes that could entail a dynamic conception of memory are ignored, as is the consideration of context in a broad sense of the term. On the other hand, causal theories make some implicit assumptions that are obviously questionable (e.g., the difference between facts and events) and leave some of their key concepts unexplained, like the nature of the causality, the isomorphism between the memory trace and the memory event, etc. Nonetheless, the principal point that we criticized is the idea of a correspondence between a past event or representation of it, the correspondent memory trace and the memory representation of it. Psychological models of memory, with the exception of the spatial analogy, question some of these assumptions and suggest that memory information is organized in a way other than that implied by the causal theory. But it is distributed connectionism that actually challenges the causal theory of memory, not only through some of its basic postulates, like the superpositional conception of memory traces, their mutual influence and constant state of change, but also because it is compatible with a more situated approach to memory phenomena as well as with the idea that memory in reality accomplishes a variety of functions that go beyond the representational one. The explicit and implicit ideas provided by distributed connectionism, together with the previous criticisms that we made to of causal theory, clearly suggest the possibility of developing another kind of philosophical theory of memory, different from existing theories. This task, nonetheless, implies: (a) A much deeper study of connectionism principles and memory models, as well as of the rival candidate, symbolism, plus the evaluation of these models in the light of psychological and neurophysiological empirical research, which unfortunately I have not dealt with in this work. (b) An analysis of the metaphysical nature of events and facts, in order to see if we should decide in favor of a realist or a non-realist account. It is also clear that this decision can’t be taken without an examination of perception studies (and not exclusively philosophical ones) which would be crucial for determining what is encoded in memory, and studies devoted to language, which, I suspect, plays an important role in the segmentation of reality. (c) A clarification of the notion of causality, because we wouldn’t like to deny any kind entail the complete disappearance of boundaries between memory and imagination. And even if I suspect that my future work will question the existence of absolute boundaries between these two capacities, the notion of causality can’t be completely erase from the theory of memory without erasing the memory phenomenon itself. But this causality, as we’ve already showed, can’t consist in the naïve conception adopted by the causal accounts of memory, nor can it be explained in counterfactual terms. If we think of the reconsolidation concept, of the interdependence between all the memory system, and of the deciding influence of context, it’s almost sure that more than one notion of causality will need to be invoked in order to explain memory. (d) Because I do not want to reduce the study of memory exclusively to cases of remembering, it’s essential to analyze how memory intervenes not only in imagination, but also in perception, reasoning and future projection, for example. In this respect, we could quote the interesting articles of Atance & O’Neill (2001) and Bucker & Carroll (2006), the first concerning the relationship between episodic memory and episodic future thinking, the second concerning the relationship between episodic memory and all kinds of self-projection, such as navigation and theory of mind, relations that would also have to be explored from a philosophical perspective, in particular to overcome the tendency to suppose that representation is the only function of memory. I will also mention the necessity of combining the notion of metamemory with that of memory to some extent; as I have already remarked, it is implausible to suppose that a subject who consciously remembers something is unable to exert any kind of control over his memory reports. (e) Finally, in order to integrate a situated conception of memory into a philosophical account, it will also be indispensable to take into consideration what is known as “everyday memory research” as well as studies in social and collective memory. (shrink)

Causal reasoning and game-theoretic reasoning are fundamental topics in artificial intelligence, among many other disciplines: this paper is concerned with their intersection. Despite their importance, a formal framework that supports both these forms of reasoning has, until now, been lacking. We offer a solution in the form of (structural) causal games, which can be seen as extending Pearl's causal hierarchy to the game-theoretic domain, or as extending Koller and Milch's multi-agent influence diagrams to the causal domain. (...) We then consider three key questions: i) How can the (causal) dependencies in games - either between variables, or between strategies - be modelled in a uniform, principled manner? ii) How may causal queries be computed in causal games, and what assumptions does this require? iii) How do causal games compare to existing formalisms? To address question i), we introduce mechanised games, which encode dependencies between agents' decision rules and the distributions governing the game. In response to question ii), we present definitions of predictions, interventions, and counterfactuals, and discuss the assumptions required for each. Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support. Finally, we highlight possible applications of causal games, aided by an extensive open-source Python library. (shrink)

A given causal system can be represented in a variety of ways. How do agents determine which variables to include in their causal representations, and at what level of granularity? Using techniques from Bayesian networks, information theory, and decision theory, we develop a formal theory according to which causal representations reflect a trade-off between compression and informativeness, where the optimal trade-off depends on the decision-theoretic value of information for a given agent in (...) a given context. This theory predicts that, all else being equal, agents prefer causal models that are as compressed as possible. When compression is associated with information loss, however, all else is not equal, and our theory predicts that agents will favor compressed models only when the information they sacrifice is not informative with respect to the agent’s anticipated decisions. We then show, across six studies reported here (N=2,364) and one study reported in the supplemental materials (N=182), that participants’ preferences over causal models are in keeping with the predictions of our theory. Our theory offers a unification of different dimensions of causal evaluation identified within the philosophy of science (proportionality and stability), and contributes to a more general picture of human cognition according to which the capacity to create compressed (causal) representations plays a central role. (shrink)

The best-known argument for Evidential Decision Theory (EDT) is the ‘Why ain’cha rich?’ challenge to rival Causal Decision Theory (CDT). The basis for this challenge is that in Newcomb-like situations, acts that conform to EDT may be known in advance to have the better return than acts that conform to CDT. Frank Arntzenius has recently proposed an ingenious counter argument, based on an example in which, he claims, it is predictable in advance that acts that (...) conform to EDT will do less well than acts that conform to CDT. We raise two objections to Arntzenius’s example. We argue, first, that the example is subtly incoherent, in a way that undermines its effectiveness against EDT; and, second, that the example relies on calculating the average return over an inappropriate population of acts. (shrink)

Seeking a decision theory that can handle both the Newcomb problems that challenge evidential decision theory and the unstable problems that challenge causal decision theory, some philosophers recently have turned to ‘graded ratifiability’. However, the graded ratifiability approach to decision theory is, despite its virtues, unsatisfactory; for it conflicts with the platitude that it is always rationally permissible for an agent to knowingly choose their best option.

This article examines how specific realist and projectivist versions of manipulability theories of causation deal with the problem of objectivity. Does an agent-dependent concept of manipulability imply that conflicting causal claims made by agents with different capacities can come out as true? In defence of the projectivist stance taken by the agency view, I argue that if the agent’s perspective is shown to be uniform across different agents, then the truth-values of causal claims do not vary arbitrarily and, (...) thus, reach a satisfactory level of objectivity. My argument connects Price’s considerations on the situation of deliberation, whose structure, common to all agents, is the same with respect to both decision making and causal claims on a concept inspired by Douglas’s classification of objectivity of thought processes: the perspective of the detached agent. I further argue that, despite his agent-independent concept of intervention, Woodward’s claim of a stronger objectivity standard cannot be achieved, as the relativity of causal concepts to a variable set brings about the issue of the agent’s choice of variables. Consequently, a more permissive objectivity standard applies to both views. (shrink)

A well-known trilemma faces the interpretation of Kant’s theory of affection, namely whether the objects that affect us are empirical, noumenal, or both. I argue that according to Kant, the things that affect us and cause representations in us are not empirical objects. I articulate what I call the Causal Power Argument, according to which empirical objects cannot affect us because they do not have the right kind of power to cause representations. All the causal powers that (...) empirical objects have are moving powers, and such powers can only have spatial effects. According to Kant, however, the representations that arise in us as a result of the affection of our sensibility are non-spatial. I show that this argument is put forward by Kant in a number of passages, and figures as a decisive reason for rejecting empirical affection and instead endorsing affection by the things in themselves. (shrink)

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 Chloe 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 which is symptomatic of rationality.

I show that Kavka's toxin puzzle raises a problem for the “Responsibility Theodicy,” which holds that the reason God typically does not intervene to stop the evil effects of our actions is that such intervention would undermine the possibility of our being significantly responsible for overcoming and averting evil. This prominent theodicy seems to require that God be able to do what the agent in Kavka's toxin story cannot do: stick by a plan to do some action at a future (...) time even though when that time comes, there will be no good reason for performing that action. I assess various approaches to solving this problem. Along the way, I develop an iterated variant of Kavka's toxin case and argue that the case is not adequately handled by standard causal decision theory. (shrink)