We present a conservative extension of a Bayesian account of confirmation that can deal with the problem of old evidence and new theories. So-called open-minded Bayesianism challenges the assumption—implicit in standard Bayesianism—that the correct empirical hypothesis is among the ones currently under consideration. It requires the inclusion of a catch-all hypothesis, which is characterized by means of sets of probability assignments. Upon the introduction of a new theory, the former catch-all is decomposed into a new empirical hypothesis and a new (...) catch-all. As will be seen, this motivates a second update rule, besides Bayes’ rule, for updating probabilities in light of a new theory. This rule conserves probability ratios among the old hypotheses. This framework allows for old evidence to confirm a new hypothesis due to a shift in the theoretical context. The result is a version of Bayesianism that, in the words of Earman, “keep[s] an open mind, but not so open that your brain falls out”. (shrink)

A concept that can be expressed by the term ‘rationality’ plays a central role in both epistemology and ethics -- and especially in formal epistemology and decision theory. It is argued here that when the term is used in this way, the concept of “rationality” is the concept of a kind of virtue, with all the central features that are ascribed to the virtues by Plato and Aristotle, among others. Interpreting rationality as a kind of virtue helps to solve several (...) problems, such as the relations between “rationality” and “rational requirements” and between “propositional” and “doxastic justification”, and to answer objections to the thesis that “rationality” is a normative concept that are based on the principle that ‘ought’ implies ‘can’. (shrink)

I shall offer some very plausible assumptions for the measure of confirmation and show that they imply that E confirms H relative to background K to degree f/PK), where f is a strictly increasing function. An additional assumption about how measures of confirmation combine then makes f be proportional to a logarithm.

This commentary focuses on the parts of psychological game theory dealing with preference, as illustrated by team reasoning, and supports the conclusion that these theoretical notions do not contribute above and beyond existing theory in understanding social interaction. In particular, psychology and games are already bridged by a comprehensive, formal, and inherently psychological theory, interdependence theory (Kelley & Thibaut 1978; Kelley et al. 2003), which has been demonstrated to account for a wide variety of social interaction phenomena.

I investigate whether degreed beliefs are able to play the predictive, explanatory, and modeling roles that they are frequently taken to play. The investigation focuses on evidence—both from sources familiar in epistemology as well as recent work in behavioral economics and cognitive psychology—of variability in agents' apparent degrees of belief. Although such variability has been noticed before, there has been little philosophical discussion of its breadth or of the psychological mechanisms underlying it. Once these are appreciated, the inadequacy of degrees (...) of belief becomes clear. I offer a theoretical alternative to degrees of belief, what I call the filter theory. (shrink)

In this paper we show that the coherence measures of Olsson (J Philos 94:246–272, 2002), Shogenji (Log Anal 59:338–345, 1999), and Fitelson (Log Anal 63:194–199, 2003) satisfy the two most important adequacy requirements for the purpose of assessing theories. Following Hempel (Synthese 12:439–469, 1960), Levi (Gambling with truth, New York, A. A. Knopf, 1967), and recently Huber (Synthese 161:89–118, 2008) we require, as minimal or necessary conditions, that adequate assessment functions favor true theories over false theories and true and informative (...) theories over true but uninformative theories. We then demonstrate that the coherence measures of Olsson, Shogenji, and Fitelson satisfy these minimal conditions if we confront the hypotheses with a separating sequence of observational statements. In the concluding remarks we set out the philosophical relevance, and limitations, of the formal results. Inter alia, we discuss the problematic implications of our precondition that competing hypotheses must be confronted with a separating sequence of observational statements, which also leads us to discuss theory assessment in the context of scientific antirealism. (shrink)

This chapter contains sections titled: The Decision ‐ Theoretic Picture The Decision ‐ plus ‐ Deliberation Picture A Common Mistake References.

Recent advances in causal reasoning have given rise to a computational model that emulates the process by which humans generate, evaluate, and distinguish counterfactual sentences. Contrasted with the “possible worlds” account of counterfactuals, this “structural” model enjoys the advantages of representational economy, algorithmic simplicity, and conceptual clarity. This introduction traces the emergence of the structural model and gives a panoramic view of several applications where counterfactual reasoning has benefited problem areas in the empirical sciences.

Judea Pearl won the 2010 Rumelhart Prize in computational cognitive science due to his seminal contributions to the development of Bayes nets and causal Bayes nets, frameworks that are central to multiple domains of the computational study of mind. At the heart of the causal Bayes nets formalism is the notion of a counterfactual, a representation of something false or nonexistent. Pearl refers to Bayes nets as oracles for intervention, and interventions can tell us what the effect of action will (...) be or what the effect of counterfactual possibilities would be. Counterfactuals turn out to be necessary to understand thought, perception, and language. This selection of papers tells us why, sometimes in ways that support the Bayes net framework and sometimes in ways that challenge it. (shrink)

In the first section of this paper I follow an important trajectory in the development of Davidson's notion of radical interpretation: From being interpretationally concerned only with language, like Quine's radical translation that precedes it, through involving the ascription of belief in increasingly complex ways, to finally incorporating desire and preference. In the second section of the paper I show that Davidson falls short of incorporating non-linguistic action in radical interpretation, I assess his motivations for doing so, and I criticize (...) these motivations. In the third and final section I propose a unified interpretation scheme for language, action and mind. (shrink)

Logic is the study of the quality of arguments. An argument consists of a set of premises and a conclusion. The quality of an argument depends on at least two factors: the truth of the premises, and the strength with which the premises confirm the conclusion. The truth of the premises is a contingent factor that depends on the state of the world. The strength with which the premises confirm the conclusion is supposed to be independent of the state of (...) the world. Logic is only concerned with this second, logical factor of the quality of arguments. (shrink)

It seems natural to choose whether to have a child by reflecting on what it would be like to actually have a child. I argue that this natural approach fails. If you choose to become a parent, and your choice is based on projections about what you think it would be like for you to have a child, your choice is not rational. If you choose to remain childless, and your choice is based upon projections about what you think it (...) would be like for you to have a child, your choice is not rational. This suggests we should reject our ordinary conception of how to make this life-changing decision, and raises general questions about how to rationally approach important life choices. (shrink)

Forty years ago, Bayesian philosophers were just catching a new wave of technical innovation, ushering in an era of scoring rules, imprecise credences, and infinitesimal probabilities. Meanwhile, down the hall, Gettier’s 1963 paper [28] was shaping a literature with little obvious interest in the formal programs of Reichenbach, Hempel, and Carnap, or their successors like Jeffrey, Levi, Skyrms, van Fraassen, and Lewis. And how Bayesians might accommodate the discourses of full belief and knowledge was but a glimmer in the eye (...) of Isaac Levi. Forty years later, scoring rules, imprecise credences, and infinitesimal probabilities are all the rage. And the formal and “informal” traditions are increasingly coming together as Bayesian arguments spill over into debates about the foundations of empirical knowledge, skepticism, and more. Relatedly, Bayesian interest in full belief and knowledge has never been greater. Much more besides has happened in the last forty years of Bayesian philosophy, .. (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.

A simple counterfactual theory of causation fails because of problems with cases of preemption. This might lead us to expect that preemption will raise problems for counterfactual theories of other concepts that have a causal dimension. Indeed, examples are easy to find. But there is one case where we do not find this. Several versions of causal decision theory are formulated using counterfactuals. This might lead us to expect that these theories will yield the wrong recommendations in cases of preemption. (...) But they do not. The explanation, I argue, is that the ‘cause’ that has been the target of counterfactual analyses is a specific relation, ‘actual causation’, that is not needed for prospective deliberation. A simple counterfactual theory of causation seems to capture the notion of cause needed for causal decision theory. This shows, in opposition to some critics, that counterfactual theories of causation are not barking up the wrong tree. (shrink)

I formulate a counterfactual version of the notorious 'Ramsey Test'. Whereas the Ramsey Test for indicative conditionals links credence in indicatives to conditional credences, the counterfactual version links credence in counterfactuals to expected conditional chance. I outline two forms: a Ramsey Identity on which the probability of the conditional should be identical to the corresponding conditional probabihty/expectation of chance; and a Ramsey Bound on which credence in the conditional should never exceed the latter.Even in the weaker, bound, form, the counterfactual (...) Ramsey Test makes counterfactuals subject to the very argument that Lewis used to argue against the indicative version of the Ramsey Test. I compare the assumptions needed to run each, pointing to assumptions about the time-evolution of chances that can replace the appeal to Bayesian assumptions about credence update in motivating the assumptions of the argument.I finish by outlining two reactions to the discussion: to indicativize the debate on counterfactuals; or to counterfactualize the debate on indicatives. (shrink)

In “A Subjectivist’s Guide to Objective Chance,” David Lewis says that he is “led to wonder whether anyone but a subjectivist is in a position to understand objective chance.” The present essay aims to motivate this same Lewisean attitude, and a similar degree of modest subjectivism, with respect to objective causation. The essay begins with Newcomb problems, which turn on an apparent tension between two principles of choice: roughly, a principle sensitive to the causal features of the relevant situation, and (...) a principle sensitive only to evidential factors. Two-boxers give priority to causal beliefs, and one-boxers to evidential beliefs. The essay notes that a similar issue can arise when the modality in question is chance, rather than causation. In this case, the conflict is between decision rules based on credences guided solely by chances, and rules based on credences guided by other sorts of probabilistic evidence. Far from excluding cases of the latter kind, Lewis’s Principal Principle explicitly allows for them, in the form of the caveat that credences should follow beliefs about chances only in the absence of “inadmissible evidence.” The essay then exhibits a tension in Lewis’s views on these two matters, by presenting a class of decision problems—some of them themselves Newcomb problems—in which Lewis’s view of the relevance of inadmissible evidence seems in tension with his causal decision theory. It offers a diagnosis for this dilemma and proposes a remedy, based on an extension of a proposal due to Ned Hall and others from the case of chance to that of causation. The remedy suggests a new view of the relation between causal decision theory and evidential decision theory, namely, that they stand to each other much as chance stands to credence, being objective and subjective faces of the same practical coin. This has much the same metaphysical benefits as Lewis’s own view of chance and also throws interesting new light on Newcomb problems, providing an irenic resolution of the apparent disagreement between causal and evidential decision rules. (shrink)

What should we do when we won't do as we ought? Suppose it ought to be that the procrastinating professor accept the task of reviewing a book, and actually review the book. It seems clear that given he won't review it, he ought not to accept the task. That is a genuine moral obligation in light of less than perfect circumstances. I want to entertain the possibility that a set of such obligations form something like a 'practical morality'; that which (...) we ought to do given that we're unlikely or unwilling to do much of what ideal morality demands. If it is possible to give a coherent account of these kinds of obligations, then it is possible to entertain the idea that these obligations are in fact what morality demands. The conceptual truths about justice (good, right, fairness) that come from ideals are one thing; the actions that morality demands of people given their actual circumstances are quite another. In this paper I will ask about the kinds of facts that can be used to establish a more circumscribed set of obligations than we get from the orthodox view about moral obligation. (shrink)

In this paper, I explore a question about deductive reasoning: why am I in a position to immediately infer some deductive consequences of what I know, but not others? I show why the question cannot be answered in the most natural ways of answering it, in particular in Descartes’s way of answering it. I then go on to introduce a new approach to answering the question, an approach inspired by Hume’s view of inductive reasoning.

Among Bayesian confirmation theorists, several quantitative measures of the degree to which an evidential proposition E confirms a hypothesis H have been proposed. According to one popular recent measure, s, the degree to which E confirms H is a function of the equation P(H|E) − P(H|~E). A consequence of s is that when we have two evidential propositions, E1 and E2, such that P(H|E1) = P(H|E2), and P(H|~E1) ≠ P(H|~E2), the confirmation afforded to H by E1 does not equal the (...) confirmation afforded to H by E2. I present several examples that demonstrate the unacceptability of this result, and conclude that we should reject s (and other measures that share this feature) as a measure of confirmation. (shrink)

Suppose that it is rational to choose or intend a course of action if and only if the course of action maximizes some sort of expectation of some sort of value. What sort of value should this definition appeal to? According to an influential neo-Humean view, the answer is “Utility”, where utility is defined as a measure of subjective preference. According to a rival neo-Aristotelian view, the answer is “Choiceworthiness”, where choiceworthiness is an irreducibly normative notion of a course of (...) action that is good in a certain way. The neo-Humean view requires preferences to be measurable by means of a utility function. Various interpretations of what exactly a “preference” is are explored, to see if there is any interpretation that supports the claim that a rational agent’s “preferences” must satisfy the “axioms” that are necessary for them to be measurable in this way. It is argued that the only interpretation that supports the idea that the rational agent’s preferences must meet these axioms interprets “preferences” as a kind of value-judgment. But this turns out to be version of the neo-Aristotelian view, rather than the neo-Humean view. Rational intentions maximize expected choiceworthiness, not expected utility. (shrink)

In the first paper, I discussed the basic claims of Bayesianism (that degrees of belief are important, that they obey the axioms of probability theory, and that they are rationally updated by either standard or Jeffrey conditionalization) and the arguments that are often used to support them. In this paper, I will discuss some applications these ideas have had in confirmation theory, epistemol- ogy, and statistics, and criticisms of these applications.

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

Andy Egan has recently produced a set of alleged counterexamples to causal decision theory in which agents are forced to decide among causally unratifiable options, thereby making choices they know they will regret. I show that, far from being counterexamples, CDT gets Egan's cases exactly right. Egan thinks otherwise because he has misapplied CDT by requiring agents to make binding choices before they have processed all available information about the causal consequences of their acts. I elucidate CDT in a way (...) that makes it clear where Egan goes wrong, and which explains why his examples pose no threat to the theory. My approach has similarities to a modification of CDT proposed by Frank Arntzenius, but it differs in the significance that it assigns to potential regrets. I maintain, contrary to Arntzenius, that an agent facing Egan's decisions can rationally choose actions that she knows she will later regret. All rationality demands of agents it that they maximize unconditional causal expected utility from an epistemic perspective that accurately reflects all the available evidence about what their acts are likely to cause. This yields correct answers even in outlandish cases in which one is sure to regret whatever one does. (shrink)

This paper argues that just as full beliefs can constitute knowledge, so can properties of your credence distribution. The resulting notion of probabilistic knowledge helps us give a natural account of knowledge ascriptions embedding language of subjective uncertainty, and a simple diagnosis of probabilistic analogs of Gettier cases. Just like propositional knowledge, probabilistic knowledge is factive, safe, and sensitive. And it helps us build knowledge-based norms of action without accepting implausible semantic assumptions or endorsing the claim that knowledge is interest-relative.

Sometimes, we think of belief as a phenomenon that comes in degrees – that is, in the many different levels of confidence that a thinker might have in various different propositions. Sometimes, we think of belief as a simple two-place relation that holds between a thinker and a proposition – that is, as what I shall here call "outright belief".

This paper compares two alternative explanations of pragmatic encroachment on knowledge (i.e., the claim that whether an agent knows that p can depend on pragmatic factors). After reviewing the evidence for such pragmatic encroachment, we ask how it is best explained, assuming it obtains. Several authors have recently argued that the best explanation is provided by a particular account of belief, which we call pragmatic credal reductivism. On this view, what it is for an agent to believe a proposition is (...) for her credence in this proposition to be above a certain threshold, a threshold that varies depending on pragmatic factors. We show that while this account of belief can provide an elegant explanation of pragmatic encroachment on knowledge, it is not alone in doing so, for an alternative account of belief, which we call the reasoning disposition account, can do so as well. And the latter account, we argue, is far more plausible than pragmatic credal reductivism, since it accords far better with a number of claims about belief that are very hard to deny. (shrink)

Let us take an example that Bernard Williams (1981: 102) made famous. Suppose that you want a gin and tonic, and you believe that the stuff in front of you is gin. In fact, however, the stuff is not gin but petrol. So if you drink the stuff (even mixed with tonic), it will be decidedly unpleasant, to say the least. Should you choose to drink the stuff or not?

Starting from the sixties of the past century theory change has become a main concern of philosophy of science. Two of the best known formal accounts of theory change are the post-Popperian theories of verisimilitude (PPV for short) and the AGM theory of belief change (AGM for short). In this paper, we will investigate the conceptual relations between PPV and AGM and, in particular, we will ask whether the AGM rules for theory change are effective means for approaching the truth, (...) i.e., for achieving the cognitive aim of science pointed out by PPV. First, the key ideas of PPV and AGM and their application to a particular kind of propositional theories - the so called "conjunctive propositions" - will be illustrated. Afterwards, we will prove that, as far as conjunctive propositions are concerned, AGM belief change is an effective tool for approaching the truth. (shrink)

The conjunction fallacy has been a key topic in debates on the rationality of human reasoning and its limitations. Despite extensive inquiry, however, the attempt of providing a satisfactory account of the phenomenon has proven challenging. Here, we elaborate the suggestion (first discussed by Sides et al., 2001) that in standard conjunction problems the fallacious probability judgments experimentally observed are typically guided by sound assessments of confirmation relations, meant in terms of contemporary Bayesian confirmation theory. Our main formal result is (...) a confirmation-theoretic account of the conjuntion fallacy which is proven robust (i.e., not depending on various alternative ways of measuring degrees of confirmation). The proposed analysis is shown distinct from contentions that the conjunction effect is in fact not a fallacy and is compared with major competing explanations of the phenomenon, including earlier references to a confirmation-theoretic account. (shrink)

This paper provides new foundations for Bayesian Decision Theory based on a representation theorem for preferences defined on a set of prospects containing both factual and conditional possibilities. This use of a rich set of prospects not only provides a framework within which the main theoretical claims of Savage, Ramsey, Jeffrey and others can be stated and compared, but also allows for the postulation of an extended Bayesian model of rational belief and desire from which they can be derived as (...) special cases. The main theorem of the paper establishes the existence of a such a Bayesian representation of preferences over conditional prospects, i.e. the existence of a pair of real-valued functions respectively measuring the agent’s degrees of belief and desire and which satisfy the postulated rationality conditions on partial belief and desire. The representation of partial belief is shown to be unique and that of partial desire, unique up to a linear transformation. (shrink)

The paper attempts to rationalize cooperation in the one-shot prisoners' dilemma (PD). It starts by introducing (and preliminarily investigating) a new kind of equilibrium (differing from Aumann's correlated equilibria) according to which the players' actions may be correlated (sect. 2). In PD the Pareto-optimal among these equilibria is joint cooperation. Since these equilibria seem to contradict causal preconceptions, the paper continues with a standard analysis of the causal structure of decision situations (sect. 3). The analysis then raises to a reflexive (...) point of view according to which the agent integrates his own present and future decision situations into the causal picture of his situation (sect. 4). This reflexive structure is first applied to the toxin puzzle and then to Newcomb's problem, showing a way to rationalize drinking the toxin and taking only one box with-out assuming causal mystery (sect. 5). The latter result is finally extended to a rationalization of cooperation in PD (sect. 6). (shrink)

In deciding whether to read this paper, it might seem reasonable for you to base your decision on your confidence (i) that, if you read this paper, you will become a better person. It might also seem reasonable for you to base your decision on your confidence (ii) that, if you were to read this paper, you would become a better person. Is there a difference between (i) and (ii)? If so, are you rationally required to base your decision on (...) your confidence in one of the two conditionals, rather than the other? Keith DeRose (2010) proposes a provocative pair of answers to these questions. In this paper, I argue that DeRose’s answers are incorrect, and I defend a rival pair of answers. (shrink)

Representation theorems are often taken to provide the foundations for decision theory. First, they are taken to characterize degrees of belief and utilities. Second, they are taken to justify two fundamental rules of rationality: that we should have probabilistic degrees of belief and that we should act as expected utility maximizers. We argue that representation theorems cannot serve either of these foundational purposes, and that recent attempts to defend the foundational importance of representation theorems are unsuccessful. As a result, we (...) should reject these claims, and lay the foundations of decision theory on firmer ground. (shrink)

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

Abstract: In the course of seeking an answer to the question "How do you know you are not a zombie?" Floridi (2005) issues an ingenious, philosophically rich challenge to artificial intelligence (AI) in the form of an extremely demanding version of the so-called knowledge game (or "wise-man puzzle," or "muddy-children puzzle")—one that purportedly ensures that those who pass it are self-conscious. In this article, on behalf of (at least the logic-based variety of) AI, I take up the challenge—which is to (...) say, I try to show that this challenge can in fact be met by AI in the foreseeable future. (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)

Sections 1 through 3 present all of the main ideas behind the probabilistic logic of evidential support. For most readers these three sections will suffice to provide an adequate understanding of the subject. Those readers who want to know more about how the logic applies when the implications of hypotheses about evidence claims (called likelihoods) are vague or imprecise may, after reading sections 1-3, skip to section 6. Sections 4 and 5 are for the more advanced reader who wants a (...) detailed understanding of some telling results about how this logic may bring about convergence to the truth. (shrink)

In “Bayesianism, Infinite Decisions, and Binding”, Arntzenius et al. (Mind 113:251–283, 2004 ) present cases in which agents who cannot bind themselves are driven by standard decision theory to choose sequences of actions with disastrous consequences. They defend standard decision theory by arguing that if a decision rule leads agents to disaster only when they cannot bind themselves, this should not be taken to be a mark against the decision rule. I show that this claim has surprising implications for a (...) number of other debates in decision theory. I then assess the plausibility of this claim, and suggest that it should be rejected. (shrink)

*This is a project I hope to come back to one day. It stalled, a bit, on the absence of a positive theory of update I could be satisfied with* When should we believe a indicative conditional, and how much confidence in it should we have? Here’s one proposal: one supposes actual the antecedent; and sees under that supposition what credence attaches to the consequent. Thus we suppose that Oswald did not shot Kennedy; and note that under this assumption, Kennedy (...) was assassinated by someone other than Oswald. Thus we are highly confident in the indicative: if Oswald did not kill Kennedy, someone else did. (shrink)

Bryne & Hajek (1997) argue that Lewis’s (1988; 1996) objections to identifying desire with belief do not go through if our notion of desire is ‘causalized’ (characterized by causal, rather than evidential, decision theory). I argue that versions of the argument go through on certain assumptions about the formulation of decision theory. There is one version of causal decision theory where the original arguments cannot be formulated—the ‘imaging’ formulation that Joyce (1999) advocates. But I argue this formulation is independently objectionable. (...) If we want to maintain the desire as belief thesis, there’s no shortcut through causalization. (shrink)

I argue that one central aspect of the epistemology of causation, the use of causes as evidence for their effects, is largely independent of the metaphysics of causation. In particular, I use the formalism of Bayesian causal graphs to factor the incremental evidential impact of a cause for its effect into a direct cause-to-effect component and a backtracking component. While the “backtracking” evidence that causes provide about earlier events often obscures things, once we our restrict attention to the cause-to-effect component (...) it is true to say promoting (inhibiting) causes raise (lower) the probabilities of their effects. This factoring assumes the same form whether causation is given an interventionist, counterfactual or probabilistic interpretation. Whether we think about causation in terms of interventions and causal graphs, counterfactuals and imaging functions, or probability raising against the background of causally homogenous partitions, if we describe the essential features of a situation correctly then the incremental evidence that a cause provides for its effect in virtue of being its cause will be the same. (shrink)

Various theories try to give accounts of how measures of this confidence do or ought to behave, both as far as the internal mental consistency of the agent as ...

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)

Bayesian confirmation theory—abbreviated to in these notes—is the predominant approach to confirmation in late twentieth century philosophy of science. It has many critics, but no rival theory can claim anything like the same following. The popularity of the Bayesian approach is due to its flexibility, its apparently effortless handling of various technical problems, the existence of various a priori arguments for its validity, and its injection of subjective and contextual elements into the process of confirmation in just the places where (...) critics of earlier approaches had come to think that subjectivity and sensitivity to context were necessary. (shrink)

Rational choice theory enjoys unprecedented popularity and influence in the behavioral and social sciences, but it generates intractable problems when applied to socially interactive decisions. In individual decisions, instrumental rationality is defined in terms of expected utility maximization. This becomes problematic in interactive decisions, when individuals have only partial control over the outcomes, because expected utility maximization is undefined in the absence of assumptions about how the other participants will behave. Game theory therefore incorporates not only rationality but also common (...) knowledge assumptions, enabling players to anticipate their co-players' strategies. Under these assumptions, disparate anomalies emerge. Instrumental rationality, conventionally interpreted, fails to explain intuitively obvious features of human interaction, yields predictions starkly at variance with experimental findings, and breaks down completely in certain cases. In particular, focal point selection in pure coordination games is inexplicable, though it is easily achieved in practice; the intuitively compelling payoff-dominance principle lacks rational justification; rationality in social dilemmas is self-defeating; a key solution concept for cooperative coalition games is frequently inapplicable; and rational choice in certain sequential games generates contradictions. In experiments, human players behave more cooperatively and receive higher payoffs than strict rationality would permit. Orthodox conceptions of rationality are evidently internally deficient and inadequate for explaining human interaction. Psychological game theory, based on nonstandard assumptions, is required to solve these problems, and some suggestions along these lines have already been put forward. Key Words: backward induction; Centipede game; common knowledge; cooperation; epistemic reasoning; game theory; payoff dominance; pure coordination game; rational choice theory; social dilemma. (shrink)

Arguably no concept is more fundamental to science than that of causality, for investigations into cases of existence, persistence, and change in the natural world are largely investigations into the causes of these phenomena. Yet the metaphysics and epistemology of causality remain unclear. For example, the ontological categories of the causal relata have been taken to be objects (Hume 1739), events (Davidson 1967), properties (Armstrong 1978), processes (Salmon 1984), variables (Hitchcock 1993), and facts (Mellor 1995). (For convenience, causes and effects (...) will usually be understood as events in what follows.) Complicating matters, causal relations may be singular (Socrates’s drinking hemlock caused Socrates’s death) or general (Drinking hemlock causes death); hence the relata might be tokens (e.g., instances of properties) or types (e.g., types of events) of the category in question. Other questions up for grabs are: Are singular causes metaphysically and/or epistemologically prior to general causes or vice versa (or neither)? What grounds the intuitive asymmetry of the causal relation? Are macro-causal relations reducible to micro-causal relations? And perhaps most importantly: Are causal facts (e.g., the holding of causal relations) reducible to non-causal facts (e.g., the holding of certain spatiotemporal relations)? (shrink)

outlined. This account is partly inspired by the work of C.S. Peirce. When we want to consider how degree of confirmation varies with changing I show that a large class of quantitative Bayesian measures of con-.

Bayes' Theorem is a simple mathematical formula used for calculating conditional probabilities. It figures prominently in subjectivist or Bayesian approaches to epistemology, statistics, and inductive logic. Subjectivists, who maintain that rational belief is governed by the laws of probability, lean heavily on conditional probabilities in their theories of evidence and their models of empirical learning. Bayes' Theorem is central to these enterprises both because it simplifies the calculation of conditional probabilities and because it clarifies significant features of subjectivist position. Indeed, (...) the Theorem's central insight — that a hypothesis is confirmed by any body of data that its truth renders probable — is the cornerstone of all subjectivist methodology. (shrink)