James M. Joyce

Accuracy and Coherence: Prospects for an Alethic Epistemology of Partial Belief

Traditional epistemology is both dogmatic and alethic. It is dogmatic in the sense that it takes the fundamental doxastic attitude to be full belief, the state in which a person categorically accepts some proposition as true. It is alethic in the sense that it evaluates such categorical beliefs on the basis of what William James calls the ‘two great commandments’ of epistemology: Believe the truth! Avoid error! Other central concepts of dogmatic epistemology – knowledge, justification, reliability, sensitivity, and so on – are understood in terms of their relationships to this ultimate standard of truth or accuracy. Some epistemologists, inspired by Bayesian approaches in decision theory and statistics, have sought to replace the dogmatic model with a probabilistic one in which partial beliefs, or credences, play the leading role. A person’s credence in a proposition X is her level of confidence in its truth. This corresponds, roughly, to the degree to which she is disposed to presuppose X in her theoretical and practical reasoning. Credences are inherently gradational: the strength of a partial belief in X can range from certainty of truth, through maximal uncertainty (in which X and its negation ∼X are believed equally strongly), to complete certainty of falsehood. These variations in confidence are warranted by differing states of evidence, and they rationalize different choices among options whose outcomes depend on X . It is a central normative doctrine of probabilistic epistemology that rational credences should obey the laws of probability. In the idealized case where a believer has a numerically precise credence b(X) for every proposition X in some Boolean algebra of propositions,1 these laws are as follows:

LEVI ON CAUSAL DECISION THEORY AND THE POSSIBILITY OF PREDICTING ONE'S OWN ACTIONS

Isaac Levi has long criticized causal decisiontheory on the grounds that it requiresdeliberating agents to make predictions abouttheir own actions. A rational agent cannot, heclaims, see herself as free to choose an actwhile simultaneously making a prediction abouther likelihood of performing it. Levi is wrongon both points. First, nothing in causaldecision theory forces agents to makepredictions about their own acts. Second,Levis arguments for the ``deliberation crowdsout prediction thesis rely on a flawed modelof the measurement of belief. Moreover, theability of agents to adopt beliefs about theirown acts during deliberation is essentialto any plausible account of human agency andfreedom. Though these beliefs play no part inthe rationalization of actions, they arerequired to account for the causalgenesis of behavior. To explain the causes ofactions we must recognize that (a) an agentcannot see herself as entirely free in thematter of A unless she believes herdecision to perform A will cause A,and (b) she cannot come to a deliberatedecision about A unless she adoptsbeliefs about her decisions. FollowingElizabeth Anscombe and David Velleman, I arguethat an agents beliefs about her own decisionsare self-fulfilling, and that this can beused to explain away the seeming paradoxicalfeatures of act probabilities.

Regret and instability in causal decision theory

Andy Egan has recently produced a set of alleged counterexamples to causal decision theory (CDT) 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.

Are Newcomb problems really decisions

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

Why We Still Need the Logic of Decision

In The Logic of Decision Richard Jeffrey defends a version of expected utility theory that advises agents to choose acts with an eye to securing evidence for thinking that desirable results will ensue. Proponents of causal decision theory have argued that Jeffreys account is inadequate because it fails to properly discriminate the causal features of acts from their merely evidential properties. Jeffreys approach has also been criticized on the grounds that it makes it impossible to extract a unique probability/utility representation from a sufficiently rich system of preferences (given a zero and unit for measuring utility). The existence of these problems should not blind us to the fact that Jeffreys system has advantages that no other decision theory can match: it can be underwritten by a particularly compelling representation theorem proved by Ethan Bolker; and it has a property called partition invariance that every reasonable theory of rational choice must possess. I shall argue that the non-uniqueness problem can be finessed, and that it is impossible to adequately formulate causal decision theory, or any other, without using Jeffreys theory as ones basic analysis of rational desire.

The Development of Subjective Bayesianism

Publisher Summary Bayesianism effectively offers a powerful and compelling set of methods for drawing inductive inferences. Its unifying ideas include (1) Pascals recognition that uncertainty is best expressed probabilistically and that values of unknown quantities are best estimated using the principle of mathematical expectation, and (2) Bayess insight that learning and inductive inference can be fruitfully modeled using conditional probabilities and Bayess theorem. The two central challenges for Bayesianism are the problem of the priors and the development of general methods for Bayesian conditioning. In context to the problem of priors, Bayesians propose the use of ignorance priors that are justified “a priori,” embracing a radical subjectivism in which probabilities are mere degrees of coherent credence, or have sought refuge in the idea that subjective prejudices can wash out as evidence increases. All Bayesians agree that inductive reasoning involves drawing conclusions from new data on the basis of prior information using update rules that require conditioning on the evidence.

Causal Decision Theory

Many will find the answer easy—though they may disagree with each other on which the answer is. A standard line on the prisoner’s dilemma rests on dominance: What you do won’t affect what Twin does. Twin may rat or keep mum, but in either case, you yourself will do better to rat. Whichever Twin is doing, you would spend less time in jail if you were to rat than if you were to keep mum. Therefore the rational way to minimize your own time in jail is to rat.