Theories of subjective probability are viewed as formal languages for analyzing evidence and expressing degrees of belief. This article focuses on two probability langauges, the Bayesian language and the language of belief functions (Shafer, 1976). We describe and compare the semantics (i.e., the meaning of the scale) and the syntax (i.e., the formal calculus) of these languages. We also investigate some of the designs for probability judgment afforded by the two languages.

Can there be good reasons for judging one set of probabilistic assertions more reliable than a second? There are many candidates for measuring "goodness" of probabilistic forecasts. Here, I focus on one such aspirant: calibration. Calibration requires an alignment of announced probabilities and observed relative frequency, e.g., 50 percent of forecasts made with the announced probability of.5 occur, 70 percent of forecasts made with probability.7 occur, etc. To summarize the conclusions: (i) Surveys designed to display calibration curves, from which a (...) recalibration is to be calculated, are useless without due consideration for the interconnections between questions (forecasts) in the survey. (ii) Subject to feedback, calibration in the long run is otiose. It gives no ground for validating one coherent opinion over another as each coherent forecaster is (almost) sure of his own long-run calibration. (iii) Calibration in the short run is an inducement to hedge forecasts. A calibration score, in the short run, is improper. It gives the forecaster reason to feign violation of total evidence by enticing him to use the more predictable frequencies in a larger finite reference class than that directly relevant. (shrink)

While there is now considerable anxiety about whether the psychological theory presupposed by virtue ethics is empirically sustainable, analogous issues have received little attention in the virtue epistemology literature. This paper argues that virtue epistemology encounters challenges reminiscent of those recently encountered by virtue ethics: just as seemingly trivial variation in context provokes unsettling variation in patterns of moral behavior, trivial variation in context elicits unsettling variation in patterns of cognitive functioning. Insofar as reliability is a condition on epistemic virtue, (...) we have reason to doubt that human beings possess the cognitive materials required for epistemic virtue, and thereby reason to think that virtue epistemology is threatened by skepticism. We conclude that while virtue epistemology has resources for addressing this challenge, exploiting these resources forces tradeoffs between empirical and normative adequacy. (shrink)

This experiment elicits beliefs about other people’s overconfidence and abilities. We find that most people believe that others are unbiased, and only few think that others are overconfident. There is a remarkable heterogeneity between these groups. Those people who think others are underconfident or unbiased are overconfident themselves. Those who think others are overconfident are underconfident themselves. Despite this heterogeneity, people overestimate on average the abilities of others as they do their own ability. One driving force behind this result is (...) the refusal to process information about oneself: not only does this lead to overestimation of one’s own ability, but by means of social projection also to overestimation of others’ abilities. (shrink)

Decision makers often rely on expert opinion when making forecasts under uncertainty. In doing so, they confront two methodological challenges: the elicitation problem, which requires them to extract meaningful information from experts; and the aggregation problem, which requires them to combine expert opinion by resolving disagreements. Linear averaging is a justifiably popular method for addressing aggregation, but its robust simplicity makes two requirements on elicitation. First, each expert must offer probabilistically coherent forecasts; second, each expert must respond to all our (...) queries. In practice, human judges (even experts) may be incoherent, and may prefer to assess only the subset of events about which they are comfortable offering an opinion. In this paper, a new methodology is developed for combining expert assessment of chance. The method retains the conceptual and computational simplicity of linear averaging, but generalizes the standard approach by relaxing the requirements on expert elicitation. The method also enjoys provable performance guarantees, and in experiments with real-world forecasting data is shown to offer both computational efficiency and competitive forecasting gains as compared to rival aggregation methods. This paper is relevant to the practice of decision analysis, for it enables an elicitation methodology in which judges have freedom to choose the events they assess. (shrink)

How should an agent decide what to do when she is uncertain not just about morally relevant empirical matters, like the consequences of some course of action, but about the basic principles of morality itself? This question has only recently been taken up in a systematic way by philosophers. Advocates of moral hedging claim that an agent should weigh the reasons put forward by each moral theory in which she has positive credence, considering both the likelihood that that theory is (...) true and the strength of the reasons it posits. The view that it is sometimes rational to hedge for one's moral uncertainties, however, has recently come under attack both from those who believe that an agent should always be guided by the dictates of the single moral theory she deems most probable and from those who believe that an agent's moral beliefs are simply irrelevant to what she ought to do. Among the many objections to hedging that have been pressed in the recent literature is the worry that there is no non-arbitrary way of making the intertheoretic comparisons of moral value necessary to aggregate the value assignments of rival moral theories into a single ranking of an agent's options. -/- This dissertation has two principal objectives: First, I argue that, contra these recent objections, an agent's moral beliefs and uncertainties are relevant to what she rationally ought to do, and more particularly, that agents are at least sometimes rationally required to hedge for their moral uncertainties. My principal argument for these claims appeals to the enkratic conception of rationality, according to which the requirements of practical rationality derive from an agent's beliefs about the objective, desire-independent value or choiceworthiness of her options. Second, I outline a new general theory of rational choice under moral uncertainty. Central to this theory is the idea of content-based aggregation, that the principles according to which an agent should compare and aggregate rival moral theories are grounded in the content of those theories themselves, including not only their value assignments but also the metaethical and other non-surface-level propositions that underlie, justify, or explain those value assignments. (shrink)