Ambiguity and rationality

Recently, several theories of decision making and probability judgment have been proposed that take into account ambiguity (Einhorn and Hogarth, 1985; Gardenfors and Sahlin, 1982). However, none of these theories explains exactly what the psychological causes of ambiguity are or addresses the issue of whether ambiguity effects are rational. In this paper, we define ambiguity as the subjective experience of missing information relevant to a prediction. We show how this definition can explain why ambiguity affects decisions in the ways it does. We argue that there are a variety of rational reasons ambiguity affects probability judgments and choices in the ways it does. However, we argue that the ambiguity effect does not cast doubt on the claim that utility theory is a standard of rational choice. Rather, we suggest that the effect of ambiguity on decisions highlights the fact that utility theory, like any normative model of decision making only prescribes the optimal decision, given what one knows.     

Reliability of test scores in nonparametric item response theory

Three methods for estimating reliability are studied within the context of nonparametric item response theory. Two were proposed originally by Mokken (1971) and a third is developed in this paper. Using a Monte Carlo strategy, these three estimation methods are compared with four classical lower bounds to reliability. Finally, recommendations are given concerning the use of these estimation methods.The authors are grateful for constructive comments from the reviewers and from Charles Lewis.     

An evaluation of the reliability of probability judgments across response modes and over time

Despite the importance of probability assessment methods in behavioral decision theory and decision analysis, little attention has been directed at evaluating their reliability and validity. In fact, no comprehensive study of reliability has been undertaken. Since reliability is a necessary condition for validity, this oversight is significant. The present study was motivated by that oversight. We investigated the reliability of probability measures derived from three response modes: numerical probabilities, pie diagrams, and odds. Unlike previous studies, the experiment was designed to distinguish systematic deviations in probability judgments, such as those due to experience or practice, from random deviations. It was found that subjects assessed probabilities reliably for all three assessment methods regardless of the reliability measures employed. However, a small but statistically significant decrease over time in the magnitudes of assessed probabilities was observed. This effect was linked to a decrease in subjects overconfidence during the course of the experiment.     

Testing whether independent treatment groups have equal medians

The paper suggests new methods for comparing the medians corresponding to independent treatment groups. The procedures are based on the Harrell-Davis estimator in conjunction with a slight modification and extension of the bootstrap calibration technique suggested by Loh. Alternatives to the Harrell-Davis estimator are briefly discussed. For the special case of two treatment groups, the proposed procedure always had more power than the Fligner-Rust solution, as well as the procedure examined by Wilcox and Charlin. Included is an illustration, using real data, that comparing medians, rather than means, can yield a substantially different conclusion as to whether two distributions differ in terms of some measure of central location.     

I did not expect to be dreaming: Explaining realization in lucid dreams with a Bayesian framework

The commonsense view is that a lucid dream starts when the dreamer realizes that they are currently dreaming. The notion of realization, however, has been accepted at face value, with little consideration of whether the dreamer realizes that they are dreaming in the sense of actual reasoning, or if it is a mere epiphenomenon of lucid dream initiation. This article offers a solution to this problem by, first, arguing that the transition to lucidity can occur as a result of successful reasoning, and second, building a model of this reasoning in terms of probabilistic reasoning. The established Bayesian model explains realization in lucid dreams taking under consideration two factors: the beliefs that the dreamer holds on what is generally probable and improbable, and the dreamer’s admissibility of being in a dream. Defended against important objections, the model offers an explanation of lucid dream initiation, relevant for future research on dreaming.     

The influence of hierarchy on probability judgment

Consider the task of predicting which soccer team will win the next World Cup. The bookmakers may judge Brazil to be the team most likely to win, but also judge it most likely that a European rather than a Latin American team will win. This is an example of a non-aligned hierarchy structure: the most probable event at the subordinate level (Brazil wins) appears to be inconsistent with the most probable event at the superordinate level (a European team wins). In this paper we exploit such structures to investigate how people make predictions based on uncertain hierarchical knowledge. We distinguish between aligned and non-aligned environments, and conjecture that people assume alignment. Participants were exposed to a non-aligned training set in which the most probable superordinate category predicted one outcome, whereas the most probable subordinate category predicted a different outcome. In the test phase participants allowed their initial probability judgments about category membership to shift their final ratings of the probability of the outcome, even though all judgments were made on the basis of the same statistical data. In effect people were primed to focus on the most likely path in an inference tree, and neglect alternative paths. These results highlight the importance of the level at which statistical data are represented, and suggest that when faced with hierarchical inference problems people adopt a simplifying heuristic that assumes alignment.     

Nonparametric IRT: Testing the bi-isotonicity of isotonic probabilistic models (ISOP)

Nonparametric tests for testing the validity of polytomous ISOP-models (unidimensional ordinal probabilistic polytomous IRT-models) are presented. Since the ISOP-model is a very general nonparametric unidimensional rating scale model the test statistics apply to a great multitude of latent trait models. A test for the comonotonicity of item sets of two or more items is suggested. Procedures for testing the comonotonicity of two item sets and for item selection are developed. The tests are based on Goodman-Kruskal's gamma index of ordinal association and are generalizations thereof. It is an essential advantage of polytomous ISOP-models within probabilistic IRT-models that the tests of validity of the model can be performed before and without the model being fitted to the data. The new test statistics have the further advantage that no prior order of items or subjects needs to be known.     

Second order inductive logic and Wilmers' principle

We extend the framework of Inductive Logic to Second Order languages and introduce Wilmers' Principle, a rational principle for probability functions on Second Order languages. We derive a representation theorem for functions satisfying this principle and investigate its relationship with the first order principles of Regularity and Super Regularity.     

Probabilities of counterfactuals and counterfactual probabilities

Probabilities figure centrally in much of the literature on the semantics of conditionals. I find this surprising: it accords a special status to conditionals that other parts of language apparently do not share. I critically discuss two notable ‘probabilities first’ accounts of counterfactuals, due to Edgington and Leitgeb. According to Edgington, counterfactuals lack truth values but have probabilities. I argue that this combination gives rise to a number of problems. According to Leitgeb, counterfactuals have truth conditions-roughly, a counterfactual is true when the corresponding conditional chance is sufficiently high. I argue that problems arise from the disparity between truth and high chance, between approximate truth and high chance, and from counterfactuals for which the corresponding conditional chances are undefined. However, Edgington, Leitgeb and I can unite in opposition to Stalnaker and Lewis-style ‘similarity’ accounts of counterfactuals.     

Statistical manifold as an affine space: A functional equation approach

A statistical manifold M_μ consists of positive functions f such that defines a probability measure. In order to define an atlas on the manifold, it is viewed as an affine space associated with a subspace of the Orlicz space L^Φ. This leads to a functional equation whose solution, after imposing the linearity constrain in line with the vector space assumption, gives rise to a general form of mappings between the affine probability manifold and the vector (Orlicz) space. These results generalize the exponential statistical manifold and clarify some foundational issues in non-parametric information geometry.