qualitative decision theory: its motivating tasks and issues, its antecedents, and its prospects. Qualitative decision theory studies qualitative approaches to problems of decision making and their sound and effective reconciliation and integration with quantitative approaches. Although it inherits from a long tradition, the field offers a new focus on a number of important unanswered questions...

This paper reviews and contrasts the basic elements of statistical decision theory [1–4] and statistical learning theory [5–7]. It is not intended to be a comprehensive treatment of either subject, but rather just enough to draw comparisons between the two. Throughout this paper, let X denote the input to a decision-making process and Y denote the correct response or output (e.g., the value of ...

We argue that Bayesian decision theory provides a good theoretical framework for visual perception. Such a theory involves a likelihood function specifying how the scene generates the image(s), a prior assumption about the scene, and a decision rule to determine the scene interpretation. This is illustrated by describing Bayesian theories for individual visual cues and showing that perceptual b...

This paper reviews applications in computer science that decision theorists have addressed for years, discusses the requirements posed by these applications that place great strain on decision theory/social science methods, and explores applications in the social and decision sciences of newer decision-theoretic methods developed with computer science applications in mind. The paper deals with ...

Decision theory is the analysis of the behavior of an individual facing nonstrategic uncertainty—that is, uncertainty that is due to what we term “Nature” (a stochastic natural event such as a coin flip, seasonal crop loss, personal illness, and the like) or, if other individuals are involved, their behavior is treated as a statistical distribution known to the decision maker. Decision theory d...

Using experimental data on choices between pairs of lotteries, we compare a new parameterized Quantum Decision Theory (QDT) with Rank Dependent Utility (RDU) and Cumulative Prospect (CPT). At the aggregate level, CPT-based QDT outperforms. individual considerable heterogeneity across subjects is best described by RDU-based QDT, at odds conclusion using representative agent approach. The quantum...

Two recent articles by Geisler and Diehl use Bayesian statistical decision theory to model the co-evolution of predator and prey in a simple, game-like environment. The prey is characterized by its coloration. The predator is characterized by the chromatic sensitivity of its visual system and its willingness to attack. The authors demonstrate how the coloration of prey and the perceptual system...

Let X be a nonempty finite set, ∆(X) = { p : X → [0, 1] | Px∈X p(x) = 1}, and let % denote a binary relation on ∆(X). As usual, Â and ∼ denote the asymmetric and symmetric parts of % . In our nomenclature elements of X are outcomes (or consequences or prizes), elements of ∆(X) are lotteries, and % is the preference relation. Axioms: A1 the relation % is complete and transitive. A2 (continuity) ...