We propose a new parametrization of Quantum Decision Theory (QDT), based on Rank Dependent Utility Theory (RDU). Using … experimental data made of choices between pairs of lotteries, we compare QDT with "classical" decision theories, RDU and Cumulative … decision makers as either RDU, RDU-based QDT, CPT or CPT-based QDT. Our major findings are the following: the quantum …

are information seeking otherwise. Because belief updating depends on the decision problem in which new information is …

Quantum decision theory (QDT) is a recently developed theory of decision making based on the mathematics of Hilbert … decision making. QDT describes a decision maker's choice as a stochastic event occurring with a probability that is the sum of … an objective utility factor and a subjective attraction factor. QDT offers a prediction for the average effect of …

This paper focuses on information acquisition and individual decision making in ambiguous situations and presents a …, subjects can reduce or even eliminate the ambiguity and turn the decision situation into one of risk. Under the assumption that … an ambiguity averse subject should reduce ambiguity within a decision process we predicted that these subjects would …

(subjective) probabilities weight multiplicatively the utilities of the alternatives offered to the decision maker, although … evidence suggest that probability weights and utilities are often not separable in the mind of the decision maker. In this … context, we introduce a simple and efficient framework on how to describe the inherently probabilistic human decision …

This paper analyzes dynamic choice for ambiguity-sensitive decision makers. It demonstrates that unambiguous behavioral … predictions can be obtained, even in the face of dynamic inconsistency, by taking the individual's preferences over decision trees … assumption about the way individuals resolve conflicts between preferences at different decision points. The main result employs …

Economists often estimate a subset of their model parameters outside the model and let the decision-makers inside the … misspecification of the decision problem, and leads to post-decision disappointment. We develop a framework to explore, evaluate, and … optimize decision rules that explicitly account for the uncertainty in the first step estimation using statistical decision …

the model’s implications for optimal decision-making. This practice ignores model ambiguity, exposes the decision problem … to misspecification, and ultimately leads to post-decision disappointment. Using statistical decision theory, we develop … a framework to explore, evaluate, and optimize robust decision rules that explicitly account for estimation uncertainty …