The goal programming (GP) is a well-known approach applied to multi-criteria decision making (M-DM). It has been used in many domains and the literature offers diverse extensions of this procedure. On the other hand, so far, some evident analogies between M-DM under certainty and scenario-based...

Quantum decision theory (QDT) is a recently developed theory of decision making based on the mathematics of Hilbert spaces, a framework known in physics for its application to quantum mechanics. This framework formalizes the concept of uncertainty and other effects that are particularly manifest...

We present the first calibration of quantum decision theory (QDT) to an empirical data set. The data comprise 91 choices between two lotteries (two "prospects") presented in 91 random pairs made by 142 subjects offered at two separated times. First, we quantitatively account for the fraction of...

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 Prospect Theory (CPT). At the aggregate...

Humans are notoriously bad at understanding probabilities, exhibiting a host of biases and distortions that are context dependent. This has serious consequences on how we assess risks and make decisions. Several theories have been developed to replace the normative rational expectation theory at...

We consider a hypothetical company that is assumed to have just manufactured and sold a number of copies of a product. It is known that, with a small probability, the company has committed a manufacturing fault that will require a recall. The company is able to observe the expiration times of...

We apply utility indifference pricing to solve a contingent claim problem, valuing a connected pair of gas fields where the underlying process is not standard Geometric Brownian motion and the assumption of complete markets is not fulfilled. First, empirical data are often characterized by...

We apply utility indifference pricing to solve a contingent claim problem, valuing a connected pair of gas fields where the underlying process is not standard Geometric Brownian motion and the assumption of complete markets is not fulfilled. First, empirical data are often characterized by...

A new methodology is presented to solve an important model of dynamic decision-making with a continuous unknown parameter (or state). The methodology centers on the concepts of “continuation-value function” (which gives the expected value-to-go as a function of the parameter under a feasible...

Adjustable robust optimization (ARO) is a technique to solve dynamic (multistage) optimization problems. In ARO, the decision in each stage is a function of the information accumulated from the previous periods on the values of the uncertain parameters. This information, however, is often...