609,638 research outputs found
Theory of the decision/problem state
A theory of the decision-problem state was introduced and elaborated. Starting with the basic model of a decision-problem condition, an attempt was made to explain how a major decision-problem may consist of subsets of decision-problem conditions composing different condition sequences. In addition, the basic classical decision-tree model was modified to allow for the introduction of a series of characteristics that may be encountered in an analysis of a decision-problem state. The resulting hierarchical model reflects the unique attributes of the decision-problem state. The basic model of a decision-problem condition was used as a base to evolve a more complex model that is more representative of the decision-problem state and may be used to initiate research on decision-problem states
A Quantum-Conceptual Explanation of Violations of Expected Utility in Economics
The expected utility hypothesis is one of the building blocks of classical
economic theory and founded on Savage's Sure-Thing Principle. It has been put
forward, e.g. by situations such as the Allais and Ellsberg paradoxes, that
real-life situations can violate Savage's Sure-Thing Principle and hence also
expected utility. We analyze how this violation is connected to the presence of
the 'disjunction effect' of decision theory and use our earlier study of this
effect in concept theory to put forward an explanation of the violation of
Savage's Sure-Thing Principle, namely the presence of 'quantum conceptual
thought' next to 'classical logical thought' within a double layer structure of
human thought during the decision process. Quantum conceptual thought can be
modeled mathematically by the quantum mechanical formalism, which we illustrate
by modeling the Hawaii problem situation, a well-known example of the
disjunction effect, and we show how the dynamics in the Hawaii problem
situation is generated by the whole conceptual landscape surrounding the
decision situation.Comment: 9 pages, no figure
Approaches for the Joint Evaluation of Hypothesis Tests: Classical Testing, Bayes Testing, and Joint Confirmation
The occurrence of decision problems with changing roles of null and alternative hypotheses has increased interest in extending the classical hypothesis testing setup. Particularly, confirmation analysis has been in the focus of some recent contributions in econometrics. We emphasize that confirmation analysis is grounded in classical testing and should be contrasted with the Bayesian approach. Differences across the three approaches â traditional classical testing, Bayes testing, joint confirmation â are highlighted for a popular testing problem. A decision is searched for the existence of a unit root in a time-series process on the basis of two tests. One of them has the existence of a unit root as its null hypothesis and its non-existence as its alternative, while the roles of null and alternative are reversed for the other hypothesis test.Confirmation analysis, Decision contours, Unit roots
Regularized parametric system identification: a decision-theoretic formulation
Parametric prediction error methods constitute a classical approach to the
identification of linear dynamic systems with excellent large-sample
properties. A more recent regularized approach, inspired by machine learning
and Bayesian methods, has also gained attention. Methods based on this approach
estimate the system impulse response with excellent small-sample properties. In
several applications, however, it is desirable to obtain a compact
representation of the system in the form of a parametric model. By viewing the
identification of such models as a decision, we develop a decision-theoretic
formulation of the parametric system identification problem that bridges the
gap between the classical and regularized approaches above. Using the
output-error model class as an illustration, we show that this
decision-theoretic approach leads to a regularized method that is robust to
small sample-sizes as well as overparameterization.Comment: 10 pages, 8 figure
Optimal path for a quantum teleportation protocol in entangled networks
Bellman's optimality principle has been of enormous importance in the
development of whole branches of applied mathematics, computer science, optimal
control theory, economics, decision making, and classical physics. Examples are
numerous: dynamic programming, Markov chains, stochastic dynamics, calculus of
variations, and the brachistochrone problem. Here we show that Bellman's
optimality principle is violated in a teleportation problem on a quantum
network. This implies that finding the optimal fidelity route for teleporting a
quantum state between two distant nodes on a quantum network with bi-partite
entanglement will be a tough problem and will require further investigation.Comment: 4 pages, 1 figure, RevTeX
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