The k-essence theories admit in general the superluminal propagation of the perturbations on classical backgrounds. We show that in spite of the superluminal propagation the causal paradoxes do not arise in these theories and in this respect they are not less safe than General Relativity.

Uncertainty theory is a branch of mathematics for modeling uncertainty in human reasoning, and uncertain statistics is a methodology for collecting and interpreting experts’ experimental data by uncertainty theory. In this paper, we will first discuss how to collect experts’ data. We apply this method to a model for estimating the distance between Beijing and Tianjin. Based on the experts’ expe...

We review the definition of the Full Bayesian Significance Test (FBST), and summarize its main statistical and epistemological characteristics. We review also the Abstract Belief Calculus (ABC) of Derwiche and Ginsberg, and use it to analyze the FBST’s value of evidence. This analysis helps us understand the FBST properties, specially its consistency with the Onus Probandi principle, and why it...

This paper investigates the minimum cost multicommodity flow problem with uncertain costs and uncertain capacities. Uncertainty theory is used to deal with indeterminacy factors in uncertain network. An (α, β)-minimum cost multicommodity flow model is formulated. Some properties of the model are analyzed. An equivalence relationship between the (α, β)-minimum cost multicommodity flow and the mi...

We present an operator formalism for the recently developed kinetic information theory, construct Poisson brackets between the Liouville L̃ and information Ĩ operators in μ space, proposing its quantum version. Making use of the universal quantum of time, the Planck time τp, a pseudo-energy-time uncertainty relation is constructed. It suggests that tiny amounts of information production may caus...

The option-pricing problem is always an important part in modern finance. Assuming that the stock diffusion is a constant, some literature has introduced many stock models and given corresponding option pricing formulas within the framework of the uncertainty theory. In this paper, we propose a new stock model with uncertain stock diffusion for uncertain markets. Some option pricing formulas on...

In the contemporary measurement theory, the new methods of accuracy determination are still investigated. It is possible, that the theory of uncertainty is a universal tool for the estimation of accuracy [1, 2]. This theory allows for the analysis of random interactions, even if the measurement model is strongly nonlinear. According to the theory of uncertainty, the standard uncertainty plays t...

Traditional accounts of decision-making under uncertainty have taken the Von Neumann and Morgenstern approach of Expected Utility Theory that considers how decisions under uncertainty should be made. This prescriptive model states that, when faced with a choice, a rational decision maker will pick the prospect that offers the highest expected utility. But as has been demonstrated by Kahnemann a...