Decision-theoretic rough set theory is quickly becoming a research direction in rough set theory, which is a general and typical probabilistic rough set model with respect to its threshold semantics and decision features. However, unlike the Pawlak rough set, the positive region, the boundary region and the negative region of a decision-theoretic rough set are not monotonic as the number of att...

Entropy is a measure of the uncertainty associated with a variable whose value cannot be exactly predicated. In uncertainty theory, it has been quantified so far by logarithmic entropy. However, logarithmic entropy sometimes fails to measure the uncertainty. This paper will propose another type of entropy named sine entropy as a supplement, and explore its properties. After that, the maximum en...

Within the framework of uncertainty-based geographic information science, this paper addresses modeling uncertainties in integrating multiple sources of data, modeling uncertainty in overlay analysis, modeling uncertainty in line simplification, uncertainty-based spatial data mining, uncertainty-based spatial queries, theory and methods for controlling the quality of spatial data, modeling unce...

In uncertainty theory, quadratic entropy is a type of entropy that provide a quantitative measurement of the uncertainty of uncertain variables. This paper presents the maximum entropy principle for quadratic entropy of uncertain variables, that is, out of all the uncertainty distributions satisfying given constraints, choose the maximum quadratic entropy one.

This paper develops a model of demand estimation in which consumers learn about their true preferences through consumption experiences. We develop a theoretical model of Bayesian updating, perform comparative statics over the model, and show how the theoretical model can be consistently incorporated into a reduced form econometric model. We then estimate the model using data collected for two ...

Reliability analysis of a system based on probability theory has been widely studied and used. Nevertheless, it sometimes meets with one problem that the components of a system may have only few or even no samples, so that we cannot estimate their probability distributions via statistics. Then reliability analysis of a system based on uncertainty theory has been proposed. However, in a general ...

A natural idea of decision making under uncertainty is to assign a fair price to different alternatives, and then to use these fair prices to select the best alternative. In this paper, we show how to assign a fair price under different types of uncertainty. 1 Decision Making under Uncertainty: Formulation of the Problem Need for decision making. In many practical situations, we have several al...

Based on uncertainty theory, a twostage supply chain model is presented, where the customers’ demands are characterized as uncertain variables. The objective is to minimize the combined costs incurred in the manufacturing and logistics phases. When these uncertain variables are linear, the objective function and constraints can be converted into crisp equivalents, then can be solved by traditio...

This paper clari es the connection between multiple criteria decisionmaking and decision under uncertainty in a qualitative setting relying on a nite value scale. While their mathematical formulations are very similar, the underlying assumptions di er and the latter problem turns out to be a special case of the former. Sugeno integrals are very general aggregation operations that can represent ...

We apply random set theory to an analysis of future climate change. Bounds on cumulative probability are used to quantify uncertainties in natural and socio-economic factors that influence estimates of global mean temperature. We explore the link of random sets to lower envelopes of probability families bounded by cumulative probability intervals. By exploiting this link, a random set for a sim...