After considering some problems facing exiting versions of consequentialism, I present the schematic outline of an alternative version of consequentialism, which I call Actual Rule Consequentialism (ARC). I then consider three ways in which one might attempt to ground ARC by deriving it from some more fundamental principle. The first attempt is to derive ARC from a principle of fairness, the se...

design of a logistics network in proper way provides a proper platform for efficient and effective supply chain management. this paper studies a multi-period, multi echelon and multi-product integrated forward-reverse logistics network under uncertainty. first, an efficient complex mixed-integer linear programming (milp) model by considering some real-world assumptions is developed for the inte...

the traditional event tree analysis uses a single probability to represent each top event. however, it is unrealistic to evaluate the occurrence of each event by using a crisp value without considering the inherent uncertainty and imprecision a state has. the fuzzy set theory is universally applied to deal with this kind of phenomena. the main purpose of this study is to construct an easy metho...

In this article, a method is presented to analyse relationships between detection or discrimination frequencies and reaction times in psychophysical tasks. It is shown in three empirical data sets that reaction time decreases as a linear function of the absolute value of the logit transforms of the response probabilities. Such a function stresses the characteristic uncertainty associated with s...

Machine learning such as data based classification is a diagnosis solution useful to monitor complex systems when designing a model is a long and expensive process. When used for process monitoring the processed data are available thanks to sensors. But in many situations it is hard to get an exact measure from these sensors. Indeed measure is done with a lot of noise that can be caused by the ...

Characterization of measurement uncertainty in terms of root sums of squares of both unknown systematic as well as random error components is given meaning in the sense of prediction intervals. Both types of errors are commonly encountered with industrial hygiene air monitoring of hazardous substances. Two extreme types of measurement methods are presented for illustrating how confidence levels...

The decision theory is defined typically as the combination of utility theory and probability theory. In this paper we generalize the decision theory as the performance measure theory and uncertainty theory. Intelligent agents look for approximate optimal decisions under bounded resources and uncertainty. The $-calculus process algebra for problem solving applies the cost performance measures t...

We address the problem of estimating the uncertainty of optical flow algorithm results. Our method estimates the error magnitude at all points in the image. It can be used as a confidence measure. It is based on bootstrap resampling, which is a computational statistical inference technique based on repeating the optical flow calculation several times for different randomly chosen subsets of pix...

We present a new approach to measure uncertainty/information applicable to theories based on convex sets of probability distributions, also called credal sets. A definition of a total disaggregated uncertainty measure on credal sets is proposed in this paper motivated by recent outcomes. This definition is based on the upper and lower values of Shannon’s entropy for a credal set. We justify the...

Relying on the well-established theoretical result that uncertainty has a common and an idiosyncratic component, we propose a new measure of earnings forecast uncertainty as the sum of dispersion among analysts and the variance of mean forecast errors estimated by a GARCH model. The new measure is based on both common and private information available to analysts at the time they make their for...