Expert Rule Versus Majority Rule Under Partial Information, II

The main purpose of this paper is clarifying the connection between some characteristics of a deciding body and the probability of its making correct decisions. In our model a group of decision makers is required to select one of two alternatives. We assume the probabilities of the decision makers being correct are independent random variables distributed according to the same given distribution rule. This distribution belongs to a general family, containing the uniform distribution as a particular case. We investigate the behavior of the probability of the expert rule being optimal, as well as that of the majority rule, both as functions of the distribution parameter and the group size. The main result is that for any value of the distribution parameter the expert rule is far more likely to be optimal than the majority rule, especially as the deciding body becomes larger.     

Multivariate Gaussian criteria in SMAA

We consider stochastic multicriteria decision-making problems with multiple decision makers. In such problems, the uncertainty or inaccuracy of the criteria measurements and the partial or missing preference information can be represented through probability distributions. In many real-life problems the uncertainties of criteria measurements may be dependent. However, it is often difficult to quantify these dependencies. Also, most of the existing methods are unable to handle such dependency information.In this paper, we develop a method for handling dependent uncertainties in stochastic multicriteria group decision-making problems. We measure the criteria, their uncertainties and dependencies using a stochastic simulation model. The model is based on decision variables and stochastic parameters with given distributions. Based on the simulation results, we determine for the criteria measurements a joint probability distribution that quantifies the uncertainties and their dependencies. We then use the SMAA-2 stochastic multicriteria acceptability analysis method for comparing the alternatives based on the criteria distributions. We demonstrate the use of the method in the context of a strategic decision support model for a retailer operating in the liberated European electricity market.     

Consensus of opinion

In a decision analysis, it is often necessary to combine a group of individuals' beliefs into a consensus probability distribution. This paper addresses the question whether it is possible to base such consensus distributions only upon the information present within the group or must some arbitrary rule be used to resolve disagreement. Some earlier work on modifying beliefs in the light of another's opinion is developed to apply to groups of n people. Using this as a “benchmark of rationality”, standard methods of forming group consensus probability distributions are found somewhat arbitrary. Furthermore it is argued that the possibility of constructing better procedures is remote.     

Convergence properties and practical estimation of the probability of rank reversal in pairwise comparisons for multi-criteria decision making problems

In this paper, we address the impact of uncertainty introduced when the experts complete pairwise comparison matrices, in the context of multi-criteria decision making. We first discuss how uncertainty can be quantified and modeled and then show how the probability of rank reversal scales with the number of experts. We consider the impact of various aspects which may affect the estimation of probability of rank reversal in the context of pairwise comparisons, such as the uncertainty level, alternative preference scales and different weight estimation methods. We also consider the case where the comparisons are carried out in a fuzzy manner. It is shown that in most circumstances, augmenting the size of the expert group beyond 15 produces a small change in the probability of rank reversal. We next address the issue of how this probability can be estimated in practice, from information gathered simply from the comparison matrices of a single expert group. We propose and validate a scheme which yields an estimate for the probability of rank reversal and test the applicability of this scheme under various conditions. The framework discussed in the paper can allow decision makers to correctly choose the number of experts participating in a pairwise comparison and obtain an estimate of the credibility of the outcome.     

Dempster's rule of combination

The theory of belief functions is a generalization of probability theory; a belief function is a set function more general than a probability measure but whose values can still be interpreted as degrees of belief. Dempster's rule of combination is a rule for combining two or more belief functions; when the belief functions combined are based on distinct or “independent” sources of evidence, the rule corresponds intuitively to the pooling of evidence. As a special case, the rule yields a rule of conditioning which generalizes the usual rule for conditioning probability measures. The rule of combination was studied extensively, but only in the case of finite sets of possibilities, in the author's monograph A Mathematical Theory of Evidence. The present paper describes the rule for general, possibly infinite, sets of possibilities. We show that the rule preserves the regularity conditions of continuity and condensability, and we investigate the two distinct generalizations of probabilistic independence which the rule suggests.     

Potential variability of decisional skills in uncertain dichotomous choice situations

The main concern of this paper is the selection of optimal decision rules for groups of individuals with identical preferences but diverse and potentially variable independent decisional skills. Employing the uncertain dichotomous choice model the main results illustrate how optimality and sensitivity analysis can be pursued while explicitly recognizing decision-making costs associated with potential variability of decisional skills. For panels of experts consisting of three members our analysis focuses on three special cases of potential variability in individual skills. The extended optimality problem is analyzed resolving the dilemma which of the two common rules, the simple majority rule or the expert rule, is the better selection for the group. The sensitivity of the two rules to variability of decisional skills is also investigated.     

Robust dynamic pairs trading with cointegration

This paper investigates the robust optimal pairs trading using the concept of equivalent probability measures and a penalty function associated with the confidence in parameter estimates when the parameters in the drift term of the continuous-time cointegration model are estimated with errors. A closed-form solution is derived for the robust pairs trading rule. We compare the robust pairs trading rule against its non-robust counterpart using simulations and real data. The robust strategy is empirically more stable and less volatile.     

Warp effects on calculating interval probabilities

In real-life decision analysis, the probabilities and utilities of consequences are in general vague and imprecise. One way to model imprecise probabilities is to represent a probability with the interval between the lowest possible and the highest possible probability, respectively. However, there are disadvantages with this approach; one being that when an event has several possible outcomes, the distributions of belief in the different probabilities are heavily concentrated toward their centres of mass, meaning that much of the information of the original intervals are lost. Representing an imprecise probability with the distribution’s centre of mass therefore in practice gives much the same result as using an interval, but a single number instead of an interval is computationally easier and avoids problems such as overlapping intervals. We demonstrate why second-order calculations add information when handling imprecise representations, as is the case of decision trees or probabilistic networks. We suggest a measure of belief density for such intervals. We also discuss properties applicable to general distributions. The results herein apply also to approaches which do not explicitly deal with second-order distributions, instead using only first-order concepts such as upper and lower bounds.     

Threats,counter-threats and strategic manipulation for non-binary group decision rules

We prove that a class of unanimity rules (the k-Pareto rule, 1≤ k ≤ N) is the only class of group decision functions which is non-manipulable even after the possibility of counter-threats is taken into consideration. We also prove that the 1-Pareto rule is the only group decision function which is strictly nonmanipulable after the possibility of counter-threats is taken into consideration.     

Newsvendor-type models with decision-dependent uncertainty

Models for decision-making under uncertainty use probability distributions to represent variables whose values are unknown when the decisions are to be made. Often the distributions are estimated with observed data. Sometimes these variables depend on the decisions but the dependence is ignored in the decision maker??s model, that is, the decision maker models these variables as having an exogenous probability distribution independent of the decisions, whereas the probability distribution of the variables actually depend on the decisions. It has been shown in the context of revenue management problems that such modeling error can lead to systematic deterioration of decisions as the decision maker attempts to refine the estimates with observed data. Many questions remain to be addressed. Motivated by the revenue management, newsvendor, and a number of other problems, we consider a setting in which the optimal decision for the decision maker??s model is given by a particular quantile of the estimated distribution, and the empirical distribution is used as estimator. We give conditions under which the estimation and control process converges, and show that although in the limit the decision maker??s model appears to be consistent with the observed data, the modeling error can cause the limit decisions to be arbitrarily bad.     

Primal and dual linear decision rules in stochastic and robust optimization

Linear stochastic programming provides a flexible toolbox for analyzing real-life decision situations, but it can become computationally cumbersome when recourse decisions are involved. The latter are usually modeled as decision rules, i.e., functions of the uncertain problem data. It has recently been argued that stochastic programs can quite generally be made tractable by restricting the space of decision rules to those that exhibit a linear data dependence. In this paper, we propose an efficient method to estimate the approximation error introduced by this rather drastic means of complexity reduction: we apply the linear decision rule restriction not only to the primal but also to a dual version of the stochastic program. By employing techniques that are commonly used in modern robust optimization, we show that both arising approximate problems are equivalent to tractable linear or semidefinite programs of moderate sizes. The gap between their optimal values estimates the loss of optimality incurred by the linear decision rule approximation. Our method remains applicable if the stochastic program has random recourse and multiple decision stages. It also extends to cases involving ambiguous probability distributions.     

Inferring probability densities from expert opinion

When experts are asked to assess a situation, they often express their opinions providing estimates of the probability of observing the occurrence of a random variable in given intervals, sometimes up to a range of values, rather than simply providing point estimates. The problem we face is how to translate that expert opinion into probability distributions. We examine a novel way of solving that problem by making use of the maximum entropy method in the data to deal with expert opinions expressed with or without uncertainty bands. Our method allows us to unveil underlying probability distributions driving expert opinions expressed with and without uncertainty.     

Eliciting density ratio classes

The probability distributions of uncertain quantities needed for predictive modelling and decision support are frequently elicited from subject matter experts. However, experts are often uncertain about quantifying their beliefs using precise probability distributions. Therefore, it seems natural to describe their uncertain beliefs using sets of probability distributions. There are various possible structures, or classes, for defining set membership of continuous random variables. The Density Ratio Class has desirable properties, but there is no established procedure for eliciting this class. Thus, we propose a method for constructing Density Ratio Classes that builds on conventional quantile or probability elicitation, but allows the expert to state intervals for these quantities. Parametric shape functions, ideally also suggested by the expert, are then used to bound the nonparametric set of shapes of densities that belong to the class and are compatible with the stated intervals. This leads to a natural metric for the size of the class based on the ratio of the total areas under upper and lower bounding shape functions. This ratio will be determined by the characteristics of the shape functions, the scatter of the elicited values, and the explicit expert imprecision, as characterized by the width of the stated intervals. We provide some examples, both didactic and real, and conclude with recommendations for the further development and application of the Density Ratio Class.     

Selecting the best choice in the full information group interview problem

We consider the problem of selecting the single best choice when several groups of choices are presented sequentially for evaluation. In the so-called group interview problem, we assume that the values of choices are random observations from a known distribution function and derive the optimal search strategy that maximizes the probability of selecting the best among all choices. Under the optimal search strategy derived by means of a dynamic programming technique, a decision maker simply selects the best choice in the group under consideration if its value is higher than the pre-specified decision value for that group. We also consider the optimal ordering strategy for the case where the decision maker is permitted to rearrange the sequence of groups for evaluation. We show that the optimal search and ordering strategies can be applied to many sequential decision problems such as the store location problem.     

基于贝叶斯信息更新的风险规避库存策略研究

在贝叶斯库存控制研究中一个著名的结论是：当缺货需求不能被观测到时,最优贝叶斯库存水平总会高于短视策略库存水平,原因是决策者需要通过多订货来获取对需求分布的认识. 这是基于风险中性的研究,然后现实中决策者都期望规避风险. 基于贝叶斯信息更新研究了风险规避背景下需求部分可观测的多周期报童问题,决策者的周期内效用函数满足独立可加性公理. 通过引入非正规化概率,研究发现,对风险规避的决策者,当其效用函数具有不变绝对风险规避特征时,最优贝叶斯库存水平也会高于短视策略库存水平. 非正规化概率简化了动态规划方程与结果的证明.     

Decision-dependent probabilities in stochastic programs with recourse

Stochastic programming with recourse usually assumes uncertainty to be exogenous. Our work presents modelling and application of decision-dependent uncertainty in mathematical programming including a taxonomy of stochastic programming recourse models with decision-dependent uncertainty. The work includes several ways of incorporating direct or indirect manipulation of underlying probability distributions through decision variables in two-stage stochastic programming problems. Two-stage models are formulated where prior probabilities are distorted through an affine transformation or combined using a convex combination of several probability distributions. Additionally, we present models where the parameters of the probability distribution are first-stage decision variables. The probability distributions are either incorporated in the model using the exact expression or by using a rational approximation. Test instances for each formulation are solved with a commercial solver, BARON, using selective branching.     

集团统一审计、审计风险与组成部分重要性水平

针对集团审计的特性,运用贝叶斯定理构建集团审计模型GUAM,并用伽玛概率分布函数表示先验函数、后验函数与似然函数,用于规范集团各组成部分的重要性水平核算,将模型从单一组织扩展到集团层面的审计,并建立了各组成部分重要性水平与相对可变成本的计算公式,通过各组成部分的资产等标准分配权重,进而确定合适的先验水平使得集团整体的可变成本降低,以达到提高集团审计效率,通过集团层面的控制,使得在完成集团整体目标置信水平的基础上达到最佳审计效果的目的.     

大型群体决策问题中的简单多数规则（3个评选对象场合）

一个群体决策问题取决于两个因素,一个是群体决策的规则,另一个是投票。当选定群体决策规则时,一个群体决策问题由投票完全决定,此时,群体决策问题与投票之间一一对应。简单多数规则是个简单且被广泛采用的群体决策规则,但它有缺陷,我们可举出些群体决策问题使用简单多数规则没法从投票得到最后群体决策的结果。这里我们将给出一个简单多数规则的有趣性质,即在3个评选对象场合,使用简单多数规则没法从投票得到最后群体决策结果的n个评选人的群体决策问题的个数与所有n个评选人的群体决策问题的个数之比当评选人个数n趋向无穷时趋于零,这说明3个评选对象的大型群体决策场合,简单多数规则的缺陷不严重。     

基于粗糙集的应急案例中概率规则挖掘方法

应急案例作为描述突发事件发生、发展及应对过程的文本,蕴含了潜在的规律与宝贵的经验。为了挖掘应急案例中各要素间潜在的关联关系,构建出基于粗糙集的应急案例中概率规则挖掘方法。首先,构建出应急案例知识五元组,描述应急案例共性特征,并将诸多应急案例信息组织成一张应急案例决策表;然后,应用遗传算法对应急案例决策表进行属性约简,进而获取概率规则;最后,以大兴安岭林区50起重特大火灾案例为例,阐述方法的具体执行过程,并通过两组测试实验证明了方法的可行性和有效性。该方法描述了应急案例的共性本体特征,具有较高的可重用性,有利于为决策者采取应急管理措施提供决策支持。     

Discrete time Markov chains with interval probabilities

The parameters of Markov chain models are often not known precisely. Instead of ignoring this problem, a better way to cope with it is to incorporate the imprecision into the models. This has become possible with the development of models of imprecise probabilities, such as the interval probability model. In this paper we discuss some modelling approaches which range from simple probability intervals to the general interval probability models and further to the models allowing completely general convex sets of probabilities. The basic idea is that precisely known initial distributions and transition matrices are replaced by imprecise ones, which effectively means that sets of possible candidates are considered. Consequently, sets of possible results are obtained and represented using similar imprecise probability models.We first set up the model and then show how to perform calculations of the distributions corresponding to the consecutive steps of a Markov chain. We present several approaches to such calculations and compare them with respect to the accuracy of the results. Next we consider a generalisation of the concept of regularity and study the convergence of regular imprecise Markov chains. We also give some numerical examples to compare different approaches to calculations of the sets of probabilities.