A Note on Chance-Constrained Programming

A stochastic linear programme with chance constraints is considered. By allowing for the possibility of sampling, a so-called dynamic statistical decision model is developed, and the existence of an optimal decision rule is shown under appropriate continuity and compactness assumptions.     

Structured policies in the sequential design of experiments

A general control model under uncertainty is considered. Using a Bayesian approach and dynamic programming, we investigate structural properties of optimal decision rules. In particular, we show the monotonicity of the total expected reward and of the so-called Gittins-Index. We extend the stopping rule and the stay-on-a-winner rule, which are well-known in bandit problems. Our approach is based on the multivariate likelihood ratio order andTP ₂ functions.     

Rough set based rule induction from two decision tables

We study rule induction from two decision tables as a basis of rough set analysis of more than one decision tables. We regard the rule induction process as enumerating minimal conditions satisfied with positive examples but unsatisfied with negative examples and/or with negative decision rules. From this point of view, we show that seven kinds of rule induction are conceivable for a single decision table. We point out that the set of all decision rules from two decision tables can be split in two levels: a first level decision rule is positively supported by a decision table and does not have any conflict with the other decision table and a second level decision rule is positively supported by both decision tables. To each level, we propose rule induction methods based on decision matrices. Through the discussions, we demonstrate that many kinds of rule induction are conceivable.     

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。     

渐近最优的经验贝叶斯决策

本文获得了刻度指数族变量带误差情形下的贝叶斯决策,且利用解卷积的核方法构造出了经验贝叶斯决策.在适当的条件下,证明了经验贝叶斯决策的渐近最优性.     

On the empirical Bayes approach to multiple decision problems with sequential components

The empirical Bayes approach to multiple decision problems with a sequential decision problem as the component is studied. An empirical Bayesm-truncated sequential decision procedure is exhibited for general multiple decision problems. With a sequential component, an empirical Bayes sequential decision procedure selects both a stopping rule function and a terminal decision rule function for use in the component. Asymptotic results are presented for the convergence of the Bayes risk of the empirical Bayes sequential decision procedure.     

Incomplete decision contexts: Approximate concept construction,rule acquisition and knowledge reduction

Incomplete decision contexts are a kind of decision formal contexts in which information about the relationship between some objects and attributes is not available or is lost. Knowledge discovery in incomplete decision contexts is of interest because such databases are frequently encountered in the real world. This paper mainly focuses on the issues of approximate concept construction, rule acquisition and knowledge reduction in incomplete decision contexts. We propose a novel method for building the approximate concept lattice of an incomplete context. Then, we present the notion of an approximate decision rule and an approach for extracting non-redundant approximate decision rules from an incomplete decision context. Furthermore, in order to make the rule acquisition easier and the extracted approximate decision rules more compact, a knowledge reduction framework with a reduction procedure for incomplete decision contexts is formulated by constructing a discernibility matrix and its associated Boolean function. Finally, some numerical experiments are conducted to assess the efficiency of the proposed method.     

Optimal decision rule in forming an insurance portfolio

The paper is devoted to finding an optimal decision rule for accepting/rejecting potential insureds when the demand for the insurance provision is a stochastic variable. A criterion to be maximized is the mean-variance utility function of the insurer. It is shown that the optimal decision rule is a stopping rule with some finite protection level.     

Imaging and Sleeping Beauty: A case for double-halfers

The aim of this paper is to provide a case for the double-halfer position in the sleeping beauty. This case relies on the use of the so-called imaging rule for probabilistic dynamics as a substitute for conditionalization. It is argued that the imaging rule is the appropriate one for dealing with belief change in sleeping beauty and that under natural assumptions, this rule results in the double-halfer position.     

Performance evaluation of some special classes of weighted majority rules

The main concern of this paper is the performance evaluation of four classes of decision rules: the expert rule, the balanced expert rules, the simple majority rule, and the restricted simple majority rules. Employing the uncertain dichotomous choice model we first establish the necessary and sufficient conditions for the optimality of these four types of decision rules.For small groups consisting of less than six members the optimality conditions cover all the potentially optimal decision rules. Consequently, we are able to pursue a complete analysis of the small group cases. The analysis of the special (small group) cases as well as that of the general (n-member group) cases is based on the assumption that individual decisional skills are uniformly distributed. In evaluating the quality of a decision rule we resort to four alternative criteria: the expected optimality likelihood of the rule, the expected probability of yielding a correct collective decision given complete information on decisional skills, the expected probability of yielding a correct collective judgement given complete inability of skills verification, and, finally, the sensitivity of the rule to skills verifiability.     

基于粗集的决策分析

粗糙集理论研究的重要内容是约简,目的在于获取优良的规则集合。本文描述了决策规则的多种指标,分析了他们体现的性质,并提出了规则集合的决策度量,从整体上体现了一个规则集合的性能,为多知识库决策奠定了基础。     

Bankruptcy and the per capita nucleolus: The claim-and-right rules family

This article describes the per capita nucleolus for bankruptcy games as a bankruptcy rule. This rule, called the clights rule, is based on the well-known constrained equal awards principle and it takes into account a vector of clights, a new term which is a blend of claims and rights. These clights only depend on the vector of claims while the height of the estate determines whether the clights should be interpreted as modified claims or as rights. It is shown that both the clights rule and the Aumann–Maschler rule can be captured within the family of so-called claim-and-right rules.     

Optimality of the Expert Rule Under Partial Information

We study the uncertain dichotomous choice model. In this model a group of decision makers is required to select one of two alternatives. The applications of this model are relevant to a wide variety of areas, such as medicine, management and banking. The decision rule may be the simple majority rule; however, it is also possible to assign more weight to the opinion of members known to be more qualified. The extreme example of such a rule is the expert decision rule. We are concerned with the probability of the expert rule to be optimal. Our purpose is to investigate the behaviour of this probability as a function of the group size for several rather general types of distributions. One such family of distributions is that where the density function of the correctness probability is a polynomial (on the interval [1/2,1]). Our main result is an explicit formula for the probability in question. This contains formerly known results as very special cases.     

A heuristic for triggering emergency orders in an inventory system

This paper considers a single-echelon inventory system with a warehouse facing compound Poisson customer demand. Normally the warehouse replenishes from an outside supplier according to a continuous review reorder point policy. However, it is also possible to use emergency orders. Such orders incur additional costs but have a much shorter lead time. We consider standard holding and backorder costs as well as ordering costs. A heuristic decision rule for triggering emergency orders is suggested. The decision rule minimizes the expected costs under the assumption that there is only a single possibility for an emergency replenishment, but the rule is used repeatedly as a heuristic. Given a certain reorder point policy for normal replenishments, our decision rule will always reduce the expected costs. A simulation study illustrates that the suggested technique performs well under different conditions.     

Decision making in arranged marriages with a stochastic reservation quality level

Recently, Batabyal [1] has shown that when the decision to get married in an arranged marriage is analyzed in an intertemporal and stochastic setting, it is possible that a marrying agent will never get married. This result arises because the marrying agent in [1] maximizes the probability of accepting the best possible marriage proposal. What happens when a marrying agent uses the following decision rule: Get married as long as the quality of a marriage proposal exceeds a stochastic reservation quality level? In this note, we provide an interesting answer to this question. First, we show that the probability of getting married with this decision rule is always positive. Even so, we point out that on average, an agent who uses this decision rule will end up single.     

On a Heuristic Inventory-replenishment Rule for Items with a Linearly Increasing Demand Incorporating Shortages

A heuristic decision rule is derived for the replenishment of items with a linearly increasing demand rate over a finite planning horizon during which shortages are allowed. When compared with the exact decision rule, the heuristic is found to incur negligible cost penalty for the numerical example which is given to illustrate the use of the heuristic.     

Detection of multivariate outliers with location slippage or scale inflation in left orthogonally invariant or elliptically contoured distributions

This paper is concerned with two kinds of multiple outlier problems in multivariate regression. One is a multiple location-slippage problem and the other is a multiple scale-inflation problem. A multi-decision rule is proposed. Its optimality is shown for the first problem in a class of left orthogonally invariant distributions and is also shown for the second problem in a class of elliptically contoured distributions. Thus the decision rule is robust against departures from normality. Further the null robustness of the decision statistic which the rule is based on is pointed out in each problem.     

Rough set theory: a novel approach for extraction of robust decision rules based on incremental attributes

Rough set theory is a new data mining approach to manage vagueness. It is capable to discover important facts hidden in the data. Literature indicate the current rough set based approaches can’t guarantee that classification of a decision table is credible and it is not able to generate robust decision rules when new attributes are incrementally added in. In this study, an incremental attribute oriented rule-extraction algorithm is proposed to solve this deficiency commonly observed in the literature related to decision rule induction. The proposed approach considers incremental attributes based on the alternative rule extraction algorithm (AREA), which was presented for discovering preference-based rules according to the reducts with the maximum of strength index (SI), specifically the case that the desired reducts are not necessarily unique since several reducts could include the same value of SI. Using the AREA, an alternative rule can be defined as the rule which holds identical preference to the original decision rule and may be more attractive to a decision-maker than the original one. Through implementing the proposed approach, it can be effectively operating with new attributes to be added in the database/information systems. It is not required to re-compute the updated data set similar to the first step at the initial stage. The proposed algorithm also excludes these repetitive rules during the solution search stage since most of the rule induction approaches generate the repetitive rules. The proposed approach is capable to efficiently and effectively generate the complete, robust and non-repetitive decision rules. The rules derived from the data set provide an indication of how to effectively study this problem in further investigations.     

Contractually stable networks

We consider situations where players are part of a network and belong to coalitions in a given coalition structure. We propose the concept of contractual stability to predict the networks that are going to emerge at equilibrium when the consent of coalition partners is needed for adding or deleting links. Two different decision rules for consent are analyzed: simple majority and unanimity. We characterize the coalition structures that make the strongly efficient network contractually stable under the unanimity decision rule and the coalition structures that sustain some critical network as contractually stable under the simple majority decision rule and under any decision rule requiring the consent of any proportion of coalition partners. Requiring the consent of coalition members may help to reconcile stability and efficiency in some classical models of network formation.     

Condorcet Winners for Public Goods

In this work, we consider a public facility allocation problem decided through a voting process under the majority rule. A location of the public facility is a majority rule winner if there is no other location in the network where more than half of the voters would have been closer to than the majority rule winner. We develop fast algorithms for interesting cases with nice combinatorial structures. We show that the computing problem and the decision problem in the general case, where the number of public facilities is more than one and is considered part of the input size, are all NP-hard. Finally, we discuss majority rule decision making for related models.