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.     

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.     

Strategic control and interests,its effects on decision outcomes

Application of the model to artificial data shows that actors with strong preferences in the center have more possibilities to realize good outcomes than other actors. On the basis of an empirical application it is shown that a Nash equilibrium does not always arise after a large number of iterations unless actors have learning capabilities or are severely restricted in their strategic behavior.

In political systems and large organizations, ultimate decision makers are usually just a small subset of all actors in the social system. To arrive at acceptable decisions, decision makers have to take into account the preferences of other actors in the system. Typically preferences of more interested and more powerful actors are weighted heavier than those of less interested and powerful actors. This implies that the total leverage of an actor on the decision is determined by the combination of his power (his potential) and his interest (his willingness to mobilize his power). As the exact level of an actor's leverage is difficult to estimate for the other actors in the system, an actor is able to optimize his effects on outcomes of decisions by providing strategic informatioa

In this paper, first an analytic solution is presented for the optimization of strategic leverage in collective decision making by one single actor. In this solution, the actor makes assumptions about the leverage other actors will show in decision making. Subsequently, the actor optimizes the outcomes of decisions by manipulating the distribution of his leverage over a set of issues.

The analytic solution can be theoretically interpreted by decomposing the solution into three terms, the expected external leverage of the other actors on the issue, the evaluation of the deviance of the expected from the preferred outcome of the issue, and the restrictions on the distribution of leverage over the issues. The higher the expectation of the leverages the other actors will allocate to the issue, the less an actor is inclined to allocate leverage to the issue. The higher the evaluation of the deviance, the more an actor is inclined to allocate leverage to the issue. This is restricted, however, by the required distribution of leverages over the issues. The researcher is able to manipulate these restrictions to investigate its consequences for the outcomes.

In the next step, we investigate whether we can find a Nash equilibrium if all actors optimize their leverage simultaneously. Under certain conditions, a Nash equilibrium can be found by an iterative process in which actors update their estimates oh each other's leverages on the basis of what the other actors have shown in previous iterations.     

动态联合库存的决策模型及算法

考虑生产商、销售商联合库存的动态优化问题,建立的随机需求下生产-销售运作系统的排队模型,得到了系统的稳态概率分布和队长分布.以成本最小化为目标,模型算法找到了最优的运作策略和机器使用数量.数值模拟的结果表明,依赖于指定机器数量的动态调整策略明显优于静态系统.     

Heterogeneous trading agents

In this article, we present a multiagent system (MAS) simulation of a financial market and investigate the requirements to obtain realistic data. The model consists of autonomous, interactive agents that buy stock on a financial market. Transaction decisions are based on a number of individual and collective elements, the former being risk aversion and a set of decision rules reflecting their anticipation of the future evolution of prices and dividends and the latter the information arriving on the market influencing the decision making process of each trader. We specifically look at this process and the following observations hold: The market behavior is determined by the information arriving at the market and agent heterogeneity is required in order to obtain the right statistical properties of the price and return time series. The observed results are not sensitive to changes in the parameter values. © 2003 Wiley Periodicals, Inc.     

A Bayesian decision model with hurricane forecast updates for emergency supplies inventory management

Hurricane forecasts are intended to convey information that is useful in helping individuals and organizations make decisions. For example, decisions include whether a mandatory evacuation should be issued, where emergency evacuation shelters should be located, and what are the appropriate quantities of emergency supplies that should be stockpiled at various locations. This paper incorporates one of the National Hurricane Center's official prediction models into a Bayesian decision framework to address complex decisions made in response to an observed tropical cyclone. The Bayesian decision process accounts for the trade-off between improving forecast accuracy and deteriorating cost efficiency (with respect to implementing a decision) as the storm evolves, which is characteristic of the above-mentioned decisions. The specific application addressed in this paper is a single-supplier, multi-retailer supply chain system in which demand at each retailer location is a random variable that is affected by the trajectory of an observed hurricane. The solution methodology is illustrated through numerical examples, and the benefit of the proposed approach compared to a traditional approach is discussed.     

Financial modelling: Where to go? With an illustration for portfolio management

The definition of Financial Modelling chosen by the EURO working group on financial modelling is ‘the development and implementation of tools supporting firms, investors, intermediaries, governments and others in their financial-economic decision making, including the validation of the premises behind these tools and the measurement of the effectivity of the use of these tools’. Clearly, in this definition, the decision and its solution is central. Unlike financial modelling in our definition, the theory of finance is not so much concerned with individual decisions, but rather with the effects of the decisions and actions of many individuals on the formation of prices in financial markets. It is therefore no wonder that the assumptions underlying financial theory, which at best describe ‘average individuals’ and ‘average decision situations’, are not suited to describe specific individual decision problems. In our view it is the role of financial modelling to support individual decision making, taking account of the peculiarities of the actual case, where possible taking benefit from the results of the financial theory. This philosophy towards financial modelling is illustrated by a framework for portfolio management.     

Stochastic linear programs with restricted recourse

Stochastic programs with recourse provide an effective modeling paradigm for sequential decision problems with uncertain or noisy data, when uncertainty can be modeled by a discrete set of scenarios. In two-stage problems the decision variables are partitioned into two groups: a set of structural, first-stage decisions, and a set of second-stage, recourse decisions. The structural decisions are scenario-invariant, but the recourse decisions are scenario-dependent and can vary substantially across scenarios. In several applications it is important to restrict the variability of recourse decisions across scenarios, or to investigate the tradeoffs between the stability of recourse decisions and expected cost of a solution.We present formulations of stochastic programs with restricted recourse that trade off recourse stability with expected cost. The models generate a sequence of solutions to which recourse robustness is progressively enforced via parameterized, satisficing constraints. We investigate the behavior of the models on several test cases, and examine the performance of solution procedures based on the primal-dual interior point method.     

Fuzzy optimization approach to component selection of fault-tolerant software system

In developing software systems, a manager’s goal is to design software using limited resources and meet the user requirements. One of the important user requirements concerns the reliability of the software. The decision to choose the right software modules (components) becomes extremely difficult because of the number of parameters to be considered while making the decision. If suitable components are not available, then the decision process is further complicated with build versus buy decisions. In this paper, we have formulated a fuzzy multi-objective approach to optimal decision “build-or-buy” for component selection for a fault-tolerant modular software system under the consensus recovery block scheme. A joint optimization model is formulated where the two objectives are maximization of system reliability and minimization of the system cost with a constraint on delivery time. An example of developing a retail system for small-and-medium-size enterprises is used to illustrate the proposed methodology.     

Classification methods for random utility models with i.i.d. disturbances under the most probable alternative rule

In this paper random utility maximization based on maximization of correct classification of the choice decisions over a given data set is considered. It is shown that if the disturbance vector in the random utility model is independent and identically distributed, then preference determination based on the most probable alternative reduces to deterministic utility maximization. As a consequence of the above equivalence, the form of the error distribution (normal, Weibull, uniform etc.) plays no role in the determination of the preferred alternative. Parameter estimation under the most probable alternative rule is carried out using two methods. The first is based on the solution of an appropriately defined system of linear inequalities and the second one is based on the function optimization of a newly proposed function, whose optimum is achieved when the number of correctly classified individuals is maximized. The ability to use these algorithms in the framework of pattern recognition and machine learning is pointed out. Simulations and a real case study involving intercity travel behavior are employed to assess the proposed methods.     

IP-MAGS: an incomplete preference-based multiple attribute group support system

In this research, we develop a group decision support system to deal with multiple attribute group decision-making problems, which involve getting incomplete judgements of individual preference and aggregating the judgements by means of the additive preference model. Dominance-based decision-making rules are built in and applied to obtain a group's preferred alternative. The proposed system, above all, allows for more various forms of incomplete judgements than prior systems that were designed to handle group decision problems. A user-friendly graphical interface enables users to easily encode their incomplete judgements. Further, the system helps individuals revise their preference judgements by referring them to their own decision results in comparison with the group's aggregated decision result. The system is a web-based application system, which enables bidirectional communications between individuals and the system. Any individual who is involved in a group decision-making problem is able to participate in the decision-making process from a remote site. Furthermore, we present a real-life case study on the selection of a branch office server that has been carried out using the proposed system.     

A genetic algorithm for cellular manufacturing design and layout

Cellular manufacturing (CM) is an approach that can be used to enhance both flexibility and efficiency in today’s small-to-medium lot production environment. The design of a CM system (CMS) often involves three major decisions: cell formation, group layout, and group schedule. Ideally, these decisions should be addressed simultaneously in order to obtain the best results. However, due to the complexity and NP-complete nature of each decision and the limitations of traditional approaches, most researchers have only addressed these decisions sequentially or independently. In this study, a hierarchical genetic algorithm is developed to simultaneously form manufacturing cells and determine the group layout of a CMS. The intrinsic features of our proposed algorithm include a hierarchical chromosome structure to encode two important cell design decisions, a new selection scheme to dynamically consider two correlated fitness functions, and a group mutation operator to increase the probability of mutation. From the computational analyses, these proposed structure and operators are found to be effective in improving solution quality as well as accelerating convergence.     

改进的多属性群决策共识方法及其在图书供应商选择中的应用

针对多属性群决策问题,提出了一种改进的加型集结共识方法.在每轮决策中,共识一致性指标较小的决策个体都修改其偏差较大的偏好,能快速使参与决策的个体对各属性形成满意的一致性意见.数值实验也表明该共识方法是合理有效的.     

Aggregation of utility-based individual preferences for group decision-making

Multi-attribute utility theory (MAUT) elicits an individual decision maker’s preferences for single attributes and develops a utility function by mathematics formulation to add up the preferences of the entire set of attributes when assessing alternatives. A common aggregation method of MAUT for group decisions is the simple additive weighting (SAW) method, which does not consider the different preferential levels and preferential ranks for individual decision makers’ assessments of alternatives in a decision group, and thus seems too intuitive in achieving the consensus and commitment for group decision aggregation. In this paper, the preferential differences denoting the preference degrees among different alternatives and preferential priorities denoting the favorite ranking of the alternatives for each decision maker are both considered and aggregated to construct the utility discriminative values for assessing alternatives in a decision group. A comparative analysis is performed to compare the proposed approach to the SAW model, and a satisfaction index is used to investigate the satisfaction levels of the final two resulting group decisions. In addition, a feedback interview is conducted to understand the subjective perceptions of decision makers while examining the results obtained from these two approaches for the second practical case. Both investigation results show that the proposed approach is able to achieve a more satisfying and agreeable group decision than that of the SAW method.     

基于制造商资金有约束的替代产品的最优生产决策

考虑一个单周期的生产决策模型,在该模型中有一个制造商生产两种可替代的产品.面对随机的市场需求,制造商要在需求到来之前制定出两种产品的生产决策来最大化自己的期望利润.在制造商的资金有、无约束两种情形下,证明了制造商的收益函数的期望是关于两种产品生产数量的凹函数,探讨了资金的约束以及产品的替代给制造商的生产决策所带来的影响,给出了最优生产数量的若干性质.另外,针对需求分布为均匀分布的特殊情形给出了制造商最优生产决策的简单表达形式.     

Competing Between Two Groups of Individuals Following Frailty Models

This paper studies the competing system between two groups of individuals, each of them follows multivariate frailty models. We investigate the distribution of the number of individuals in one group to survive some failure in the other group and how the frailty vector of each group has an impact on the distribution by using stochastic comparisons. Applications in candidates selection by ranking and engineering reliability are presented as well.     

Optimal Criterion Weights in Repetitive Multicriteria Decision-Making

In repetitive judgmental discrete decision-making with multiple criteria, the decision maker usually behaves as if there is a set of appropriate criterion weights such that the decisions chosen are based on the weighted sum of all the criteria. Many different procedures for estimating these implied criterion weights have been proposed. Most of these procedures emphasize the preference trade-off among the multiple criteria of the decision maker, and thus the criterion weights obtained are not directly related to the hit ratio of matching decisions. Based on past data, statistical discriminant analysis can be used to determine the implied criterion weights that would reflect the past decisions. The most interesting performance measure is the hit ratio. In this work, we use the integer linear goal-programming technique to determine optimal criterion weights which minimize the number of misclassification of decisions. The linear goal-programming formulation has m constraints and m + k + 1 variables, where m is the number of cases and k is the number of criteria. Empirical study is done by using two different procedures on the actual past admission data of an M.B.A. programme. The hit ratios of the different procedures are compared.     

Learning and transfer in dynamic decision environments

An important aspect of learning is the ability to transfer knowledge to new contexts. However, in dynamic decision tasks, such as bargaining, firefighting, and process control, where decision makers must make repeated decisions under time pressure and outcome feedback may relate to any of a number of decisions, such transfer has proven elusive. This paper proposes a two-stage connectionist model which hypothesizes that decision makers learn to identify categories of evidence requiring similar decisions as they perform in dynamic environments. The model suggests conditions under which decision makers will be able to use this ability to help them in novel situations. These predictions are compared against those of a one-stage decision model that does not learn evidence categories, as is common in many current theories of repeated decision making. Both models' predictions are then tested against the performance of decision makers in an Internet bargaining task. Both models correctly predict aspects of decision makers' learning under different interventions. The two-stage model provides closer fits to decision maker performance in a new, related bargaining task and accounts for important features of higher-performing decision makers' learning. Although frequently omitted in recent accounts of repeated decision making, the processes of evidence category formation described by the two-stage model appear critical in understanding the extent to which decision makers learn from feedback in dynamic tasks. Faison (Bud) Gibson is an Assistant Professor at College of Business, Eastern Michigan University. He has extensive experience developing and empirically testing models of decision behavior in dynamic decision environments.     

Effects of proactive decision making on life satisfaction

Proactive decision making, a concept recently introduced to behavioral operational research and decision analysis, addresses effective decision making during its phase of generating alternatives. It is measured on a scale comprising six dimensions grouped into two categories: proactive personality traits and proactive cognitive skills. Personality traits are grounded on theoretical constructs such as proactive attitude and proactive behavior; cognitive skills reflect value-focused thinking and decision quality. These traits and skills have been used to explain decision satisfaction, although their antecedents and other consequences have not yet been the subject of rigorous hypotheses and testing.This paper embeds proactive decision making within a model of three possible consequences. We consider—and empirically test—decision satisfaction, general self-efficacy, and life satisfaction by conducting three studies with 1300 participants. We then apply structural equation modeling to show that proactive decision making helps to account for life satisfaction, an explanation mediated by general self-efficacy and decision satisfaction. Thus proactive decision making fosters greater belief in one's abilities and increases satisfaction with one's decisions and with life more generally. These results imply that it is worthwhile to help individuals enhance their decision-making proactivity.Demonstrating the positive effects of proactive decision making at the individual level underscores how important the phase of generating alternatives is, and it also highlights the merit of employing “decision quality” principles and being proactive during that phase. Hence the findings presented here confirm the relevance of OR, and of decision-analytic principles, to the lives of ordinary people.     

Representation of decision making processes in an ecological evaluation system

This paper deals with the problems of knowledge representation for a decision support system (DSS) applicable in a dynamic environment. Some special principles concerning environment applications are considered in order to understand better human decision making and behavior. The approach of representing static and dynamic aspects of a system and reflecting them using deep knowledge representation is proposed. The formalization of multiple objective decision making mechanisms is considered. The results of modeling cognitive processes leading to decisions are demonstrated by an example developed during the design stages of an ecological evaluation system.