Investigation of decision making process: a hypermedia approach

Research in decision making has concentrated mainly on the decision outcome (choice) rather than on the process of the decision making. This pattern is primarily due to the lack of an acceptable unobtrusive tool for investigation of the decision process. Further, the lack of a consistent decision aid in empirical works has hindered the comparison of results. The purpose of this paper is to present a flexible and adaptable computer-based system for empirical examination of information processing. The main thrust of the proposed system is to provide guidelines to the restrictive/channeling approach to system design. This system can be easily modified to suit different experimental research requirements. It should support more realistic decision tasks, which often are criticized in decision experiments. Furthermore, availability of this system should encourage more research work on the process of decision making. The nature of this support system is linked to the literature, and specifications and components of the system are provided.     

A view of group decision making process and bivoting approach

Many characteristics of Group Decision Making (GDM) are different from those of individual decision. The literature dealing with the stages of GDM is rather scant. This paper presents a view about the stages of group decision processes, characterizing GDM as a dynamic process. We discuss particularly the aggregation stage of preferences and reports a bivoting approach' used in practising group decision support systems (GDSS). Finally, a concrete example is given to illustrate each stage of group decision process and tile 'bivoting approach' in consensus reaching stage.     

Multicriteria decision making under uncertainty: a visual approach

Decision problems at the strategic level tend to have multiple criteria and outcomes that are uncertain. Many of the current decision‐making tools are too simplistic to incorporate the important features. This paper considers a multicriteria decision‐making scenario in which the outcomes of the decisions, evaluated on different criteria, are uncertain. The main contribution of this paper is the presentation of a tool that enables decision makers to visualize the expected payoff and likelihood that the payoff of a decision does not fall short of a preset target value. Furthermore, it presents decision makers with a tool that shows the tradeoff between expected payoff and downside risk. A variety of solution techniques are suggested that build upon this visualization.     

Systematic decision process for intelligent decision making

In this paper, Systematic decision process (SDP) for solving Multiple Criteria Decision Making problems with application for manufacturing location selection is introduced. SDP is a comprehensive approach which is based on eliciting strength of preferences for assessing additive utility functions. SDP consists of three steps: I. assessing weights, II. assessing qualitative criteria, and III. ranking alternatives using the assessed additive utility function. Strengths of preferences can be expressed by using either qualitative or numerical ratings. If the decision maker is inconsistent in his/her responses, such inconsistencies are identified by the method. It is shown that the method has advantages in terms of simplicity and accuracy compared to existing methods such as Analytical Hierarchy Process. Furthermore, a quadratic optimization method for assessing weights of additive utility function by use of pair comparison of actual alternatives is developed. Computational experiments are provided.     

Adaptation in IMMPS as a decision making process

Intelligent Multimedia Presentation Systems (IMMPS) are characterised by their capability to dynamically adapt at run-time, based on the state of several aspects of user-computer interaction, such as the user profile, task and information characteristics, etc. In this paper, we treat adaptation as a decision making process, and propose the employment of established techniques from the domain of Decision Making to assist this process. It is argued that, following this approach, the adaptation process can be easily modified, customised and re-used in different application domains and for different user interaction requirements. The designer is enabled to incorporate different design strategies, which can be dynamically and automatically modified, according to the run-time requirements of the system. The application of the proposed approach is exemplified in the media/modalities allocation process.     

Markov decision process: Fuzzy approach

This paper propose a fuzzy concept of return cost of Markov Decision Process (MDP) model which is an application of dynamic programming to the solution of probabilistic decision process. The return structure of the process is measured by Triangular Fuzzy Number (TFN). The comparison method is based on the ranking method.

The goal of this research is to provide the optimal solution for a finite stage and infinite stage which can be manipulated to study the real-world situation for the purpose of aiding the decision maker [6,7].     

Allocation of shared costs among decision making units: a DEA approach

An issue of considerable importance, both from a practical organizational standpoint and from a costs research perspectives, involves the allocation of fixed resources or costs across a set of competing entities in an equitable manner. Cook and Kress (Eur. J. Oper. Res. 119 (1999) 652) propose a data envelopment analysis (DEA) approach to obtain a theoretical framework for such cost allocation problems. Their approach cannot be used directly to determine a cost allocation among the decision making units (DMUs), but rather to examine existing costing rules for equity. The current paper extends the Cook and Kress (Eur. J. Oper. Res. 119 (1999) 652) approach, and provides a practical approach to the cost allocation problem. It is shown that an equitable cost allocation can be achieved using DEA principles.     

Optimizing logistic processes using a fuzzy decision making approach

This paper addresses the problem of optimizing logistic processes that can be modeled as a birth-and-death process. A fuzzy decision making algorithm is proposed to assign components to orders, which is a common task in a large number of logistic processes. The dynamic assignment of components to orders is the key issue in optimizing logistic processes. This paper proposes several criteria for this optimization. These criteria are combined using weighted fuzzy aggregation in a fuzzy decision making environment. First, a simple but illustrative example shows that the proposed techniques can be applied with good results to this type of processes. Then, the proposed method is applied to a real-world logistic process at Fujitsu-Siemens Computers.     

一类基于优势关系的不完全信息多属性决策方法

针对属性权重和属性值信息均不完全,且含有非线性形式的多属性决策问题,提出一种基于优势关系实现方案择优与排序的决策方法.首先定义方案优势、弱优势和潜在优关系,构建等价的非线性规划模型,确定优势和潜在优方案;然后揭示、论证了非劣集与潜在优集的关系,提出了方案优势度指标,并据此给出了方案择优与排序的实现步骤;最后,提出采用变量替换法求解非线性决策模型.实例计算结果表明,所提出的方法是可行且有效的.     

Recursive approach to combine expert knowledge and data-driven RSW weldability certification decision making process

Data-driven techniques have shown promising results in the analysis and understanding of complex welding processes. Data analytics play a significant role to turn data into valuable insights to assist in the weldability certification decision-making for Resistance Spot Welding (RSW) as well. However, to successfully perform the associated data analytics, domain knowledge is essential to construct more ‘sense-making’ analytics models, as often the models cannot properly capture the nuances of the domain and do not properly indicate the relationship among the RSW concepts and parameters. Thus, machine learning models developed from rough experimental data often do not provide models meaningful and sensible to the domain expert. In this article, we employ a recursive approach between the domain experts and data-driven models so that the knowledge of the domain experts can be integrated into the weldability certification decision-making process. An ontology-based semantic knowledge framework supports this recursive communication while helping the experts to instil more confidence in the developed analytics models. The collaborative and recursive approach implemented in this study helps the domain experts to tap into their domain knowledge and form expert opinions using the formalized semantic RSW concepts and decision rules. The expert opinions are then used to learn new knowledge about the RSW domain and transform the RSW datasets by incorporating significant features that were not included in the earlier models. The transformed datasets help us to develop improved machine learning models, which in turn work as a new source of semantic knowledge, as we have discovered through our pilot implementation.     

一般军事决策过程的协调理论模型

联合作战要求参战各方克服组织、经验、资源和技能的差异,共同协作。基于协调理论的军事决策过程建模有助于理解作战活动的共同结构,有助于识别作战活动的差异及其形成差异的原因。在分析协调理论应用的基础上,根据基于协调理论的军事决策过程建模方法,使用UML2.0建立了一般军事决策过程的协调理论模型,并着重考虑了协调活动及其依赖、协调机制及其异常处理等方面的内容。模型的建立有利于分析军事决策过程中的核心任务及其依赖属性,为指挥员准确地制订和确定作战方案提供依据,从而提高军事决策过程的适应性。     

A protocol for multi-agent negotiation in a group-choice decision making process

Negotiation is one of the most important features of agent interactions found in multi-agent systems, because it provides the basis for managing the expectations of the individual negotiating agents, and it enables selecting solutions that satisfy all the agents as much as possible. In order for negotiation to take place between two or more agents there is need for a negotiation protocol that defines the rules of the game; consequently, a variety of agent negotiation protocols have been proposed in literature. However, most of them are inappropriate for Group-Choice Decision Making (GCDM) because they do not explicitly exploit tradeoff to achieve social optimality, and their main focus is solving two-agent negotiation problems such as buyer–seller negotiation. In this paper we present an agent negotiation protocol that facilitates the solving of GCDM problems. The protocol is based on a hybrid of analytic and artificial intelligence techniques. The analytic component of the protocol utilizes a Game Theory model of an n-person general-sum game with complete information to determine the agreement options, while the knowledge-based (artificial intelligence) component of the protocol is similar to the strategic negotiation protocol. Moreover, this paper presents a tradeoff algorithm based on Qualitative Reasoning, which the agents employ to determine the ‘amount’ of tradeoff associated with various agreement options. Finally, the paper presents simulation results that illustrate the operational effectiveness of our agent negotiation protocol.     

Using a multi-criteria decision making approach to evaluate mobile phone alternatives

The arrival of the mobile phone and its rapid and widespread growth may well be seen as one of the most significant developments in the fields of communication and information technology over the past two decades. The aim of this study is to propose a multi-criteria decision making (MCDM) approach to evaluate the mobile phone options in respect to the users' preferences order. Firstly, the most desirable features influencing the choice of a mobile phone are identified. This is realized through a survey conducted among the target group, the experiences of the telecommunication sector experts and the studies in the literature. Two MCDM methods are then used in the evaluation procedure. More precisely, Analytic Hierarchy Process (AHP) is applied to determine the relative weights of evaluation criteria and the extension of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to rank the mobile phone alternatives. A case study illustrates the effectiveness of the proposed approach.     

A systematic approach to embedded biomedical decision making

An embedded decision making is a key feature for many biomedical systems. In most cases human life directly depends on correct decisions made by these systems, therefore they have to work reliably. This paper describes how we applied systems engineering principles to design a high performance embedded classification system in a systematic and well structured way. We introduce the structured design approach by discussing requirements capturing, specifications refinement, implementation and testing. Thereby, we follow systems engineering principles and execute each of these processes as formal as possible. The requirements, which motivate the system design, describe an automated decision making system for diagnostic support. These requirements are refined into the implementation of a support vector machine (SVM) algorithm which enables us to integrate automated decision making in embedded systems. With a formal model we establish functionality, stability and reliability of the system. Furthermore, we investigated different parallel processing configurations of this computationally complex algorithm. We found that, by adding SVM processes, an almost linear speedup is possible. Once we established these system properties, we translated the formal model into an implementation. The resulting implementation was tested using XMOS processors with both normal and failure cases, to build up trust in the implementation. Finally, we demonstrated that our parallel implementation achieves the speedup, predicted by the formal model.     

Pythagorean fuzzy TODIM approach to multi-criteria decision making

Recently, the TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) approach, which can characterize the decision makers’ psychological behaviours under risk, has been introduced to handle multi-criteria decision making (MCDM) problems. Moreover, Pythagorean fuzzy set is an effective tool for depicting uncertainty of the MCDM problems. In this paper, based on the prospect theory, we first extend the TODIM approach to solve the MCDM problems with Pythagorean fuzzy information. Then, we conduct simulation tests to analyze how the risk attitudes of the decision makers exert the influence on the results of MCDM under uncertainty. Finally, a case study on selecting the governor of Asian Infrastructure Investment Bank is made to show the applicability of the proposed approach.     

一种新的灰色多属性决策方法

针对模糊群体多属性决策问题,提出一种新的灰色多属性决策方法.该方法依据一般的灰色关联分析方法的基本思路,将灰色关联度和欧氏距离有机结合,构造出一种平均相似度对方案进行评价.平均相似度同时反映了方案与正负理想方案之间的位置和数据曲线的相似性差异,其物理意义更加明确.最后通过算例表明了所提出方法的可行性和有效性.     

直觉模糊多属性决策方法综述

直觉模糊多属性决策是当前决策领域的一个研究热点,在实际决策中有着广泛的应用.按照直觉模糊集的发展形式:直觉模糊集、区间直觉模糊集、直觉三角模糊数和直觉梯形模糊数,分别介绍它们在多属性决策与群决策中的研究现状,并对其未来的发展方向进行了探讨与展望.     

Impersonate human decision making process: an interactive context-aware recommender system

A considerable amount of information is quickly disseminated worldwide and users struggled to survive on such data tsunami. Context-recommender-aware systems (CAR) are then developed which enabling users to locate valuable and useful information from a large amount of disordered data. However, human decision-making contains multiple steps and a recursive loop, most users tend to adjust their decision many times instead of achieving the final decision-making immediately. Therefore, to replicate such a recursive process among multiple steps, the traditional CAR system should be altered as an interactive CAR (iCAR) system for improving the recommendation accuracy. In view of the deficiency in the present CAR, this study leads the concept of human-computer interaction in tradition CAR and establishes an interactive context-aware recommender System (iCAR). To validate the feasibility and applicability of the proposed iCAR system, a car rental website which is designed based on iCAR is shown as a demonstration. According to the car rental case shown, after couples of iterations, the decision criteria can be gradually clarified by the proposed algorithm of inferring engine. Also, iCAR can find users a car that most satisfies their requirements by using the contexts information. iCAR can improve the accuracy of traditional CAR system and provide user more precise recommendation results according to 3-dimensions information, including: user, item and context information. The iCAR system can be further expected to apply to various fields, such as online shopping or travel packages recommendations, to optimize recommendations results.     

Multiple-attribute decision making under uncertainty: the evidential reasoning approach revisited

In multiple-attribute decision making (MADM) problems, one often needs to deal with decision information with uncertainty. During the last decade, Yang and Singh (1994) have proposed and developed an evidential reasoning (ER) approach to deal with such MADM problems. Essentially, this approach is based on an evaluation analysis model and Dempster's rule of combination in the Dempster-Shafer (D-S) theory of evidence. This paper reanalyzes the ER approach explicitly in terms of D-S theory and then proposes a general scheme of attribute aggregation in MADM under uncertainty. In the spirit of such a reanalysis, previous ER algorithms are reviewed and two other aggregation schemes are discussed. Theoretically, it is shown that new aggregation schemes also satisfy the synthesis axioms, which have been recently proposed by Yang and Xu (2002) for which any rational aggregation process should grant. A numerical example traditionally examined in published sources on the ER approach is used to illustrate the discussed techniques.