A Topsis‐Based Approach for Prioritized Aggregation in Multi‐Criteria Decision‐Making Problems

Aggregation in multi‐criteria decision‐making environments is a process of combining the values of a set of attributes into one representative value for the entire set of attributes. Many aggregation methods—ranging from the simple averaging approach to more sophisticated methods, such as ordered weighted averaging—have been applied in previous research. One challenge in aggregation arises in special cases of prioritized aggregation, wherein the prioritized relationships between attributes must be considered during aggregation. This paper presents a new approach to aggregating attributes with prioritized relationships. First, an overview of past research is conducted to identify different aggregation methods, classes and properties. Next, the concept of prioritized aggregation is explained in detail. A prioritized aggregation method utilizing the technique of order preference by similarity to ideal solution is then presented. Subsequently, the presented prioritized aggregation method is applied on an actual case study. According to the results, the aggregation method presented in this paper is, through the application of technique of order preference by similarity to ideal solution, capable of quantifying and considering the prioritized relationship between a set of attributes undergoing aggregation. Finally, conclusions are stated, and a discussion describing future work pertinent to this paper is presented. Copyright © 2016 John Wiley & Sons, Ltd.     

A Fuzzy Topsis Method for Prioritized Aggregation in Multi‐Criteria Decision Making Problems

Aggregation in a decision making environment requires the fusion of opinions of a group of decision makers. The group of decision makers are required to analyse a set of interrelated criteria that are usually measured on a linguistic scale. This process requires, in many instances, to capture experts experience, intuition and thinking that are traditionally expressed in a linguistic fashion rather than a numerical fashion. Furthermore, the necessity of considering the relationship between the criteria to the overall decision must be considered by the group of decision makers. This paper extends the application of fuzzy numbers, fuzzy relative importance scores (FRIS), fuzzy relative weights (FRW) and the fuzzy technique of order preference by similarity to ideal solution (TOPSIS) in prioritized aggregation. This extension provides a mean to systematically aggregate a group of decision makers' views for a set of interrelated criteria that are measured on a linguistic scale. First, an overview of the application of fuzzy numbers and the characteristics of aggregating fuzzy numbers in multi‐criteria decision making problems are presented. Then, the application of TOPSIS in fuzzy environments is presented. Next, past research is highlighted to present prioritized aggregation and the different aggregation operators' classes. Subsequently, a new prioritized aggregation method is presented. This method utilizes fuzzy TOPSIS with prioritized aggregation in fuzzy environments. Finally, the fuzzy prioritized aggregation method presented in this paper is applied on an actual case study. According to the results, the method presented in this paper provides a systematic approach to capture the uncertainty and imprecision associated with quantifying linguistic measurements in multi‐criteria decision making problems. Furthermore, it considers the relationship between the set of linguistically measured criteria undergoing prioritized aggregation in a fuzzy environment. Lastly, findings, conclusions and future work are presented. Copyright © 2016 John Wiley & Sons, Ltd.     

A hybrid neutrosophic multiple criteria group decision making approach for project selection

The project selection is one of the most important phases of a project life cycle. The project selection is considered as a Multi-Criteria Decision-Making (MCDM) problem. This research aims to study the integration between Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) into Decision-Making Trial and Evaluation Laboratory (DEMATEL) under neutrosophic environment to provide a new technique for making a decision regarding the choice of appropriate project (project selection) as one of the most important phases of the project life cycle. Projects are selected by comparing them against many criteria. Criteria are evaluated based on expert’s opinion. Sometimes experts cannot give reliable information due to the non-deterministic environment. The neutrosophic set theory will be used to handle and overcome the ambiguity or lack of confirmation of information. The criteria are weighted by DEMATEL, then the best project alternative is selected using TOPSIS. In the proposed model, each pairwise comparison judgments is symbolized as a trapezoidal neutrosophic number. Experts will focus only on (n − 1) judgments for n alternatives to overcome the difficulties of [(n * (n − 1))/2] consistence judgments in case of increasing number of alternatives. A numerical example is developed to show the validation of the suggested model in the neutrosophic environment.     

Attribute Partitioning in Multiple Attribute Decision Making Problems for a Decision with a Purpose – a Fuzzy Approach

This work introduces a methodology to find solutions corresponding to different purposes in a multiple attribute decision‐making problem under fuzzy environment. The discernment of purpose‐based solutions becomes important when the problem is defined vaguely and solution is targeted to heterogeneous population. Depending on the purpose, for which the solution is sought, the attributes are identified and weighted in an appropriate proportion. The level of similarity between a pair of attributes plays an important role to determine the aggregated value of attributes specific to a purpose. Our work determines the similarity levels between a pair of attributes by calculating their maximum attainability in presence of each other. The achievement of an attribute in presence of another is represented as a fuzzy set in the unit interval. The crisp equivalents of the fuzzy sets in the unit interval are used to define their simultaneous satisfaction denoted as 1‐step relation. The 1‐step relation is extended to (m‐1)‐step relation to calculate the degree of attainability of the same pair of attributes in the presence of m (all) attributes. The different levels of (m‐1)‐step relations generate several partitions of the attributes corresponding to multiple purposes in the multiple attribute decision‐making problems. The degree of fulfilment of the purposes in the alternatives are numerically derived by first taking weighted average of attributes within the equivalence classes of a partition and then aggregating the values corresponding to equivalence classes through ordered weighted averaging. The methodology is illustrated with a numerical example. Copyright © 2016 John Wiley & Sons, Ltd.     

Prioritized and Non-prioritized Multiple Change on Belief Bases

In this article we explore multiple change operators, i.e., operators in which the epistemic input is a set of sentences instead of a single sentence. We propose two types of change: prioritized change, in which the input set is fully accepted, and symmetric change, where both the epistemic state and the epistemic input are equally treated. In both kinds of operators we propose a set of postulates and we present different constructions: kernel changes and partial meet changes.     

Group‐analytic network process for balancing stakeholder views on fire protection of steel‐framed buildings

To select appropriate fire protection options for buildings during their design stage, economic, safety, environmental, and societal criteria need to be accounted for. The divergent and sometimes conflictual desires from different fire design stakeholders involved in the process present a multicriteria decision problem. Design decision criteria and fire protection options can be interdependent, and so there is a need to manage these desires with an advanced decision analysis technique, thereby reducing uncertainties in the complex decision‐making process. The aim of this paper is to use the weighted/geometric mean method‐analytic network process (W/GMM‐ANP) to balance the opinions of fire design stakeholders extracted from 42 structured stakeholder interviews on selecting the most suitable fire protection option for buildings constructed of steel frames. Different categories of interdependent decision elements were developed from 22 design decision criteria and 5 proposed fire protection options to produce a network of decision clusters for multicriteria decision analysis. In the synthesis and ranking of fire protection options, the W/GMM‐ANP accounted for the multiple interdependencies of weighted and unweighted stakeholder desires and managed the complexity of the decision‐making problem. The technique is proposed for approaching suitable group decisions in structural fire design of steel‐framed buildings as well as other performance‐based engineering decision making that may involve multidisciplinary stakeholders.     

Local Change

An agent can usually hold a very large number of beliefs. However, only a small part of these beliefs is used at a time. Efficient operations for belief change should affect the beliefs of the agent locally, that is, the changes should be performed only in the relevant part of the belief state. In this paper we define a local consequence operator that only considers the relevant part of a belief base. This operator is used to define local versions of the operations for belief change. Representation theorems are given for the local operators.     

Rational consensual procedure: Argumentation or weighted averaging?

The following is a defense of Jurgen Habermas' argumentational consensual procedure against Keith Lehrer and Carl Wagner's weighted averaging consensual procedure (and, I tentatively claim, against any weighted averaging consensual procedure). The argument is twofold: if Lehrer and Wagner intend, implicity, to replace what is for Habermas the metatheoretical stage of a discussion with the aggregation of judgments of respect, then their procedure fails to make use of all available information and the participants are not committed to the weighted average position on these grounds; if, on the other hand, they do not intend to replace metatheoretical discussion by aggregation, then the conditions under which the discussion could conceivably have come to a halt are such as to provide no support for the claim that it is rational to aggregate, rather than to consider the discussion unresolved until more information is available.An earlier and abbreviated draft of this paper was read at the 10th Annual International Wittgenstein Symposium, August 1985, in Kirchberg, Austria.     

构建科学的冠心病介入治疗临床决策

临床决策已成为临床医学的重要内容,但目前还为许多冠心病介入医生所忽视。阐述了临床决策的概念,分析了冠心病介入治疗决策中存在的问题及原因,探讨了如何构建科学的冠心病介入治疗决策,为冠心病介入治疗合理应用提供科学的决策方法。     

Normative and Descriptive Models of Military Decisions to Deploy Precision Strike Capabilities

Precision strike capabilities represent a significant and highly controversial part of present day military operations. And yet, there is a surprising dearth of empirical research on military decision making in this domain. In this article, we therefore review different psychological perspectives on how these decisions can be made. Specifically, we compare the application of normative models of judgment and choice against the empirical research on human decision making, which suggests that people are more likely to employ heuristic strategies. We suggest that several features of decision tasks in the precision strike domain evoke the use of intuitive (heuristic) decision making whereas other features such as the sometimes unfamiliar (or novel) nature of the decision task requires analytic strategies to generate good solutions. Therefore, decisions about precision strike capabilities are best made with a mixture of intuitive and analytic thought, a mode of thinking known as quasirationality.     

Response to the Comments by Larichev,Korhonen and Vargas on ‘Power Relations and Group Aggregation in the Multiplicative AHP and SMART’

We are pleased to continue the open discussion in this journal and we hope that it will contribute to the clarification of the main issues. Aggregation by geometric means has been accepted as a technically sound procedure. The critical comments now seem to concentrate on the establishment of the Multiplicative AHP, the publication of a method for weighted voting before empirical verification, and the significance of negotiations in decision making. Several numerical examples illustrate the arithmetic-mean and geometric-mean calculations, and the legitimacy of rank reversal is revisited. We discuss the issues in the same order here. © 1997 by John Wiley & Sons, Ltd. J. Multi-Crit. Decis. Anal. 6 : 171–174 (1997) No. of Figures: 0. No. of Tables: 5. No. of References: 9.     

Answer Sets and Qualitative Decision Making

Logic programs under answer set semantics have become popular as a knowledge representation formalism in Artificial Intelligence. In this paper we investigate the possibility of using answer sets for qualitative decision making. Our approach is based on an extension of the formalism, called logic programs with ordered disjunction (LPODs). These programs contain a new connective called ordered disjunction. The new connective allows us to represent alternative, ranked options for problem solutions in the heads of rules: A × B intuitively means: if possible A, but if A is not possible then at least B. The semantics of logic programs with ordered disjunction is based on a preference relation on answer sets. We show that LPODs can serve as a basis for qualitative decision making.     

基于遗传算法的模糊综合评价在心理测量中的应用

提出了运用模糊数学对利克特量表数据进行分析的方法, 探讨了人们在进行模糊综合评价时, 所采用的算子和对各个自变量的权重分配, 并且运用遗传算法(GA)来确定相关的权重。以大学生对康师傅红烧牛肉面的评价数据为例, 运用基于遗传算法的模糊综合评价方法, 发现男生采用了“最大最小”合成算子, 女生采用了“有界和、取小”合成算子。研究结果表明, 基于遗传算法的模糊综合评价方法可以对利克特量表的心理测量数据进行有效的分析。     

Measurement scales and scale independence in the analytic hierarchy process

One approach to evaluate the relative performance of decision alternatives with respect to multiple criteria is provided by the analytic hierarchy process. The method is based on pairwise comparisons between attributes, and several numerical measurement scales for the ratio statements have been proposed. The choice of measurement scale is re‐examined, and new arguments supporting the measurement scale of geometric progression are derived. Separately from the measurement scale considerations, the effects of the scale parameter in geometric measurement scale are also studied. By using a regression model for pairwise comparisons data, it is shown that the statistical inference does not depend on the value of the scale parameter in the case of a single pairwise comparison matrix. It is also shown when the scale independence of statistical inference can be achieved in a decision hierarchy. This requires the use of the geometric‐mean aggregation rule instead of the traditional arithmetic‐mean aggregation. The results of the case study demonstrate that the measurement scale and the aggregation rule have potentially large impacts on decision support. Copyright © 2000 John Wiley & Sons, Ltd.     

A neural cognitive model of argumentation with application to legal inference and decision making

Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (ii) neural networks can be used to combine argumentation, quantitative reasoning and statistical learning. At the same time, non-standard logic models of argumentation started to emerge. In this paper, we propose a connectionist cognitive model of argumentation that accounts for both standard and non-standard forms of argumentation. The model is shown to be an adequate framework for dealing with standard and non-standard argumentation, including joint-attacks, argument support, ordered attacks, disjunctive attacks, meta-level attacks, self-defeating attacks, argument accrual and uncertainty. We show that the neural cognitive approach offers an adequate way of modelling all of these different aspects of argumentation. We have applied the framework to the modelling of a public prosecution charging decision as part of a real legal decision making case study containing many of the above aspects of argumentation. The results show that the model can be a useful tool in the analysis of legal decision making, including the analysis of what-if questions and the analysis of alternative conclusions. The approach opens up two new perspectives in the short-term: the use of neural networks for computing prevailing arguments efficiently through the propagation in parallel of neuronal activations, and the use of the same networks to evolve the structure of the argumentation network through learning (e.g. to learn the strength of arguments from data).     

To change or not to change: A matter of decision maker’s role

The status quo effect derived from loss aversion is common in decision making. However, we propose that advisors (vs. personal decision makers) are less susceptible to such an effect because they are less loss-averse. The difference in loss aversion between personal decision makers and advisors is reflected in both the query order and content. Compared to advisors, personal decision makers produce more queries favoring the status quo, at an earlier time, than those favoring the new option. As hypothesized, the status quo effect was observed among personal decision makers, but not among advisors (Studies 1 and 2). Query order and content were found to mediate the impact of decision maker’s role on the status quo effect (Study 2). When personal decision makers and advisors made queries in the same order (Study 3) or of the same content (Studies 4a and 4b), the difference between self–other decision making disappeared.     

What is adaptive about adaptive decision making? A parallel constraint satisfaction account

There is broad consensus that human cognition is adaptive. However, the vital question of how exactly this adaptivity is achieved has remained largely open. Herein, we contrast two frameworks which account for adaptive decision making, namely broad and general single-mechanism accounts vs. multi-strategy accounts. We propose and fully specify a single-mechanism model for decision making based on parallel constraint satisfaction processes (PCS-DM) and contrast it theoretically and empirically against a multi-strategy account. To achieve sufficiently sensitive tests, we rely on a multiple-measure methodology including choice, reaction time, and confidence data as well as eye-tracking. Results show that manipulating the environmental structure produces clear adaptive shifts in choice patterns – as both frameworks would predict. However, results on the process level (reaction time, confidence), in information acquisition (eye-tracking), and from cross-predicting choice consistently corroborate single-mechanisms accounts in general, and the proposed parallel constraint satisfaction model for decision making in particular.     

跨期选择中偏好反转的心理机制

通过直接测量近期和远期选择中备选项激活的情绪、间隔的时间知觉及对备选项金额差异和时间间隔的重视程度,以探究跨期选择中偏好反转产生的心理机制。结果表明：（1）近期选择中,备选项激活的情绪强度差异显著,而远期选择中两者的差异不显著;（2）近期和远期选择中,情绪强度差异和时间知觉差异能够预测偏好反转的发生。研究结果支持了偏好反转的情绪激活差异假说和时间知觉差异假说。     

Neural affective decision theory: Choices, brains, and emotions

We present a theory and neurocomputational model of how specific brain operations produce complex decision and preference phenomena, including those explored in prospect theory and decision affect theory. We propose that valuation and decision making are emotional processes, involving interacting brain areas that include two expectation-discrepancy subsystems: a dopamine-encoded system for positive events and a serotonin-encoded system for negative ones. The model provides a rigorous account of loss aversion and the shape of the value function from prospect theory. It also suggests multiple distinct neurological mechanisms by which information framing may affect choices, including ones involving anticipated pleasure. It further offers a neural basis for the interactions among affect, prior expectations and counterfactual comparisons explored in decision affect theory. Along with predicting the effects of particular brain disturbances and damage, the model suggests specific neurological explanations for individual differences observed in choice and valuation behaviors.