Learning models for decentralized decision making

Models for decentralized decision making under uncertainty are introduced. Decision makers interact with each other and update their decisions using known learning schemes, rendering analysis of the group behavior tractable. Synchronous models are shown to lead to problems in stochastic games. Specific examples of such models are explored in detail and simulation results are given. Other situations in which only one decision maker acts at a time are also presented, and it is suggested that the decentralized control of Markov chains can be viewed in terms of such a model.     

How cognitive and affective aspects can influence the outcome of the group decision‐making process

Supporting group decision‐making when the decision makers are spread around the world is a complex process. The mechanisms of automated negotiation, such as argumentation, can be used in Ubiquitous Group Decision Support Systems (UbiGDSS) to help decision makers find a solution based on their preferences. However, the decision‐making process is much more than just a simple criteria and alternative analysis. There are many cognitive and affective issues that affect the outcome, and these issues should not be ignored; otherwise, the quality of the decision could be compromised. In this paper, we detail an UbiGDSS architecture and explore 2 cognitive and affective methods that are essential to the group decision‐making process. We explain how agents can reason about self‐expertise and other decision makers' credibility, and how agents can verify and react to tendencies throughout the decision‐making process. We intend agents to achieve higher quality and more consensual decisions. In any simulation environment that we tested, agents that analysed credibility, expertise, and/or analysed tendencies always achieved a higher consensus compared to agents that used neither of the proposed methods. Likewise, agents that used neither of the proposed methods or only performed tendencies analysis obtained the worst average satisfaction levels for each simulation environment.     

Developing a fuzzy TOPSIS approach based on subjective weights and objective weights

Multiple criteria decision making (MCDM) is widely used in ranking one or more alternatives from a set of available alternatives with respect to multiple criteria. Inspired by MCDM to systematically evaluate alternatives under various criteria, we propose a new fuzzy TOPSIS for evaluating alternatives by integrating using subjective and objective weights. Most MCDM approaches consider only decision maker’s subjective weights. However, the end-user attitude can be a key factor. We propose a novel approach that involves end-user into the whole decision making process. In this proposed approach, the subjective weights assigned by decision makers (DM) are normalized into a comparable scale. In addition, we also adopt end-user ratings as an objective weight based on Shannon’s entropy theory. A closeness coefficient is defined to determine the ranking order of alternatives by calculating the distances to both ideal and negative-ideal solutions. A case study is performed showing how the propose method can be used for a software outsourcing problem. With our method, we provide decision makers more information to make more subtle decisions.     

Decision making on business issues with foresight perspective; an application of new hybrid MCDM model in shopping mall locating

Shopping malls are one of the glories of metropolises with their attractive shops and a wide variety people who are walking in order to purchase goods. Location of a shopping mall is one of the critical criteria, because it can influence the success of the project. In addition, selecting an appropriate location to establish a new shopping mall is a sophisticated, time consuming and risky decision. Commonly multi-factors should be considered in the decision making model. Thus, a comprehensive model should be considered for similar studies. Moreover, the foresight perspective can be necessary for the future competitiveness of the project. Decision makers need powerful tools for the process of the decision making, for this aim two Multiple Criteria Decision Making (MCDM) methods are applied in our model. Stepwise Weight Assessment Ratio Analysis (SWARA) is applied to decision making in order to prioritize and calculating the relative importance of the criteria. Then, Weighted Aggregated Sum Product Assessment (WASPAS) methodology is used to evaluate potential alternatives. Tehran is considered as a real example of this research and potential places for this mean considered in research. This brand-new hybrid MCDM method is presented in this research as a powerful framework is decision making. This framework can be useful as an appropriate framework for solving locating issues in other companies.     

Extension of VIKOR method in intuitionistic fuzzy environment for robot selection

Decision making is the process of finding the best option among the feasible alternatives. In classical multiple-criteria decision making methods, the ratings and the weights of the criteria are known precisely. However, if decision makers are not able to involve uncertainty in the defining of linguistic variables based on fuzzy sets, the intuitionistic fuzzy set theory can do this job very well. In this paper, VIKOR method is extended in intuitionistic fuzzy environment, aiming at solving multiple-criteria decision making problems in which the weights of criteria and ratings of alternatives are taken as triangular intuitionistic fuzzy set. For application and verification, this study presents a robot selection problem for material handling task to verify our proposed method.     

Using Gower Plots and Decision Balls to rank alternatives involving inconsistent preferences

Ranking alternatives involving inconsistent preferences is one of the most important topics in decision-making. Determining how to assist decision makers in understanding the decision context and adjusting inconsistencies in judgment are two important issues in ranking alternatives. This study proposes a visualization approach which will assist decision makers in ranking alternatives involving inconsistent preferences. Gower Plots are adopted to detect alternatives involving inconsistencies. An adjusting model is developed to provide suggestions for simultaneously improving ordinal and cardinal inconsistencies. A Decision Ball model is applied to visualize the decision context. By a graphical and interactive interface, decision makers can iteratively detect inconsistencies, choose the preferred way to adjust inconsistencies, observe relationships among alternatives, and then rank alternatives.     

Creating a Decision-Making Model Using Association Rules

Being a highly significant and complex function of management, decision making requires methods and techniques that simplify the process of selecting one choice among all available options. Decision making is therefore selection of that particular choice over any of several alternatives. Because of the process complexity, a continuous research and improvement of the methods and techniques modern decision making involves is required. One of many modern business challenges is to discover any possible improvement in the decision-making process managers shall use in making the right decision. Any decision made by managers directly impacts the realized profit, business, and company’s position on the market. The fact is that mankind faces the decision-making problem in each phase of its social development, which has resulted in increased need for learning more about it. In this work, both the significance and application of association rules will be analyzed on an example of car sales business. The research was conducted on a sample of 1728 transactions in order to recognize and establish the association rules and then determine their impact on the sales and profit. For the purpose of this research, a large car sales database was used as a source of information, which is also described in this work. Once these association rules were established, they were then used to create a better and more complete market supply. The main contribution of the paper is providing business intelligence model for performing association rules in real-term business settings.     

Extension of TOPSIS to determine weight of decision maker for group decision making problems with uncertain information

In traditional TOPSIS method, the ideal solutions for alternatives are expressed in vectors. An important step in the process of group decision making is to determine the relative importance of each decision maker. In this paper, the weights of decision makers derived from individual decision are determined by using an extended TOPSIS method with interval numbers. The ideal decisions for all individual decisions are expressed in matrices. The positive ideal decision is the intersection of all individual decisions; the negative ideal decision is the union of all individual decisions. Comparisons with other methods are also made. A numerical example is examined to show the potential applications of the proposed method.     

Decision‐theoretic rough sets under Pythagorean fuzzy information

Decision‐theoretic rough sets (DTRSs), which provide a classical model of three‐way decisions (3WDs), play an important role in risk decision‐making problems. The risk is associated with the loss function of DTRSs, which is evaluated by the decision makers. As a new extension of fuzzy sets, Pythagorean fuzzy sets can handle uncertain information more flexibly than intuitionistic fuzzy sets in the process of decision making and it gives a new measure for the determination of loss functions of DTRSs. More specifically, we take into account the loss functions of DTRSs with Pythagorean fuzzy numbers and propose a Pythagorean fuzzy decision‐theoretic rough set (PFDTRS) model. Some properties of the expected losses are carefully investigated. Then we further design three approaches for deriving 3WDs with the PFDTRS model. The group decision making (GDM) based on the PFDTRS model is also discussed. It provides a novel interpretation for the determination of loss functions. With the aid of the Pythagorean fuzz weighted averaging operator, we aggregate the loss functions, as suggested by the all experts, which support a coherent way of designing information granules in the presence of numerics. An algorithm for 3WDs in GDM based on the PFDTRS model is designed. Then, an example is presented to elaborate on 3WDs with the PFDTRS model.     

Establishing dominance and potential optimality in multi-criteria analysis with imprecise weight and value

The model presented in this paper does not require exact estimations of decision parameters such as attribute weights and values that may often be considerable cognitive burden of human decision makers. Information on the decision parameters is only assumed to be in the form of arbitrary linear inequalities which form constraints in the model. We consider two criteria, dominance and potential optimality, to check whether or not each alternative is outperform for a fixed feasible region denoted by the constraints. In particular, we develop a method to identify potential optimality of alternatives when all (or subsets) of the attribute values as well as weights are imprecisely know. This formulation becomes a nonlinear programming problem hard to be solved generally so that we provide in this paper how this problem is transformed into a linear programming equivalent.Scope and purposeMost managerial decisions involve choosing an optimal alternative from a number of available alternatives. Researchers have proposed a lot of methods to assist decision makers in choice making with a set of, usually conflicting, criteria or attributes. Many of these approaches require exact (or precise) information about either or both attribute values and/or trade-off weights. In some practice, however, it is not easy for decision makers to provide such exact data because, for example, intangible attributes to reflect social and environmental impacts may be included. To cope with such problem, a mathematical programming model-based approach to multi-criteria decision analysis is presented in this paper when both attribute weights and marginal values are imprecisely identified. A weighted additive rule is used to evaluate the performance of alternatives. We then show how to obtain non-dominated and potentially optimal alternatives in order to support choice making.     

动态环境下的群组专家多准则变权决策方法

为克服经典多准则决策（MCDM）方法不适应动态的决策环境、难以反映方案集对准则集的非线性反馈效应等方面缺陷,通过运用网络分析和数据包络分析技术,提出一种动态环境下的群组专家多准则变权决策方法。较之于经典MCDM方法,新方法主要创新之处在于：给出了MCDM模型的动态演化机理;通过专家对方案所处准则状态予以有偏好（无偏好）判断,提出一种保证信息无损的群组专家信息提取方式;实现了对方案的变权评价,有效反映出蕴含在系统内部的准则集与方案集的非线性交互作用关系。实例验证结果表明,所提方法是科学可行的,能够有效解决救灾方案动态优选、供应商动态评价等实践问题。     

Constructing,evaluating and visualizing value and utility functions for decision support

Formal methods of decision analysis can help to structure a decision making process and to communicate reasons for decisions transparently. Objectives hierarchies and associated value and utility functions are useful instruments for supporting such decision making processes by structuring and quantifying the preferences of decision makers or stakeholders. Common multi-attribute decision analysis software products support such decision making processes but they can often not represent complex preference structures and visualize uncertainty induced by uncertain predictions of the consequences of decision alternatives. To stimulate strengthening these aspects in decision support processes, we propose a set of visualization tools and provide a software package for constructing, evaluating and visualizing value and utility functions. In these tools we emphasize flexibility in value aggregation schemes and consideration and communication of prediction uncertainty. The use of these tools is demonstrated with an illustrative example of river management decision support.     

Unifying approaches to consensus across different preference representations

Consensus measures can be useful in group decision making problems both to guide users toward more reasonable judgments and to give an overall indication of the support for the final decision. The level of consensus between decision makers can be measured in contexts where preferences over alternatives are expressed either as evaluations or scores, pairwise preferences, and weak orders, however these different representations often call for different approaches to consensus measurements. In this paper, we look at the distance metrics used to construct consensus measures in each of these settings and how consistent these are for preference profiles when they are converted from one representation to another. We develop some methods for consistent approaches across decision making settings and provide an example to help investigate differences between some of the commonly used distances.     

Novel intuitionistic fuzzy decision making models in the framework of decision field theory

The intuitionistic fuzzy decision making problems have gained great popularity recently. Most of the current methods depend on various aggregation operators that provide collective intuitionistic fuzzy values of alternatives to be ranked. Such collective information only depicts the overall characteristics of the alternatives but ignores the detailed contrasts among them. Most important of all, the current decision making procedure is not in accordance with the way that the decision makers (DMs) think about the decision making problems. In this paper, we develop a novel intuitionistic fuzzy decision making model in the framework of decision field theory. The decision making model emphasizes the contrasts among alternatives with respect to each attribute that competes and influences each other, and thus, the preferences for alternatives can dynamically evolve and provide the final optimal result. After that, we develop an intuitionistic fuzzy group decision making model based on decision field theory, and then make a practical case study on the application of the developed models to the “one belt, one road” investment decision making problems. Finally, we point out the characteristics and the limitations of our models in detail.     

Managing architectural decision models with dependency relations, integrity constraints, and production rules

Software architects consider capturing and sharing architectural decisions increasingly important; many tacit dependencies exist in this architectural knowledge. Architectural decision modeling makes these dependencies explicit and serves as a foundation for knowledge management tools. In practice, however, text templates and informal rich pictures rather than models are used to capture the knowledge; a formal definition of model entities and their relations is missing in the current state of the art. In this paper, we propose such a formal definition of architectural decision models as directed acyclic graphs with several types of nodes and edges. In our models, architectural decision topic groups, issues, alternatives, and outcomes form trees of nodes connected by edges expressing containment and refinement, decomposition, and triggers dependencies, as well as logical relations such as (in)compatibility of alternatives. The formalization can be used to verify integrity constraints and to organize the decision making process; production rules and dependency patterns can be defined. A reusable architectural decision model supporting service-oriented architecture design demonstrates how we use these concepts. We also present tool support and give a quantitative evaluation.     

Concepts of decision support for river rehabilitation

River rehabilitation decisions, like other decisions in environmental management, are often taken by authorities without sufficient transparency about how different goals, predictions, and concerns were considered during the decision making process. This can lead to lack of acceptance or even opposition by stakeholders. In this paper, a concept is outlined for the use of techniques of decision analysis to structure scientist and stakeholder involvement in river rehabilitation decisions. The main elements of this structure are (i) an objectives hierarchy that facilitates and stimulates explicit discussion of goals, (ii) an integrative probability network model for the prediction of the consequences of rehabilitation alternatives, and (iii) a mathematical representation of preferences for possible outcomes elicited from important stakeholders. This structure leads to transparency about expectations of outcomes by scientists and valuations of these outcomes by stakeholders and decision makers. It can be used (i) to analyze synergies and conflict potential between stakeholders, (ii) to analyze the sensitivity of alternative-rankings to uncertainty in prediction and valuation, and (iii) as a basis for communicating the reasons for the decision. These analyses can be expected to support consensus-building among stakeholders and stimulate the creation of alternatives with a greater degree of consensus. Because most decisions in environmental management are characterized by similarly complex scientific problems and diverse stakeholders, the outlined methodology will be easily transferable to other settings.     

The scope of human values and human activities in decision making

Decision making was long in need of methods of prioritization but now needs to complete the scope of its structures to become a dependable science that can be helpful on the micro level. Most of the factors that determine the structure of a decision depend largely on the feelings and memories of the decision makers and that leaves room for doubt about the completeness of the decision. The decision makers would be helped by having available a general well-researched list of factors for some of their complex decisions in order to have greater assurance that their decisions are comprehensive and right. To do that, they must have wide exposure and be familiar with the full spectrum of human values and environmental opportunities. Our concern is about structuring decisions in a reliable way to serve the needs of decision makers. A very broad framework is provided in this paper to address this issue.     

Using arguments for making and explaining decisions

Arguments play two different roles in day life decisions, as well as in the discussion of more crucial issues. Namely, they help to select one or several alternatives, or to explain and justify an already adopted choice.This paper proposes the first general and abstract argument-based framework for decision making. This framework follows two main steps. At the first step, arguments for beliefs and arguments for options are built and evaluated using classical acceptability semantics. At the second step, pairs of options are compared using decision principles. Decision principles are based on the accepted arguments supporting the options. Three classes of decision principles are distinguished: unipolar, bipolar or non-polar principles depending on whether i) only arguments pros or only arguments cons, or ii) both types, or iii) an aggregation of them into a meta-argument are used. The abstract model is then instantiated by expressing formally the mental states (beliefs and preferences) of a decision maker. In the proposed framework, information is given in the form of a stratified set of beliefs. The bipolar nature of preferences is emphasized by making an explicit distinction between prioritized goals to be pursued, and prioritized rejections that are stumbling blocks to be avoided. A typology that identifies four types of argument is proposed. Indeed, each decision is supported by arguments emphasizing its positive consequences in terms of goals certainly satisfied and rejections certainly avoided. A decision can also be attacked by arguments emphasizing its negative consequences in terms of certainly missed goals, or rejections certainly led to by that decision. Finally, this paper articulates the optimistic and pessimistic decision criteria defined in qualitative decision making under uncertainty, in terms of an argumentation process. Similarly, different decision principles identified in multiple criteria decision making are restated in our argumentation-based framework.     

Extension of Fuzzy ELECTRE based on VIKOR method

Many decision problems in real-world deal with conflicting criteria, uncertainty and imprecise information. Some also allow a group of decision makers (DMs) to make their opinions independently. Multi-criteria decision making (MCDM) is a well known decision method that can make the quality of group multiple criteria decisions better by creating a more explicit, rational and efficient process. A group of MCDM models known as “outranking methods” have been used to rank a set of alternatives. ELECTRE I is an outranking method which is simple, but provides partial ranking. So we consider VIKOR and try to mitigate this problem with regard to relations between VIKOR and ELECTRE. The objective of this paper is to extend ELECTRE I method based on VIKOR to rank a set of alternatives versus a set of criteria to show the decision maker’s preferences.     

On group decision making with four formats of incomplete preference relations

In this paper, a new approach is proposed to solve group decision making (GDM) problems where the preference information on alternatives provided by decision makers (DMs) is represented in four formats of incomplete preference relations, i.e., incomplete multiplicative preference relations, incomplete fuzzy preference relations, incomplete additive linguistic preference relations, incomplete multiplicative linguistic preference relations. In order to make the collective opinion close each decision maker’s opinion as near as possible, an optimization model is constructed to integrate the four different formats of incomplete preference relations and to compute the collective ranking values of the alternatives. The ranking of alternatives or selection of the most desirable alternative(s) is directly obtained from the derived collective ranking values. A numerical example is also used to illustrate the applicability of the proposed approach.