A Distance-Based Consensus Model with Flexible Choice of Rank-Position Weights

In this paper we propose a preference aggregation procedure for those cases in which the decision-makers express their preferences by means of a ranking of alternatives. Among the most applied methods for this purpose are those inspired by the Borda–Kendall rule, which attach to each alternative an aggregated value of the votes received in the different rank positions, and those based on distance measures between individual and collective preferences, which look for the solution that maximizes the consensus. The main idea here is to integrate these two approaches. Taking into account that the information about the values of weights or utilities assigned to each rank position is imprecise, we propose an evaluation of the alternatives using that vector of weights that minimizes the disagreement between DMs. In order to solve the problem, mixed-integer linear programming models are constructed. Two numerical examples are examined to illustrate the applicability of the proposed procedure.     

Monte Carlo Simulation Techniques in a Decision Support System for Group Decision Making

This paper describes a group decision support system based on an additive multi-attribute utility model for identifying a consensus strategy in group decision-making problems where several decision-makers or groups of decision-makers elicit their own preferences separately. On the one hand, the system provides procedures to quantify the DMs or group of DMs preferences separately. This involves assessing the DMs or group of DMs component utilities that represent their preferences regarding the respective possible attribute values and objective weights that represent the relative importance of the criteria. On the other hand, we propose Monte Carlo simulation techniques for identifying a consensus strategy. An iterative process will be carried out, where, after the simulations have been performed, the imprecise component utilities and weights corresponding to the different DMs or groups of DMs are tightened to output more meaningful information in the next simulations to achieve a consensus strategy. Finally, an application to the evaluation of remedial strategies for restoring contaminated aquatic ecosystems illustrates the usefulness and flexibility of this decision support tool.     

A purchase decision support model considering consumer personalization about aspirations and risk attitudes

Ranking alternative products to help consumers make better purchase choices is a valuable research topic. Most previous decision support models cannot be well applied to heterogeneous consumers. This paper focuses on establishing a personalized interactive model to assist consumers make better buying decisions with less effort. For the alternative products provided by consumers, we collect online reviews and parameter configurations of alternative products and then obtain the fusing evaluative information. As consumers are dominated by bounded rationality, they only provide partially key attribute weights, based on which, we construct an optimizing model to obtain the optimal attribute weights of customers for products. Then, a satisfaction function is proposed by uniting aspiration levels and risk attitudes of consumers and a compensatory decision rules is established to rank and recommend the brands to consumers. Finally, practicability of this study is illustrated with a real car purchase case. Through the case study, it can be seen that the proposed decision support model generates a personalized list of alternatives based on consumer's own utility function about risk attitudes, aspiration levels, and preferences for product attributes, which further confirms that the proposed model can capture the personalized needs of consumers. Theoretical and managerial implications of this model as well as advantages are further illustrated.     

For the Greater Good? A Test for Strategic Bias in Group Environmental Decisions

This paper presents a study which tests for strategic bias in group decision support models. Strategic bias occurs when individuals provide preference information to a group decision model which, they perceive, will improve their own outcomes and not necessarily those of the group. A test is made for strategic bias in a decision model used to allocate funds amongst 14 natural resource management regions in Queensland. The funds are a crucial source of revenue for the regions to achieve environmental objectives. In this real decision problem representatives from each region supplied criteria weights for a multiple criteria analysis model. Results reveal moderate to weak presence of strategic bias. Regions mostly selected weights that would improve their outcome relative to the weights of other regions. But this was not overly pronounced and there were exceptions. Whilst some degree of strategic bias existed these results show a willingness to separate individual and group preferences when interacting with formal decision procedures. Further research is required to see how this changes under unstructured negotiation and arbitration as opposed to a formal model.     

Using Bids,Arguments and Preferences in Sensitive Multi-unit Assignments: A p-Equitable Process and a Course Allocation Case Study

Bonus distribution in enterprises or course allocation at universities are examples of sensitive multi-unit assignment problems, where a set of resources is to be allocated among a set of agents having multi-unit demands. Automatic processes exist, based on quantitative information, for example bids or preference ranking, or even on lotteries. In sensitive cases, however, decisions are taken by persons also using qualitative information. At present, no multi-unit assignment system supports both quantitative and qualitative information. In this paper, we propose MUAP-LIS, an interactive process for multi-assignment problems where, in addition to bids and preferences, agents can give arguments to motivate their choices. Bids are used to automatically make pre-assignments, qualitative arguments and preferences help decision makers break ties in a founded way. A group decision support system, based on Logical Information Systems, allows decision makers to handle bids, arguments and preferences in a unified interface. We say that a process is p-equitable for a property p if all agents satisfying p are treated equally. We formally demonstrate that MUAP-LIS is p-equitable for a number of properties on bids, arguments and preferences. It is also Pareto-efficient and Gale–Shapley-stable with respect to bids. A successful course allocation case study is reported. It spans over two university years. The decision makers were confident about the process and the resulting assignment. Furthermore, the students, even the ones who did not get all their wishes, found the process to be equitable.     

An Extended PROMETHE II Multi-Criteria Group Decision Making Technique Based on Intuitionistic Fuzzy Logic for Sustainable Energy Planning

The implementations of Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) category to complex multi-criteria group decision making (MCGDM) scenarios have been included in thousands areas. Outranking methods such as PROMETHEE II are also greatly employed in energy planning application. In MCGDM methods if decision makers (DMs) are not able to treat precise data in order to define their preferences, the intuitionistic fuzzy set (IFS) theory enables them. IFS attributes are connected with the degree of membership and non-membership, and can be used to draw uncertainty in group decision-making situations. In this paper, a new version of the PROMETHEE II method is proposed, aiming at solving MCGDM problems. Linguistic variables are expressed in the membership function and non-membership function of IFS which are used to assess the weights of all criteria and the ratings of each alternative with respect to each criteria. Conditional normalized Euclidean distance measure is adopted to measure deviations between alternatives on intuitionistic fuzzy set. Then, a ranking algorithm is applied to indicate the order of superiority of alternatives. Finally, a practical example is given to an application of sustainable energy planning to verify our proposed method. Additionally, a comparative analysis is done among the proposed PROMETHEE II method and the intuitionistic fuzzy technique for order preference by similarity to ideal solution (IF-TOPSIS) method and elimination and choice translating reality method (IF-ELECTRE).     

Community differences in business births and business growths

The published literature relating to the location of a business tends to support two different kinds of theories: (1) that business locations are selected to minimize costs; or (2) that decision-makers select locations because of personal preferences. This study attempts to find explanations as to why certain communities have grown faster than others, and to provide a model for the location decision of a start-up business.We find a negative correlation of two entrepreneurship measures to environmental factors that are usually considered to be desirable, i.e., health care and the environment, climate and terrain, recreation, and low crime. We find a weak correlation between community attitudes and the entrepreneurship measures. We also find a positive correlation of entrepreneurship with a high number of college graduates; a negative correlation when a high proportion of the population is over age 65.Population mobility and low unemployment are also positively correlated with the measures, but those factors seem at least as likely to be results as to be causes of business births and business growth.We believe that start-ups are vital for any community that wishes to grow, therefore the location decision of start-up businesses seems important. We propose a model of the location decision of a start-up, a model that emphasizes the individuality of the decision maker and the specific success requirements of the business. The driving force behind a start-up can be a decision-maker's desire for personal gain, a problem that begs a solution, or a solution that is looking for a problem. In some cases, the reason for starting a business will dictate its location. In other cases, success requirements of the business will dominate. We believe that researchers can gain a true understanding of the location decision only by considering both the preference of the decision-maker and the requirements of the specific business.     

Do family members agree on family purchase decision? – an empirical study among families in Hong Kong

Family decision making is one of the most important consumer decisions. It is complicated because all family members can be involved in the decision‐making process. The current study examined the impact of perceived buying preferences of individual family member on perceived family buying preferences. A new family decision‐making model with family members’ buying preference is proposed based on resources theory, social learning theory and family system theory. It is found that there is a synergy effect in a family decision‐making process. The synergy effect is expressed as positive correlations between individual family members buying preferences. Quota sampling was adopted to collect primary data in Hong Kong using triadic approach. Managerial implications and future research directions are suggested.     

Fuzzy Group Decision Making for the Selection of Facility Location

In this paper, fuzzy group decision making based on extension of TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method which was proposed by Chen (Fuzzy Sets Syst, 114:1–9, 2000) is adopted for facility location selection. In this method, the ratings of various alternatives versus various subjective criteria and the weights of all criteria are assessed in linguistic variables represented by fuzzy numbers. By fuzzy numbers, it has been tried to resolve the ambiguity of concepts that are associated with human being’s judgments. To determine the order of the alternatives, closeness coefficient is defined by calculating the distances to the fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS). In Chen’s approach, the distance between two fuzzy numbers is calculated with vertex method. But in this study, different distance measurement methods are used and the results are compared. Finally the proposed method has been applied to a facility location selection problem of a textile company in Turkey.     

Multiple Attribute Group Decision Making Method Based on Utility Theory Under Interval-Valued Intuitionistic Fuzzy Environment

In this paper, a kind of multiple attribute group decision making problem is studied, where there is no original information about the weights of importance of the attributes and the decision makers (DMs), and the attribute values are given in the form of interval-valued intuitionistic fuzzy numbers (IVIFNs). To solve this problem, a new method is proposed based on utility theory. In the proposed method, the weights of importance of the DMs and the attributes are all determined by using the intuitionistic indexes of related IVIFNs. And then, the alternatives are compared by using their composite interval indexes which are generated based on utility theory. Finally, two numerical examples are proposed to demonstrate the effectiveness of the proposed method.     

Interval-Valued Intuitionistic Fuzzy WASPAS Method: Application in Reservoir Flood Control Management Policy

Interval-valued intuitionistic fuzzy sets (IVIFSs) are very flexible tool to cope with the uncertainty arises in multi-criteria decision making (MCDM) problems. In recent times, MCDM problems with interval-valued intuitionistic fuzzy information have achieved more attention from researchers in different areas and consequently, several MCDM methods have been extended for IVIFSs. In this paper, a novel approach based on WASPAS method is developed under IVIFSs. The developed method is based on the operators of IVIFSs, some amendments in the classical WASPAS method and a new process for calculation of criteria and decision experts’ weights. In process for calculating weights, new procedures is propoesd to compute the decision experts’ weights and criteria weights based on interval-valued intuitionistic fuzzy information measures (entropy, divergence and similarity measures) to achieve more realistic weights. Innovative information measures are developed based on the exponential function for IVIFSs to determine the weights of the criteria and decision experts. Since the uncertainty is an unavoidable feature of MCDM problems, the developed method can be a constructive tool for decision-making in an uncertain environment. Further, an uncertain decision making problem of reservoir flood control management policy is implemented with interval-valued intuitionistic fuzzy information, which reveals the effectiveness and reliability of the proposed IVIF-WASPAS method. To validate the result, comparative analysis with existing methods and sensitivity analysis are presented under interval-valued intuitionistic fuzzy environment.     

Investigating brand preferences across social groups and consumption contexts

Using a unique dataset on U.S. beer consumption, we investigate brand preferences of consumers across various social group and context related consumption scenarios (??scenarios??). As sufficient data are not available for each scenario, understanding these preferences requires us to share information across scenarios. Our proposed modeling framework has two main building blocks. The first is a standard continuous random coefficients logit model that the framework reduces to in the absence of information on social groups and consumption contexts. The second component captures variations in mean preferences across scenarios in a parsimonious fashion by decomposing the deviations in preferences from a base scenario into a low dimensional brand map in which the brand locations are fixed across scenarios but the importance weights vary by scenario. In addition to heterogeneity in brand preferences that is reflected in the random coefficients, heterogeneity in preferences across scenarios is accounted for by allowing the brand map itself to have a discrete heterogeneity distribution across consumers. Finally, heterogeneity in preferences within a scenario is accounted for by allowing the importance weights to vary across consumers. Together, these factors allow us to parsimoniously account for preference heterogeneity across brands, consumers and scenarios. We conduct a simulation study to reassure ourselves that using the kind of data that is available to us, our proposed estimator can recover the true model parameters from those data. We find that brand preferences vary considerably across the different social groups and consumption contexts as well as across different consumer segments. Despite the sparse data on specific brand-scenario combinations, our approach facilitates such an analysis and assessment of the relative strengths of brands in each of these scenarios. This could provide useful guidance to the brand managers of the smaller brands whose overall preference level might be low but which enjoy a customer franchise in a particular segment or in a particular context or a social group setting.     

The Use of Ranking Veto Concept to Mitigate the Compensatory Effects of Additive Aggregation in Group Decisions on a Water Utility Automation Investment

The use of additive models for aggregating group decisions implies they have a compensatory effect in the process of aggregating all decision makers’ (DMs’) preferences. In this kind of model, the final result may produce some extremely undesirable alternatives for one or more DMs. Such alternatives may emerge with a higher ranking than desirable ones, thus generating conflicts and regrets. To overcome this problem the concept of ranking veto is introduced based on a reduction factor combined with the utility of the alternative in order to penalize conflicting alternatives and reduce disagreements in an additive model. A water utility problem was considered as a numerical application to illustrate the model. A decision group method based on MAUT, utility thresholds and a reduction factor is proposed to support group decision in selecting regions that will receive investments in automation over the next 4 years.     

Conjoint models of family decision making

The objective of this research is to develop viable approaches to modeling joint decisions. Using conjoint-analysis-type preference data, three methods are developed to combine individual preferences to approximate joint preferences and predict joint decisions. The first is an equal weighting model, which is a simple average of individual members' part-worth utilities. The second is a relative influence model, which combines individual utility functions using a measure of derived influence. The third is a conflict resolution model, which combines utility functions using a measure of conflict. In addition to these three combination models, individual member models and a joint model based on the joint preferences are available.The application area in which the models are operationalized is family decision making. The decision involves choice of a job by MBA students and spouses at a major private university. The models are first calibrated using preference data on hypothetical jobs from MBAs, spouses, and couples and then evaluated on their ability to predict the actual job chosen.     

A family of IOWA operators with reliability measurement under interval-valued group decision-making environment

Aggregation operators play an essential role in the aggregation of various individual input arguments in group decision-making (GDM). In this paper, we have proposed a family of IOWA operators with reliability measurement to aggregate uncertain decision information represented by interval numbers in GDM problems. In particular, we introduce the reliability-induced uncertain OWA (R-IUOWA) operator and the clusters’ reliability-induced uncertain OWA (CR-IUOWA) operator. This type of operators uses the reliability measurement representing the opinion consensus of individuals as the associated order-inducing variable and considers the decision-makers’ preference in the calculation of the position weights. Thus, the aggregation results have a higher consensus level. The R-IUOWA and CR-IUOWA operators have three primary properties such as commutativity, idempotency and boundness. The generalized formulas and some special cases of the two operators are outlined. Finally, the proposed operators are applied to a GDM problem regarding the selection of an investment company. The validity of the two operators is illustrated by comparing the aggregation results with that of other operators.

     

Multiple Criteria Assessment of Insulating Materials with a Group Decision Framework Incorporating Outranking Preference Model and Characteristic Class Profiles

We present a group decision making framework for evaluating sustainability of the insulating materials. We tested thirteen materials on a model that was applied to retrofit a traditional rural building through roof’s insulation. To evaluate the materials from the socio-economic and environmental viewpoints, we combined life cycle costing and assessment with an adaptive comfort evaluation. In this way, the performances of each coating material were measured in terms of an incurred reduction of costs and consumption of resources, maintenance of the cultural and historic significance of buildings, and a guaranteed indoor thermal comfort. The comprehensive assessment of the materials involved their assignment to one of the three preference-ordered sustainability classes. For this purpose, we used a multiple criteria decision analysis approach that accounted for preferences of a few tens of rural buildings’ owners. The proposed methodological framework incorporated an outranking-based preference model to compare the insulating materials with the characteristic class profiles while using the weights derived from the revised Simos procedure. The initial sorting recommendation for each material was validated against the outcomes of robustness analysis that combined the preferences of individual stakeholders either at the output or at the input level. The analysis revealed that the most favorable materials in terms of their overall sustainability were glass wool, hemp fibres, kenaf fibres, polystyrene foam, polyurethane, and rock wool.     

A Study on Aggregation of TOPSIS Ideal Solutions for Group Decision-Making

The technique for order preference by similarity to ideal solution (TOPSIS) has become a popular multi-criteria decision making (MCDM) technique, since it has a comprehensible theoretical structure and is able to provide an exact model for decision making. For the use of TOPSIS in group decisions, the common approaches in aggregating individual decision makers’ judgments are the geometric and the arithmetic mean methods, although these are too intuitive and do not consider either preference levels or preference priorities among alternatives for individual decision makers. In this paper, a TOPSIS group decision aggregation model is proposed in which the construction consists of three stages: (1) The weight differences are calculated first as the degrees of preferences among different alternatives for each decision maker; (2) The alternative priorities are then derived, and the highest one can be denoted as the degree to which a decision maker wants his most favorite alternative to be chosen; (3) The group ideal solutions approach in TOPSIS is used for the aggregation of similarities obtained from different decision makers. A comparative analysis is performed, and the proposed aggregation model seems to be more satisfactory than the traditional aggregation model for solving compromise-oriented decision problems.     

Arguing over Goals for Negotiation: Adopting an Assumption-Based Argumentation Decision Support System

Computational models of argumentation has been put forward as a promising approach to support decision making. In this context several recent works have proposed argumentation-based frameworks for decision making. In this paper we describe an application based on an argumentation-based mechanism for decision-making to concede. Adopting the assumption-based approach of argumentation, we propose an argumentation framework in which preferences are attached to goals. Arguments are defined as tree-like structures. Our framework is equipped with a computational counterpart for solving a decision problem, modeling the intuition that high-ranked goals are preferred to low-ranked goals which can be withdrawn. In this way, our framework suggests some decisions and provides an interactive and intelligible explanation of this choice. Our implementation, called MARGO, has been used for service selection within the ArguGRID project. We illustrate our approach with an industrial application, and illustrate the operation of the system with a running example.     

Using the Analytic Hierarchy Process as a Two-phase Integrated Decision Approach for Large Nominal Groups

When decision makers who comprise a large nominal group face an unstructured decision problem and no simultaneous interactive communications are available, problem identification and consensus building are difficult, if not impossible. Few tools are available to assist decision makers in this situation. The Analytic Hierarchy Process (AHP) has typically been used to evaluate a set of alternatives after a decision problem has been structured as a hierarchy with various levels of criteria above the alternatives. With a group of decision makers, AHP has been used to evaluate those alternatives either by consensus building or by combining judgments or priorities using the geometric mean to aggregate their preferences. In this paper, we extend the use of AHP to a situation involving a large nominal group of dispersed decision makers where the entire hierarchy is not defined at the outset. In particular, we use the AHP as an integrative approach to identify the priorities of the various criteria and then use those priorities to screen and consolidate a large set of potential alternatives. This results in considering a reduced set of alternatives that will be affected by the more important criteria. The consolidated set of alternatives is evaluated by each individual in the group using AHP, combined using the geometric mean, and the results are synthesized to obtain the overall priorities of the alternatives. The approach is demonstrated and evaluated in a case study to select an alunmi anniversary gift to the U.S. Coast Guard Academy with a large nominal group of decision-makers dispersed throughout the United States.     

A model of decision-making incorporating ethical values

A model is presented which describes the process decision-makers follow when faced with problems containing ethical dimensions. The model, based upon the empirical literature, is designed to provide guidance to researchers studying ethical behavior in business. The model portrays the decision-maker with a set of personal values which are mediated by elements of the organization's culture. The combination of personal values and organizational influences yields decisions which may be significantly different from those made based upon personal values alone. Inclusion of the personal values of the decision-maker as the dominant individual input and an explicit discussion of the ethical decision process make this model more comprehensive than other recent ethics models. David J. Fritzsche is Visiting Professor in the Department of Managing and Organization at the University of Washington. His articles have appeared in numerous journals and books. He is also a co-author of the Business Policy Game, a strategic management simulation.