A Comparative Study on Consensus Measures in Group Decision Making

The file attached to this record is the author's final peer reviewed version.In group decision making problems it is desirable to obtain a high level of consensus among experts before reaching a solution. It is customary to construct consensus measures by using similarity functions to quantify the closeness of experts preferences. In such process the use of a metric that describes the distance between experts preferences allows the definition of similarity and dissimilarity -distance- functions. Different distance functions have been proposed in order to implement consensus measures. This paper examines how the use of different aggregation operators affects the level of consensus achieved by experts through different distance functions, once the number of experts has been established in the decision making problem. The experimental study conducted states that the speed of the consensus process is significantly affected by the use of diverse aggregation operators and distance functions. Several decision support rules which can be useful in controling the convergence speed of the consensus process are also derived

An alternative calculation of the consensus degree in group decision making problems

In a problem of group decision-making it is desirable to obtain a solution with the highest possible degree of agreement – consensus- among the participants. For this aim, it is necessary to have tools that facilitate the calculation of the degree of consensus in a reliable way. This study proposes a consensus index based on a statistical measure of variability of the preferences expressed by the experts in a group decision-making process and performs a specific comparative study between this index and several known consensus measures. The analysis shows that in this specific situation the proposed measure behaves in a similar way to the previous ones and it could play their role in a process of decision making in group.European Commission TIN2016-75850-

Information Technology and Quantitative Management (ITQM 2017): An alternative calculation of the consensus degree in group decision making problems

In a problem of group decision-making it is desirable to obtain a solution with the highest possible degree of agreement – consensus- among the participants. For this aim, it is necessary to have tools that facilitate the calculation of the degree of consensus in a reliable way. This study proposes a consensus index based on a statistical measure of variability of the preferences expressed by the experts in a group decision-making process and performs a specific comparative study between this index and several known consensus measures. The analysis shows that in this specific situation the proposed measure behaves in a similar way to the previous ones and it could play their role in a process of decision making in group.The authors would like to acknowledge FEDER financial support from the Project TIN2016-75850-R

An analysis of consensus approaches based on different concepts of coincidence

The file attached to this record is the author's final peer reviewed version.Soft consensus is a relevant topic in group decision making problems. Soft consensus measures are utilized to reflect the different agreement degrees between the experts leading the consensus reaching process. This may determine the final decision and the time needed to reach it. The concept of coincidence has led to two main approaches to calculating the soft consensus measures, namely, concordance among expert preferences and concordance among individual solutions. In the first approach the coincidence is obtained by evaluating the similarity among the expert preferences, while in the second one the concordance is derived from the measurement of the similarity among the solutions proposed by these experts. This paper performs a comparative study of consensus approaches based on both coincidence approaches. We obtain significant differences between both approaches by comparing several distance functions for measuring expert preferences and a consensus measure over the set of alternatives for measuring the solutions provided by experts. To do so, we use the nonparametric Wilcoxon signed-ranks test. Finally, these outcomes are analyzed using Friedman mean ranks in order to obtain a quantitative classification of the considered measurements according to the convergence criterion considered in the consensus reaching process

Factors influencing communication and decision-making about life-sustaining technology during serious illness: A qualitative study

© 2016, BMJ Publishing Group. All rights reserved. Objectives: We aimed to identify factors influencing communication and decision-making, and to learn how physicians and nurses view their roles in deciding about the use of life-sustaining technology for seriously ill hospitalised patients and their families. Design: The qualitative study used Flanagan\u27s critical incident technique to guide interpretive description of open-ended in-depth individual interviews. Setting: Participants were recruited from the medical wards at 3 Canadian hospitals. Participants: Interviews were completed with 30 healthcare professionals (9 staff physicians, 9 residents and 12 nurses; aged 25-63 years; 73% female) involved in decisions about the care of seriously ill hospitalised patients and their families. Measures: Participants described encounters with patients and families in which communication and decision-making about life-sustaining technology went particularly well and unwell (ie, critical incidents). We further explored their roles, context and challenges. Analysis proceeded using constant comparative methods to form themes independently and with the interprofessional research team. Results: We identified several key factors that influenced communication and decision-making about life-sustaining technology. The overarching factor was how those involved in such communication and decision-making (healthcare providers, patients and families) conceptualised the goals of medical practice. Additional key factors related to how preferences and decision-making were shaped through relationships, particularly how people worked toward \u27making sense of the situation\u27, how physicians and nurses approached the inherent and systemic tensions in achieving consensus with families, and how physicians and nurses conducted professional work within teams. Participants described incidents in which these key factors interacted in dynamic and unpredictable ways to influence decision-making for any particular patient and family. Conclusions: A focus on more meaningful and productive dialogue with patients and families by (and between) each member of the healthcare team may improve decisions about life-sustaining technology. Work is needed to acknowledge and support the non-curative role of healthcare and build capacity for the interprofessional team to engage in effective decision-making discussions

A comparative analysis between two statistical deviation–based consensus measures in group decision making problems

The mean absolute deviation and the standard deviation, two statistical measures commonly used in quantifying variability, may become an interesting tool when defining consensus measures. Two consensus indexes which obtain the level of consensus in some problems of Group Decision Making are introduced in this paper by expanding the aforementioned statistical concepts. A comparative analysis reveals that the levels of consensus derived from these indexes are close to those obtained employing distance functions when a fuzzy preference relations frame is considered, so they turn out to be a useful tool in this context. In addition, these indexes are different from each other and with the distance functions considered. Thus, they are applicable tools in the calculation of consensus in our context and are different from those commonly used

Influence Analysis in Consensus Search - A Multi Criteria Group Decision Making Approach in Environmental Management

The environmental decision problems often are divisive, even in a technical realm, decision makers with strong personalities influence outcomes. The purpose of this study is to define and quantify the factors that affect the conservation objectives of a national natural park located in Colombia, South America adding the judgments of six decision makers with different knowledge (every decision maker is also a stakeholder representative). This paper uses a hybrid multiple criteria group decision making model (MCDM), combining the social network analysis (SNA), analytic hierarchy process (AHP) and similarity measures to solve the consensus and anchoring problem among environmental decision makers. The SNA technique is used to build an influential network relation map among decision makers and to obtain their weights for applying a weighted analytic hierarchy process. Then, the final decision matrices for every decision maker are compared between them in order to identify the consensus level of the problem.Romero Gelvez, JI.; García Melón, M. (2016). Influence Analysis in Consensus Search - A Multi Criteria Group Decision Making Approach in Environmental Management. International Journal of Information Technology and Decision Making. 15(4):791-813. doi:10.1142/S0219622016400034S791813154Regan, H. M., Colyvan, M., & Markovchick-Nicholls, L. (2006). A formal model for consensus and negotiation in environmental management. Journal of Environmental Management, 80(2), 167-176. doi:10.1016/j.jenvman.2005.09.004Reed, M. S. (2008). Stakeholder participation for environmental management: A literature review. Biological Conservation, 141(10), 2417-2431. doi:10.1016/j.biocon.2008.07.014Gomez-Navarro, T., & Garcia-Melon, M. (2011). DESIGN OF AN EFFICIENCY INDEX FOR THE RANK ORDER OF SOIL REMEDIATION TECHNIQUES. Environmental Engineering and Management Journal, 10(5), 603-613. doi:10.30638/eemj.2011.083Aragonés-Beltrán, P., García-Melón, M., & Estruch-Guitart, V. (2015). ANALYSIS OF THE PARTICIPATION OF STAKEHOLDERS IN ENVIRONMENTAL MANAGEMENT BASED ON ANP: APPLICATION TO A SPANISH NATURAL PARK. International Journal of the Analytic Hierarchy Process, 7(1). doi:10.13033/ijahp.v7i1.276Belton, V., & Stewart, T. J. (2002). Multiple Criteria Decision Analysis. doi:10.1007/978-1-4615-1495-4Ginevičius, R., & Podvezko, V. (2009). EVALUATING THE CHANGES IN ECONOMIC AND SOCIAL DEVELOPMENT OF LITHUANIAN COUNTIES BY MULTIPLE CRITERIA METHODS. Technological and Economic Development of Economy, 15(3), 418-436. doi:10.3846/1392-8619.2009.15.418-436Ramzan, N., Degenkolbe, S., & Witt, W. (2008). Evaluating and improving environmental performance of HC’s recovery system: A case study of distillation unit. Chemical Engineering Journal, 140(1-3), 201-213. doi:10.1016/j.cej.2007.09.042Sólnes, J. (2003). Environmental quality indexing of large industrial development alternatives using AHP. Environmental Impact Assessment Review, 23(3), 283-303. doi:10.1016/s0195-9255(03)00004-0Beccali, M., Cellura, M., & Mistretta, M. (2003). Decision-making in energy planning. Application of the Electre method at regional level for the diffusion of renewable energy technology. Renewable Energy, 28(13), 2063-2087. doi:10.1016/s0960-1481(03)00102-2Varvasovszky, Z. (2000). A stakeholder analysis. Health Policy and Planning, 15(3), 338-345. doi:10.1093/heapol/15.3.338Prell, C., Hubacek, K., & Reed, M. (2009). Stakeholder Analysis and Social Network Analysis in Natural Resource Management. Society & Natural Resources, 22(6), 501-518. doi:10.1080/08941920802199202Charnley, S., & Engelbert, B. (2005). Evaluating public participation in environmental decision-making: EPA’s superfund community involvement program. Journal of Environmental Management, 77(3), 165-182. doi:10.1016/j.jenvman.2005.04.002Reed, M. S., Graves, A., Dandy, N., Posthumus, H., Hubacek, K., Morris, J., … Stringer, L. C. (2009). Who’s in and why? A typology of stakeholder analysis methods for natural resource management. Journal of Environmental Management, 90(5), 1933-1949. doi:10.1016/j.jenvman.2009.01.001Bonner, B. L., & Bolinger, A. R. (2013). Separating the confident from the correct: Leveraging member knowledge in groups to improve decision making and performance. Organizational Behavior and Human Decision Processes, 122(2), 214-221. doi:10.1016/j.obhdp.2013.07.005Kirchler, E., & Davis, J. H. (1986). The influence of member status differences and task type on group consensus and member position change. Journal of Personality and Social Psychology, 51(1), 83-91. doi:10.1037/0022-3514.51.1.83Sniezek, J. A., & Henry, R. A. (1989). Accuracy and confidence in group judgment. Organizational Behavior and Human Decision Processes, 43(1), 1-28. doi:10.1016/0749-5978(89)90055-1Bonner, B. L., Sillito, S. D., & Baumann, M. R. (2007). Collective estimation: Accuracy, expertise, and extroversion as sources of intra-group influence. Organizational Behavior and Human Decision Processes, 103(1), 121-133. doi:10.1016/j.obhdp.2006.05.001Burgman, M. (2005). Risks and Decisions for Conservation and Environmental Management. doi:10.1017/cbo9780511614279Mitchell, R. K., Agle, B. R., & Wood, D. J. (1997). Toward a Theory of Stakeholder Identification and Salience: Defining the Principle of who and What Really Counts. Academy of Management Review, 22(4), 853-886. doi:10.5465/amr.1997.9711022105Bryson, J. M. (2004). What to do when Stakeholders matter. Public Management Review, 6(1), 21-53. doi:10.1080/14719030410001675722Biggs, S., & Matsaert, H. (1999). An actor-oriented approach for strengthening research and development capabilities in natural resource systems. Public Administration and Development, 19(3), 231-262. doi:10.1002/(sici)1099-162x(199908)19:33.0.co;2-eWu, J., & Chiclana, F. (2014). A social network analysis trust–consensus based approach to group decision-making problems with interval-valued fuzzy reciprocal preference relations. Knowledge-Based Systems, 59, 97-107. doi:10.1016/j.knosys.2014.01.017Sabidussi, G. (1966). The centrality index of a graph. Psychometrika, 31(4), 581-603. doi:10.1007/bf02289527Chiclana, F., Tapia García, J. M., del Moral, M. J., & Herrera-Viedma, E. (2013). A statistical comparative study of different similarity measures of consensus in group decision making. Information Sciences, 221, 110-123. doi:10.1016/j.ins.2012.09.014Wu, J., Chiclana, F., & Herrera-Viedma, E. (2015). Trust based consensus model for social network in an incomplete linguistic information context. Applied Soft Computing, 35, 827-839. doi:10.1016/j.asoc.2015.02.023Xu, J., & Wu, Z. (2011). A discrete consensus support model for multiple attribute group decision making. Knowledge-Based Systems, 24(8), 1196-1202. doi:10.1016/j.knosys.2011.05.007Herrera, F., Herrera-Viedma, E., & Verdegay, J. . (1998). Choice processes for non-homogeneous group decision making in linguistic setting. Fuzzy Sets and Systems, 94(3), 287-308. doi:10.1016/s0165-0114(96)00251-5Chiclana, F., Herrera-Viedma, E., Herrera, F., & Alonso, S. (2007). Some induced ordered weighted averaging operators and their use for solving group decision-making problems based on fuzzy preference relations. European Journal of Operational Research, 182(1), 383-399. doi:10.1016/j.ejor.2006.08.032Xu, Z. (2000). On consistency of the weighted geometric mean complex judgement matrix in AHP. European Journal of Operational Research, 126(3), 683-687. doi:10.1016/s0377-2217(99)00082-xRosso, M., Bottero, M., Pomarico, S., La Ferlita, S., & Comino, E. (2014). Integrating multicriteria evaluation and stakeholders analysis for assessing hydropower projects. Energy Policy, 67, 870-881. doi:10.1016/j.enpol.2013.12.00

Water Policies and Conflict Resolution of Public Participation Decision-Making Processes Using Prioritized Ordered Weighted Averaging (OWA) Operators

[EN] There is a growing interest in environmental policies about how to implement public participation engagement in the context of water resources management. This paper presents a robust methodology, based on ordered weighted averaging (OWA) operators, to conflict resolution decision-making problems under uncertain environments due to both information and stakeholders' preferences. The methodology allows integrating heterogeneous interests of the general public and stakeholders on account of their different degree of acceptance or preference and level of influence or power regarding the measures and policies to be adopted, and also of their level of involvement (i.e., information supply, consultation and active involvement). These considerations lead to different environmental and socio-economic outcomes, and levels of stakeholders' satisfaction. The methodology establishes a prioritization relationship over the stakeholders. The individual stakeholders' preferences are aggregated through their associated weights, which depend on the satisfaction of the higher priority decision maker. The methodology ranks the optimal management strategies to maximize the stakeholders' satisfaction. It has been successfully applied to a real case study, providing greater fairness, transparency, social equity and consensus among actors. Furthermore, it provides support to environmental policies, such as the EU Water Framework Directive (WFD), improving integrated water management while covering a wide range of objectives, management alternatives and stakeholders.Llopis Albert, C.; Merigó-Lindahl, JM.; Liao, H.; Xu, Y.; Grima-Olmedo, J.; Grima-Olmedo, C. (2018). Water Policies and Conflict Resolution of Public Participation Decision-Making Processes Using Prioritized Ordered Weighted Averaging (OWA) Operators. Water Resources Management. 32(2):497-510. https://doi.org/10.1007/s11269-017-1823-2S497510322Amin GR, Sadeghi H (2010) Application of prioritized aggregation operators in preference voting. Int J Intell Syst 25(10):1027–1034Chen TY (2014) A prioritized aggregation operator-based approach to multiple criteria decision making using interval-valued intuitionistic fuzzy sets: A comparative perspective. Inf Sci 281:97–112Chen LH, Xu ZS (2014) A prioritized aggregation operator based on the OWA operator and prioritized measures. J Intell Fuzzy Syst 27:1297–1307Chen LH, Xu ZS, Yu XH (2014a) Prioritized measure-guided aggregation operators. IEEE Trans Fuzzy Syst 22:1127–1138Chen LH, Xu ZS, Yu XH (2014b) Weakly prioritized measure aggregation in prioritized multicriteria decision making. Int J Intell Syst 29:439–461CHJ (2016). Júcar river basin authority http://www.chj.es/CHS (2016). Segura river basin authority http://www.chsegura.es/Dong JY, Wan SP (2016) A new method for prioritized multi-criteria group decision making with triangular intuitionistic fuzzy numbers. J Intell Fuzzy Syst 30:1719–1733EC (2000). Directive 2000/60/EC of the European Parliament and of the Council of October 23 2000 Establishing a Framework for Community Action in the Field of Water Policy. Official Journal of the European Communities, L327/1eL327/72 22.12.2000Jackson S, Tan P-L, Nolan S (2012) Tools to enhance public participation and confidence in the development of the Howard East aquifer water plan, Northern Territory. J Hydrol 474:22–28Jin FF, Ni ZW, Chen HY (2016) Note on “Hesitant fuzzy prioritized operators and their application to multiple attribute decision making”. Knowl-Based Syst 96:115–119Kentel E, Aral MM (2007) Fuzzy Multiobjective Decision-Making Approach for Groundwater Resources Management. J Hydrol Eng 12(2):206–217. https://doi.org/10.1061/(ASCE)1084-0699(2007)12:2(206).Kirchherr J, Charles KJ, Walton MJ (2016) Multi-causal pathways of public opposition to dam project in Asia: A fuzzy set qualitative comparative analysis (fsQCA). Glob Environ Chang 41:33–45. https://doi.org/10.1016/j.gloenvcha.2016.08.001Llopis-Albert C, Pulido-Velazquez D (2015) Using MODFLOW code to approach transient hydraulic head with a sharp-interface solution. Hydrol Process 29(8):2052–2064. https://doi.org/10.1002/hyp.10354Llopis-Albert C, Palacios-Marqués D, Soto-Acosta P (2015) Decision-making and stakeholders constructive participation in environmental projects. J Bus Res 68:1641–1644. https://doi.org/10.1016/j.jbusres.2015.02.010Llopis-Albert C, Merigó JM, Xu Y, Huchang L (2017) Improving regional climate projections by prioritized aggregation via ordered weighted averaging operators. Environ Eng Sci. https://doi.org/10.1089/ees.2016.0546Maia R (2017) The WFD Implementation in the European Member States. Water Resour Manag 31(10):3043–3060. https://doi.org/10.1007/s11269-017-1723-5Malczewski J, Chapman T, Flegel C, Walters D, Shrubsole D, Healy MA (2003) GIS - multicriteria evaluation with ordered weighted averaging (OWA): case study of developing watershed management strategies. Environ Plan A 35:1769–1784. https://doi.org/10.1068/a35156Merigó JM, Casanovas M (2011) The uncertain generalized owa operator and its application to financial decision making. Int J Inf Technol Decis Mak 10(2):211–230Merigó JM, Yager RR (2013) Generalized moving averages, distance measures and OWA operators. Int J Uncertain, Fuzziness Knowl-Based Syst 21(4):533–559Merigó JM, Palacios-Marqués D, Ribeiro-Navarrete B (2015) Aggregation systems for sales forecasting. J Bus Res 68:2299–2304Mesiar R, Stupnanová A, Yager RR (2015) Generalizations of OWA Operators. IEEE Trans Fuzzy Syst 23(6):2154–2162O’Hagan M (1988) Aggregating Template Rule Antecedents in Real-time Expert Systems with Fuzzy Set Logic. In: Proceedings of 22nd annual IEEE Asilomar Conference on Signals. IEEE and Maple Press, Pacific Grove, Systems and Computers, pp 681–689Rahmani MA, Zarghami M (2013) A new approach to combine climate change projections by ordered weighting averaging operator; applications to northwestern provinces of Iran. Glob Planet Chang 102:41–50Ran LG, Wei GW (2015) Uncertain prioritized operators and their application to multiple attribute group decision making. Technol Econ Dev Econ 21:118–139Ruiz-Villaverde, A., García-Rubio, M.A. (2017). Public Participation in European Water Management: from Theory to Practice. Water Resour Manag 31(8), 2479–2495. https://doi.org/10.1007/s11269-016-1355-1Sadiq R, Tesfamariam S (2007) Probability density functions based weights for ordered weighted averaging (OWA) operators: An example of water quality indices. Eur J Oper Res 182:1350–1368Sadiq R, Rodríguez MJ, Tesfamariam S (2010) Integrating indicators for performance assessment of small water utilities using ordered weighted averaging (OWA) operators. Expert Syst Appl 37:4881–4891Verma R, Sharma B (2016) Prioritized information fusion method for triangular fuzzy information and its application to multiple attribute decision making. Int J Uncertain, Fuzziness Knowl-Based Syst 24:265–290Wang HM, Xu YJ, Merigó JM (2014) Prioritized aggregation for non-homogeneous group decision making in water resource management. Econ Comput Econ Cybern Stud Res 48(1):247–258Wei GW (2012) Hesitant fuzzy prioritized operators. Knowl-Based Syst 31:176–182Wei CP, Tang XJ (2012) Generalized prioritized aggregation operators. Int J Intell Syst 27:578–589Xu ZS (2005) An Overview of Methods for Determining OWA Weights. Int J Intell Syst 20:843–865Yager RR (1988) On ordered weighted averaging aggregation operators in multi-criteria decision making, IEEE Transactions on Systems. Man Cybern B 18(1988):183–190Yager RR (2008) Prioritized Aggregation Operators. Int J Approx Reason 48:263–274Yan H-B, Huynh V-N, Nakamori Y, Murai T (2011) On prioritized weighted aggregation in multi-criteria decision making. Expert Syst Appl 38(1):812–823Ye J (2014) Prioritized aggregation operators of trapezoidal intuitionistic fuzzy sets and their application to multicriteria decision-making. Neural Comput & Applic 25:1447–1454Yu XH, Xu ZS, Liu SS (2013) Prioritized multi-criteria decision making based on preference relations. Comput Ind Eng 66:104–115Zadeh LA (1983) A Computational Approach to Fuzzy Quantifiers in Natural Languages. Comput Math Appl 9:149–184Zarghami M, Szidarovszky F (2009) Revising the OWA operator for multi criteria decision making problems under uncertainty. Eur J Oper Res 198:259–265Zarghami M, Ardakanian R, Memariani A, Szidarovszky F (2008) Extended OWA Operator for Group Decision Making on Water Resources Projects. J Water Resour Plan Manag 134(3):266–275. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:3(266)Zarghami M, Szidarovszky F, Ardakanian R (2009) Multi-attribute decision making on inter-basin water transfer projects. Transaction E. Ind Eng 16(1):73–80Zhao XF, Li QX, Wei GW (2014) Some prioritized aggregating operators with linguistic information and their application to multiple attribute group decision making. J Intell Fuzzy Syst 26:1619–1630Zhao N, Xu ZS, Ren ZL (2016) On typical hesitant fuzzy prioritized “or” operator in multi-attribute decision making. Int J Intell Syst 31:73–100Zhou LY, Lin R, Zhao XF, Wei GW (2013) Uncertain linguistic prioritized aggregation operators and their application to multiple attribute group decision making. Int J Uncertain, Fuzziness Knowl-Based Syst 21:603–627Zhou LG, Merigó JM, Chen HY, Liu JP (2016) The optimal group continuous logarithm compatibility measure for interval multiplicative preference relations based on the COWGA operator. Inf Sci 328:250–26

Promises, pitfalls and shortfalls of the guaranteed maximum price approach: A comparative case study

The relative merits of the guaranteed maximum price (GMP) mechanism as a contractual incentive in construction have been much contested. This question was investigated using a comparative case study of two building projects in Hong Kong. Data was collected through semi-structured interviews, review of project documentation and communications, and passive observation of project meetings. The findings suggest that the GMP mechanism has low incentive intensity from an instrumental rationality perspective and high incentive intensity from a value-expressive perspective. Further analysis of the findings leads to two main conclusions about the potential value of the GMP mechanism to a client: (a) it can provide some flexibility in responding to short-term market changes and other idiosyncratic factors and (b) it can be a useful instrument for project work group integration. Based on current approaches to GMP projects in Hong Kong, the ultimate compensation for the additional risk transfer to the contractor should come from the applied mark up or fee rather than any expectation or possibility of financial reward for net cost savings