research-article

A bipolar consensus approach for group decision making problems

Authors:

Yasmina Bouzarour-Amokrane,

Ayeley Tchangani,

François PeresAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 42, Issue 3

Pages 1759 - 1772

https://doi.org/10.1016/j.eswa.2014.09.061

Published: 15 February 2015 Publication History

Abstract

Adaptive consensus seeking in group decision making.Mutual influence modeling.Taking into account human attitude.Multi-criteria problem using bipolarity concept.Consensus processes with or not individual changes. This paper addresses the collaborative group decision making problems considering a consensus processes to achieve a common legitimate solution. The proposed resolution model is based on individual bipolar assessment. Each decision maker evaluates alternatives through selectability and rejectability measures which respectively represent the positive and negative aspects of alternatives considering objectives achievement. The impact of human behavior (influence, individualism, fear, caution, etc.) on decisional capacity has been taken into account. The influence degrees exerted mutually by decision makers are modeled through concordance and discordance measures. The individualistic nature of decision makers has been taken into account from the individualism degree. In order to achieve a common solution(s), models of consensus building are proposed based on the satisficing game theory formalism for collective decision problems. An application example is given to illustrate the proposed concepts.

References

[1]

S. Alonso, E. Herrera-Viedma, F. Chiclana, F. Herrera, A web based consensus support system for group decision making problems and incomplete preferences, Information Sciences, 180 (2010) 4477-4495.

Digital Library

[2]

S. Alonso, I.J. Pérez, A linguistic consensus model for Web 2.0 communities, Applied Soft Computing, 13 (2013) 149-157.

Digital Library

[3]

J.F. Bard, S.F. Sousk, A tradeoff analysis for rough terrain cargo handlers using the AHP: An example of group decision making, IEEE Transactions on Engineering Management, 37 (1990) 222.

[4]

D. Ben-Arieh, Z. Chen, Linguistic-labels aggregation and consensus measure for autocratic decision making using group recommendations, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 36 (2006) 558-568.

Digital Library

[5]

D. Ben-Arieh, T. Easton, B. Evans, Minimum cost consensus with quadratic cost functions, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 39 (2009) 210-217.

Digital Library

[6]

E.J. Bonano, G.E. Apostolakis, Theoretical foundations and practical issues for using expert judgements in uncertainty analysis of high-level radioactive waste disposal, Radioactive Waste Management and the Nuclear Fuel Cycle, 16 (1991) 137-159.

[7]

G. Bordogna, M. Fedrizzi, G. Pasi, A linguistic modeling of consensus in group decision making based on OWA operators, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 27 (1997) 126-133.

Digital Library

[8]

Bouzarour-Amokrane, Y., Tchangani, A., & Pérès, F. (2012). Defining and measuring risk and opportunity in BOCR framework for decision analysis. In Proceedings of 9th international conference on modeling, optimization and simulation, MOSIM 2012, Bordeaux, France.

[9]

Bouzarour-Amokrane, Y., Tchangani, A., & Pérès, F. (2013). Résolution des problémes de décision de groupe par analyse bipolaire. In 5émes Journées Doctorales (JDJN) GDRMACS, Strasbourg, France.

[10]

N. Bryson, Group decision-making and the analytic hierarchy process: Exploring the consensus-relevant information content, Computers & Operations Research, 23 (1996) 27-35.

Digital Library

[11]

C.L. Butler, A. Rothstein, On conflict and consensus: A handbook on formal consensus decision making, Food Not Bombs Publishing, 1987.

[12]

f.J. Cabrerizo, s. alonso, E. herrera-viedma, A consensus model for group decision making problems with unbalanced fuzzy linguistic information, International Journal of Information Technology & Decision Making, 08 (2009) 109-131.

[13]

F.J. Cabrerizo, I.J. Pérez, E. Herrera-Viedma, Managing the consensus in group decision making in an unbalanced fuzzy linguistic context with incomplete information, Knowledge-Based Systems, 23 (2010) 169-181.

Digital Library

[14]

C. Carlsson, D. Ehrenberg, P. Eklund, M. Fedrizzi, P. Gustafsson, P. Lindholm, Consensus in distributed soft environments, European Journal of Operational Research, 61 (1992) 165-185.

[15]

H.-V.H. Chiclana, E. Herrera-viedma, F. Herrera, F. Chiclana, A consensus model for multiperson decision making with different preference structures, IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans, 32 (2002) 394-402.

Digital Library

[16]

F. Chiclana, F. Mata, L. Martinez, E. Herrera-Viedma, S. Alonso, Integration of a consistency control module within a consensus model, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 16 (2008) 35.

[17]

A.K. Choudhury, R. Shankar, M.K. Tiwari, Consensus-based intelligent group decision-making model for the selection of advanced technology, Decision Support Systems, 42 (2006) 1776-1799.

Digital Library

[18]

R.B. Cialdini, The science of persuasion, Scientific American, 2001.

[19]

L. Coch, J.R.P. French, Overcoming resistance to change, Human Relations, 1 (1948) 512-532.

[20]

W.D. Cook, M. Kress, Ordinal ranking with intensity of preference, Management Science, 31 (1985) 26-32.

Digital Library

[21]

De Brucker, K. & C. Macharis. (2010). Multi-actor multi-criteria analysis (MAMCA) as a means to cope with societal complexity. In EURO XXIV, Lisbon, Portugal.

[22]

M.H. Degroot, Reaching a consensus, Journal of the American Statistical Association, 69 (1974) 118-121.

[23]

P. Eklund, A. Rusinowska, H. de. Swart, A consensus model of political decision-making, Annals of Operations Research, 158 (2008) 5-20.

[24]

M. Fedrizzi, J. Kacprzyk, S. Zadro¿ny, An interactive multi-user decision support system for consensus reaching processes using fuzzy logic with linguistic quantifiers, Decision Support Systems, 4 (1988) 313-327.

Digital Library

[25]

J.R.P. French, A formal theory of social power, Psychological Review, 63 (1956) 181-194.

[26]

S. French, Consensus of opinion, European Journal of Operational Research, 7 (1981) 332-340.

[27]

C. Fu, S.-L. Yang, The group consensus based evidential reasoning approach for multiple attributive group decision analysis, European Journal of Operational Research, 206 (2010) 601-608.

[28]

C. Fu, S. Yang, An evidential reasoning based consensus model for multiple attribute group decision analysis problems with interval-valued group consensus requirements, European Journal of Operational Research, 223 (2012) 167-176.

[29]

Z.-W. Gong, J. Forrest, Y.-J. Yang, The optimal group consensus models for 2-tuple linguistic preference relations, Knowledge-Based Systems, 37 (2013) 427-437.

Digital Library

[30]

F. Harary, On the measurement of structural balance, Behavioral Science, 4 (1959) 316-323.

[31]

A. Hatami-Marbini, M. Tavana, An extension of the Electre I method for group decision-making under a fuzzy environment, Omega, 39 (2011) 373-386.

[32]

F. Herrera, S. Alonso, F. Chiclana, E. Herrera-Viedma, Computing with words in decision making: Foundations, trends and prospects, Fuzzy Optimization and Decision Making, 8 (2009) 337-364.

Digital Library

[33]

F. Herrera, E. Herrera-Viedma, Linguistic decision analysis: Steps for solving decision problems under linguistic information, Fuzzy Sets and Systems, 115 (2000) 67-82.

Digital Library

[34]

F. Herrera, E. Herrera-Viedma, F. Chiclana, Multiperson decision-making based on multiplicative preference relations, European Journal of Operational Research, 129 (2001) 372-385.

[35]

F. Herrera, E. Herrera-Viedma, J.L. Verdegay, A model of consensus in group decision making under linguistic assessments, Fuzzy Sets and Systemss, 78 (1996) 73-87.

Digital Library

[36]

F. Herrera, E. Herrera-Viedma, J.L. Verdegay, Direct approach processes in group decision making using linguistic OWA operators, Fuzzy Sets and Systems, 79 (1996) 175-190.

Digital Library

[37]

F. Herrera, E. Herrera-Viedma, J.L. Verdegay, A rational consensus model in group decision making using linguistic assessments, Fuzzy Sets and Systems, 88 (1997) 31-49.

Digital Library

[38]

F. Herrera, E. Herrera-Viedma, J.L. Verdegay, Linguistic measures based on fuzzy coincidence for reaching consensus in group decision making, International Journal of Approximate Reasoning, 16 (1997) 309-334.

[39]

E. Herrera-Viedma, S. Alonso, F. Chiclana, F. Herrera, A consensus model for group decision making with incomplete fuzzy preference relations, IEEE Transactions on Fuzzy Systems, 15 (2007) 863-877.

[40]

E. Herrera-Viedma, F.J. Cabrerizo, J. Kacprzyk, W. Pedrycz, A review of soft consensus models in a fuzzy environment, Information Fusion, 17 (2014) 4-13.

Digital Library

[41]

E. Herrera-Viedma, F. Herrera, F. Chiclana, A consensus model for multiperson decision making with different preference structures, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 32 (2002) 394-402.

Digital Library

[42]

E. Herrera-Viedma, L. Martinez, F. Mata, F. Chiclana, A consensus support system model for group decision-making problems with multigranular linguistic preference relations, IEEE Transactions on Fuzzy Systems, 13 (2005) 644-658.

[43]

Hsi-Mei Hsu, Chen-Tung Chen, Aggregation of fuzzy opinions under group decision making, Fuzzy Sets and Systems, 79 (1996) 279-285.

Digital Library

[44]

Y. Jiang, Z. Xu, X. Yu, Compatibility measures and consensus models for group decision making with intuitionistic multiplicative preference relations, Applied Soft Computing (2013).

[45]

J. Kacprzyk, Group decision making with a fuzzy linguistic majority, Fuzzy Sets and Systems, 18 (1986) 105-118.

Digital Library

[46]

J. Kacprzyk, On some fuzzy cores and "soft" consensus measures in group decision making (1987) 119-130.

[47]

J. Kacprzyk, M. Fedrizz, Soft consensus measure for monitoring real consensus reaching processes under fuzzy preferences, Control and Cybernetics (1986) 309-323.

[48]

J. Kacprzyk, M. Fedrizzi, A 'soft' measure of consensus in the setting of partial (fuzzy) preferences, European Journal of Operational Research, 34 (1988) 316-325.

[49]

J. Kacprzyk, M. Fedrizzi, A 'human-consistent' degree of consensus based on fuzzy login with linguistic quantifiers, Mathematical Social Sciences, 18 (1989) 275-290.

[50]

J. Kacprzyk, M. Fedrizzi, H. Nurmi, Group decision making and consensus under fuzzy preferences and fuzzy majority, Fuzzy Sets and Systems, 49 (1992) 21-31.

Digital Library

[51]

S. Khorshid, Soft consensus model based on coincidence between positive and negative ideal degrees of agreement under a group decision-making fuzzy environment, Expert Systems with Applications, 37 (2010) 3977-3985.

Digital Library

[52]

J.A. Kline, Orientation and group consensus, Central States Speech Journal, 23 (1972) 44-47.

[53]

Korpela, J., & Tuominen, M. (1997). Group decision support for analysing logistics development projects. In Proceedings of the thirtieth Hawaii international conference on system sciences (Vol. 2, pp. 493-502).

[54]

M. Ko, A. Tiwari, J. Mehnen, A review of soft computing applications in supply chain management, Applied Soft Computing, 10 (2010) 661-674.

Digital Library

[55]

V.S. Lai, B.K. Wong, W. Cheung, Group decision making in a multiple criteria environment: A case using the AHP in software selection, European Journal of Operational Research, 137 (2002) 134-144.

[56]

H.-S. Lee, Optimal consensus of fuzzy opinions under group decision making environment, Fuzzy Sets and Systems, 132 (2002) 303-315.

Digital Library

[57]

A.H.I. Lee, H. Chen, H. Kang, Multi-criteria decision making on strategic selection of wind farms, Renewable Energy, 34 (2009) 120-126.

[58]

K. Lehrer, C. Wagner, Rational consensus in science and society: A philosophical and mathematical study, Springer Science & Business Media, 1981.

[59]

Leyva Lopez, J. C. (2010). A consensus model for group decision support based on valued outranking relations. In EURO XXIV, Lisbon, Portugal.

[60]

D.-F. Li, Compromise ratio method for fuzzy multi-attribute group decision making, Applied Soft Computing, 7 (2007) 807-817.

Digital Library

[61]

B. Loewer, R. Laddaga, Destroying the consensus, Synthese, 62 (1985) 79-95.

[62]

F. Mata, L. Martinez, E. Herrera-Viedma, An adaptive consensus support model for group decision-making problems in a multigranular fuzzy linguistic context, IEEE Transactions on Fuzzy Systems, 17 (2009) 279-290.

Digital Library

[63]

J.H. Moon, C.S. Kang, Use of fuzzy set theory in the aggregation of expert judgments, Annals of Nuclear Energy, 26 (1999) 461-469.

[64]

S.K. Pal, A. Ghosh, Soft computing data mining, Information Sciences, 163 (2004) 444.

[65]

I. Palomares, F.J. Estrella, L. Martínez, F. Herrera, Consensus under a fuzzy context: Taxonomy, analysis framework AFRYCA and experimental case of study, Information Fusion, 20 (2014) 252-271.

[66]

I. Palomares, L. Martinez, F. Herrera, A consensus model to detect and manage noncooperative behaviors in large-scale group decision making, IEEE Transactions on Fuzzy Systems, 22 (2014) 516-530.

Digital Library

[67]

J.H. Park, I.Y. Park, Y.C. Kwun, X. Tan, Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment, Applied Mathematical Modelling, 35 (2011) 2544-2556.

[68]

R. Parreiras, P. Ekel, F. Bernardes, A dynamic consensus scheme based on a nonreciprocal fuzzy preference relation modeling, Information Sciences, 211 (2012) 1-17.

Digital Library

[69]

I.J. Pérez, F.J. Cabrerizo, E. Herrera-Viedma, Group decision making problems in a linguistic and dynamic context, Expert Systems with Applications, 38 (2011) 1675-1688.

Digital Library

[70]

R.K. Ragade, Fuzzy sets in communication systems and in consensus formation systems, Cybernetics and Systems, 6 (1976) 21-38.

[71]

L. Roselló, F. Prats, N. Agell, M. Sánchez, Measuring consensus in group decisions by means of qualitative reasoning, International Journal of Approximate Reasoning, 51 (2010) 441-452.

Digital Library

[72]

M. Roubens, Fuzzy sets and decision analysis, Fuzzy Sets and Systems, 90 (1997) 199-206.

Digital Library

[73]

Saaty, T. (2001). The analytic network process. Fundamentals of decision making and priority theory.

[74]

T. Saaty, M. Özdemir, The encyclicon; a dictionary of applications of decision making with dependence and feedback based on the analytic network process, RWS Publications, 2005.

[75]

S. Saint, Rules for reaching consensus: A modern approach to decision making, Pfeiffer, Amsterdam, San Diego, 1994.

[76]

E. Szmidt, J. Kacprzyk, A consensus-reaching process under intuitionistic fuzzy preference relations, International Journal of Intelligent Systems, 18 (2003) 837-852.

[77]

J.M. Tapia GarcíA, M.J. Del Moral, M.A. MartíNez, E. Herrera-Viedma, A consensus model for group decision-making problems with interval fuzzy preference relations, International Journal of Information Technology & Decision Making, 11 (2012) 709-725.

[78]

M. Tavana, D.T. Kennedy, P. Joglekar, A group decision support framework for consensus ranking of technical manager candidates, Omega, 24 (1996) 523-538.

[79]

A. Tchangani, Evaluation model for multiattributes-multiagents decision making: Satisficing game approach, International Journal of Information Technology and Decision Making, 8 (2009) 73-91.

[80]

A. Tchangani, Bipolarity in decision analysis: A way to cope with human judgment, exploring innovative and successful applications of soft computing, IGI Global (2013) 216-244.

[81]

A. Tchangani, Y. Bouzarour-Amokrane, F. Pérés, Evaluation model in decision analysis: Bipolar approach, Informatica, 23 (2012) 461-485.

Digital Library

[82]

Tchangani, A., Bouzarour-Amokrane, Y., & Pérés, F. (2011). Bipolar evaluation model in decision analysis. In 12éme Congrés annuel de la société française de recherché opérationnelle et d'aide á la décision, ROADEF 2011, Saint Etienne, France.

[83]

Urfalino, P. (2007). L'esprit des régles de décision. Vincent Descombes. Questions disputées, 199235.

[84]

D. Verma, Decision making style: Social and creative dimensions, Global India Publications, 2009.

[85]

Wheeler, T. A., Hora, S. C., Cramond, W. R., & Unwin, S. D. (1989). Analysis of core damage frequency from internal events: Expert judgment elicitation: Part 1, Expert panel results, Part 2, Project staff results.

[86]

D.J.D. Wijnmalen, Analysis of benefits, opportunities, costs, and risks (BOCR) with the AHP-ANP: A critical validation, Mathematical and Computer Modelling, 46 (2007) 892-905.

Digital Library

[87]

Z. Wu, J. Xu, A concise consensus support model for group decision making with reciprocal preference relations based on deviation measures, Fuzzy Sets and Systems, 206 (2012) 58-73.

Digital Library

[88]

Y. Xu, K.W. Li, H. Wang, Distance-based consensus models for fuzzy and multiplicative preference relations, Information Sciences, 253 (2013) 56-73.

Digital Library

[89]

J. Xu, Z. Wu, A discrete consensus support model for multiple attribute group decision making, Knowledge-Based Systems, 24 (2011) 1196-1202.

Digital Library

[90]

R.R. Yager, On ordered weighted averaging aggregation operators in multicriteria decisionmaking, IEEE Transactions on Systems, Man and Cybernetics, 18 (1988) 183-190.

Digital Library

[91]

J.-M. Yeh, B. Kreng, C. Lin, A consensus approach for synthesizing the elements of comparison matrix in the Analytic Hierarchy Process, International Journal of Systems Science, 32 (2001) 1353-1363.

[92]

Z. Yue, An extended TOPSIS for determining weights of decision makers with interval numbers, Knowledge-Based Systems, 24 (2011) 146-153.

Digital Library

[93]

Z. Yue, Deriving decision maker's weights based on distance measure for interval-valued intuitionistic fuzzy group decision making, Expert Systems with Applications, 38 (2011) 11665-11670.

Digital Library

[94]

Z. Yue, Approach to group decision making based on determining the weights of experts by using projection method, Applied Mathematical Modelling, 36 (2012) 2900-2910.

[95]

Z. Yue, Extension of TOPSIS to determine weight of decision maker for group decision making problems with uncertain information, Expert Systems with Applications, 39 (2012) 6343-6350.

[96]

Z. Yue, Y. Jia, An application of soft computing technique in group decision making under interval-valued intuitionistic fuzzy environment, Applied Soft Computing (2012).

[97]

L. Yu, K.K. Lai, A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support, Decision Support Systems, 51 (2011) 307-315.

Digital Library

[98]

L.A. Zadeh, Fuzzy sets, Information and Control, 8 (1965) 338-353.

Cited By

Tran TFelfernig ALe V(2024)An overview of consensus models for group decision-making and group recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-023-09380-z34:3(489-547)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11257-023-09380-z
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Tchangani A(2019)Bipolar Fuzzy Nominal Classification (BFNC) frameworkIntelligent Decision Technologies10.3233/IDT-19035513:1(117-130)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/IDT-190355
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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 42, Issue 3

February 2015

817 pages

ISSN:0957-4174

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Copyright © Elsevier Ltd.

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Pergamon Press, Inc.

United States

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Published: 15 February 2015

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Cited By

Tran TFelfernig ALe V(2024)An overview of consensus models for group decision-making and group recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-023-09380-z34:3(489-547)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11257-023-09380-z
Wang ZShen Y(2023)Fuzzy Labeling Semantics for Quantitative ArgumentationLogic and Argumentation10.1007/978-3-031-40875-5_12(193-210)Online publication date: 10-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-40875-5_12
Tchangani A(2019)Bipolar Fuzzy Nominal Classification (BFNC) frameworkIntelligent Decision Technologies10.3233/IDT-19035513:1(117-130)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/IDT-190355
Wu ZXu J(2016)An interactive consensus reaching model for decision making under hesitation linguistic environmentJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-15170831:3(1635-1644)Online publication date: 1-Jan-2016
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Gupta MMohanty B(2016)An algorithmic approach to group decision making problems under fuzzy and dynamic environmentExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.02.00255:C(118-132)Online publication date: 15-Aug-2016
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https://dl.acm.org/doi/10.1016/j.dss.2016.02.011

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