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Modeling the minimum cost consensus problem in an asymmetric costs context. (English) Zbl 1403.91105

Summary: The unit costs of up- and down-adjustments are not equal in some real-life minimum cost consensus (MCC) problems, such as when each individual has two different cost coefficients that depend on the adjustment direction of his/her opinion. To solve these problems, the MCC model with directional constraints (MCCM-DC) is constructed on the basis of goal programming theory and the rectilinear distance function. To analyse the impact of individuals’ limited compromises and tolerance behaviors on the consensus modeling, we further develop the \(\varepsilon\)-MCCM-DC and the threshold-based (TB)-MCCM-DC. Then, the relationships and transformation conditions of these models are investigated. Furthermore, the validity of the proposed models is demonstrated by the case of trans-boundary pollution control negotiations in China’s Taihu Lake Basin. The analysis results show the following: first, the consensus opinion obtained from MCCM-DC is more inclined to the lower cost direction, and its total consensus costs will no longer ascend after reaching a critical point with the increase of unit adjustment costs. Second, the optimal solution of MCCM-DC is the lower bound of \(\varepsilon\)-MCCM-DC and the upper bound of TB-MCCM-DC. Compared with consensus models without directional constraints, the proposed models can obtain a better consensus opinion at lower costs due to the flexibility in adjusting individual opinions and can also characterize the MCC problems in a more realistic way.

MSC:

91B06 Decision theory
Full Text: DOI

References:

[1] Ben-Arieh, D.; Easton, T., Multi-criteria group consensus under linear cost opinion elasticity, Decision support systems, 43, 3, 713-721, (2007)
[2] Ben-Arieh, D.; Easton, T.; Evans, B., Minimum cost consensus with quadratic cost functions, IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 39, 1, 210-217, (2009)
[3] Chen, E.; Budescu, D. V.; Lakshmikanth, S. K.; Mellers, B. A.; Tetlock, P. E., Validating the contribution-weighted model: robustness and cost-benefit analyses, Decision Analysis, 13, 2, 128-152, (2016)
[4] Cheng, D.; Zhou, Z.; Cheng, F.; Wang, J., Deriving heterogeneous experts weights from incomplete linguistic preference relations based on uninorm consistency, Knowledge-Based Systems, in press, (2018)
[5] Cheng, L.-C.; Chen, Y.-L.; Chiang, Y.-C., Identifying conflict patterns to reach a consensus-a novel group decision approach, European Journal of Operational Research, 254, 2, 622-631, (2016) · Zbl 1346.91040
[6] Dong, Y.; Chen, X.; Herrera, F., Minimizing adjusted simple terms in the consensus reaching process with hesitant linguistic assessments in group decision making, Information Sciences, 297, 95-117, (2015)
[7] Dong, Y.; Ding, Z.; Chiclana, F.; Herrera-Viedma, E., Dynamics of public opinions in an online and offline social network, IEEE Transactions on Big Data, in press, (2018)
[8] Dong, Y.; Ding, Z.; Martínez, L.; Herrera, F., Managing consensus based on leadership in opinion dynamics, Information Sciences, 397, 187-205, (2017) · Zbl 1429.91260
[9] Dong, Y.; Xu, J., Consensus building in group decision making: searching the consensus path with minimum adjustments, (2015), Springer
[10] Dong, Y.; Xu, Y.; Li, H.; Feng, B., The OWA-based consensus operator under linguistic representation models using position indexes, European Journal of Operational Research, 203, 2, 455-463, (2010) · Zbl 1177.90211
[11] Dong, Y.; Zhang, H.; Herrera-Viedma, E., Integrating experts’ weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors, Decision Support Systems, 84, 1-15, (2016)
[12] Gong, Z.; Xu, C.; Chiclana, F.; Xu, X., Consensus measure with multi-stage fluctuation utility based on china’s urban demolition negotiation, Group Decision and Negotiation, 26, 2, 379-407, (2017)
[13] Gong, Z.; Xu, X.; Zhang, H.; Ozturk, U. A.; Herrera-Viedma, E.; Xu, C., The consensus models with interval preference opinions and their economic interpretation, Omega, 55, 81-90, (2015)
[14] Gong, Z.; Zhang, H.; Forrest, J.; Li, L.; Xu, X., Two consensus models based on the minimum cost and maximum return regarding either all individuals or one individual, European Journal of Operational Research, 240, 1, 183-192, (2015) · Zbl 1339.91039
[15] Herrera-Viedma, E.; Cabrerizo, F. J.; Kacprzyk, J.; Pedrycz, W., A review of soft consensus models in a fuzzy environment, Information Fusion, 17, 4-13, (2014)
[16] Joshi, D.; Kumar, S., Interval-valued intuitionistic hesitant fuzzy Choquet integral based TOPSIS method for multi-criteria group decision making, European Journal of Operational Research, 248, 1, 183-191, (2016) · Zbl 1346.91051
[17] Kacprzyk, J.; Fedrizzi, M., A ‘soft’ measure of consensus in the setting of partial (fuzzy) preferences, European Journal of Operational Research, 34, 3, 316-325, (1988)
[18] Kwok, P.; Lau, H., Modified delphi-AHP method based on minimum-cost consensus model and vague set theory for road junction control method evaluation criteria selection, Journal of Industrial and Intelligent Information, 4, 1, 76-82, (2016)
[19] Li, Y.; Zhang, H.; Dong, Y., The interactive consensus reaching process with the minimum and uncertain cost in group decision making, Applied Soft Computing, 60, 202-212, (2017)
[20] Liu, J.; Chan, F. T.; Li, Y.; Zhang, Y.; Deng, Y., A new optimal consensus method with minimum cost in fuzzy group decision, Knowledge-Based Systems, 35, 357-360, (2012)
[21] Montibeller, G.; Winterfeldt, D., Cognitive and motivational biases in decision and risk analysis, Risk Analysis, 35, 7, 1230-1251, (2015)
[22] Palomares, I.; Martinez, L.; Herrera, F., A consensus model to detect and manage noncooperative behaviors in large-scale group decision making, IEEE Transactions on Fuzzy Systems, 22, 3, 516-530, (2014)
[23] Sun, B.; Ma, W., An approach to consensus measurement of linguistic preference relations in multi-attribute group decision making and application, Omega, 51, 83-92, (2015)
[24] Tan, X.; Gong, Z.; Chiclana, F.; Zhang, N., Consensus modeling with cost chance constraint under uncertainty opinions, Applied Soft Computing, 49, 1-20, (2017)
[25] Wagner, H. M., Global sensitivity analysis, Operations Research, 43, 6, 948-969, (1995) · Zbl 0852.90122
[26] Ward, J. E.; Wendell, R. E., Approaches to sensitivity analysis in linear programming, Annals of Operations Research, 27, 1, 3-38, (1990) · Zbl 0722.90075
[27] Zhang, B.; Dong, Y., Consensus rules with minimum adjustments for multiple attribute group decision making, Procedia Computer Science, 17, 473-481, (2013)
[28] Zhang, B.; Dong, Y.; Xu, Y., Maximum expert consensus models with linear cost function and aggregation operators, Computers & Industrial Engineering, 66, 1, 147-157, (2013)
[29] Zhang, B.; Dong, Y.; Xu, Y., Multiple attribute consensus rules with minimum adjustments to support consensus reaching, Knowledge-Based Systems, 67, 35-48, (2014)
[30] Zhang, G.; Dong, Y.; Xu, Y.; Li, H., Minimum-cost consensus models under aggregation operators, IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 41, 6, 1253-1261, (2011)
[31] Zhao, L.; Li, C.; Huang, R.; Si, S.; Xue, J.; Huang, W.; Hu, Y., Harmonizing model with transfer tax on water pollution across regional boundaries in a china’s lake basin, European Journal of Operational Research, 225, 2, 377-382, (2013) · Zbl 1292.91142
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