IDEAS home Printed from https://ideas.repec.org/a/spr/grdene/v26y2017i2d10.1007_s10726-016-9486-6.html
   My bibliography  Save this article

Consensus Measure with Multi-stage Fluctuation Utility Based on China’s Urban Demolition Negotiation

Author

Listed:
  • Zaiwu Gong

    (Nanjing University of Information Science and Technology)

  • Chao Xu

    (Nanjing University of Information Science and Technology)

  • Francisco Chiclana

    (De Montfort University)

  • Xiaoxia Xu

    (Nanjing University of Information Science and Technology)

Abstract

Utility functions are often used to reflect decision makers’ (DMs’) preferences. They have the following two merits: one refers to the representation of the DM’s utility (satisfaction) level, the other one to the measuring of the consensus level in a negotiation process. Taking the background of China’s urban house demolition, a new kind of consensus model is established by using different types of multi-stage fluctuation utility functions, such as concave, convex, S-shaped, reversed S-shaped, reversed U-shaped as well as their combinations, to reveal negotiators’ dynamic physiological preferences and consensus level. Moreover, the effects of the decision-making budget and the individual compensation tolerance on the consensus level are also discussed in this paper. Compared with previous research, the proposed model takes both the negotiation cost and DM’s preference structure into consideration, and most importantly, it is computational less complex.

Suggested Citation

  • Zaiwu Gong & Chao Xu & Francisco Chiclana & Xiaoxia Xu, 2017. "Consensus Measure with Multi-stage Fluctuation Utility Based on China’s Urban Demolition Negotiation," Group Decision and Negotiation, Springer, vol. 26(2), pages 379-407, March.
    • Handle: RePEc:spr:grdene:v:26:y:2017:i:2:d:10.1007_s10726-016-9486-6
    DOI: 10.1007/s10726-016-9486-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10726-016-9486-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10726-016-9486-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Feenstra, Robert C., 2003. "A homothetic utility function for monopolistic competition models, without constant price elasticity," Economics Letters, Elsevier, vol. 78(1), pages 79-86, January.
    2. Chang, Ching-Ter, 2011. "Multi-choice goal programming with utility functions," European Journal of Operational Research, Elsevier, vol. 215(2), pages 439-445, December.
    3. Xu, Zeshui, 2005. "Deviation measures of linguistic preference relations in group decision making," Omega, Elsevier, vol. 33(3), pages 249-254, June.
    4. Chang, Ching-Ter, 2007. "Binary fuzzy goal programming," European Journal of Operational Research, Elsevier, vol. 180(1), pages 29-37, July.
    5. Faruk Gul & Wolfgang Pesendorfer, 2014. "Expected Uncertain Utility Theory," Econometrica, Econometric Society, vol. 82(1), pages 1-39, January.
    6. Chang, Ching-Ter & Lin, Teng-Chiao, 2009. "Interval goal programming for S-shaped penalty function," European Journal of Operational Research, Elsevier, vol. 199(1), pages 9-20, November.
    7. Yucheng Dong & Cong-Cong Li & Yinfeng Xu & Xin Gu, 2015. "Consensus-Based Group Decision Making Under Multi-granular Unbalanced 2-Tuple Linguistic Preference Relations," Group Decision and Negotiation, Springer, vol. 24(2), pages 217-242, March.
    8. Arthur Charpentier & Alfred Galichon & Marc Henry, 2016. "Local Utility and Multivariate Risk Aversion," Mathematics of Operations Research, INFORMS, vol. 41(2), pages 466-476, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Bowen & Dong, Yucheng & Zhang, Hengjie & Pedrycz, Witold, 2020. "Consensus mechanism with maximum-return modifications and minimum-cost feedback: A perspective of game theory," European Journal of Operational Research, Elsevier, vol. 287(2), pages 546-559.
    2. Wenqi Liu & Hengjie Zhang & Haiming Liang & Cong-cong Li & Yucheng Dong, 2022. "Managing Consistency and Consensus Issues in Group Decision-Making with Self-Confident Additive Preference Relations and Without Feedback: A Nonlinear Optimization Method," Group Decision and Negotiation, Springer, vol. 31(1), pages 213-240, February.
    3. Cheng, Dong & Yuan, Yuxiang & Wu, Yong & Hao, Tiantian & Cheng, Faxin, 2022. "Maximum satisfaction consensus with budget constraints considering individual tolerance and compromise limit behaviors," European Journal of Operational Research, Elsevier, vol. 297(1), pages 221-238.
    4. Shaojian Qu & Yefan Han & Zhong Wu & Hassan Raza, 2021. "Consensus Modeling with Asymmetric Cost Based on Data-Driven Robust Optimization," Group Decision and Negotiation, Springer, vol. 30(6), pages 1395-1432, December.
    5. Du, Junliang & Liu, Sifeng & Liu, Yong, 2022. "A limited cost consensus approach with fairness concern and its application," European Journal of Operational Research, Elsevier, vol. 298(1), pages 261-275.
    6. Zhang, Huanhuan & Kou, Gang & Peng, Yi, 2019. "Soft consensus cost models for group decision making and economic interpretations," European Journal of Operational Research, Elsevier, vol. 277(3), pages 964-980.
    7. Zhang, Hengjie & Dong, Yucheng & Chiclana, Francisco & Yu, Shui, 2019. "Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design," European Journal of Operational Research, Elsevier, vol. 275(2), pages 580-598.
    8. Weijun Xu & Xin Chen & Yucheng Dong & Francisco Chiclana, 2021. "Impact of Decision Rules and Non-cooperative Behaviors on Minimum Consensus Cost in Group Decision Making," Group Decision and Negotiation, Springer, vol. 30(6), pages 1239-1260, December.
    9. Guo, Weiwei & Gong, Zaiwu & Zhang, Wei-Guo & Xu, Yanxin, 2023. "Minimum cost consensus modeling under dynamic feedback regulation mechanism considering consensus principle and tolerance level," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1279-1295.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yan, Hong-Bin & Ma, Tieju & Huynh, Van-Nam, 2017. "On qualitative multi-attribute group decision making and its consensus measure: A probability based perspective," Omega, Elsevier, vol. 70(C), pages 94-117.
    2. Chang, Ching-Ter, 2011. "Multi-choice goal programming with utility functions," European Journal of Operational Research, Elsevier, vol. 215(2), pages 439-445, December.
    3. Fanyong Meng & Aiqing Zeng & Jie Tang & Witold Pedrycz, 2023. "Ranking Objects from Individual Linguistic Dual Hesitant Fuzzy Information in View of Optimal Model-Based Consistency and Consensus Iteration Algorithm," Group Decision and Negotiation, Springer, vol. 32(1), pages 5-44, February.
    4. Wu, Zhibin & Xu, Jiuping, 2016. "Managing consistency and consensus in group decision making with hesitant fuzzy linguistic preference relations," Omega, Elsevier, vol. 65(C), pages 28-40.
    5. Zheng, Xiao-Xue & Chang, Ching-Ter, 2021. "Topology design of remote patient monitoring system concerning qualitative and quantitative issues," Omega, Elsevier, vol. 98(C).
    6. Chen, Natalie & Juvenal, Luciana, 2022. "Markups, quality, and trade costs," Journal of International Economics, Elsevier, vol. 137(C).
    7. Rajeev K. Goel & Shoji Haruna, 2021. "Unmasking the demand for masks: Analytics of mandating coronavirus masks," Metroeconomica, Wiley Blackwell, vol. 72(3), pages 580-591, July.
    8. Brissimis, Sophocles N. & Kosma, Theodora S., 2014. "A unified framework for analysing price interdependence, innovative activity and exchange rate pass-through," Economics Letters, Elsevier, vol. 122(2), pages 159-162.
    9. Pang, Jifang & Liang, Jiye, 2012. "Evaluation of the results of multi-attribute group decision-making with linguistic information," Omega, Elsevier, vol. 40(3), pages 294-301.
    10. Ni Li & Minghui Sun & Zhuming Bi & Zeya Su & Chao Wang, 2014. "A new methodology to support group decision-making for IoT-based emergency response systems," Information Systems Frontiers, Springer, vol. 16(5), pages 953-977, November.
    11. Jewitt, Ian & Mukerji, Sujoy, 2017. "Ordering ambiguous acts," Journal of Economic Theory, Elsevier, vol. 171(C), pages 213-267.
    12. Mion, Giordano & Jacob, Nick, 2020. "On the productivity advantage of cities," CEPR Discussion Papers 14644, C.E.P.R. Discussion Papers.
    13. Head, Keith & Mayer, Thierry, 2014. "Gravity Equations: Workhorse,Toolkit, and Cookbook," Handbook of International Economics, in: Gopinath, G. & Helpman, . & Rogoff, K. (ed.), Handbook of International Economics, edition 1, volume 4, chapter 0, pages 131-195, Elsevier.
    14. Wenqi Liu & Hengjie Zhang & Haiming Liang & Cong-cong Li & Yucheng Dong, 2022. "Managing Consistency and Consensus Issues in Group Decision-Making with Self-Confident Additive Preference Relations and Without Feedback: A Nonlinear Optimization Method," Group Decision and Negotiation, Springer, vol. 31(1), pages 213-240, February.
    15. Zeshui Xu, 2013. "Compatibility Analysis of Intuitionistic Fuzzy Preference Relations in Group Decision Making," Group Decision and Negotiation, Springer, vol. 22(3), pages 463-482, May.
    16. Fabio Ghironi, 2018. "Macro needs micro," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 34(1-2), pages 195-218.
    17. Banri Ito & Katsunori Shirai, 2023. "Patent law harmonization and international trade," International Journal of Economic Policy Studies, Springer, vol. 17(1), pages 289-306, February.
    18. Hsieh Hui-ting & Lai Ching-chong & Chen Kuan-jen, 2015. "A Macroeconomic Model of Imperfect Competition with Patent Licensing," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 15(4), pages 1579-1618, October.
    19. Xunjie Gou & Zeshui Xu & Huchang Liao, 2019. "Hesitant Fuzzy Linguistic Possibility Degree-Based Linear Assignment Method for Multiple Criteria Decision-Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 35-63, January.
    20. Matsuyama, Kiminori & Ushchev, Philip, 2020. "Constant Pass-Through," CEPR Discussion Papers 15475, C.E.P.R. Discussion Papers.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:grdene:v:26:y:2017:i:2:d:10.1007_s10726-016-9486-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.