Skip to main content
Log in

Decentralized group decision making using blockchain

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Group decision making (GDM) involving consensus reaching process (CRP) attempts to achieve a consensus among Decision-Makers (DMs) before coming to a final decision. Computer-based decision support systems are present to support the decision-making process called the Group Decision Support Systems (GDSS). The traditional GDSS being centralized is subject to security, transparency, and trust issues, such as vulnerable to security risks providing attackers with a single target to attack, a single point of failure, and biases. In this regard, this paper identifies and discusses such issues. To address these issues, we introduce a novel idea of a decentralized group decision-making structure using blockchain technology. We proposed a consensus model suitable for the blockchain platform. For validation, we implement the proposed work using the Ethereum blockchain. Furthermore, a theoretical security analysis of the proposed model is also done to validate that the system eliminates possible security attacks. The possible experiments show that the proposed work minimizes the gas cost by minimizing the feedback cost. To the best of our knowledge, this work is the first step toward introducing the idea, and the advanced approaches will be the natural consequences of this work.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Li Y, Zhang H, Dong Y (2017) The interactive consensus reaching process with the minimum and uncertain cost in group decision making. Appl Soft Comput 60:202–212

    Article  Google Scholar 

  3. Zhang H, Zhao S, Kou G, Li C-C, Dong Y, Herrera F (2020) An overview on feedback mechanisms with minimum adjustment or cost in consensus reaching in group decision making: research paradigms and challenges. Inf Fusion 60:65–79

    Article  Google Scholar 

  4. Parreiras RO, Ekel PY, Martini JSC, Palhares RM (2010) A flexible consensus scheme for multicriteria group decision making under linguistic assessments. Inf Sci (Ny) 180(7):1075–1089

    Article  Google Scholar 

  5. Kacprzyk J, Fedrizzi M (1988) A ‘soft’measure of consensus in the setting of partial (fuzzy) preferences. Eur J Oper Res 34(3):316–325

    Article  MathSciNet  Google Scholar 

  6. Mata F, Martínez L, Herrera-Viedma E (2009) An adaptive consensus support model for group decision-making problems in a multigranular fuzzy linguistic context. IEEE Trans Fuzzy Syst 17(2):279–290. https://doi.org/10.1109/TFUZZ.2009.2013457

    Article  Google Scholar 

  7. Zadrożny S, Kacprzyk J (2003) An internet-based group decision and consensus reaching support system. In: Applied decision support with soft computing. Springer, Cham, pp. 263–276.

  8. Palomares I, Martinez L (2013) A semisupervised multiagent system model to support consensus-reaching processes. IEEE Trans Fuzzy Syst 22(4):762–777

    Article  Google Scholar 

  9. Carvalho G, Vivacqua AS, Souza JM, Medeiros SPJ (2008) LaSca: A large scale group decision support system. In: 2008 12th International Conference on Computer Supported Cooperative Work in Design, pp. 289–294.

  10. Palomares I, Martínez L, Herrera F (2014) MENTOR: A graphical monitoring tool of preferences evolution in large-scale group decision making. Knowledge-Based Syst 58:66–74

    Article  Google Scholar 

  11. Urena R, Chiclana F, Herrera-Viedma E (2020) DeciTrustNET: a graph based trust and reputation framework for social networks. Inf Fusion 61:101–112

    Article  Google Scholar 

  12. Li X, Zheng Z, Dai H-N (2021) When services computing meets blockchain: challenges and opportunities. J Parallel Distrib Comput 150:1–14

    Article  Google Scholar 

  13. Salah K, Rehman MHU, Nizamuddin N, Al-Fuqaha A (2019) Blockchain for AI: review and open research challenges. IEEE Access 7:10127–10149

    Article  Google Scholar 

  14. “The Facebook and Cambridge Analytica scandal, explained with a simple diagram.” https://www.vox.com/policy-and-politics/2018/3/23/17151916/facebook-cambridge-analytica-trump-diagram. Accessed May 07, 2023.

  15. Xie S, Zheng Z, Chen W, Wu J, Dai H-N, Imran M (2020) Blockchain for cloud exchange: a survey. Comput Electr Eng 81:106526

    Article  Google Scholar 

  16. Srivastava HK, Yadav R, Baranwal G (2021) Service Selection using Ethereum. In: IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom) 2021, pp 1–3

  17. Lu Y, Xu Y, Herrera-Viedma E, Han Y (2021) Consensus of large-scale group decision making in social network: the minimum cost model based on robust optimization. Inf Sci (Ny) 547:910–930

    Article  MathSciNet  MATH  Google Scholar 

  18. Chen Y, Li Q, Wang H (2018) Towards trusted social networks with blockchain technology. arXiv Prepr. arXiv1801.02796.

  19. Bai Y, Hu Q, Seo S-H, Kang K, Lee JJ (2021) Public participation consortium blockchain for smart city governance. IEEE Internet Things J 9(3):2094–2108

    Article  Google Scholar 

  20. Christidis K, Devetsikiotis M (2016) Blockchains and smart contracts for the internet of things. Ieee Access 4:2292–2303

    Article  Google Scholar 

  21. Pérez IJ, Cabrerizo FJ, Alonso S, Herrera-Viedma E (2013) A new consensus model for group decision making problems with non-homogeneous experts. IEEE Trans Syst Man Cybern Syst 44(4):494–498.

  22. Herrera-Viedma E, Herrera F, Chiclana F (2002) A consensus model for multiperson decision making with different preference structures. IEEE Trans Syst Man Cybern A Syst Humans 32(3):394–402.

  23. Zhang H, Dong Y, Chiclana F, Yu S (2019) Consensus efficiency in group decision making: acomprehensive comparative study and its optimal design. Eur J Oper Res 275(2):580–598

    Article  MathSciNet  MATH  Google Scholar 

  24. Dong Y, Xu Y, Li H, Feng B (2010) The OWA-based consensus operator under linguistic representation models using position indexes. Eur J Oper Res 203(2):455–463

    Article  MATH  Google Scholar 

  25. Zhang Z, Guo C (2016) Consistency and consensus models for group decision-making with uncertain 2-tuple linguistic preference relations. Int J Syst Sci 47(11):2572–2587

    Article  MathSciNet  MATH  Google Scholar 

  26. Mata F, Chiclana F (2005) A consensus support system model for group decision-making problems with multigranular linguistic preference relations. IEEE Trans Fuzzy Syst 13(5):644–658

    Article  Google Scholar 

  27. Tang M, Liao H, Xu J, Streimikiene D, Zheng X (2020) Adaptive consensus reaching process with hybrid strategies for large-scale group decision making. Eur J Oper Res 282(3):957–971

    Article  MathSciNet  MATH  Google Scholar 

  28. Liu Y, Liang C, Chiclana F, Wu J (2017) A trust induced recommendation mechanism for reaching consensus in group decision making. Knowledge-Based Syst 119:221–231

    Article  Google Scholar 

  29. Gupta M (2017) Consensus building process in group decision making—an adaptive procedure based on group dynamics. IEEE Trans Fuzzy Syst 26(4):1923–1933

    Article  Google Scholar 

  30. Dong Q, Cooper O (2016) A peer-to-peer dynamic adaptive consensus reaching model for the group AHP decision making. Eur J Oper Res 250(2):521–530

    Article  MathSciNet  MATH  Google Scholar 

  31. Wu X, Liao H (2019) A consensus-based probabilistic linguistic gained and lost dominance score method. Eur J Oper Res 272(3):1017–1027

    Article  MathSciNet  MATH  Google Scholar 

  32. Herrera-Viedma E, Herrera F, Chiclana F, Luque M (2004) Some issues on consistency of fuzzy preference relations. Eur J Oper Res 154(1):98–109

    Article  MathSciNet  MATH  Google Scholar 

  33. Orlovsky S (1978) Decision-making with a fuzzy preference relation. Fuzzy sets Syst 1(3):155–167

    Article  MathSciNet  MATH  Google Scholar 

  34. Świtalski Z (1999) Rationality of fuzzy reciprocal preference relations. Fuzzy Sets Syst 107(2):187–190

    Article  MathSciNet  MATH  Google Scholar 

  35. Aggarwal S, Kumar N (2021) Blockchain 2.0: smart contracts. Adv Comput 121:301–322.

  36. Lin X (2017) Semi-centralized blockchain smart contracts: centralized verification and smart computing under chains in the ethereum blockchain. Dep. Inf. Eng. Natl. Taiwan Univ, Taiwan, ROC

    Google Scholar 

  37. Vacca A, Di Sorbo A, Visaggio CA, Canfora G (2021) A systematic literature review of blockchain and smart contract development: techniques, tools, and open challenges. J Syst Softw 174:110891

    Article  Google Scholar 

  38. Bellini E, Iraqi Y, Damiani E (2020) Blockchain-based distributed trust and reputation management systems: a survey. IEEE Access 8:21127–21151

    Article  Google Scholar 

  39. Cabrerizo FJ, Pérez IJ, Herrera-Viedma E (2010) Managing the consensus in group decision making in an unbalanced fuzzy linguistic context with incomplete information. Knowledge-Based Syst 23(2):169–181

    Article  Google Scholar 

  40. Choudhury AK, Shankar R, Tiwari MK (2006) Consensus-based intelligent group decision-making model for the selection of advanced technology. Decis Support Syst 42(3):1776–1799

    Article  Google Scholar 

  41. Kacprzyk J, Zadrożny S (2010) Soft computing and web intelligence for supporting consensus reaching. Soft Comput 14(8):833–846

    Article  Google Scholar 

  42. Palomares I, Liu J, Xu Y, Martínez L (2012) Modelling experts’ attitudes in group decision making. Soft Comput 16(10):1755–1766

    Article  MATH  Google Scholar 

  43. Palomares I, Rodríguez RM, Martínez L (2013) An attitude-driven web consensus support system for heterogeneous group decision making. Expert Syst Appl 40(1):139–149

    Article  Google Scholar 

  44. Saad M, Spaulding J, Njilla L, Kamhoua CA, Nyang D, Mohaisen A (2019) Overview of attack surfaces in blockchain. Blockchain Distrib. Syst. Secur., pp. 51–66.

  45. Li G, Kou G, Peng Y (2016) A group decision making model for integrating heterogeneous information. IEEE Trans Syst Man Cybern Syst 48(6):982–992.

  46. Dong Y, Zhang H, Herrera-Viedma E (2016) Integrating experts’ weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors. Decis Support Syst 84:1–15

    Article  Google Scholar 

  47. Dong Y et al (2018) Consensus reaching in social network group decision making: research paradigms and challenges. Knowledge-Based Syst 162:3–13

    Article  Google Scholar 

  48. Ramanathan R, Ganesh LS (1994) Group preference aggregation methods employed in AHP: an evaluation and an intrinsic process for deriving members’ weightages. Eur J Oper Res 79(2):249–265

    Article  MATH  Google Scholar 

  49. Tang M, Liao H, Fujita H (2021) Delegation Mechanism-Based Large-Scale Group Decision Making With Heterogeneous Experts and Overlapping Communities. IEEE Trans Syst Man Cybern Syst 52(6):3542–3555.

  50. Yu L, Lai KK (2011) A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support. Decis Support Syst 51(2):307–315

    Article  Google Scholar 

  51. Almadhoun R, Kadadha M, Alhemeiri M, Alshehhi M, Salah K (2018) A user authentication scheme of IoT devices using blockchain-enabled fog nodes. In: 2018 IEEE/ACS 15th international Conference on Computer Systems and Applications (AICCSA), pp. 1–8.

  52. Dib O, Brousmiche K-L, Durand A, Thea E, Ben Hamida E (2018) Consortium blockchains: overview, applications and challenges. Int J Adv Telecommun 11(1&2):51–64.

  53. Zheng Z, Xie S, Dai H-N, Chen X, Wang H (2018) Blockchain challenges and opportunities: a survey. Int J Web Grid Serv 14(4):352–375

    Article  Google Scholar 

Download references

Funding

The authors have no relevant financial or non-financial interests to disclose.

Author information

Authors and Affiliations

Authors

Contributions

MS involved in conceptualization, methodology, software, validation, investigation, writing—original draft, and visualization; GB involved in conceptualization, validation, writing—review and editing, and visualization; AKT involved in conceptualization, validation, writing—review and editing, visualization, and supervision.

Corresponding author

Correspondence to Gaurav Baranwal.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, M., Baranwal, G. & Tripathi, A.K. Decentralized group decision making using blockchain. J Supercomput 79, 20141–20178 (2023). https://doi.org/10.1007/s11227-023-05426-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-023-05426-6

Keywords

Navigation