Consensus achievement strategy of opinion dynamics based on deep reinforcement learning with time constraint

References

• AskariSichani, O., & Jalili, M. (2015). Influence maximization of informed agents in social network. Applied Mathematics and Computation, 254, 229–239. https://doi.org/10.1016/j.amc.2014.12.139
   Web of Science ®Google Scholar
• Barberá, P., Jost, J. T., Nagler, J., Joshua, A. T., & Richard, B. (2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological Science, 26(10), 1531–1542.
   PubMed Web of Science ®Google Scholar
• Bellman, R. (1957). A Markov decision process. Journal of Mathematical Mechanics, 6, 679–684.
   Google Scholar
• Bellman, R. (1966). Dynamic programming. Science (New York, N.Y.), 153(3731), 34–37.
   PubMed Web of Science ®Google Scholar
• Chen, S., Glass, D. H., & McCartney, M. (2016). Characteristics of successful opinion leaders in a bounded confidence model. Physica A: Statistical Mechanics and Its Applications, 449, 426–436. https://doi.org/10.1016/j.physa.2015.12.107
   Web of Science ®Google Scholar
• Chen, X., Ding, Z. G., Dong, Y. C., & Liang, H. M. (2021). Managing consensus with minimum adjustments in group decision making with opinions evolution. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(4), 2299–2311. https://doi.org/10.1109/TSMC.2019.2912231
   Web of Science ®Google Scholar
• Deffuant, G., Neau, D., Amblard, F., & Weisbuch, G. (2000). Mixing beliefs among interacting agents. Advances in Complex Systems, 03(01n04), 87–98. https://doi.org/10.1142/S0219525900000078
   Google Scholar
• DeGroot, M. H. (1974). Reaching a consensus. Journal of the American Statistical Association, 69(345), 118–121. https://doi.org/10.1080/01621459.1974.10480137
   Web of Science ®Google Scholar
• Ding, Z. G., Dong, Y. C., Kou, G., Palomares, I., & Yu, S. (2018). Consensus formation in opinion dynamics with online and offline interactions at complex networks. International Journal of Modern Physics C, 29(7), 1850046. https://doi.org/10.1142/S0129183118500468
   Web of Science ®Google Scholar
• Dong, Y. C., Chen, X., Liang, H. M., & Li, C. C. (2016). Dynamics of linguistic opinion formation in bounded confidence model. Information Fusion, 32, 52–61. https://doi.org/10.1016/j.inffus.2016.03.001
   Web of Science ®Google Scholar
• Dong, Y. C., Zhan, M., Kou, G., Ding, Z. G., & Liang, H. M. (2018). A survey on the fusion process in opinion dynamics. Information Fusion, 43, 57–65. https://doi.org/10.1016/j.inffus.2017.11.009
   Web of Science ®Google Scholar
• Fan, K., & Pedrycz, W. (2016). Opinion evolution influenced by informed agents. Physica A: Statistical Mechanics and Its Applications, 462, 431–441. https://doi.org/10.1016/j.physa.2016.06.110
   Web of Science ®Google Scholar
• Fortunato, S. (2005). Damage spreading and opinion dynamics on scale-free networks. Physica A: Statistical Mechanics and Its Applications, 348, 683–690. https://doi.org/10.1016/j.physa.2004.09.007
   Web of Science ®Google Scholar
• Gupta, A., Dengre, V., Kheruwala, H. A., & Shah, M. (2020). Comprehensive review of text-mining applications in finance. Financial Innovation, 6, 39. https://doi.org/10.1080/01605682.2020.1750310
   Web of Science ®Google Scholar
• Hegselmann, R., & Krause, U. (2002). Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulation, 5(3).
   Web of Science ®Google Scholar
• Howard, R. A. (1960). Dynamic programming and Markov processes. MIT Press.
   Google Scholar
• Keck, S., & Tang, W. J. (2020). Elaborating or aggregating? The joint effects of group decision-making structure and systematic errors on the value of group interactions. Management Science. https://doi.org/10.1287/mnsc.2020.3688
   PubMed Web of Science ®Google Scholar
• Li, T., Kou, G., Peng, Y., & Shi, Y. (2020). Classifying with adaptive hyper-spheres: An incremental classifier based on competitive learning. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(4), 1218–1229.
   Web of Science ®Google Scholar
• Liang, H. M., Dong, Y. C., Ding, Z. G., Ureña, R., Chiclana, F., & Herrera-Viedma, E. (2020). Consensus reaching with time constraints and minimum adjustments in group with bounded confidence effects. IEEE Transactions on Fuzzy Systems, 28(10), 2466–2479. https://doi.org/10.1109/TFUZZ.2019.2939970
   Web of Science ®Google Scholar
• Liang, H. M., Li, C. C., Dong, Y. C., & Jiang, Y. P. (2016). The fusion process of interval opinions based on the dynamic bounded confidence. Information Fusion, 29, 112–119. https://doi.org/10.1016/j.inffus.2015.08.010
   Web of Science ®Google Scholar
• Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., Wierstra, D. (2016). Continuous control with deep reinforcement learning. In Proceedings of the International Conference on Learning Representations.
   Google Scholar
• Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, L., Wierstra, D., & Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.
   Google Scholar
• Pineda, M., Toral, R., & Hernández-García, E. (2009). Noisy continuous-opinion dynamics. Journal of Statistical Mechanics: Theory and Experiment, 2009(08), P08001. https://doi.org/10.1088/1742-5468/2009/08/P08001
   Google Scholar
• Pineda, M., Toral, R., & Hernández-García, E. (2013). The noisy Hegselmann-Krause model for opinion dynamics. The European Physical Journal B, 86(12), 1–10. https://doi.org/10.1140/epjb/e2013-40777-7
   Web of Science ®Google Scholar
• Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
   Google Scholar
• Tang, M., Liao, H. C., Mi, X. M., Lev, B., & Pedrycz, W. (2021). A hierarchical consensus reaching process for group decision making with noncooperative behaviors. European Journal of Operational Research, 293(2), 632–642. https://doi.org/10.1016/j.ejor.2020.12.028
   Web of Science ®Google Scholar
• Tang, M., Liao, H. C., Xu, J. P., Dalia, S., & Zheng, X. S. (2020). Adaptive consensus reaching process with hybrid strategies for large-scale group decision making. European Journal of Operational Research, 282(3), 957–971. https://doi.org/10.1016/j.ejor.2019.10.006
   Web of Science ®Google Scholar
• Van Hasselt, H., Guez, A., & Silver, D. (2016). Deep reinforcement learning with double q-learning. In Proceedings of the AAAI Conference on Artificial Intelligence, 30(1).
   Google Scholar
• Wang, L. X., & Mendel, J. M. (2016). Fuzzy opinion networks: A mathematical framework for the evolution of opinions and their uncertainties across social networks. IEEE Transactions on Fuzzy Systems, 24(4), 880–905. https://doi.org/10.1109/TFUZZ.2015.2486816
   Web of Science ®Google Scholar
• Wang, M. W., Li, F. S., & Liang, D. C. (2020). Opinion dynamics and consensus achievement strategy based on reinforcement learning [Paper presentation]. In 2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 1–6. https://doi.org/10.1109/ISSPIT51521.2020.9408808
   Google Scholar
• Wang, Z. Y., Schaul, T., Hessel, M., van Hasselt, H., Lanctot, M., de Freitas, N. (2016). Dueling network architectures for deep reinforcement learning. In Proceedings of International Conference on Machine Learning, PMLR, 1995–2003.
   Google Scholar
• Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3-4), 229–256. https://doi.org/10.1007/BF00992696
   Web of Science ®Google Scholar
• Wu, Z. B., Huang, S., & Xu, J. P. (2019). Multi-stage optimization models for individual consistency and group consensus with preference relations. European Journal of Operational Research, 275(1), 182–194. https://doi.org/10.1016/j.ejor.2018.11.014
   Web of Science ®Google Scholar
• Zha, Q. B., Dong, Y. C., Zhang, H. J., Chiclana, F., & Herrera-Viedma, E. (2019). A personalized feedback mechanism based on bounded confidence learning to support consensus reaching in group decision making. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 1–11.
   Google Scholar
• Zha, Q. B., Kou, G., Zhang, H. J., Liang, H. M., Chen, X., Li, C. C., & Dong, Y. C. (2020). Opinion dynamics in finance and business: A literature review and research opportunities. Financial Innovation, 6, 44. https://doi.org/10.1186/s40854-020-00211-3
   Web of Science ®Google Scholar
• Zhang, H. J., Dong, Y. C., Xiao, J., Chiclana, F., & Herrera-Viedma, E. (2021). Consensus and opinion evolution-based failure mode and effect analysis approach for reliability management in social network and uncertainty contexts. Reliability Engineering & System Safety, 208, 107425.
   Web of Science ®Google Scholar
• Zhang, H. J., Zhao, S. H., Kou, G., Li, C. C., Dong, Y. C., & Herrera, F. (2020). An overview on feedback mechanisms with minimum adjustment or cost in consensus reaching in group decision making: Research paradigms and challenges. Information Fusion, 60, 65–79.
   Web of Science ®Google Scholar