A new method for enhancing collective intelligence using expert’s knowledge

References

• Arima, Y. (2021). Group collective intelligence. In Y. Arima (Ed.), Psychology of group and collective intelligence (pp. 95–119). Springer International Publishing.
   Google Scholar
• Bernstein, E., Shore, J., & Lazer, D. (2018). How intermittent breaks in interaction improve collective intelligence. Proceedings of the National Academy of Sciences, 115(35), 8734–8739. https://doi.org/10.1073/pnas.1802407115
   PubMed Web of Science ®Google Scholar
• Bhatt, S., Chen, K., Shalin, V. L., Sheth, A. P., & Minnery, B. (2019). Who should Be the captain this week?Leveraging inferred diversity-enhanced crowd wisdom for a fantasy premier league captain prediction. Proceedings of the International AAAI Conference on Web and Social Media, 13, 103–113. https://doi.org/10.1609/icwsm.v13i01.3213
   Google Scholar
• Bhatt, S., Minnery, B., Nadella, S., Bullemer, B., Shalin, V., & Sheth, A. (2017). Enhancing crowd wisdom using measures of diversity computed from social media data, pp. 907–913. Proceedings of the International Conference on Web Intelligence, WI ‘17, Leipzig, Germany, 23-26 August 2017.
   Google Scholar
• Bossaerts, F., Yadav, N., Bossaerts, P., Nash, C., Todd, T., Rudolf, T., Hutchins, R., Ponsonby, A.-L., & Mattingly, K. (2024). Price formation in field prediction markets: The wisdom in the crowd. Journal of Financial Markets, 100881. https://doi.org/10.1016/j.finmar.2023.100881
   Google Scholar
• Bottazzi, G., & Giachini, D. (2019). Far from the madding crowd: Collective wisdom in prediction markets. Quantitative Finance, 19(9), 1461–1471. https://doi.org/10.1080/14697688.2019.1622285
   Web of Science ®Google Scholar
• Budescu, D. V., & Chen, E. (2014). Identifying expertise to extract the wisdom of crowds. Management Science, 61(2), 267–280. https://doi.org/10.1287/mnsc.2014.1909
   Web of Science ®Google Scholar
• Cooke, N. A., & Teichmann, J. J. (2012). 9 – Keeping current. In N. A. Cooke & T. I. P. Teichmann (Eds.), Chandos information professional series (pp. 99–116). Chandos Publishing.
   Google Scholar
• Dezecache, G., Dockendorff, M., Ferreiro, D. N., Deroy, O., & Bahrami, B. (2022). Democratic forecast: Small groups predict the future better than individuals and crowds. Journal of Experimental Psychology: Applied, 28(3), 525–537. 10.1037xap0000424
   PubMed Web of Science ®Google Scholar
• Duong, T. H., Nguyen, N. T., & Jo, G. (2010). Constructing and mining a semantic-based academic social network. Journal of Intelligent & Fuzzy Systems, 21(3), 197–207. https://doi.org/10.3233/IFS-2010-0451
   Web of Science ®Google Scholar
• Erdős, P., & Rényi, A. (1960). On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences, 5, 17–60. https://doi.org/10.2307/1999405
   Google Scholar
• Galton, F. (1907). Vox Populi (The Wisdom of Crowds). Nature, 75(1949), 450–451. 10.1038075450a0
   Google Scholar
• Hansen, M. J., & Vaagen, H. (2016). Collective intelligence in project groups: Reflections from the field. Procedia Computer Science, 100, 840–847. https://doi.org/10.1016/j.procs.2016.09.233
   Google Scholar
• Hong, H., Du, Q., Wang, G., Fan, W., & Xu, D. (2016). Crowd wisdom: The impact of opinion diversity and participant independence on crowd performance. 22nd Americas Conference on Information Systems, AMCIS 2016, San Diego, California, 11-14 August 2016.
   Google Scholar
• Hong, H., Ye, Q., Du, Q., Wang, G. A., & Fan, W. (2020). Crowd characteristics and crowd wisdom: Evidence from an online investment community. Journal of the Association for Information Science and Technology, 71(4), 423–435. https://doi.org/10.1002/asi.24255
   Web of Science ®Google Scholar
• Hong, L., & Page, S. E. (2004). Groups of diverse problem solvers Can outperform groups of high-ability problem solvers. Proceedings of the National Academy of Sciences, 101(46), 16385–16389. https://doi.org/10.1073/pnas.0403723101
   PubMed Web of Science ®Google Scholar
• Kameda, T., Toyokawa, W., & Scott Tindale, R. (2022). Information aggregation and collective intelligence beyond the wisdom of crowds. Nature Reviews Psychology, 1(6), 345–357. https://doi.org/10.1038/s44159-022-00054-y
   Google Scholar
• Kawamura, H., & Ohuchi, A. (2000). Evolutionary emergence of collective intelligence with artificial pheromone communication, pp. 2831–2836. 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation, Nagoya, Japan, 22-28 October 2000.
   Google Scholar
• Lee, J.-Y., & Jin, C.-H. (2019). How collective intelligence fosters incremental innovation. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), 53. 10.3390joitmc5030053
   Google Scholar
• Levy, P. (1997). Collective intelligence: Mankind’s emerging world in cyberspace. Perseus Books.
   Google Scholar
• Maleszka, M., & Nguyen, N. T. (2015). Integration computing and collective intelligence. Expert Systems with Applications, 42(1), 332–340. 10.1016j.eswa.2014.07.036
   Web of Science ®Google Scholar
• Malone, W. T., & Michael Bernstein, S. (2015). Handbook of collective intelligence. The MIT Press.
   Google Scholar
• Meza, J., Jimenez, A., Mendoza, K., & Vaca-Cárdenas, L. (2018). Collective intelligence education, enhancing the collaborative learning, pp. 24–30. 2018 International Conference on eDemocracy & eGovernment (ICEDEG), Ambato, Ecuador, 4-6 April 2018.
   Google Scholar
• Nguyen, Ngoc Thanh, and Janusz Sobecki. 2003. Using consensus methods to construct adaptive interfaces in multimodal Web-based systems. Universal Access in the Information Society 2(4):342–358. https://doi.org/10.1007/s10209-003-0050-1.
   Google Scholar
• Nguyen, N. T. (2002). Metody Wyboru Consensusu i Ich Zastosowanie w Rozwiązywaniu Konfliktów w Systemach Rozproszonych. Oficyna Wydawnicza PWr.
   Google Scholar
• Nguyen, V. D., Merayo, M. G., & Nguyen, N. T. (2017). Intelligent collective: The role of diversity and collective cardinality, pp. 83–92. Computational Collective Intelligence – 9th International Conference, Nicosia, Cyprus, 27-29 September 2017.
   Google Scholar
• Nguyen, V. D., & Nguyen, N. T. (2017). The impact of diversity on the quality of collective prediction, pp. 149–154. IEEE International Conference on Innovations in Intelligent SysTems and Applications, Gdynia, Poland, 3-5 July 2017.
   Google Scholar
• Nguyen, V. D., & Nguyen, N. T. (2018a). An influence analysis of diversity and collective cardinality on collective performance. Information Sciences, 430, 487–503. https://doi.org/10.1016/j.ins.2017.11.053
   Web of Science ®Google Scholar
• Nguyen, V. D., & Nguyen, N. T. (2018b). Intelligent collectives: Theory, applications, and research challenges. Cybernetics and Systems, 49(5-6), 261–279. https://doi.org/10.1080/01969722.2017.1418254
   Web of Science ®Google Scholar
• Nguyen, V. D., Tran, V. C., Truong, H. B., & Nguyen, N. T. (2022). Social networks as platforms for enhancing collective intelligence. Cybernetics and Systems, 53(5), 425–442. https://doi.org/10.1080/01969722.2021.2018544
   Web of Science ®Google Scholar
• Nguyen, V. D., & Truong, H. B. (2023). Intelligent collectives: Impact of independence on collective performance. Cybernetics and Systems, 1–16. https://doi.org/10.1080/01969722.2022.2162735
   Web of Science ®Google Scholar
• Nik, V. M., & Moazami, A. (2021). Using collective intelligence to enhance demand flexibility and climate resilience in urban areas. Applied Energy, 281, 116106. https://doi.org/10.1016/j.apenergy.2020.116106
   Web of Science ®Google Scholar
• Nofer, M., & Hinz, O. (2014). Are crowds on the internet wiser than experts? The case of a stock prediction community. Journal of Business Economics, 84, 303–338. https://doi.org/10.1007/s11573-014-0720-x
   Google Scholar
• Palak, R., & Nguyen, N. T. (2017). Prediction markets as a vital part of collective intelligence, pp. 137–142. Proceedings of INISTA 2017, Gdynia, Poland, 3-5 July, 2017.
   Google Scholar
• Palak, R., & Nguyen, N. T. (2019a). An independence measure for expert collections based on social media profiles. Proceedings of ACIIDS 2019, Yogyakarta, Indonesia, 8-11 April 2019, pp.15–25.
   Google Scholar
• Palak, R., & Nguyen, N. T. (2019b). Independency aspect as a vital feature of intelligent collectives. BT – 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019, Bari, Italy, October 6-9, 2019.
   Google Scholar
• Palak, R., & Nguyen, V. S. (2021). Centralization measures for social networks. Cybernetics and Systems, 52(5), 398–418. https://doi.org/10.1080/01969722.2020.1871228
   Web of Science ®Google Scholar
• Passig, D., & Zoref, L. (2020). Using social networks as a collective intelligence tool for a decision-making process about adult lifelong education. Interactive Learning Environments, 30, 1–9. https://doi.org/10.1080/10494820.2020.1746672
   Web of Science ®Google Scholar
• Pescetelli, N., Rutherford, A., & Rahwan, I. (2020). Diversity Promotes Collective Intelligence in Large Groups but Harms Small Ones. PsyArXiv, https://doi.org/10.31234/osf.io/b8q2c.
   Google Scholar
• Radcliffe, K., Lyson, H. C., Barr-Walker, J., & Sarkar, U. (2019). Collective intelligence in medical decision-making: A systematic scoping review. BMC Medical Informatics and Decision Making, 19(1), 1–11. https://doi.org/10.1186/s12911-019-0882-0
   PubMed Web of Science ®Google Scholar
• Restocchi, V., McGroarty, F., & Gerding, E. (2019). The stylized facts of prediction markets: Analysis of price changes. Physica A: Statistical Mechanics and Its Applications, 515, 159–170. https://doi.org/10.1016/j.physa.2018.09.183
   Web of Science ®Google Scholar
• Russell, P. (2000). The global brain awakens: Our next evolutionary leap. Shaftesbury: Element Books Ltd.
   Google Scholar
• Sandelands, L. E., & Stablein, R. E. (1987). The concept of organization mind. Research in the Sociology of Organizations, 5, 239–255.
   Google Scholar
• Sassi, S., Ivanovic, M., Chbeir, R., Prasath, R., & Manolopoulos, Y. (2022). Collective intelligence and knowledge exploration: An introduction. International Journal of Data Science and Analytics, 14(2), 99–111. https://doi.org/10.1007/s41060-022-00338-9
   PubMedGoogle Scholar
• Silver, I., Mellers, B. A., & Tetlock, P. E. (2021). Wise teamwork: Collective confidence calibration predicts the effectiveness of group discussion. Journal of Experimental Social Psychology, 96, 104157. https://doi.org/10.1016/j.jesp.2021.104157
   Web of Science ®Google Scholar
• Simmons, J. P., Nelson, L. D., Galak, J., & Frederick, S. (2011). Intuitive biases in choice versus estimation: Implications for the wisdom of crowds. Journal of Consumer Research, 38(1), 1–15. https://doi.org/10.1086/658070
   Web of Science ®Google Scholar
• Sitarczyk, M., & Nguyen, N. T. (2021). The impact of the collective members’ independence on the prediction accuracy. IEEE Access, 9, 111975–111984. https://doi.org/10.1109/ACCESS.2021.3102850
   Web of Science ®Google Scholar
• Surowiecki, J. (2005). The wisdom of crowds. Anchor.
   Google Scholar
• Williams, W. M., & Sternberg, R. J. (1988). Group intelligence: Why some groups Are better than others. Intelligence, 12(4), 351–377. https://doi.org/10.1016/0160-2896(88)90002-5
   Web of Science ®Google Scholar
• Woodley, M. A., & Bell, E. (2011). Is collective intelligence (mostly) the general factor of personality? A comment on Woolley, Chabris, Pentland, Hashmi and Malone (2010). Intelligence, 39(2), 79–81. https://doi.org/10.1016/j.intell.2011.01.004
   Web of Science ®Google Scholar
• Woolley, A. W., & Fuchs, E. (2011). Collective intelligence in the organization of science. Organization Science, 22(5), 1359–1367. https://doi.org/10.1287/orsc.1110.0648
   Web of Science ®Google Scholar
• Wu, Z., & Xu, J. (2012). A concise consensus support model for group decision making with reciprocal preference relations based on deviation measures. Fuzzy Sets and Systems, 206, 58–73. https://doi.org/10.1016/j.fss.2012.03.016
   Web of Science ®Google Scholar
• Zhao, M., Zhang, L., & Gu, J. (2020). Emergence of Web collective intelligence and Its impact on technology foresight, pp. 909–915. Proceedings – 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020, Melbourne, Australia, 14-17 December 2020.
   Google Scholar