Intelligent Collectives: Impact of Independence on Collective Performance

References

• Arrow, J. K., R. Forsythe, M. Gorham, R. Hahn, R. Hanson, O. J. Ledyard, S. Levmore, R. Litan, P. Milgrom, D. F. Nelson, et al. 2008. The promise of prediction markets. Science (New York, N.Y.) 320 (5878):877–8.
   PubMed Web of Science ®Google Scholar
• Berg, J., R. Forsythe, F. Nelson, and T. Rietz. 2008. Chapter 80 results from a dozen years of election futures markets research (C. R. Plott and V. L. B. T.-H. of E. E. R. Smith eds.). Vol. 1, 742–51. Amsterdam, The Netherlands: Elsevier.
   Google Scholar
• Bernstein, E., J. Shore, and D. Lazer. 2018. How intermittent breaks in interaction improve collective intelligence. Proceedings of the National Academy of Sciences of the United States of America 115 (35):8734–9. doi:10.1073/pnas.1802407115.
   PubMed Web of Science ®Google Scholar
• Bhatt, S., B. Minnery, S. Nadella, B. Bullemer, V. Shalin, and A. Sheth. 2017. Enhancing crowd wisdom using measures of diversity computed from social media data. doi:10.1145/3106426.3106491.
   Google Scholar
• Bhatt, S., K. Chen, V. L. Shalin, A. P. Sheth, and B. Minnery. 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 (01 SE-Full Papers), 103–13.
   Google Scholar
• Centola, D. 2022. The network science of collective intelligence. Trends in Cognitive Sciences 26 (11):923–41. doi:10.1016/j.tics.2022.08.009.
   PubMed Web of Science ®Google Scholar
• Dortheimer, J. 2022. Collective intelligence in design crowdsourcing. Mathematics 10 (4):539. doi:10.3390/math10040539.
   Web of Science ®Google Scholar
• Duong, T. H., N. T. Nguyen, and G. S. Jo. 2008. A Method for integration of wordnet-based ontologies using distance measures. Proceedings of KES 2008. Lecture Notes in Artificial Intelligence 5177:210–9.
   Google Scholar
• Erdős, P., and A. Rényi. 1960. On the evolution of random graphs. Publication of the Mathematical Institute of the Hungarian Academy of Sciences 5 (1):17–60.
   Google Scholar
• Hong, H., Q. Du, G. Wang, W. Fan, and D. Xu. 2016. Crowd wisdom: The impact of opinion diversity and participant independence on crowd performance. BT – 22nd Americas Conference on Information Systems, AMCIS 2016, San Diego, CA, August 11–14, 2016. http://aisel.aisnet.org/amcis2016/Social/Presentations/11.
   Google Scholar
• Hong, H., Q. Ye, Q. Du, G. A. Wang, and W. Fan. 2020. Crowd characteristics and crowd wisdom: Evidence from an online investment community. Journal of the Association for Information Science and Technology 71 (4):423–35. doi:10.1002/asi.24255.
   Web of Science ®Google Scholar
• Kawamura, H., and A. Ohuchi. 2000. Evolutionary emergence of collective intelligence with artificial pheromone communication. 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies, 4, 2831–2836. doi:10.1109/IECON.2000.972447.
   Google Scholar
• Lorenz, J., H. Rauhut, F. Schweitzer, and D. Helbing. 2011. How social influence can undermine the wisdom of crowd effect. Proceedings of the National Academy of Sciences of the United States of America 108 (22):9020–5. doi:10.1073/pnas.1008636108.
   PubMed Web of Science ®Google Scholar
• Malone, W. T., and S. M. Bernstein. 2015. Handbook of collective intelligence. Cambridge: The MIT Press.
   Google Scholar
• Narayanan, A., M. S. Korium, D. C. Melgarejo, H. M. Hussain, A. S. D. Sena, P. E. G. Silva, D. Gutierrez-Rojas, M. Ullah, A. E. Nezhad, M. Rasti, et al. 2022. Collective intelligence using 5G: Concepts, applications, and challenges in sociotechnical environments. IEEE Access. 10:70394–417. doi:10.1109/ACCESS.2022.3184035.
   Web of Science ®Google Scholar
• Nguyen, N. T. 2006. Conflicts of ontologies – Classification and consensus-based methods for resolving. Proceedings of KES 2006, Lecture Notes in Artificial Intelligence 4252:267–74.
   Google Scholar
• Nguyen, N. T. 2008. Advanced methods for inconsistent knowledge management. London: Springer. doi:10.1007/978-1-84628-889-0.
   Google Scholar
• Nguyen, N. T. 2009. Processing inconsistency of knowledge in determining knowledge of a collective. Cybernetics and Systems 40 (8):670–88. doi:10.1080/01969720903294593.
   Web of Science ®Google Scholar
• Nguyen, N. T., and J. Sobecki. 2003. Using consensus methods to construct adaptive interfaces in multimodal web-based systems. Universal Access in the Information Society 2 (4):342–58. doi:10.1007/s10209-003-0050-1.
   Google Scholar
• Nguyen, V. D., and N. T. Nguyen. 2017. The impact of diversity on the quality of collective prediction. BT – IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017, Gdynia, Poland, July 3–5, 2017 (pp. 149–154). doi:10.1109/INISTA.2017.8001148.
   Google Scholar
• Nguyen, V. D., and N. T. Nguyen. 2018. Intelligent collectives: Theory, applications, and research challenges. Cybernetics and Systems 49 (5-6):261–79. doi:10.1080/01969722.2017.1418254.
   Web of Science ®Google Scholar
• Nik, V. M., and A. Moazami. 2021. Using collective intelligence to enhance demand flexibility and climate resilience in urban areas. Applied Energy 281:116106. doi:10.1016/j.apenergy.2020.116106.
   Web of Science ®Google Scholar
• Nofer, M., and O. Hinz. 2014. Are crowds on the internet wiser than experts? The case of a stock prediction community. Journal of Business Economics 84:303–38.
   Google Scholar
• Page, S. E. 2007. The difference: How the power of diversity creates better groups, firms, schools, and societies. Princeton University Press, Princeton, NJ.
   Google Scholar
• Palak, R., and N. T. Nguyen. 2019a. An independence measure for expert collections based on social media profiles. In Proc. of ACIIDS 2019 (pp. 15–25).
   Google Scholar
• Palak, R., and N. T. Nguyen. 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 (pp. 82–87). doi:10.1109/SMC.2019.8914290.
   Google Scholar
• Passig, D., and L. Zoref. 2022. Using social networks as a collective intelligence tool for a decision-making process about adult lifelong education. Interactive Learning Environments 30 (9):1717–25. doi:10.1080/10494820.2020.1746672.
   Web of Science ®Google Scholar
• Pietranik, M., and N. T. Nguyen. 2014. A multi-atrribute based framework for ontology aligning. Neurocomputing 146:276–90. doi:10.1016/j.neucom.2014.03.067.
   Web of Science ®Google Scholar
• Sassi, S., M. Ivanovic, R. Chbeir, R. Prasath, and Y. Manolopoulos. 2022. Collective intelligence and knowledge exploration: an introduction. International Journal of Data Science and Analytics 14 (2):99–111. doi:10.1007/s41060-022-00338-9.
   PubMedGoogle Scholar
• Shafiq, S. I., C. Sanín, and E. Szczerbicki. 2022. Decisional DNA (DDNA) based machine monitoring and total productive maintenance in industry 4.0 framework. Cybernetics and Systems 53 (5):510–9. doi:10.1080/01969722.2021.2018549.
   Web of Science ®Google Scholar
• Sitarczyk, M., and N. T. Nguyen. 2021. The impact of the collective members’ independence on the prediction accuracy. IEEE Access. 9:111975–84. doi:10.1109/ACCESS.2021.3102850.
   Web of Science ®Google Scholar
• Sliwko, L., and N. T. Nguyen. 2007. Using multi-agent systems and consensus methods for information retrieval in internet. International Journal of Intelligent Information and Database Systems 1 (2):181–98. doi:10.1504/IJIIDS.2007.014949.
   Google Scholar
• Sunstein, C. R. 2006. Infotopia: How many minds produce knowledge. New York, NY: Oxford University Press, Inc.
   Google Scholar
• Surowiecki, J. 2005. The wisdom of crowds. New York: Doubleday/Anchor.
   Google Scholar
• Wolfers, J., and E. Zitzewitz. 2004. Prediction markets. Journal of Economic Perspectives 18 (2):107–26. doi:10.1257/0895330041371321.
   Web of Science ®Google Scholar
• Wu, Z., and J. Xu. 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. doi:10.1016/j.fss.2012.03.016.
   Web of Science ®Google Scholar