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Likelihood category game model for knowledge consensus

Author

Listed:
  • Fan, Zhong-Yan
  • Lai, Ying-Cheng
  • Tang, Wallace Kit-Sang

Abstract

To reach consensus among interacting agents is a problem of interest for social, economical, and political systems. To investigate consensus dynamics, naming game, as a computational and mathematical framework, is commonly used. Existing works mainly focus on the consensus process of vocabulary evolution in a population of agents. However, in real-world cases, naming is not an independent process but relies on perception and categorization. In order to name an object, agents must first distinguish the object according to its features. We thus articulate a likelihood category game model (LCGM) to integrate feature learning and the naming process. In the LCGM, self-organized agents can define category based on acquired knowledge through learning and use likelihood estimation to distinguish objects. The information communicated among the agents is no longer simply in some form of absolute answer, but involves one’s self perception and determination. With its distinguished features, LCGM allows coexistence of multiple categories for an observation. It also provides quantitative explanation that consensus is hard to be reached among serious agents who have a more complex knowledge formation. The proposed LCGM and this study are able to provide new insights into the emergence and evolution of consensus in complex systems.

Suggested Citation

  • Fan, Zhong-Yan & Lai, Ying-Cheng & Tang, Wallace Kit-Sang, 2020. "Likelihood category game model for knowledge consensus," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    • Handle: RePEc:eee:phsmap:v:540:y:2020:i:c:s0378437119317066
    DOI: 10.1016/j.physa.2019.123022
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    References listed on IDEAS

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    1. Lou, Yang & Chen, Guanrong & Fan, Zhengping & Xiang, Luna, 2018. "Local communities obstruct global consensus: Naming game on multi-local-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1741-1752.
    2. Lou, Yang & Chen, Guanrong & Hu, Jianwei, 2018. "Communicating with sentences: A multi-word naming game model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 857-868.
    3. Qiming Lu & G. Korniss & Boleslaw Szymanski, 2009. "The Naming Game in social networks: community formation and consensus engineering," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 4(2), pages 221-235, November.
    4. Fu, Guiyuan & Cai, Yunze & Zhang, Weidong, 2017. "Analysis of naming game over networks in the presence of memory loss," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 350-361.
    5. Zhou, Jianfeng & Lou, Yang & Chen, Guanrong & Tang, Wallace K.S., 2018. "Multi-language naming game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 620-634.
    6. Andrea Baronchelli & Vittorio Loreto & Andrea Puglisi, 2015. "Individual Biases, Cultural Evolution, and the Statistical Nature of Language Universals: The Case of Colour Naming Systems," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-19, May.
    7. Animesh Mukherjee & Francesca Tria & Andrea Baronchelli & Andrea Puglisi & Vittorio Loreto, 2011. "Aging in Language Dynamics," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-7, February.
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