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Truth and Cognitive Division of Labour: First Steps Towards a Computer Aided Social Epistemology

Author

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  • Rainer Hegselmann
  • Ulrich Krause

Abstract

The paper analyzes the chances for the truth to be found and broadly accepted under conditions of cognitive division of labour combined with a social exchange process. Cognitive division of labour means, that only some individuals are active truth seekers, possibly with different capacities. The social exchange process consists in an exchange of opinions between all individuals, whether truth seekers or not. We de- velop a model which is investigated by both, mathematical tools and computer simulations. As an analytical result the Funnel theorem states that under rather weak conditions on the social process a consensus on the truth will be reached if all individuals posses an arbitrarily small inclination for truth seeking. The Leading the pack theorem states that under certain conditions even a single truth seeker may lead all individuals to the truth. Systematic simulations analyze how close and how fast groups can get to the truth depending on the frequency of truth seekers, their capacities as truth seekers, the position of the truth (more to the extreme or more in the centre of an opinion space), and the willingness to take into account the opinions of others when exchanging and updating opinions. A tricky movie visualizes simulations results in a parameter space of higher dimensions.

Suggested Citation

  • Rainer Hegselmann & Ulrich Krause, 2006. "Truth and Cognitive Division of Labour: First Steps Towards a Computer Aided Social Epistemology," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(3), pages 1-10.
    • Handle: RePEc:jas:jasssj:2005-45-2
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    References listed on IDEAS

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    1. Katarzyna Sznajd-Weron & Józef Sznajd, 2000. "Opinion Evolution In Closed Community," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 11(06), pages 1157-1165.
    2. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    3. Gérard Weisbuch & Guillaume Deffuant & Frederic Amblard & Jean Pierre Nadal, 2001. "Interacting Agents and Continuous Opinions Dynamics," Working Papers 01-11-072, Santa Fe Institute.
    4. Lorenz, Jan, 2005. "A stabilization theorem for dynamics of continuous opinions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 217-223.
    5. Rainer Hegselmann & Ulrich Krause, 2005. "Opinion Dynamics Driven by Various Ways of Averaging," Computational Economics, Springer;Society for Computational Economics, vol. 25(4), pages 381-405, June.
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    Cited by:

    1. Kevin JS. Zollman, 2012. "Social network structure and the achievement of consensus," Politics, Philosophy & Economics, , vol. 11(1), pages 26-44, February.
    2. Edoardo Baccini & Zoé Christoff & Stephan Hartmann & Rineke Verbrugge, 2023. "The Wisdom of the Small Crowd: Myside Bias and Group Discussion," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 26(4), pages 1-7.
    3. Glass, Catherine A. & Glass, David H., 2021. "Opinion dynamics of social learning with a conflicting source," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    4. Rainer Hegselmann & Stefan König & Sascha Kurz & Christoph Niemann & Jörg Rambau, 2015. "Optimal Opinion Control: The Campaign Problem," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(3), pages 1-18.
    5. Diao, Su-Meng & Liu, Yun & Zeng, Qing-An & Luo, Gui-Xun & Xiong, Fei, 2014. "A novel opinion dynamics model based on expanded observation ranges and individuals’ social influences in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 220-228.
    6. Christoph Merdes, 2017. "Growing Unpopular Norms," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(3), pages 1-5.
    7. Catherine A. Glass & David H. Glass, 2021. "Social Influence of Competing Groups and Leaders in Opinion Dynamics," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 799-823, October.
    8. Liu, Qipeng & Wang, Xiaofan, 2013. "Social learning with bounded confidence and heterogeneous agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2368-2374.

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