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Broken Detailed Balance and Non-Equilibrium Dynamics in Noisy Social Learning Models

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  • Tushar Vaidya
  • Thiparat Chotibut
  • Georgios Piliouras

Abstract

We propose new Degroot-type social learning models with feedback in a continuous time, to investigate the effect of a noisy information source on consensus formation in a social network. Unlike the standard Degroot framework, noisy information models destroy consensus formation. On the other hand, the noisy opinion dynamics converge to the equilibrium distribution that encapsulates correlations among agents' opinions. Interestingly, such an equilibrium distribution is also a non-equilibrium steady state (NESS) with a non-zero probabilistic current loop. Thus, noisy information source leads to a NESS at long times that encodes persistent correlated opinion dynamics of learning agents. Our model provides a simple realization of NESS in the context of social learning. Other phenomena such as synchronization of opinions when agents are subject to a common noise are also studied.

Suggested Citation

  • Tushar Vaidya & Thiparat Chotibut & Georgios Piliouras, 2019. "Broken Detailed Balance and Non-Equilibrium Dynamics in Noisy Social Learning Models," Papers 1906.11481, arXiv.org, revised May 2020.
  • Handle: RePEc:arx:papers:1906.11481
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    References listed on IDEAS

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    1. 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.
    2. Arun G. Chandrasekhar & Horacio Larreguy & Juan Pablo Xandri, 2015. "Testing Models of Social Learning on Networks: Evidence from a Lab Experiment in the Field," NBER Working Papers 21468, National Bureau of Economic Research, Inc.
    3. An, Ing & Chen, Shi & Guo, Han-ying, 1984. "Search for the symmetry of the Fokker-Planck equation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 128(3), pages 520-528.
    4. Bindel, David & Kleinberg, Jon & Oren, Sigal, 2015. "How bad is forming your own opinion?," Games and Economic Behavior, Elsevier, vol. 92(C), pages 248-265.
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