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Toward a Theory of Markov Influence Systems and their Renormalization

Author Bernard Chazelle

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Keywords
  • Markov influence systems
  • nonlinear Markov chains
  • dynamical systems
  • renormalization
  • graph sequence parsing

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Abstract

Nonlinear Markov chains are probabilistic models commonly used in physics, biology, and the social sciences. In "Markov influence systems" (MIS), the transition probabilities of the chains change as a function of the current state distribution. This work introduces a renormalization framework for analyzing the dynamics of MIS. It comes in two independent parts: first, we generalize the standard classification of Markov chain states to the dynamic case by showing how to "parse" graph sequences. We then use this framework to
carry out the bifurcation analysis of a few important MIS families.
In particular, we show that irreducible MIS are almost always
asymptotically periodic. We also give an example of "hyper-torpid" mixing, where a stationary distribution is reached in super-exponential time, a timescale that cannot be achieved by any Markov chain.

Cite As Get BibTex

Bernard Chazelle. Toward a Theory of Markov Influence Systems and their Renormalization. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 94, pp. 58:1-58:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.ITCS.2018.58

Author Details

Bernard Chazelle

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