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Markov Chain Aggregation for Agent-Based Models [Paperback]

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  • Category: Books (Technology & Engineering)
  • Author:  Banisch, Sven
  • Author:  Banisch, Sven
  • ISBN-10:  3319796917
  • ISBN-10:  3319796917
  • ISBN-13:  9783319796918
  • ISBN-13:  9783319796918
  • Publisher:  Springer
  • Publisher:  Springer
  • Binding:  Paperback
  • Binding:  Paperback
  • Pub Date:  01-Apr-2018
  • Pub Date:  01-Apr-2018
  • SKU:  3319796917-11-SPRI
  • SKU:  3319796917-11-SPRI
  • Item ID: 101358157
  • List Price: $84.99
  • Seller: ShopSpell
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  • Delivery by: Jul 03 to Jul 05
  • Notes: Brand New Book. Order Now.
This self-contained text develops a Markov chain approach that makes the rigorous analysis of a class of microscopic models that specify the dynamics of complex systems at the individual level possible. It presents a general framework of aggregation in agent-based and related computational models, one which makes use of lumpability and information theory in order to link the micro and macro levels of observation. The starting point is a microscopic Markov chain description of the dynamical process in complete correspondence with the dynamical behavior of the agent-based model (ABM), which is obtained by considering the set of all possible agent configurations as the state space of a huge Markov chain. An explicit formal representation of a resulting micro-chain including microscopic transition rates is derived for a class of models by using the random mapping representation of a Markov process. The type of probability distribution used to implement the stochastic part of the model, which defines the updating rule and governs the dynamics at a Markovian level, plays a crucial part in the analysis of voter-like models used in population genetics, evolutionary game theory and social dynamics. The book demonstrates that the problem of aggregation in ABMs - and the lumpability conditions in particular - can be embedded into a more general framework that employs information theory in order to identify different levels and relevant scales in complex dynamical systems
Introduction.- Background and Concepts.- Agent-based Models as Markov Chains.- The Voter Model with Homogeneous Mixing.- From Network Symmetries to Markov Projections.- Application to the Contrarian Voter Model.- Information-Theoretic Measures for the Non-Markovian Case.- Overlapping Versus Non-Overlapping Generations.- Aggretion and Emergence: A Synthesis.- Conclusion.This self-contained text develops a Markov chain approach that makes the rigorous analysis of a class of microscopic models thatl#-
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