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Markov Chain Aggregation for Agent-Based Models

  • Livre Relié
  • 212 Nombre de pages
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This self-contained text develops a Markov chain approach that makes the rigorous analysis of a class of microscopic models that s... Lire la suite
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Description

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


Introduces and describes a new approach for modelling certain types of complex dynamical systems

Self-contained presentation and introductory level

Useful as advanced text and as self-study guide



Contenu
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.

Informations sur le produit

Titre: Markov Chain Aggregation for Agent-Based Models
Auteur:
Code EAN: 9783319248752
ISBN: 3319248758
Format: Livre Relié
Editeur: Springer International Publishing
nombre de pages: 212
Poids: 489g
Taille: H241mm x B160mm x T17mm
Année: 2016
Auflage: 1st ed. 2016

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