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portada Markov Chain Aggregation for Agent-Based Models
Type
Physical Book
Publisher
Author
Year
2016
Language
English
Pages
212
Format
Paperback
Dimensions
23.4x15.6 cm
ISBN13
9783319248769

Markov Chain Aggregation for Agent-Based Models

Sven Banisch (Author) · Springer Nature B.V. · Paperback

Markov Chain Aggregation for Agent-Based Models - Sven Banisch

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Synopsis "Markov Chain Aggregation for Agent-Based Models"

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

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