Markov Chain Aggregation for Agent-Based Models (Hardcover, 1st ed. 2016)

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

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Understanding Complex Systems
Release date:	2016
First published:	2016
Authors:	Sven Banisch
Dimensions:	235 x 155 x 18mm (L x W x T)
Format:	Hardcover
Pages:	195
Edition:	1st ed. 2016
ISBN-13:	978-3-319-24875-2
Categories:	Books > Science & Mathematics > Physics > General Books > Reference & Interdisciplinary > Communication studies > Information theory > Cybernetics & systems theory Books > Science & Mathematics > Mathematics > Applied mathematics > Non-linear science
LSN:	3-319-24875-8
Barcode:	9783319248752

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