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Markov Chain Aggregation for Agent-based Models (Hardcover) (Sven Banisch)

Markov Chain Aggregation for Agent-based Models (Hardcover) (Sven Banisch) - image 1 of 1

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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 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
Genre: Science, Mathematics
Sub-Genre: Mathematical Physics, Chaotic Behavior in Systems, Applied
Series Title: Understanding Complex Systems
Format: Hardcover
Publisher: Springer Verlag
Author: Sven Banisch
Language: English
Street Date: January 5, 2016
TCIN: 50901324
UPC: 9783319248752
Item Number (DPCI): 248-12-5056

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