國立虎尾科技大學 |

Markov Chain Aggregation for Agent-Based Models

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Markov Chain Aggregation for Agent-Based Models/ by Sven Banisch.
作者:	Banisch, Sven.
面頁冊數:	XIV, 195 p. 83 illus., 18 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Statistical physics. -
電子資源:	https://doi.org/10.1007/978-3-319-24877-6
ISBN:	9783319248776

Markov Chain Aggregation for Agent-Based Models
Banisch, Sven.

Markov Chain Aggregation for Agent-Based Models[electronic resource] /by Sven Banisch. - 1st ed. 2016. - XIV, 195 p. 83 illus., 18 illus. in color.online resource. - Understanding Complex Systems,1860-0832. - Understanding Complex 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 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.

ISBN: 9783319248776

Standard No.: 10.1007/978-3-319-24877-6doiSubjects--Topical Terms:

528048
Statistical physics.

LC Class. No.: QC174.7-175.36

Dewey Class. No.: 621

Markov Chain Aggregation for Agent-Based Models
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520 $a 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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