Markov Chain Aggregation for Agent-Based Models
Produktnummer:
1862ef49d593a24bfdbe427c29de40869d
Autor: | Banisch, Sven |
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Themengebiete: | Agent-based Modelling Contrarian Voter Model Dynamics of Complex Systems Lumpability and State-space Reduction Markov Processes Microscopic Markov Chains Scaling of Complex Dynamical Systems Voter-like Models complexity |
Veröffentlichungsdatum: | 05.01.2016 |
EAN: | 9783319248752 |
Sprache: | Englisch |
Seitenzahl: | 195 |
Produktart: | Gebunden |
Verlag: | Springer International Publishing |
Produktinformationen "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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