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Více informací o knize Markov Chain Aggregation for Agent-Based Models

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Anotace knihy

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§

Parametry knihy

Zařazení knihy Knihy v angličtině Reference, information & interdisciplinary subjects Research & information: general Information theory

• Plný název: Markov Chain Aggregation for Agent-Based Models
• Autor: Sven Banisch
• Jazyk: Angličtina
• Vazba: Pevná
• Počet stran: 195
• EAN: 9783319248752
• ISBN: 3319248758
• ID: 09523682
• Nakladatelství: Springer International Publishing AG
• Hmotnost: 472 g
• Rozměry: 167 × 244 × 18 mm
• Datum vydání: 22. December 2015

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