| 书目名称 | Markov Chain Aggregation for Agent-Based Models |
| 编辑 | Sven Banisch |
| 视频video | |
| 概述 | Introduces and describes a new approach for modelling certain types of complex dynamical systems.Self-contained presentation and introductory level.Useful as advanced text and as self-study guide.Incl |
| 丛书名称 | Understanding Complex Systems |
| 图书封面 |  |
| 描述 | 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 soci |
| 出版日期 | Book 2016 |
| 关键词 | Agent-based Modelling; Contrarian Voter Model; Dynamics of Complex Systems; Lumpability and State-space |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-24877-6 |
| isbn_softcover | 978-3-319-79691-8 |
| isbn_ebook | 978-3-319-24877-6Series ISSN 1860-0832 Series E-ISSN 1860-0840 |
| issn_series | 1860-0832 |
| copyright | Springer International Publishing Switzerland 2016 |
发表于 2025-3-21 17:02:42
