Markov Chain Aggregation for Agent-Based Models

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The book introduces a Markov chain approach for analyzing microscopic models of complex systems at the individual level. It establishes a framework for aggregation in agent-based models, utilizing lumpability and information theory to connect micro and macro observations. A detailed "micro-chain" representation is developed, focusing on transition rates and the stochastic elements that govern dynamics in "voter-like" models relevant to population genetics and social dynamics. The work emphasizes how aggregation issues in ABMs can be understood through a broader informational context, enhancing the analysis of complex systems.