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. 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.