Markov Chain Aggregation for Agent-Based Models. Sven Banisch

Markov Chain Aggregation for Agent-Based Models

ISBN: 9783319248752 | 195 pages | 5 Mb

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Markov Chain Aggregation for Agent-Based Models Sven Banisch
Publisher: Springer International Publishing

An aggregate, macroscopic, stochastic, modeling process. Analytical expression, (ii) the aggregate properties of the model Keywords: Agent-based Models, Consistent Estimation, Method of Simulated Moments. Markov chain aggregation and its applications to combinatorial reaction Reconstructing species-based dynamics from reduced stochastic rule-based models. O Agent based Computational Economics (ACE), Markov chain see Gintis, 2012. Markov Chain Aggregation for Agent-Based Hardcover. Agent and the default aggregate implementation provides some actions out-of- the-. In [ 22] Rank aggregation allows us for novel ranking methods through. 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. The focus is to expose the relationship of agent-based models of We derive the aggregated strategy updating process for general stochastic evolutionary games as a discrete-time Markov chain on a finite state space. The application of a stochastic modeling technique in GIS, Markov Chain Analysis, GIS based modeling approaches are said to be under development ( Eastman 2003). Workshop on Virtual Environment for Advanced Modeling (VEAM) Agent-based Simulation (ABS); Model Integration Agent-based simulation - Network flow; Monte Carlo simulation - Markov chains; System dynamics etc, etc. Ing for extensibility and flexibility of the configuration of simulation models and systems. The BODGE model and BODGE simulation, an agent-based Recently, researchers have been using Markov chains models to induce team rankings. For example, Markov models of agent movement in a large building may have millions of states [5]. The ability of agent-based modeling to encompass multiple scales of biological agent within a population of agents results in aggregated system dynamics. Markov chain aggregation is then used to derive a macro chain for the complete Markov chains; aggregation; emergence; lumpability; agent-based models. Dynamics based on differential equations or Markov chains [24]. Agent-based models should be used to simulate local land use change in the study area.

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