NitroFlare Markov Chain Aggregation for Agent-Based Models

Discussion in 'E-Books & Tutorials' started by kocogi, Dec 25, 2015.

  1. kocogi

    kocogi Active Member

    Joined:
    May 29, 2012
    Messages:
    17,043
    Likes Received:
    12
    Trophy Points:
    38
    [​IMG]

    Markov Chain Aggregation for Agent-Based Models
    Springer | Statistical Physics & Dynamical Systems | January 20, 2016 | ISBN-10: 3319248758 | 195 pages | pdf | 5.6 mb

    Authors: Banisch, Sven
    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


    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

    Number of Illustrations and Tables
    65 illus., 18 in colour
    Topics
    Nonlinear Dynamics
    Complex Systems
    Mathematical Methods in Physics
    Complexity

    More info and Hardcover at Springer


    Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me
    Code:
    Download ( NitroFlare )
    http://nitroflare.com/view/6B10704742F00F8/ewlfh.M.C.A.f.A.M.rar
    
    
    
    
    
    
    
    Oboom
    https://www.xxxxxxxx/XBUQ896H/ewlfh.M.C.A.f.A.M.rar
     

Share This Page