1. The Information Bottleneck Method for Optimal Prediction of Multilevel Agent-based Systems
- Author
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Robin Lamarche-Perrin, Sven Banisch, Eckehard Olbrich, Max Planck Institute for Mathematics in the Sciences (MPI-MiS), Max-Planck-Gesellschaft, and European Project: 318723,EC:FP7:ICT,FP7-ICT-2011-8,MATHEMACS(2012)
- Subjects
Structure (mathematical logic) ,Mathematical optimization ,Optimization problem ,Theoretical computer science ,Information Bottleneck ,Multilevel Systems ,Agent-based Models ,Voter Model ,[MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS] ,Complex system ,Voter model ,Information Theory ,Information bottleneck method ,Efficient Prediction ,Information theory ,Network topology ,01 natural sciences ,010305 fluids & plasmas ,System dynamics ,Control and Systems Engineering ,[INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT] ,0103 physical sciences ,010306 general physics ,Mathematics - Abstract
Because the dynamics of complex systems is the result of both decisive local events and reinforced global effects, the prediction of such systems could not do without a genuine multilevel approach. This paper proposes to found such an approach on information theory. Starting from a complete microscopic description of the system dynamics, we are looking for observables of the current state that allows to efficiently predict future observables. Using the framework of the information bottleneck (IB) method, we relate optimality to two aspects: the complexity and the predictive capacity of the retained measurement. Then, with a focus on agent-based models (ABMs), we analyze the solution space of the resulting optimization problem in a generic fashion. We show that, when dealing with a class of feasible measurements that are consistent with the agent structure, this solution space has interesting algebraic properties that can be exploited to efficiently solve the problem. We then present results of this general framework for the voter model (VM) with several topologies and show that, especially when predicting the state of some sub-part of the system, multilevel measurements turn out to be the optimal predictors.
- Published
- 2016