MiS Preprint Repository

Delve into the future of research at MiS with our preprint repository. Our scientists are making groundbreaking discoveries and sharing their latest findings before they are published. Explore repository to stay up-to-date on the newest developments and breakthroughs.

MiS Preprint

The Information Bottleneck Method for Optimal Prediction of Multilevel Agent-based Systems

Robin Lamarche-Perrin, Sven Banisch and Eckehard Olbrich


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 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, we analyse the solution space of the resulting optimisation 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 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.

Sep 2, 2015
Sep 4, 2015
MSC Codes:
60G25, 62M20, 60J10, 62B10, 94A17, 94A15
information theory, Information Bottleneck, Efficient Prediction, Multilevel Systems, agent-based models, Voter Model

Related publications

2016 Repository Open Access
Robin Lamarche-Perrin, Sven Banisch and Eckehard Olbrich

The information Bottleneck method for optimal prediction of multilevel agent-based systems

In: Advances in complex systems, 19 (2016) 1-2, p. 1650002