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Nonparametric inference of interaction laws in systems of agents from trajectory data.

ABSTRACT: Inferring the laws of interaction in agent-based systems from observational data is a fundamental challenge in a wide variety of disciplines. We propose a nonparametric statistical learning approach for distance-based interactions, with no reference or assumption on their analytical form, given data consisting of sampled trajectories of interacting agents. We demonstrate the effectiveness of our estimators both by providing theoretical guarantees that avoid the curse of dimensionality and by testing them on a variety of prototypical systems used in various disciplines. These systems include homogeneous and heterogeneous agent systems, ranging from particle systems in fundamental physics to agent-based systems that model opinion dynamics under the social influence, prey-predator dynamics, flocking and swarming, and phototaxis in cell dynamics.

SUBMITTER: Lu F

PROVIDER: S-EPMC6642354 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Nonparametric inference of interaction laws in systems of agents from trajectory data.

Lu Fei F Zhong Ming M Tang Sui S Maggioni Mauro M

Proceedings of the National Academy of Sciences of the United States of America 20190628 29

Inferring the laws of interaction in agent-based systems from observational data is a fundamental challenge in a wide variety of disciplines. We propose a nonparametric statistical learning approach for distance-based interactions, with no reference or assumption on their analytical form, given data consisting of sampled trajectories of interacting agents. We demonstrate the effectiveness of our estimators both by providing theoretical guarantees that avoid the curse of dimensionality and by tes ...[more]

PMID: 31253708

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