ArticleProceedings of the National Academy of Sciences of the United States of America2019
Nonparametric inference of interaction laws in systems of agents from trajectory data.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Inverse Statistics of Active Matter Trajectories to Distinguish Interaction Kernel Anisotropy from Emergent Correlations.Bulletin of mathematical biology · 2026Article
- Data-driven analysis of fine-scale badger movement in the UK.PLoS computational biology · 2025Article
- Swarm shepherding using bearing-only measurements.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Article
- Data-Driven Model Reduction for Stochastic Burgers Equations.Entropy (Basel, Switzerland) · 2020Article
- Bayesian differential programming for robust systems identification under uncertainty.Proceedings. Mathematical, physical, and engineering sciences · 2020Article
- Data-driven discovery of emergent behaviors in collective dynamics.Physica D. Nonlinear phenomena · 2020Article
- Analyzing collective motion with machine learning and topology.Chaos (Woodbury, N.Y.) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.