Inferring individual rules from collective behavior

By Ryan Lukeman, Yue-Xian Li, L. Edelstein-Keshet. Proceedings of the National Academy of Sciences of the United States of America, 2010

412 citations recorded by the source corpus.

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Deciphering Interactions in Moving Animal Groups (2012, methodology): “[10], this model building amounts to writing down a fairly complicated structure a priori, involving many implicit assumptions, and to fit collective data to determine effective parameters, yielding a best-fit model.”
Active Brownian particles (2012, background): “This enables not only the statistical analysis of collective motion in nature [50] but allows also to infer the interaction rules between individuals [17,55,220,183,157].”
Swarm dynamics and equilibria for a nonlocal aggregation model (2011, background): “Uniform swarms are often observed in biology; however, the precise mechanisms of their formation are rarely known (but see a recent study [32] which attempts to establish some of these mechanisms through empirical measurements).”
Inferring the structure and dynamics of interactions in schooling fish (2011, methodology): “(25) fit data on the spatial conurations of surf scoters to a zonal model and identified best-fit parameter values as well as evidence for an additional frontal zone of interaction.”
Collective States, Multistability and Transitional Behavior in Schooling Fish (2013, result): “Similar evidence exists for aggregating ducks [19] and swarming locusts [20].”