Collective memory and spatial sorting in animal groups.

By Iain D. Couzin, Jens Krause, Richard James, Graeme D. Ruxton, Nigel R. Franks. Journal of Theoretical Biology, 2002

2,132 citations recorded by the source corpus.

Subfields: Ecology, Evolution, Behavior and Systematics, Molecular Biology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/0280ea23d90ed2482ac6642011bbe8aa3e0ef9ad

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Citations in the corpus

Collective cognition in animal groups. (2009, background): “At intermediate ranges, two different collective behaviors exist for the same individual behavior; which one is found depends on previous group structure [ 10 ].”
Human Interaction With Robot Swarms: A Survey (2016, background): “In [24] the wide range of behaviors that can be generated with a simple flocking algorithm given different parameters”
Self-organised complex aerial displays of thousands of starlings: a model (2009, background): “This differs from a metric range of interaction in which individuals interact with all others in a fixed metric distance as has been the usual implementation in models of swarming (Couzin et al., 2002; Hemelrijk and Kunz, 2005; Huth and Wissel, 1994; Romey, 1995).”
Inferring the structure and dynamics of interactions in schooling fish (2011, background): “Inmodels, themost commonassumption is that animals compute theirwould-be response to each of their neighbors separately and then average these pairwise responses (with added noise) to arrive at movement decisions (4, 7) (although see ref.”
Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study (2007, background): “physics (5), control theory (6), mobile robotics (7), economics (8), and social sciences (9). How does collective behaviour emerge? Numerical models of self-organized motion, inspired both by biology (10,11,12,13,14,15,16), and by physics (17,18,19), support the idea that simple rules of interaction”
Multi-Agent Coordination by Decentralized Estimation and Control (2008, background): “demonstrated in a number of models of schooling and flocking [8], [33], [38], [40], there are few tools to guide the design of local control laws to achieve a particular desired collective behavior.”
Swarming Patterns in a Two-Dimensional Kinematic Model for Biological Groups (2004, methodology): “Similar vortex-like states have also been observed in discrete dynamic swarming models such as those in [15, 8].”
Large Scale Dynamics of the Persistent Turning Walker Model of Fish Behavior (2007, methodology): “One previously proposed models to describe fish behavior is the discrete Vicsek model (VM) [1,14, 31,51] (see also [2,10,25,27,39,42,43] for related models).”
Coherent Pattern Prediction in Swarms of Delay-Coupled Agents (2012, background): “Some of these studies have treated the problem at a single-individual level, using ordinary differential equations (ODEs) or delay differential equations to describe their trajectories [10], [20]–[22].”
Safety in numbers: the dilution effect and other drivers of group life in the face of danger (2016, background): “However, if individuals are able to vary their spatial positioning, which is common in groups (Couzin et al. 2002) , the average safety in selfish groups can be higher than for solitary individuals (Rubenstein 1978).”
Collective States, Multistability and Transitional Behavior in Schooling Fish (2013, methodology): “Nevertheless, the concept that local interactions reduce to common non-equilibrium collective states is a key insight provided by computational modeling of collective animal behavior [3,4,15] which relates more generally to phase transition theory in non-equilibrium systems [16].”