Understanding how social influence
shapes biological processes is a central challenge in contemporary science,
essential for achieving progress in a variety of fields ranging from the
organization and evolution of coordinated collective action among cells, or
animals, to the dynamics of information exchange in human societies. Using an
integrated experimental and theoretical approach I will address how, and why,
animals exhibit highly-coordinated collective behavior. I will demonstrate new
imaging technology that allows us to reconstruct (automatically) the dynamic,
time-varying networks that correspond to the visual cues employed by organisms
when making movement decisions . Sensory networks are shown to provide a
much more accurate representation of how social influence propagates in groups,
and their analysis allows us to identify, for any instant in time, the most
socially-influential individuals within groups, and to predict the magnitude of
complex behavioral cascades before they actually occur . I will also
investigate the coupling between spatial and information dynamics in groups and
reveal that emergent problem solving is the predominant mechanism by which
mobile groups sense, and respond to complex environmental gradients .
Evolutionary modeling demonstrates such ‘physical computation’ readily evolves
within populations of selfish organisms, and allowing individuals to compute
collectively the spatial distribution of resources and to allocate themselves
effectively among distinct, and distant, resource patches, without requiring
information about the number, location or size of patches . Finally I will
reveal the critical role uninformed, or unbiased, individuals play in effecting
fast and democratic consensus decision-making in collectives [5-7], and will
test these predictions with experiments involving schooling fish  and wild
1) Strandburg-Peshkin, A., Twomey, C.R.,
Bode, N.W., Kao, A.B., Katz, Y., Ioannou, C.C., Rosenthal, S.B., Torney, C.J.,
Wu, H., Levin, S.A. & Couzin, I.D. (2013) Visual sensory networks and
effective information transfer in animal groups, Current Biology 23(17),
2) Rosenthal, S.B., Twomey, C.R.,
Hartnett, A.T., Wu, H.S. & Couzin, I.D. (2015) Revealing the hidden
networks of interaction in mobile animal groups allows prediction of complex
behavioral contagion, PNAS 112(15), 4690-4695.
3) Berdahl, A., Torney, C.J., Ioannou,
C.C., Faria, J. & Couzin, I.D. (2013) Emergent sensing of complex
environments by mobile animal groups, Science 339(6119) 574-576.
4) Hein, A. M., Rosenthal, S.B.,
Hagstron, G.I., Berdahl, A., Torney, C.J. & Couzin, I.D. (2015) The
evolution of distributed sensing and collective computation in animal
populations, eLife e10955.
5) Couzin, I.D., Krause, J., Franks,
N.R. & Levin, S.A. (2005) Effective leadership and decision making in
animal groups on the move. Nature 433, 513-516.
6) Couzin, I.D., Ioannou, C.C., Demirel,
G., Gross, T., Torney, C.J., Hartnett, A., Conradt, L., Levin, S.A. &
Leonard, N.E. (2011) Uninformed individuals promote democratic consensus in
animal groups. Science 334(6062) 1578-1580.
7) Strandburg-Peshkin, A., Farine, D.R.,
Couzin, I.D. & Crofoot, M.C. (2015) Shared decision-making drives
collective movement in wild baboons. Science 348(6241), 1358-1361.
8) Strandburg-Peshkin, A., Farine, D.R.,
Crofoot, M. C. & Couzin, I.D. (2017) Habitat and social factors shape
individual decisions and emergent group structure during baboon collective
movement, eLife 6:e19505.