Adaptive network models of collective decision making
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Adaptive network models of collective decision making
Güven Demirel

22 December 2011, G035, 13:40


Networks have been used as a metaphor for describing complex systems
from a vast range of fields including sociology, management science, biology and
physics. Many studies considered the dynamics of networks, where the network
itself is treated as a dynamical system. Other works focused on the dynamics on
networks, where the dynamical states of nodes evolve according to the network
topology. Adaptive networks are formed by the coupling of these two processes
[1]. In this talk, we will focus on the adaptive network models of collective
decision making. We will start with the simplest opinion formation model [2]
and illustrate the validity of different analytical approximation schemes. We will
introduce cyclic dominance into this model and show that a “swarm stupidity”
scenario emerges where there are global oscillations between different opinions
[3]. We will then move to a model in a more specified setting, where a fish
school makes a collective decision between two sources of food amid internal
group conflict. We will thereby emphasize the role of uninformed individuals in
achieving consensus in mobile animal groups [4]. We will finish by underscoring
the similarity of decision making to epidemic spreading and illustrate the
interplay between epidemics and network topology in a growing population.
References
[1] T. Gross and B. Blasius, Journal of the Royal Society: Interface 5, 259
(2008).
[2] F. Vazquez, V. M. Eguluz, and M. San Miguel, Phys. Rev. Lett. 100, 108702
(2008).
[3] G. Demirel, R. Prizak, P. N. Reddy, and T. Gross, Eur. Phys. J. B, 84,
541–548 (2011).
[4] I. D. Couzin, C. C. Ioannou, G. Demirel, T. Gross, C. J. Torney, A. Hartnett,
L. Conradt, S. A. Levin, and N. E. Leonard, Science, 334, 1578–1580
(2011).

Biographical Sketch
G¨uven Demirel received his B.Sc. degree from Industrial Engineering at
Bo˘gazi¸ci University, Turkey, in 2005. He obtained his M.Sc. degree from the
same department in 2008. He is currently a Ph.D. student at the Max Planck
Institute for the Physics of Complex Systems, Germany. His research focuses
on the application of dynamical systems methods to complex networks. His
application areas cover adaptive networks of opinion formation, collective animal
behavior and epidemiology. His research findings have been published in Science,
European Physical Journal B, and Advances in Complex Systems.