2009, Yates, Christian A., Erban, Radek, Escudero, Carlos, Couzin, Iain D., Buhl, Jerome, Kevrekidis, Ioannis G., Maini, Philip K., Sumpter, David J. T.

Among the most striking aspects of the movement of many animal groups are their sudden coherent changes in direction. Recent observations of locusts and starlings have shown that this directional switching is an intrinsic property of their motion. Similar direction switches are seen in self-propelled particle and other models of group motion. Comprehending the factors that determine such switches is key to understanding the movement of these groups. Here, we adopt a coarse-grained approach to the study of directional switching in a self-propelled particle model assuming an underlying one-dimensional Fokker-Planck equation for the mean velocity of the particles. We continue with this assumption in analyzing experimental data on locusts and use a similar systematic Fokker-Planck equation coefficient estimation approach to extract the relevant information for the assumed Fokker-Planck equation underlying that experimental data. In the experiment itself the motion of groups of 5 to 100 locust nymphs was investigated in a homogeneous laboratory environment, helping us to establish the intrinsic dynamics of locust marching bands. We determine the mean time between direction switches as a function of group density for the experimental data and the self-propelled particle model. This systematic approach allows us to identify key differences between the experimental data and the model, revealing that individual locusts appear to increase the randomness of their movements in response to a loss of alignment by the group. We give a quantitative description of how locusts use noise to maintain swarm alignment. We discuss further how properties of individual animal behavior, inferred by using the Fokker-Planck equation coefficient estimation approach, can be implemented in the self-propelled particle model to replicate qualitatively the group level dynamics seen in the experimental data.

2009, Couzin, Iain D.

The remarkable collective action of organisms such as swarming ants, schooling fish and flocking birds has long captivated the attention of artists, naturalists, philosophers and scientists. Despite a long history of scientific investigation, only now are we beginning to decipher the relationship between individuals and group-level properties. This interdisciplinary effort is beginning to reveal the underlying principles of collective decision-making in animal groups, demonstrating how social interactions, individual state, environmental modification and processes of informational amplification and decay can all play a part in tuning adaptive response. It is proposed that important commonalities exist with the understanding of neuronal processes and that much could be learned by considering collective animal behavior in the framework of cognitive science.

2009, Nabet, Benjamin, Leonard, Naomi E., Couzin, Iain D., Levin, Simon A.

We analyze a continuous-time model of a multi-agent system motivated by simulation studies on dynamics of decision making in animal groups in motion. Each individual moves at constant speed in the plane and adjusts its heading in response to relative headings of others in the population. The population includes two subgroups that are “informed” such that individuals in each subgroup have a preferred direction of motion. The model exhibits fast and slow time scales allowing for a reduction in the dimension of the problem. The stable solutions for the reduced model correspond to compromise by individuals with conflicting preferences. We study the global phase space for the proposed reduced model by computing equilibria and exploring stability and bifurcations.

2008-05-06, Sumpter, David J. T., Buhl, Jerome, Biro, Dora, Couzin, Iain D.

Moving animal groups provide some of the most intriguing and difficult to characterise examples of collective behaviour. We review some recent (and not so recent) empirical research on the motion of animal groups, including fish, locusts and homing pigeons. An important concept which unifies our understanding of these groups is that of transfer of directional information. Individuals which change their direction of travel in response to the direction taken by their near neighbours can quickly transfer information about the presence of a predatory threat or food source. We show that such information transfer is optimised when the density of individuals in a group is close to that at which a phase transition occurs between random and ordered motion. Similarly, we show that even relatively small differences in information possessed by group members can lead to strong collective-level decisions for one of two options. By combining the use of self-propelled particle and social force models of collective motion with thinking about the evolution of flocking we aim to better understand how complexity arises within these groups.

2009, Deisboeck, Thomas S., Couzin, Iain D.

In recent years the argument has been made that malignant tumors represent complex dynamic and self-organizing biosystems. Furthermore, there is increasing evidence that collective cell migration is common during invasion and metastasis of malignant tumors. Here, we argue that cancer systems may be capable of developing multicellular collective patterns that resemble evolved adaptive behavior known from other biological systems including collective sensing of environmental conditions and collective decision-making. We present a concept as to how these properties could arise in tumors and why the emergence of such swarm-like patterns would confer advantageous properties to the spatiotemporal expansion of tumors, and consequently, why understanding and ultimately targeting such collectivity should be of interest for basic and clinical cancer research alike.

2009, Erra, Ugo, Frola, Bernardino, Scarano, Vittorio, Couzin, Iain D.

In this work we describe a GPU implementation for an individual-based model for fish schooling. In this model each fish aligns its position and orientation with an appropriate average of its neighbors' positions and orientations. This carries a very high computational cost in the so-called nearest neighbors search. By leveraging the GPU processing power and the new programming model called CUDA we implement an efficient framework which permits to simulate the collective motion of high-density individual groups. In particular we ...

2009, Dyer, John R.G, Johansson, Anders, Helbing, Dirk, Couzin, Iain D., Krause, Jens

This paper reviews the literature on leadership in vertebrate groups, including recent work on human groups, before presenting the results of three new experiments looking at leadership and decision making in small and large human groups. In experiment 1, we find that both group size and the presence of uninformed individuals can affect the speed with which small human groups (eight people) decide between two opposing directional preferences and the likelihood of the group splitting. In experiment 2, we show that the spatial positioning of informed individuals within small human groups (10 people) can affect the speed and accuracy of group motion. We find that having a mixture of leaders positioned in the centre and on the edge of a group increases the speed and accuracy with which the group reaches their target. In experiment 3, we use large human crowds (100 and 200 people) to demonstrate that the trends observed from earlier work using small human groups can be applied to larger crowds. We find that only a small minority of informed individuals is needed to guide a large uninformed group. These studies build upon important theoretical and empirical work on leadership and decision making in animal groups.

2009, Conradt, Larissa, Krause, J., Couzin, Iain D., Roper, Timothy J.

Self-organizing-system approaches have shed significant light on the mechanisms underlying synchronized movements by large groups of animals, such as shoals of fish, flocks of birds, or herds of ungulates. However, these approaches rarely consider conflicts of interest between group members, although there is reason to suppose that such conflicts are commonplace. Here, we demonstrate that, where conflicts exist, individual members of self-organizing groups can, in principle, increase their influence on group movement destination by strategically changing simple behavioral parameters (namely, movement speed, assertiveness, and social attraction range). However, they do so at the expense of an increased risk of group fragmentation and a decrease in movement efficiency. We argue that the resulting trade-offs faced by each group member render it likely that group movements are led by those members for which reaching a particular destination is most crucial or group cohesion is least important. We term this phenomenon leading according to "need" or "social indifference," respectively. Both kinds of leading can occur in the absence of knowledge of or communication about the needs of other group members and without the assumption of altruistic cooperation. We discuss our findings in the light of observations on fish and other vertebrates.

2009, Couzin, Iain D., Laidre, Mark E.

2009, Torney, Colin, Neufeld, Zoltan, Couzin, Iain D.

Locating the source of an advected chemical signal is a common challenge facing many living organisms. When the advecting medium is characterized by either high Reynolds number or high Peclet number the task becomes highly non-trivial due to the generation of heterogenous, dynamically changing filamental concentrations which do not decrease monotonically with distance to the source. Defining search strategies which are effective in these environments has important implications for the understanding of animal behavior and for the design of biologically inspired technology. Here we present a strategy which is able to solve this task without the higher intelligence required to assess spatial gradient direction, measure the diffusive properties of the flow field or perform complex calculations. Instead our method is based on the collective behavior of autonomous individuals following simple social interaction rules which are modified according to the local conditions they are experiencing. Through these context-dependent interactions the group is able to locate the source of a chemical signal and in doing so displays an awareness of the environment not present at the individual level. Our model demonstrates the ability of decentralized information processing systems to solve real world problems and also illustrates an alternative pathway to the evolution of higher cognitive capacity via the emergent, group level intelligence which can result from local interactions.