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Welcome to the Collective Behavior group

Who are we and what do we do?

We are a research group led by David Sumpter and based in the mathematics department of Uppsala University. Our research is about understanding collective behaviour in biological and social systems.


What is research in collective behaviour?

Research in collective behaviour is about linking together the way we understand systems at different levels. Take a bird flock as an example. Here we see many individuals moving in unison, but there is no clear leader or global controller. The question is how the bird's interactions link together to produce the overall motion of the flock. Similar questions arise when we look at ant and bee colonies, cellular interactions, and the human economy. How do individual behaviours integrate to produce global dynamics?



Why is understanding collective behaviour important?

We live in a data rich world. We can use image analysis to track the motion of fish and GPS to track birds. There are vast databases of information about the social welfare and economic development of humans. We can measure the flow of nutrients within a microorganism. But the question is how we understand this data? How is all this individual behaviour, be it of animals, humans or cells, integrated to produce dynamics at the level of the group? These are unanswered and general questions which research on collective behaviour addresses.


What has this got to do with mathematics?

Mathematics is a rigorous way of reasoning about the world. It is also a way of reasoning which allows us to draw powerful analogies between different systems. We use a range of statistical, mathematical and computer simulation tools to quantify and then model the behaviour of groups. These tools include differential equations, stochastic models, self-propelled particle models and individual-based models.

Research

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We are a strongly interdisciplinary team: Mathematicians, biologists, computer scientists, chemists, physicists, image analysts and sociologists. We use a wide range of tools including experimental techniques, nonlinear dynamics, stochastic processes and Monte Carlo type simulations. Currently, we are focusing on four research topics :


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Rules of motion and group decision-making

Tracking and analysing fish movements
Simple rules leading to complex motion (Strömbom, 2011).
David Sumpter talking about this research in York, 2009.

Moving fish schools and bird flocks provide stunning examples of complex collective motion (King & Sumpter, 2012; Sumpter 2006). But how do individuals interact to produce these dynamics? Our research in this area started by looking at global features of moving locust swarms (Buhl et al., 2006), bird flocks (Biro et al. 2006; Sumpter et al., 2008) and fish schools. We have looked in detail at how fish make decisons about where to go and how to avoid being eaten by predators (2011).

More recently the focus has been on detailed individual interactions between animals within moving groups. We are developping methods for quantifying these interactions (Mann, 2011), and have employed these methods on fish (Herbert-Read et al., 2011), ants, prawns and pigeons. This work is part of a large grant from the European Research Council on understanding Individual Differences in Collective Animal Behaviour.

As well as work linked directly to experiments we have developed mathematical models to help explain general properties of collective motion and decision-making. For example, we have shown how directed collective motion can be generated purely by attraction between individuals (Strömbom, 2011). Another unifying theme is the use of quorum thresholds, whereby individuals wait until a threshold number of others have committed to an option before they themselves commit to it (Sumpter et al., 2008; Ward et al., 2008; Pratt & Sumpter, 2006). We have used mathematical models to show that these quorum responses allow animals to filter out poor choices and make better decisions (Sumpter & Pratt, 2009, Nicolis et al., 2011).

Several popular science articles have been written about our recent research. See, for example, ABC, Discovery, Australian Geographic, Science News, Nature and SVT (Swedish) .

Researchers involved :

  • Arianna Botinelli.

  • Richard Mann.

  • Andrea Perna.

  • Daniel Strömbom.


Collaborators :

Problem solving by biological systems

image Ants, slime moulds and bifurcations

How biological systems solve problems? For example, how do ants choose between multiple food sources when no single ant knows which one is best? For many ant species, the answer is that they lay pheromones to tell each other where to go. In one study we showed that Argentine ants can solve a maze which encodes the Towers of Hanoi problem (Reid et al., 2011). You can see a short film about this work on the science programme Catalyst, and read what both Nature and a HFSP feature | creationist make of our results.

Ant species differ in the way their colonies solve problems. Big headed ants, use multiple pheromones, which decay at different rates, to allow them to track changes in the quality and location of food (Dussutour et al., 2009). In a long term project together with Stephen Pratt in Arizona we have disected the rules through which Temnothorax ants choose a new nest (Pratt & Sumpter, 2005, 2006). Comparisons between different systems using common models has allowed us to explain why these systems sometimes appear to make 'irrational' decisions (Nicolis et al., 2011).

Current reinforced random walk solves Towers of Hanoi.

Another impressive example by problem solving by ants is the construction of networks. We have studied the structure and dynamics of networks linking different nests together (Latty et al., 2011) and those which link a central nest to many different food sources (Buhl et al., 2008). These studies led us to draw parallels between ants and acellular slime mold, an organism which builds networks of tubes linking food sources (Ito et al., 2010). From these models we have been working on models of current reinforced random walks as a general explanation of these phenomena.

Since our approach involves determining the algortihms which biological systems use to solve problems, we can then use these algorithms to design better computer systems. This idea has been the focus of an interdisciplinary project financed by the Human Frontiers Science Programme. The final report from our project can be downloaded here.

Researchers involved :

  • Anders Johansson.

  • Qi Ma.

  • Stamatios Nicolis.

  • Natalia Zabzina.


Collaborators :

Social Dynamics and Development

What is exactly development space ...

All social structures are complicated. Be it the workplace, schooling, family life, or world economics, all of these structures are characterised by complex social interactions between large numbers of individuals. When faced with tough questions about our future,such as “How do we promote economic development in the poorest parts of the world?” or “How do we promote equality and prevent social exclusion?” we can’t limit our thinking to just one or two factors. We always have to be aware of the bigger picture and how the maze of social interactions can lead us in different directions.

Our research goal is to find statistical and mathematical tools for breaking through this complexity to provide clear explanations of how social systems work.We study social interactions in everyday activities, such as gaze following (Gallup et al., 2012), clapping after a seminar (Mann et al., in prep) and answering trivia questions as a group (Granovskiy et al., in prep.), as well as in instutionalised settings, such as share trading (Nicolis & Sumpter, 2011) and political negotiations (Saam & Sumpter, 2008). In a recent project, we call Development Space,we look at how we can model human development and social well-being as a dynamical system.

Researchers involved :

  • Betty Nannyonga.

  • Shyam Ranangathan.

  • Mario Romero.

  • Stamatios Nicolis.

  • Boris Granovsky.


Collaborators :

Ecology and evolution

image Spatial population dynamics

Our most recent work in evolution has concentrated on explaining why animals form and maintain groups (Sumpter & Brännström, 2008 Cornforth et al., 2012). Detailed empirical and modelling studies of house sparrows allowed us to suggest a simple model for characterising animal group size distributions (Griesser 2011; Ma et al. 2011).

A central problem in ecology and evolution is relating the behavioural interactions of individuals, described in terms of competition, predation, interference etc., to the dynamics of the populations of these individuals, in terms of change in numbers of individuals over time (Brännström & Sumpter 2005a; 2005b; 2006). We have derived a whole range of models of population dynamics, determining how they arose from spatial distribution and competition between individuals. These derivations allow us to make a more rigorous prescription for model choice when fitting to particular data-sets .

Researchers involved :

  • Anders Johansson.

  • Qi Ma.


Collaborators :

Publications (2008 - present)

for papers before 2007, see individual pages

2013
  • Dussutour, Audrey. and Nicolis Stamatios C.. 2013. Flexibility in collective decision-making by ant colonies: Tracking food across space and time. Chaos, Solitons and Fractals, in press.

  • Mann, Richard P.; Perna, Andrea; Strömbom, Daniel; Garnett, R.,;Herbert-Read ,James E.; Sumpter, David J.T.and Ward, Ashley J.W. 2013. Multi-scale inference of interaction rules in animal groups using Bayesian model selection. Plos Computational Biology, in press.

  • Ma, Qi; Johansson, Anders; Tero, Atsushi; Nakagaki, Toshiyuki and Sumpter, David J.T.. 2013. Current-reinforced random walks for biological problem solving, Royal Society Interface, 10, 2012086.
    doi:10.1098/rsif.2012.0864

  • Viana M. P.; Fourcassié Vincent; Perna, Andrea; da Fontoura Costa, L. and Jost, Christian 2013. Accessibility in networks: a useful measure for understanding social insect nest architecture. Chaos, Solitons & Fractals, 46, 38-45.
    doi:10.1016/j.chaos.2012.11.003

  • Ma, Qi. 2013. Reinforcement in Biology: Stochastic models of group formation and network construction. Phd thesis, Uppsala University.
    [pdf]

2012
  • Granovskiy Boris. 2012. Modeling Collective Decision-Making in Animal Groups. Phd thesis, Uppsala University.
    [pdf]

  • Sumpter, David J.T. . 2012. David Sumpter. Current Biology, 22(17): R666–R667.
    [pdf]

  • N. Kolm, M. Amcoff, Richard P. Mann and G. Arnqvist. Diversification of a food-mimicking male ornament via sensory drive. Current Biology 22, 1440–1443.
    doi:10.1016/j.cub.2012.05.050

  • David J. T. Sumpter, Mann, Richard P., Andrea Perna 2012. "The modelling cycle for collective animal behaviour", Royal Society Interface.
    doi: 10.1098/​rsfs.2012.0031

  • Perna, Andrea, Granovskiy, Boris, Garnier, Simon, Nicolis, Stamatios Labédan, Marjorie, Theraulaz, Guy, Fourcassié, Vincent, & David J. T. Sumpter (2012) Individual rules for trail pattern formation in Argentine ants (Linepithema humile) PLoS Computational Biology, 8(7): e1002592.
    [pdf]

  • Gallup, Andrew C., Joe J. Hale, David J. T. Sumpter, Simon Garnier, Alex Kacelnik, John Krebs & Iain Couzin (2012) "Visual attention and information transfer in human crowds", Proceedings of the National Academy of Sciences 109 (19), 7245-7250.
    [pdf]

  • King, Andrew & David J. T. Sumpter 2012. “Murmurations” Current Biology Volume 22, Issue 4, Pages R112-R114.
    doi:10.1016/j.cub.2011.11.033

  • Granovskiy, Boris, Tanya Latty, Duncan Michael, David J. T. Sumpter and Madeleine Beekman. 2012. "How dancing honey bees keep track of changes: the role of inspector bees." Behavioral Ecology, in press.
    [pdf]

  • Betty Nannyonga, David J. T. Sumpter , Joseph YT Mugisha, Livingstone S Luboobi. 2012. "The Dynamics, Causes and Possible Prevention of Hepatitis E Outbreaks", PloS One 7 (7), e41135
    [pdf]

  • Ward, Ashley, Krause, Jens & David J. T. Sumpter (2012) Quorum Decision-Making in Foraging Fish Shoals, PloS One 7 (3), e32411
    [pdf]

  • Johansson, Anders, Ramsch, Kai, Middendorf, Martin & David J. T. Sumpter (2012) Tuning positive feedback for signal detection in noisy dynamic environments, Journal of Theoretical Biology, in press.

  • Sumpter, David J. T., Zabzina, Natalia & Nicolis, Stam (2012) Six predictions about the Decision Making of Animal and Human Groups, Managerial and Decision Economics, 33, 295-309.
    [pdf]

  • Cornforth, Daniel, David J. T. Sumpter, Sam Brown and Åke Brännström (2012) Synergy and group size in microbial cooperation, American Naturalist, 180, 296-305.
    [pdf]

    • 2011
    • Latty, Tanya, Kai Ramsch, Kentaro Ito, Toshiyuki Nakagaki, David J. T. Sumpter, Martin Middendorf and Madeleine Beekman. 2011. “Structure and Formation of Ant Transportation Networks.” Journal of the Royal Society, Interface.
      doi:10.1098/rsif.2010.0612.

    • Mann, Richard P.. 2011. “Bayesian Inference for Identifying Interaction Rules in Moving Animal Groups..” PLoS ONE 6 (8): e22827–.
      doi:10.1371/journal.pone.0022827.

    • Mann, Richard P, Robin Freeman, Michael Osborne, Roman Garnett, Chris Armstrong, Jessica Meade, Dora Biro, Tim Guilford and Stephen Roberts. 2011. “Objectively Identifying Landmark Use and Predicting Flight Trajectories of the Homing Pigeon Using Gaussian Processes.” Journal of the Royal Society, Interface 8 (55): 210–219.
      doi:10.1098/rsif.2010.0301.

    • Ward, Ashley J W, James E Herbert-Read, David J. T. Sumpter and Jens Krause. 2011. “Fast and Accurate Decisions Through Collective Vigilance in Fish Shoals.” Proceedings of the National Academy of Sciences of the United States of America 108 (6) : 2312–2315.
      doi:10.1073/pnas.1007102108.

    • Ma, Qi, Anders Johansson and David J. T. Sumpter. 2011. “A First Principles Derivation of Animal Group Size Distributions..” Journal of Theoretical Biology 283 (1) (August 21): 35–43.
      doi:10.1016/j.jtbi.2011.04.031.

    • Griesser, Michael, Qi Ma, Simone Webber, Katharine Bowgen and David J. T. Sumpter. 2011. “Understanding Animal Group-Size Distributions..” PLoS ONE 6 (8): e23438–.
      doi:10.1371/journal.pone.0023438.

    • Herbert-Read, James E, Andrea Perna, Richard P. Mann, Timothy M Schaerf, David J. T. Sumpter and Ashley J W Ward. 2011. “Inferring the Rules of Interaction of Shoaling Fish.” Proceedings of the National Academy of Sciences 108 (46): 18726–18731.
      doi:10.1073/pnas.1109355108.

    • Bousquet, Christophe A H, David J. T. Sumpter and Marta B Manser. 2011. “Moving Calls: a Vocal Mechanism Underlying Quorum Decisions in Cohesive Groups.” Proceedings of the Royal Society B-Biological Sciences 278 (1711): 1482–1488.
      doi:10.1098/rspb.2010.1739.

    • Reid, Chris R, David J. T. Sumpter and Madeleine Beekman. 2011. “Optimisation in a Natural System: Argentine Ants Solve the Towers of Hanoi.” The Journal of Experimental Biology 214 (Pt 1): 50–58.
      doi:10.1242/jeb.048173.

    • Strömbom, Daniel. 2011. “Collective Motion From Local Attraction.” Journal of Theoretical Biology 283 (1): 145–151.
      doi:10.1016/j.jtbi.2011.05.019.

    • Freeman, R, Richard P. Mann, T Guilford and D Biro. 2011. “Group Decisions and Individual Differences: Route Fidelity Predicts Flight Leadership in Homing Pigeons (Columba Livia).” Biology Letters 7 (1): 63–66.
      doi:10.1098/rsbl.2010.0627.

    • Nicolis, Stamatios C. and Audrey Dussutour. 2011. “Resource Exploitation Strategies in the Presence of Traffic Between Food Sources.” Bio Systems 103 (1): 73–78.
      doi:10.1016/j.biosystems.2010.10.002.

    • Nicolis, Stamatios C. and David J.T. Sumpter. 2011. “A Dynamical Approach to Stock Market Fluctuations.” International Journal of Bifurcation and Chaos 21 (12):3557.
      doi:10.1142/S0218127411030726.

    • Nicolis, Stamatios C. 2011. “Information Flow and Information Production in a Population System.” Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) 84 (1): 011110.
      doi:10.1103/PhysRevE.84.011110.

    • Nicolis, Stamatios C., Natalia Zabzina, Tanya Latty and David J. T. Sumpter. 2011. “Collective Irrationality and Positive Feedback.” PLoS ONE 6 (4): e18901.
      doi:10.1371/journal.pone.0018901.


    2010
    • Ito, Kentaro, David J. T. Sumpter and Toshiyuki Nakagaki. 2010. “Risk Management in Spatio-Temporally Varying Field by True Slime Mold.” Nonlinear Theory and Its Applications, IEICE 1 (1): 26–36.
      doi:10.1587/nolta.1.26.

    • Nicolis, Hélène and Stamatios C. Nicolis. 2010. “The Selfish to Egalitarian Transition in Young Children: Developmental Processes Versus Cooperative Transitions.” Nonlinear Dynamics, Psychology and Life Science 14 (3): 257–264.


    2009
    • Sumpter, David J. T. 2009. “Group Behaviour: Leadership by Those in Need.” Current Biology 19 (8) : R325–R327.
      doi:10.1016/j.cub.2009.02.049.

    • Sumpter, David J. T., and Stephen C Pratt. 2009. “Quorum Responses and Consensus Decision Making.” Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 364 (1518): 743–753.
      doi:10.1098/rstb.2008.0204.

    • Buhl, Jérôme, Kerri Hicks, Esther R Miller, Sophie Persey, Ola Alinvi, and David J. T. Sumpter. 2009. “Shape and Efficiency of Wood Ant Foraging Networks.” Behavioral Ecology and Sociobiology 63 (3): 451–460.
      doi:10.1007/s00265-008-0680-7.

    • Yates, Christian A, Radek Erban, Carlos Escudero, Iain D Couzin, Jérôme Buhl, Ioannis G Kevrekidis, Philip K Maini, and David J. T. Sumpter. 2009. “Inherent Noise Can Facilitate Coherence in Collective Swarm Motion.” Proceedings of the National Academy of Sciences of the United States of America 106 (14) 5464–5469.
      doi:10.1073/pnas.0811195106 .

    • Nicolis, C. and Stamatios C. Nicolis. 2009. “Propagation of Extremes in Space.” Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) 80 (2): 26201.
      doi:10.1103/PhysRevE.80.026201.

    • Dussutour, Audrey, Madeleine Beekman,Stamatios C. Nicolis and Bernd Meyer. 2009. “Noise Improves Collective Decision-Making by Ants in Dynamic Environments.” Proceedings of the Royal Society of London Series B-Biological Sciences 276 (1677): 4353–4361.
      doi:10.1098/rspb.2009.1235.

    • Dussutour, Audrey,Stamatios C. Nicolis, G Shephard, Madeleine Beekman and David J. T. Sumpter. 2009. “The Role of Multiple Pheromones in Food Recruitment by Ants.” Journal of Experimental Biology 212 (15): 2337–2348.
      doi:10.1242/jeb.029827.


    2008
    • Sumpter, David J. T., Jens Krause, Richard James, Iain D Couzin and Ashley J W Ward. 2008. “Consensus Decision Making by Fish.” Current Biology 18 (22): 1773–1777.
      doi:10.1016/j.cub.2008.09.064.

    • Sumpter, David J. T., Jérôme Buhl, Dora Biro, and Iain D Couzin. 2008. “Information Transfer in Moving Animal Groups.” Theory in Biosciences 127 (2): 177–186.
      doi:10.1007/s12064-008-0040-1.

    • Ward, Ashley J W, David J. T. Sumpter, Iain D Couzin, Paul J B Hart and Jens Krause. 2008. “Quorum Decision-Making Facilitates Information Transfer in Fish Shoals.” Proceedings of the National Academy of Sciences of the United States of America 105 (19): 6948–6953.
      doi:10.1073/pnas.0710344105.

    • Dussutour, Audrey, Stamatios C. Nicolis, Emma Despland and Stephen J Simpson. 2008. “Individual Differences Influence Collective Behaviour in Social Caterpillars.” Animal Behaviour 76: 5–16.
      doi:10.1016/j.anbehav.2007.12.009.

    • Nicolis, Stamatios C. and Audrey Dussutour. 2008. “Self-Organization, Collective Decision Making and Resource Exploitation Strategies in Social Insects.” European Physical Journal B 65 (3): 379–385.
      doi:10.1140/epjb/e2008-00334-3.

    • Nicolis, Stamatios C. and Catherine Nicolis. 2008. “Extreme Events in Bimodal Systems.” Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) 78 (3): 036222.
      doi:10.1103/PhysRevE.78.036222.

    • Nicolis, Stamatios C., Emma Despland and Audrey Dussutour. 2008. “Collective Decision-Making and Behavioral Polymorphism in Group Living Organisms.” Journal of Theoretical Biology 254 (3): 580–586.
      doi:10.1016/j.jtbi.2008.06.028.

A new summer school will be planned in a near future.

Summer school in collective behaviour

Uppsala, Sweden, August 19th to 25th, 2012.

This week-long intensive summer school will introduce PhD students to the techniques and tools used in understanding the collective behaviour of animals and humans. We will introduce key concepts in the study of group behavior and provide numerous case studies on bird flocks, fish schools, social insects and human society. Most importantly we will concentrate on presenting methods for data collection and analysis; for mathematical modeling and for computer simulation in a way which accessible for students of all backgrounds and disciplines.

Each day will start with a lecture by David Sumpter based on his book Collective Animal Behaviour. A long lunch will allow for discussion. Then the afternoon session will go in to details of how techniques are applied in this field by other members of the collective behaviour group. The week will be structured as follows.

Sunday

Arrival.

Monday

9:30 - 10:45Models in collective animal behaviour (chapters 1, 2 and 10)
11:00 - 12:15 Information transfer and synergy (chapter 3 and 10)
12:15 - 14:00 Discussion and lunch.
14:00 - 18:00 Tutorial: Differential equation models. (Stam Nicolis)

Tuesday

9:30 - 10:45 Collective decision-making (chapter 4)
11:00 - 12:15 Collective motion (chapter 5).
12:15 - 14:00 Discussion and lunch.
14:00 - 18:00 Tutorial: Self-propelled particle models. (Daniel Strömbom)

Wednesday

9:30 - 10:45 Guest lecture: Peter Hedström (Institute for Future Studies, Stockholm)
11:00 - 12:15 Guest lecture: Jens Krause (Humbolt University, Berlin)
12:30 - Lunch and Free afternoon.

Thursday

 9:30 - 10:45 Quantifying individuals' interactions.
11:00 - 12:15 Collective structures (chapter 7).
12:15 - 14:00 Discussion and lunch.
14:00 - 18:00 Tutorial session: Automated data analysis and tracking. (Andrea Perna)

Friday

9:30 - 10:45 Negative feedback and regulation (chapter 8).
11:00 - 12:15 Individual complexity and Agent-based models (chapter 9).
12:00 - 14:00 Discussion and lunch.
14:00 - 18:00 Tutorial: Model fitting. (Richard Mann)
19:00 - End of summer school party.

Saturday

 Departure.

The summer school will take place outside of Uppsala at Björkdala conference centre .

The summer school is free to all accepted participants. Accommodation is provided in shared rooms at the conference centre and tasty home cooked food is provided throughout your stay.

List of participants

Michael Anreiter (Barcelona), Zahedeh Bashardanesh (Uppsala), Hjalmar Eriksson (Perimeter Instiute), Josh Kirby (Delaware), Jennifer Miller (Delaware), Catherine E. Offord (Princeton), Yu Sun (Delaware), Roy Harpaz (Weizmann Institute), Klara Wanelik (Oxford), Damien Farine (Oxford), Giovanni Giusti (Barcelona) , David Wheatcroft (Chicago), Johannes Himmelreich (London School of Economics), Juta Kawalerowicz (Oxford), Quan-Xing Liu (Royal Netherlands Institute for Sea Research), Claudia Lopes (London School of Economics), Andres Quinones Paredes (Groningen), Takao Sasaki (Arizona), Arianna Bottinelli (Uppsala), Shyam ranganathan (Uppsala), Natalia Zabzina (Uppsala), James Herbert-Read (Sydney/Uppsala)