Collective dynamics in physics, biology and social sciences

May 20, 2015 to May 22, 2015
Location : CECAM-IRL, University College Dublin, Ireland. UCD O'Brien Centre for Science, Room E2.16/2.17 (Science Centre East).


  • Vladimir Lobaskin (University College Dublin, Ireland)
  • Thomas Ihle (University Greifswald, USA)



   Irish Higher Education Authority


It should be noted that significant progress in computational social sciences has been achieved in the past 15 years after introduction of agent-based models and networks from physics and applied mathematics. The later development of the research in biology and physics and, in particular, the appearance of active matter in the more recent years lead to unfortunate situation when these fields continue to grow and mature in parallel without much interaction. It is therefore timely to reset the common grounds and share the ideas developed in different communities.

The discussion topics for the meeting are:
- methodological issues related to modelling open systems: fixed vs. variable environment, conservation laws, local equilibria, energy, material and information exchange with the environment;
- phase behavior of active systems: type of dynamic phase transition, definition and detection of phases, finite size effects;
- the general principles governing the existence and nature of the steady states, extreme events or instabilities;
- ordering and disordering effects on the active groups: external fields, type of noise, symmetry and selectivity of interactions;
- computational aspects: simulation efficiency, parallel algorithms.


[1] Toner J, Tu Y and Ramaswami S 2005 Hydrodynamics and phases of flocks. Ann. Phys. 318, 170–244
[2] Ramaswamy, S. 2010 The Mechanics and Statistics of Active Matter. Annu. Rev. Condens. Matter Phys. 1, 323– 45.
[3] Vicsek T and Zafeiris A 2012 Collective motion. Phys. Rep. 517, 71;
[4] Paxton W F, Kistler K C, Olmeda C C, Sen A, St Angelo S K, Cao Y, Mallouk T E, Lammert P E and Crespi V H 2004 J. Am. Chem. Soc. 126, 13424;
[5] Howse J R, Jones R A L, Ryan A J, Gough T, Vafabakhsh R and Golestanian R 2007 Phys. Rev. Lett. 99, 048102;
[6] Golestanian R, Liverpool T B and Ajdari A 2007 New J. Phys. 9, 126
[7] A. Siria, P. Poncharal, A.-L. Biance, R. Fulcrand, X. Blase, S. Purcell, L. Bocquet 2013 Dynamic clustering in active colloidal suspensions with chemical signaling. Nature 494 455-458.
[8] Kaiser A, Peshkov A, Sokolov A, ten Hagen B, Löwen H, Aranson IS 2014 Transport Powered by Bacterial Turbulence. Phys. Rev. Lett. 112, 158101
[9] Theurkauff I, Cottin-Bizonne C, Palacci J, Ybert C and Bocquet L 2012 Phys. Rev. Lett. 108, 268303;
[10] Marchetti M C, Joanny J F, Ramaswamy S, Liverpool T B, Prost J, Rao M and Simha R A 2013 Rev. Mod. Phys. 85, 1143
[11] Helbing D, Farkas I and Vicsek T 2000 Nature 6803, 487–490
[12] Helbing D, Molnar P, Farkas I J and Bolay K 2001 Env. and Plan. 28, 361-383
[13] Deffuant G, Neau D, Amblard F and Weisbuch G 2001 Mixing beliefs among interacting agents, Advances in Complex Systems 3, 87–98
[14] Weidlich W 2002 Sociodynamics: a systematic approach to mathematical modeling in social sciences (Taylor and Francis, London)
[15] Helbing, D. 2010 Quantitative sociodynamics, 2nd ed., Springer, Netherlands
[16] Garcia D, Garas A, Schweitzer F 2014 Collective Emotions, Modeling collective emotions in online social systems, pp. 389-406, Oxford University Press
[17] Schweitzer F, Mavrodiev P, Tessone CJ 2013 How can social herding enhance cooperation? Advances in Complex Systems 16, 1350017
[18] Mavrodiev P, Tessone CJ, Schweitzer F 2012 Effects of social influence on the wisdom of crowds, Proceedings of Collective Intelligence
[19] Huet S, Deffuant G, Jager W 2008 Rejection mechanism in 2D bounded confidence provides more conformity, Advances in Complex Systems 11, 529-549.
[20] Lorenz J, Rauhut H, Schweitzer F, Helbing D 2011 How social influence can undermine the wisdom of crowd effect, Proc. Natl. Acad. Sci., doi: 10.1073/pnas.1008636108
[21] Helbing D, Mukerji P 2012 Crowd disasters as systemic failures: analysis of the Love Parade disaster, EPJ Data Science 1, 7.
[22] Couzin, I., Ioannou, C., Demirel, G., Gross, T., Torney, C., Hartnett, A., Conradt, L., Levin, S. & Leonard, N. 2011 Uninformed individuals promote democratic consensus in animal groups. Science 334, 1578-1580.
[23] Toner, J. & Tu, Y. 1998 Flocks, herds, and schools: a quantitative theory of flocking. Phys. Rev. E 58, 4828– 59.
[24] Romenskyy, M. & Lobaskin, V. 2013 Statistical properties of swarms of self-propelled particles across the order-disorder transition. Eur. Phys. J. B 86, 91.
[25] Zafeiris, A. & Vicsek, T. 2013 Group performance is maximized by hierarchical competence distribution. Nature Commun 4, 2484.
[26] Bialek, W., Cavagna, A., Giardina, I., Morad, T., Pohl, O., Silvestri, E., Viale, M. & Walczak, A. M. 2014 Social interactions dominate speed control in poising natural flocks near criticality. Proc. Natl. Acad. Sci. 111(20), 7212.