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Feedback in Complex Systems - CCS 2016 satellite

The Centre for Complexity Science will host a satellite meeting on the morning of 20 September 2016 at the Conference on Complex Systems in Amsterdam, entitled Feedback in Complex Systems. All participants are warmly invited to attend.

Programme:

10:00 - 10:35

Colm Connaughton

Instabilities, self-organisation and feedback in geophysical fluid dynamics

10:35 - 11:10

Carlos Gershenson

When slower is faster

11:10 - 11:20 Break
11:20 - 11:55

Jorge Hidalgo

Information-based feedback and criticality in communities of adaptive systems

11:55 - 12:30

Doyne Farmer

Market ecology and evolution


Abstracts:

Colm Connaughton (University of Warwick)

Instabilities, self-organisation and feedback in geophysical fluid dynamics

The emergence of coherent circulation patterns of vortices and jets is a common feature of many large scale geophysical flows. Well known examples include the Earth's jet stream and Jupiter's Great Red Spot. These large scale structures exist in a background of turbulent fluctuations which are usually caused by hydrodynamic instabilities operating at much smaller scales. Some large scale structures are believed to have originally been created by the self-organisation of these small scale turbulent fluctuations into large-scale, quasi-deterministic flows. This self-organisation transfers energy from small scales to large scales, a process known as an "inverse cascade". In this talk, I will provide a non-specialist introduction to the physics of inverse cascades and discuss how it drives self-organisation phenomena in fluid dynamics. I will illustrate the ideas with a stylised model of the formation of zonal jets - structures analogous to the Jupiter's bands - from turbulence generated by an instability at much smaller scales. The zonal jets initially grow by extracting energy from small scale turbulent fluctuations. However a negative feedback mechanism is present which inhibits the instability mechanism generating this turbulence as the jet intensity increases. Jets thus arrest the energy input feeding their own growth and the system reaches a dynamical steady state in which the instability mechanism and feedback mechanism cancel each other out on average.

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Carlos Gershenson (Universidad Nacional Autónoma de México)

When slower is faster

The slower is faster (SIF) effect occurs when a system performs worse as its components try to do better. Thus, a moderate individual efficiency actually leads to a better systemic performance. The SIF effect takes place in a variety of phenomena. We review studies and examples of the SIF effect in pedestrian dynamics, vehicle traffic, traffic light control, logistics, public transport, social dynamics, ecological systems, and adaptation. Drawing on these examples, we generalize common features of the SIF effect and suggest possible future lines of research.

Reference: Gershenson, C. and Helbing, D. (2015). When slower is faster. Complexity, 21(2):9–15. http://dx.doi.org/10.1002/cplx.21736

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Jorge Hildalgo (University of Padua)

Information-based feedback and criticality in communities of adaptive systems

The hypothesis that living systems can benefit from operating at the vicinity of critical points has gained momentum in recent years. This hypothesis is being harshly debated, but setting the problem from an information-theory point of view opens new and stimulating questions that seem worth exploring. Employing tools from statistical mechanics and information theory, we study a community of complex adaptive systems aimed at "understanding" an external environment. We show that such a community can be much more efficient in coping with diverse heterogeneous environmental conditions when operating at criticality. Even more interestingly, we observe a more robust convergence to criticality in co-evolutionary and co-adaptive set-ups in which individuals aim to "understand" and "communicate" with other agents in the community with fidelity, thereby creating a collective critical ensemble by an information-based feedback mechanism. We hypothesize that the mechanisms reported here and their relationship with social interactions may be related with the emergence of criticality at a community level.

References:
* J.Hidalgo, J. Grilli, S. Suweis, A. Maritan and M. A. Muñoz, J. Stat. Mech. (2016) 033203.
* J.Hidalgo, J. Grilli, S. Suweis, M. A. Muñoz, J. R. Banavar and A. Maritan, Proc. Natl. Acad. Sci. USA (2014) 111(28), 10095-10100.

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Doyne Farmer (INET, University of Oxford)

Market ecology and evolution

How do we understand the relationships between the different actors in a market and how they change time? I will discuss how to understand and map out market ecologies. The key insight is that market evolution is driven by second order deviations from market efficiency. Market participants who use the market for direct purposes, such as liquidity extraction or risk diversification, create inefficiencies that support a diversity of different types of arbitrageurs. Based on a simple theory of differential price formation it is possible to map out the ecological relationships between financial strategies, which can be either predator-prey or competitive, and possibly mutualistic. I will show how to estimate timescales for market evolution. I conjecture that market ecology is a key determinant of market stability. Finally I will discuss how this theory can be developed and put to practical uses.