Feedback in Complex Systems (FCS) Session 1
Time and Date: 10:00 - 12:30 on 20th Sep 2016
Room: F - Rode kamer
Chair: Samuel Johnson
|31000|| Instabilities, self-organisation and feedback in geophysical fluid dynamics
Abstract: 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.
|31001|| When slower is faster
Abstract: 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
|31002|| Brain complexity and phase transitions
Abstract: This talk will illustrate how brain functions, including some of the high-level ones setting up the mind, may be understood on well-defined grounds if one assumes analogy with scenarios that physics classifies as (non-equilibrium) phase transitions and critical phenomena. It suggests models that identify basic mechanisms and help in interpreting observations. This strategy also allows for comparison of data obtained from brains in different stages of evolution, and suggests experiments to detect significant changes of brain dynamics. References: ?Efficient transmission of subthreshold signals in complex networks of spiking neurons?, Plos One 10(3), e0121156 (2015), J.J. Torres, I. Elices, and J. Marro. ?Brain Performance versus Phase Transitions?, Sci. Rep. 5, 12216 (2015), J.J. Torres and J. Marro. Some yet unpublished work with Ana P. Millan. Physics, Nature and Society, J. Marro (Springer 2014). Nonequilibrium Phase Transitions in Lattice Systems, J. Marro and R. Dickman (Cambridge Univ. Press 2005).
|31003|| Market ecology and evolution
Abstract: 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.