Web Seminar on The New Sciences II
The Science of Complexity
A transdisciplinary exploration of Theory and Applications
June 18, 2013
|Concept | Paper | Report | Videos
Defining complexity remains a not easy task. Some are taken form publications and depend strongly on the viewpoint of the authors.
From Melanie Mitchell (2009) .
Complexity is a system in which large networks of components with no central control and simple rules of operation give rise to complex collective behavior, sophisticated information processing, and adaptation via learning or evolution.
From Roger Lewin (1993).
Complexity science offers a way of going beyond the limits of reductionism, because it understands that much of the world is not machine-like and comprehensible through a cataloging of its parts; but consists instead mostly organic and holistic systems that are difficult to comprehend by traditional scientific analysis.
From OECD Global Science Forum. Applications of Complexity Science for Public Policy: New Tools for Finding Unanticipated Consequences and Unrealized Opportunities. (2009).
Government officials and other decision makers increasingly encounter a daunting class of problems that involve systems composed of very large numbers of diverse interacting parts. These systems are prone to surprising, large-scale, seemingly uncontrollable, behaviors. These traits are the hallmarks of what scientists call complex systems.
An exciting, interdisciplinary field called complexity science has emerged and evolved over the past several decades, devoted to understanding, predicting, and influencing the behaviors of complex systems. The field deals with issues that science has previously had difficulty addressing (and that are particularly common in human systems) such as: non-linearities and discontinuities; aggregate macroscopic patterns rather than causal microscopic events; probabilistic rather than deterministic outcomes and predictions; change rather than stasis.”
The promise of complexity science for policy applications is, at its core, the hope that science can help anticipate and understand these key patterns in complex systems that involve or concern humans, thus enabling wiser decisions about policy interventions. [OECD].
Some important characteristics of complex systems are :
- adaptability : independent constituents interact changing their behaviors in reaction to those of others, and adapting to a changing environment;
- emergence : novel pattern that arise at system level not predicted by fundamental proprieties of the system’s constituents;
- self-organization : a system that operates through many mutually adapting constituents in which no entity designs it or directly controls it;
- attractors : some complex systems spontaneously and consistently revert to recognizable dynamic states known as attractors. While they might, theoretically, be capable of exhibiting a huge variety of states, in fact they mostly exhibit the constrained attractor states;
- self-organized criticality: a complex system may possess a self-organizing attractor state that has an inherent potential for abrupt transitions of a wide range of intensities. A system that is in a self-organized critical state, the magnitude of the next transition is unpredictable, but the long-term probability distribution of event magnitudes is a very regular known distribution (a “power law” );
- chaos : chaotic behavior is characterized by extreme sensitivity to initial conditions;
- non-linearity : non-linear relationships require sophisticated algorithms, probabilistic in nature. Small changes might have large effects, as well large changes could have little or no effects;
- phase transitions : system behavior changes suddenly and dramatically (and, often, irreversibly) because a “tipping point”, or phase transition point, is reached. Phase transitions are common in nature: boiling and freezing of liquids (for example, the freezing of mercury that is referred to above), the onset of superconductivity in some materials when their temperature decreases beyond a fixed value, a.o.;
- power laws : probabilistic distribution characterized by a slowly decreasing function (log-log), different from the ‘familiar’ bell-shaped one.
Tools and Techniques for Complexity Science
Some of the most important complexity tools being used in public policy domains at this time are:
- agent-based or Multi-agent Models : in computerized, agent-based simulations, a synthetic virtual “world” is populated by artificial agents who could be individuals, families, organizations, etc. The agents interact adaptively with each other and also change with the overall conditions in the environment.
- network analyses : a common feature of many complex systems is that they are best represented by networks, which have defined structural features and follow specific dynamic laws. Scientists seek to identify configurations that are especially stable (or particularly fragile); some network patterns have been identified as predictors of catastrophic failures in real-life networks : power-distribution or communication infrastructures.
Additional complexity-related techniques deserve special mention, although their use is not unique to complexity science : Data Mining, Scenario Modeling, Sensitivity Analysis, Dynamical Systems Modeling.
Possible applications in the Public Policy domain.
Several examples of application domains have been or respectively are explored, e.g. : Epidemiology & Contagion; traffic, identification of terrorist associations. Of more general interest is climate change, in particular the social and human aspects – connection between economy, finance, energy, industry and the natural world. These new degrees of sophistication can only be achieved using complexity science.
Complexity science techniques can be useful in identifying dangerous tipping points in the human-earth system, which can occur independently of purely geophysical transitions. Perhaps the most likely disruption of this type involves the management of water resources. Drought and water stresses occur regularly across large sections of Europe and the developing world. There are indications that a tipping point may be near, leading to massive long-term water shortages.
New ways of Thinking for Policymakers : very important for WAAS!!
It focuses attention on dynamic connections and evolution, not just on designing and building fixed institutions, laws, regulations and other traditional policy instruments :
- predictability : complex systems science focuses on identifying and analyzing trends and probabilities, rather than seeking to predict specific events. It will be challenging, though necessary, for policymakers and scientists alike to move beyond strict determinism if they wish to effectively engage in decision making under conditions of uncertainty and complexity.
- control : control is generally made possible by identifying cause-and-effect chains and then manipulating the causes. But cause and effect in complex systems are distributed, intermingled and not directly controllable. Complexity science offers many insights into finding and exploiting desirable attractors; identifying and avoiding dangerous tipping points; and recognizing when a system is in a critical self-organizing state.
- explanation : analyses done using complexity science methods, insights about the underlying mechanisms that lead to complex behavior are revealed. Although deterministic quantitative prediction is not generally achieved, the elucidation of the reasons for complex behavior are often more important for comprehending what might otherwise be puzzling real-world events.
Possible Topics to be discussed :
- More examples : application in (armed) conflicts, environmental & climate change, etc. ?
- New understanding in the biological sphere?
- Applications in financial crisis and their governance (EU) ?
Some References (alphabetically) :
- Castellani Brian, [http://en.wikipedia.org/wiki/File:Complexity-map_castellani_w.jpg]
- Castellani Brian & Hafferty Frederic, Sociology and Complexity Science. A new field of Enquiry. Springer 2009
- Ball P. Why Society is a Complex Matter, Springer, 2012
- Barabási Albert-László & Bonabeau Eric, Scale-Free Networks. Scientific American, May 2003
- Barabási Albert-László, Linked. The New Science of Networks. Perseus Publishing, 2002
- Chesters G. and I. Welsh, Complexity and Social Movements. Multitudes at the Edge of Chaos. Routledge, 2006
- Érdi P., Complexity Explained, Springer, Berlin, 2008
- Lewin Roger, Complexity. Life at the Edge of Chaos. Phoenix 1993
- Mainzer K., Thinking in Complexity. Springer, Berlin, 1997
- Nicolis G. & Nicolis C., Foundation of Complex Systems. World Scientific, New Jersey, 2007
- OECD, Report on : Applications of Complexity Science for Public Policy : New Tools for Finding Unanticipated Consequences and Unrealized Opportunities. September 2009
- Mitchel Melanie, Complexity. A Guided Tour. Oxford University Press, 2009
- Ramalingam B.and Jones H., Exploring the science and complexity: Ideas and implications for developments and humanitarian efforts. ODI Working Paper 285, 2008
- Watts, Duncan J., Six Degrees. The Science of a Connected Age. W.W. Norton & Co, 2003
Presentation by Raoul Weiler
Presentation by Garry Jacobs