Policy making in UK is currently making the headlines because of its very aggressive spending cuts program. The role that science could play in policy making is most often overshadowed by political dogmas. Many important questions of society would be better addressed if data and predictive models were more widely used. Here are four examples from the ECCS 2010 conference in Lisbon.
Applications of complex systems theory in policy making
“Intervention and Policy Making in Complex Socio-Economic and Technical Systems”
by Peter Allen
“Modelling the growth of distributed energy generation for a low carbon economy: Part I the approach”
by Liz Varga
The greatest challenge for the UK government is to ensure that energy demand is met and sustainable over the long-term. This assurance is currently achieved through centralized control over various gas and electricity licensees, including generators, inter-connectors, distributers, etc. The future however is dependent in a significant way upon households and organizations, taking responsibility for their use and, where possible, their generation of energy. This new decentralized model which has the opportunity for self-organization and growth, in the same way as the internet had with the deregulation of telecommunications, is known as the ‘smart grid’ in the USA. The purpose of modelling the power industry is three-fold. First, the power industry cannot be experimented upon because of its scale and the reliance placed upon it by the economy. Second, effects of potential governmental interventions will depend upon the evolving environment and other dynamics existing within the system and so are difficult to predict because of the dependence upon the context of the interventions. Third, is to demonstrate with reasonable probability given the assumptions made and given the effects of noise in the system that desired outcomes relating to low carbon, security and sustainability are achievable. Human or social agents and artificial or smart agents will be modelled at similar levels of abstraction and simulated together, allowing an understanding of the interactions between technology, information, individuals, communities and organizations, building on the use of interpretive agents who learn.
UK Transport Emission Reduction
Most often in environmental issues, not one but several policy measures need to be implemented in order to get positive effects, and the interaction between these measures becomes key in their success. Finding the set of measures that will lead to the best synergies is a very complex problem indeed. Araz Taeihagh argues that policy makers should let science help them, and use policy models in their design process. His model is based on five types of relation between policy measures: a measure can be required for the application of another (precondition); a measure can facilitate the application of another (facilitation); two measures can work well together (synergy): two measures can produce conflicting outcomes under certain conditions (potential contradiction), two measures definitely produce conflicting outcomes (contradiction).
Hospitals are complex: they involve strict codes of operations, nurses, doctors, specialized equipment, operating rooms, and patients of course. They are optimized for maximum effectiveness in healing patients. A single change in their settings can lead to chain reactions and unpredictable consequences in daily operations. How to integrate then planned refurbishments and new requirements such as reduction of energy consumption? The new study of the Open University will attempt to model hospitals using complex systems theory in order to address the challenge.