SiET
Laboratory on Territorial Complexity
Develop by Mcrit for the
Institute on Territorial Studies (IET)
SUMMARY IN ENGLISH
The Laboratory has been created because of the long interest of Albert Serratosa, Director of the IET looking for scientific support to explain and predict the complex evolutions of urban and regional phenomena.
In Social Sciences the classic analysis methods (based on regional economy and transport theories) presuppose already well-inform rational agents who do their optimum when getting their objectives in an ideal social environment. (ex. perfect competence). The system is always well balanced (there's a simultaneous balance between the offer and aggregated demand of different markets). Once the model is statistically calibrated according with the actual situation, it could evaluate the impact of determinate politics of control or the changes of the system conditions always generating a new balanced situation. These models have a limit explaining capacity, but when they're well calibrated they could have a forecast value really acceptable, always when the politics which are on study provoke marginal changes in relation with the current situation. These methods major interest is the providing of objective reference values essential for politics evaluation, as they permit the comparison of their relative impact in a consistent way. In spite of their explaining and predictive limitations, the existent analysis models and the scientific theories on which they are based, are indispensable when applying decision support processes.
From reductionist to holistic scientific approaches
During the 1980s, there was a ground swell of interest in the study of complex systems — systems in which complex patterns arise from interactions among simple parts. This surge of interest was due, in part, to new theoretical insights and to the availability of new computational tools for modeling complex systems. Research involving chaos, self-organization, adaptive systems, and non-linear dynamics are all part of this broader interest in the new "sciences of complexity."
Research into complex systems touches on some of the deepest issues in science and philosophy — order vs. chaos, randomness vs. determinacy, self-organizing vs. centrally-controlled systems. In the minds of many, the study of complexity is not just a new research branch; it is a new way of thinking about all science:
Physical Sciences (Mass, Energy)
Natural Sciences (Living organisms and ecosystems)
Social Sciences (Intelligent organisms and groups)
CAOS Interaction with simple equations
Genetic Algorithm
Application to solve a simple problem: maximizing f(x)=x2 ;
x=0,.....,31
Reductionist and Holistic views
(In back areas where traditional reductionism produce useful results)
Whereas scientists have long seen the world in terms of centralized controls and causes, the new sciences of complexity emphasize decentralized, self-organizing systems. The principles of complexity have the potential to change the way that not only scientists but also the general public thinks about natural, social, and technological systems.
From analytic methods to numerical simulation
Computers have represented a major break point in the evolution of science, since they have provided tools useful to solve problems with no analytic solution. For instance, non-linear dynamic formulations can be solved by numerical simulation. Often, numerical solutions are based on mathematical algorithms developed in previous centuries (e.g. Liapunov methods to analyse the stability of solutions to marginal changes in the initial conditions). The possibility to solve non-linear dynamic models by using computers have removed the need to simplify model’s formulation in linear versions (the ones having analytic solutions). This has open the way to a deeper exploration of reality, in particular of complex systems where non-linear dynamic models are needed.
This has produced surprises: some complex models may have simple solutions and, also, simple models may have complex solutions. Surprisingly, the iteration of simple deterministic formulations (e.g. a logistic or parabolic equation) may produce complex and even chaotic (totally unpredictable) solutions. And there may be hidden patterns of order within apparently chaotic behaviors. All together, a new scientific focus of interest has emerged in science, more interested in the exploration of complex systems as a whole, than in reducing the complexity by breaking the system into their elements and analyzing each one of them. Needless to say, all of this have deep cultural implications (e.g. traditional values such as uniformity, regularity, centralization, perfection, prediction, order, equilibrium or stability have to balanced with new values such diversity, dynamism, decentralization, spontaneity, creative errors etc.). The traditional "watch" as image of the Newtonian world is replace by the subtle "butterfly", physical metaphors are replaced by ecological ones in social sciences. In fact, computers have provided empirical proofs for the evolutionary biology theory formulated by Darwinian (life as resulting from an spontaneous process based on the successive accumulation of successful stochastic events).
Even more important than this, computers are the first opportunity human being have to have a neutral intermediator between humans, and between humans and reality. Computers can collect data by themselves (e.g. sensors) and apply human scientific knowledge (models) independently from any religious or artistic bias. Computers, at the end, more than "computing" communicate information and knowledge. The integration of computer devices with radio, TV and telephones, and their world interconnection through Internet transmission protocols, reflects such evolution.