Today I was reading the introduction of the book on Complexity, Cognition and the City by Juval Portugali. The book is about the intersection of Complexity theory, Cognitive Science and City theory. The author impression is that now what is happening on CTC (Complexity Theories of Cities) is similar to what is called “law of diminishing marginal utility”, which means we can’t get as much as we were expecting in the beginning of complexity science and city theory, while we have an increase in the rate of active people interested in complexity and city.
And as a solution to this decline he is proposing to bring new ideas from cognitive science to this discourse of complexity science and city as he thinks this part is missing.
Of course, if you take a look at the historical trends of techniques such as fractal theory, celluar automata, multi-agent based systems and other distributed approaches in modeling of complex systems including urban land use patterns modeling, traffic modeling and so on, that is clear that we had a boom by the end of 1990s but gradually it turned out that complexity in cities is more complex than that to be approached by these (complicated) methods from complexity theory.
I completely agree with this trend as Juval Portugali is describing, but completely disagree wit his solution to bring new theories into the recipe of CCT.
This is what I would like to call the lack of abstraction in the concept of modeling and complexity, when the limits and capabilities of each modeling approach is not clear for their users. In fact, what is missing is an abstract theory of urban modeling and the limits of complexity theory as a scientific paradigm, which is independent of the content and by mixing new dimensions you can’t overcome its limit but definitely, you can make it more complicated. I will write about this in some other posts as it is the main line of my PhD research.
But the point I wanted to bring in this post is that what I am observing among researchers in the fields dealing with city and urbanism is that they are acting passively when confronting the new shining technologies such as those mentioned above.
No doubt that the research around cities and urban environment is very young comparing to other classic disciplines, but in my opinion, during each historical period we have a dominant wave of technology or a specific concept, which acts like a flood and completely change the environment of other disciplines if they don’t have a well-designed ground for digesting new concepts.
Therefore, I think more than the ideas of complexity theory, during last 50 years the dominant factor in most of scientific disciplines has been computing technologies that has been evolving from macro computers to micro computers in 1990s and nowadays in a completely different form of connected computing machines, which has brought us a data deluge. I would say, the whole complexity theory got attractive because of the available computing technology and ease of numerical simulation and analysis, while it was not easy to do before.
And if you look at the trend of evolution in computing technology, you might find the strong correlation of micro-simulation techniques such as cellular automata and agent based techniques with democratization of computing power in 198-90s.
The point is that people acted passively when they saw the capabilities of simulations of complex systems by their own PCs on their desks. For example, it seems very attractive if you have fractal analysis and visualization of the land use evolution of the city you are working on, while you trust on basics of fractal theory as a complicated scientific approach, without being so critical about. And therefore, as a result you might expect an overshoot and then a decline in the applications of such technologies, when after while the community can not gain that much was expected, as it is mentioned by Juval Portugali. Now passive researchers will wait for the next shining technology…
This trend can be identified in other similar situations, when there is not enough abstract views to the whole phenomena and when people trust on some “scientifics!” without asking it. For example when during 1950-1960 people in the field of urban planning for the first time were attracted to classical physics, thanks to promise of macro-computer in that time, when “Social Physics” and gravitational models of land use, which was being advertised by Rand corporation, while it was just based on an analogy to gravitational physics of Newton and people decisions and land use dynamics in cities. Another example would be “Urban Dyanimcs” of forrester which is based on the concepts of hydraulics from the field of Mechanics and its stocks and flows models, which can be numerically analyzed using computers.
Therefore, this celebration was not so long and in late 1970s there were some strong and influential critiques against use of this complicated simulation models for urban planning. For example, one can take a look at this: “A requiem for Large scale models”.
But again, due to lack of abstraction, new forms of invasion to city theories and city models can be seen,by some physicists, mainly with a Newtonian mindset, toward “universal laws of cities” or “City Science”. And astonishingly, simple results such as linear relations between the city population and city energy consumption or GDP growth, which kill any further social discussions, are being proposed as scientific laws of the cities.
I personally believe that computers as abstract machines have brought us unbelievable capabilities and as they are abstract, it is up to designers and modelers how to use this capability and this is one of my main motivations as a computer scientist and systems modeler to engage in urban theory discourses to first understand the environment and secondly to propose new ways of looking at this beautiful capability brought us to us by Turing, Godel and Bool.
And now if we look at the main emerging trends in computational urban modeling approaches, data driven approaches, recently called Big-Data is skyrocketing. While I am personally involved in this domain, I see some researchers getting seduced with this titles and buzzwords. without a proper and clear understanding of this approach and what I think we need before getting engaged is an abstraction from the concept of modeling by mathematical and philosophical tools, otherwise Big Data can not have any considerable contribution by itself and it becomes similar to complexity theory.
Soon, I will write in more detail about history of urban modeling and simulation from point of view of complexity of modeling and from historical advancements in computer science.