Meet Michael Batty (1/2)
By: Urban Ai
Tags: Smart Cities, AI, Future, Cities, Urban

Each week, Urban AI presents you one member of its Scientific Committee.

For our third episode, we interviewed Michael Batty. Michael Batty is Bartlett Professor of Planning at UCL where he is Chair of the Centre for Advanced Spatial Analysis (CASA). His work is focussed on computer models of cities and their visualisation, and more recently how computation lies at the heart of the smart cities movement. His most recent books are The New Science of Cities (2013) and Inventing Future Cities (2018) published by MIT Press. Both have been translated into Chinese. You can follow his research is at www.spatialcomplexity.info

You have worked a lot on complexity and cities as complex systems, so could you explain what complexity is and why cities are complex systems?

If you go back in time almost 100 years, then in science there was a sense in which everything was machine-like, that the world could be thought as sets of machines. In classical physics, machines were organised and understood from the top down. This was very successful in physics and, to a lesser extent, also in economics. However, at the beginning of the last century after these successes of classical and then quantum physics, various people speculated that maybe we could adapt this model of the world as a machine really to a number of problems in psychology, biology to some extent, and certainly ecology, sociology and cities. These were relatively ill-defined problems and as time went on, people were beginning to say that we should talk about social systems as being like machines organised from the top-down. The edifice of knowledge that was created back then was thinking about all sorts of social systems as being organised from the top-down. This was referred to as the systems approach.

“If you go back in time almost 100 years, then in science there was a sense in which everything was machine-like, that the world could be thought as sets of machines”

What the systems approach did not take account of was that systems actually change and biological systems certainly change; they evolve. This model of the systems approach to cities, societies, economies, etc., was really a timeless model, static and without any dynamic focus and the world was treated as if it was in equilibrium. Of course, as we know the world is by no means in equilibrium but is changing continually, it is evolving. The model began to switch during the last half or last quarter of the 20th century and what came onto the agenda was a way of thinking about systems not from the top-down but from the bottom-up. In other words, cities rather than being organised like machines from the top-down with a grand plan, were seen as evolving from the bottom-up and growth and evolution were consistent with one another. This was the Darwinist paradigm and of course it was articulated quite clearly from the mid 19th century onwards.

Complexity theory really began by switching our focus from top-down to bottom-up and a good many of the systems we talk about, particularly cities, etc., are much easier to think about as being organised from the bottom-up. This has quite profound implications for urban planning in some senses because planners tend to think of cities and their plans from the top-down, but cities evolve from the bottom-up. They are the product of literally millions and millions of decisions that we are making all the time about how things are organised. The complexity approach thus was really the systems approach turned upside down. At the same time, because we introduced time into the equation through dynamics, systems could be forever changing and be infinitely complex. The other feature is through time, modern society has become ever more complex. We have ever more opportunities to do things, more wealth and control over resources and thus cities are becoming more complex through time. The complexity approach not only thinks of things as evolving from the bottom-up, but it also thinks of those things getting more complex as time goes on. That means that our ability to predict is always lagging behind. The complexity approach has quite profound implications for making forecasts, for making predictions: in essence it argues that complex systems are intrinsically unpredictable

In a nutshell, this is what complexity is all about. The idea that systems evolve from the bottom-up and that they are potentially infinitely complex and therefore unpredictable. The pandemic is a good example, which is surprising in some sense, though in hindsight nothing is surprising but at the time it was a very surprising phenomenon. People did not think that this kind of simple switch would ever really happen in modern life because they had never experienced it

As you said, cities are complex because they are balanced by a bottom-up dynamic, but AI is really like a kind of top-down technology. Does this mean that AI is not suitable for cities?

I think that although many of the technologies that are part of Artificial Intelligence may be configured and thought of from the top-down, there are a lot of situations in which AI really operates from the bottom-up. If you think about training a neural net, for example, then in training this net we have a very large number of factors that are continually switched, continually iterated basically, so that we can actually replicate some pattern. The iterative procedure of fixing the weights that combine all these elements together, and there may be many because we can introduce as many elements (layers) as we like in terms of these neural nets. It so happens, that in many of the problems we work on you do not have to introduce that many nodes into the network to be able to get some sort of iterative convergence, some sort of accurate prediction that is of the image or whatever it is you are trying to evolve the net towards. In other words, because everything is related to everything else in a neural net and we have a procedure for beginning to change the weights, then in some sense it is a bit like predicting the pattern from the bottom up. For me, I think AI is a set of ideas that can be applied to both systems that are organised from the top-down and the bottom-up. It very much depends on the context in which we are actually applying these ideas because, after all, what we are doing is automating something, trying to predict something in an iterative or trial and error way. We hope that the neural net will produce something that is in turn readable in terms of the reasons why things are put together to produce a certain sort of pattern but there is no guarantee that that is the case. We do not know why some of the best neural nets work, but they do work basically because we use computers to iterate over and over again, and we are then able to converge on the right sort of pattern.

You just said that AI, as well as big data analysis and neural networks try to predict the future, but you also said several times that we cannot predict the future, and actually, we can only invent it.

If you take the complexity point of view and accept the fact that systems are getting more and more complex as time goes on, because of our human intervention, with our total population expanding, we are getting more and more minds at work in terms of inventing how we do things. We are getting more control over resources, so we are getting wealthier, which gives us more opportunities. In that sense, we can assume that if systems are getting ever more complex it is going to be increasingly more difficult to predict them. Personally, I feel that many of the systems we deal with are intrinsically unpredictable anyway. If we could actually predict the future, then we would be able to control it in a way that we clearly cannot. That does not mean that we cannot invent it and indeed to some extent we do invent the future basically and we potentially have that power of control.

However, our ability to predict is very limited and there are countless examples of this, literally countless examples in the sense that there are very, very few good predictions of something like traffic. People have been building traffic models for 50, 70, 80 years and if we were to do a catalog of predictability, we would find that they have performed extremely poorly. It is the same with economic and population predictions, etc. In some sense, this lack of predictability is not something to be worried about, it is something we have to live with and deal with this in this particular context.

“This lack of predictability is not something to be worried about, it is something we have to live with”

That does not mean that we should not try to predict the future because in a way, when we are predicting the future, we are actually beginning to think about it in various ways. In other words, our models give us predictions that are conditional to an extent. The term is ‘what if’ prediction; “what if” something happens, then what would a prediction be like? We know that that would probably not happen for it would be a scenario, an ideal type. Therefore, predictability is not something to worry about, lack of predictability is something we have to live with. But many of our tools and models still enable us to probe the future and inform the debate if you like.

This is a circular argument to some extent. My own view is that in the next 50 years or so, we will move fairly rapidly towards a notion where we accept that we cannot predict the future. If you actually look at science, 150, 200 years ago, science was all about predicting the future. What has happened since then and certainly in the last 100 or 50 years, is really a retreat from predictability in some sense. This has to be qualified because there are certain contexts in which we might be able to make predictions because the organisation of the system is sufficiently well-defined to be able to manipulate it with very few numbers and variables. For example, at the level of Newtonian mechanics, the solar system is very well-defined. We know the motions of the planets and the standard gravitational model have been used to show that we can make almost, if not quite perfect predictions, and although at the very large scale we need to develop our models in relative terms, leading to a different kind of physics, in terms of the solar system can make pretty exact predictions. However, generally speaking, we can make good predictions, with this basic science, which is why we can land men on the moon using rocket technology which has good predictive applicability. For example, if we were launching a rocket to the moon and it was affected by a hail of meteorites, it would be knocked off course and prediction would just be impossible, and any predictions would probably be destroyed. In other words, what we are saying is that if we launch a man to the moon then basically, we have to assume that the system remains totally stable, otherwise the thing is not going to reach there.

When we think about this in terms of a social or economic system, it is very rare that we can have a situation where everything is well-defined and stable. If only one thing were to change and everything else remained the same, and that is almost an impossible occurrence, you have to think of this in terms of a thought experiment. Then, we might be able to have models that enable us to make a prediction of what happens to that single thing.

Prediction thus has always to be qualified. I think it is something we need to think a lot about in our world, but in every world, because there are certain areas like economics that have long‑standing traditions in prediction which have fallen into disrepute notwithstanding there necessity. Indeed, it is arguable in economics that all of this great superstructure that has been assembled over many years is often entirely irrelevant to the particular economic situation that we face. For example, the last recession 10 years or so ago, almost every element of it was unpredictable and that is largely due to the fact that we are changing the system all the time. Economy is a complex system because it is continually changing, evolving, and getting more complex as time goes by.