For a long time, I have had an interest in cities. I have always felt that cities are living, breathing organisms, and that the process of making them smarter—more advanced, more efficient, better for their citizens—is intricate and complex, akin to the complexity of living things. Cities are a confluence of wicked problems, and many urbanists have referred to cities simply as 'the urban problem'.
Despite this complexity, I've always felt that there could be a principled, formulaic way, not too different from a formal mathematical standard, of understanding cities and ultimately improving them. With the rapid growth of AI over the past few years, my view has been simultaneously supported and challenged. At times, I have felt that AI could perhaps be a fundamental tool in understanding the urban problem. On the other hand, models which simply predict the next word could hardly offer the solution to many urban problems, let alone the urban problem.
But before I go too far on whether LLMs and foundation models could offer solutions to the urban question, I'd like to take some time to define the problem at hand. It's clear to me that an understanding of the problem and the opportunity is crucial to developing these systems, because it is from this understanding that we define the parameters of execution and the criteria for success. With respect to the latter, by defining the problem, I get to outline my view of the future that has been forming in my head, no matter how fictional it may seem.
I am a trained mathematician so I will approach this as such. What is the optimization problem? Succinctly, it is to maximize urban welfare for residents. Certainly, this description should raise many questions. What determines the optimal allocation? What is the optimal allocation? Those are certainly important considerations, among many others, but I will not deal with them now.
If the objective above is unsatisfactory, we can provide a slightly more expansive objective: to maximize the gain in utility that the average immigrant to a city gains by moving in and minimize the subsequent average loss in utility to existing residents. This still is rather lacking
So what?
It is my (ever-evolving) view that any attempt to fix the city or improve it without a granular understanding of it is moot. This means that the optimization problem is no longer merely about maximizing utility, but about maximizing our understanding of the city. Our ability to improve utility for urban residents is directly tied to the ability to sufficiently model the city to a satisfactory degree of error. To be able to solve urban problems, we need to be able to learn and predict their occurrence based on historical patterns and current trends.
So, what is the optimization problem now? The problem is now: what is the most accurate representation of the city that we can form? This is, in a sense, finding the optimum in the field of formulations of the problem rather than the field of the problem itself. In this case, finding the best formulation of the urban problem is crucial to solving the urban problem. One does not merely stumble upon the optimum of providing a better life for urban residents without a comprehensive, granular understanding of the city.
A salient feature of this formulation is that it appears to be agnostic to whatever view of urban improvement one may have. This is at once beautiful and terrifying. It's so wonderfully general, yet the liberality with which such an interpretation can be applied to maximize the gain of those who already have so much does not bode well for minimizing urban inequalities.
Smart and efficient cities are those that invest time, effort, talent, and technology into developing this principled understanding of cities. This is perhaps where advanced prediction models could come into play; models with a physical understanding of the world could be even more revolutionary.