What follows is an excerpt from a short essay I wrote. I think it encompasses the purpose of this blog really well. It constitutes a proposal for adapting the concept of evolutionarily stable strategies (ESS), from evolutionary biology to urban systems. For a quick reference on what an ESS is, refer to the wikipedia article.
Consider a finite system in equilibrium, such as a small ecosystem with minimal (or nonexistent) external influence, or a self-contained urban area, such as a city (again, with minimal interaction with external factors), and also consider the introduction of a “mutant” “individual” to the system, with an agenda of maximizing its own gain. In the biology example this individual can take the form of a mutation in a species, in an urban context the individual can take the form of a new point of interest, such as an area undergoing gentrification. The individual strives for its own share of resources (these being food in the ecosystem example and human visitors in the urban context) by competing with the rest of the population for them.
By introducing to this system the new individual, the system potentially enters a state of disequilibrium. The newcomer can have either of two outcomes in the “conflicts” with each other person of the population, either win or lose. There exist 3 different system outcomes based on this condition, either the newcomer wins all conflicts, it wins some and loses the rest, or loses all. Assume that the second scenario is true, as the final outcome in the first and third scenarios is clear and definitive. In competing with the rest of the population, the newcomer wins some conflicts, thus gaining an increasing amount of rewards at first. Rewards in the context of the ecosystem can be resources such as food or territory, in an urban context it can be people visits.
For the purposes of this example, the system in question was defined as finite. As such, resources are finite as well. In the ecosystem example this can be seen as the total amount of food available, in the urban example this can be represented as the city’s total human population. Assuming that once gained, a resource cannot be lost to the person it was gained from, the total resources owned by the newcomer increase with every consequent iteration. Furthermore, taking into account the fact that the newcomer wins specific conflicts and loses the rest, and also the fact that resources are finite, the total rewards gained by the new individual have a limit, and can also be calculated, as a proportion of the total resources.
At this point, it can be considered that the system has reached a new state of equilibrium. Considering all other factors as being equal, the outcomes of all conflicts having been calculated, and the amount of resources being finite, the resources that correspond to the newcomer can be calculated, and considered non-changing. From the system’s point of view, the intruder has been assimilated. In the ecosystem example this can be seen as the population of a new species having stabilized in numbers and established a territory. In the urban context the new equilibrium can be seen as the new area having established its target audience, and people visits having reached a steady flow.
The hypothetical city in the scenario described in this essay constituted a laboratory city, in the sense that it was in a controlled environment, having no interaction with its surroundings, and being static in its inner workings. In an attempt to bridge the gap between the static city described here and the dynamic nature of cities in the real world, the above example can be placed in a more dynamic temporal context. At the temporal scale, this process of equilibrium->new component->ESS->new equilibrium can be thought of as existing at the microscopic level. At the macroscopic level, the equilibrium reached at the end of the above described process would constitute an opportunity for a different area to start developing in order to attract visitors (ie, a new individual being introduced into a stable system).
This approach is represented using simple models, and many important aspects of the city have been omitted. Similarities, however, between an evolutionarily stable strategy as applied to processes in evolutionary biology and urban processes are sufficient to validate such an approach. A more in-depth look in urban processes is in order, utilising further tools adapted from game theory and ESS. Furthermore, it seems most probable that an approach such as this should be studied through agent based models. The reason for this is that the agents in such a model can function almost intuitively as the individuals in a system, acting on a few simple and clear strategies.
in the “win all” scenario the newcomer ends up being the only individual, in the “lose all” it gets removed from the system