The model of contemporary economics is at a crossroads between the neoclassicist knowledge-based optimization, and the adaptation of more flexible structures under a model of  ‘evolutionary development’ which is aligned with looking at emerging macroeconomic behavior patterns and the embracing of new technologies.  Knowledge-based economic strategy follows a rule of constrained optimization which, although providing precision in its analysis, only evaluates behavior within a simplistic time period where there is quantifiable risk. This model can only be used to calculate using calculable data and does not take into account the economic uncertainties which can arise from the behaviors of more complex systems in its response to shifts in technology, policy, industry, and human procedure. The study of macroeconomics is the evaluation of the overall states of economic systems, which in practice quantifies the the resulting changes in perspective and procedure that come about through such shifts in society, but does not consider the behavioral origins of these shifts which are at the root of the system’s development itself. Here we see the development of the same emerging logics which we can observe in the structure of our cities, wherein the relationships between agents in the system develop clusters of rules through self-organization to create a whole which is in fact greater than the sum of its parts. As with the awareness of swarm intelligence and self-organizing systems, the hybridization of architecture and computation has been present for over half a century, and it has divided the architectural community in similar ways. Though parametricism is met with an onslaught of negative connotations due to its indifference in formal resolution to such social elements as landmarks, embedded cultural traditions and the preservation of the human history of a place, it remains merely an agent of constrained optimization, of  designing by parameters. The improper use  of this technique occurs when parametricization occurs at the scale of the overall, indicating a top-down implementation method based on the calculable data collected from the emergent system rather than forming parametric logics based on the behaviors of the agents themselves and the means by which they collaborate and exchange data. The latter approach, which is more heavily dependent on the creation of clusters of rules by independent agents for the purpose of self-organization, is directly analogous to this notion of ‘economic evolution’ which is characterized by increases in organised complexity in economic systems and accompanying rises in wealth and per capita income[i]. Computation in architecture affords designers, urbanists, psychologists and economists the ability to zoom  in on the behaviors of such clusters as a means to develop complex algorithms which can take into account, within an estimated margin, the unquantifiable uncertainties which arise through environmental, technological and political changes. As such, however ‘rational’ it might be to think of economic growth in the classical sense by evaluating the state of the system, it is impractical to omit the presence of the self-organizing economy. We must instead look for way in which to embed the economic processes into the functional clustering of micro-economies and their complex relationships. This coincides with a complete deconstruction of the traditional ways of viewing architecture as fundamentally the creation of four walls and a roof. It is not only naive to suggest that the problems with our socioeconomic situations can be solved with the development of better buildings, it its essentially counterintuitive to basic human operation. We are too big, too different, too emotional, and too dumb to assume that building a sturdy house will solve our problems.