The decisions we make with respect to housing ourselves and providing shelter and outdoor places for the huge range of human endeavors are highly complex.  Our urban areas are, in fact, large, complex multi-layered systems of people, cultures, commerce, learning, transportation, energy production, natural resources and highly adaptive, though in many instances today impoverished, natural habitats.  It is no wonder, then, that a century or more of good faith guestimates about what might work better or what might produce the biggest financial return have sub-optimized the complex, multi-layered urban systems that actually exist and have been greatly expanded over the past half century.

Even though we have seen several decades of real estate booms that have greatly expanded our cities into urban regions, many of these are unsustainable in the face of the economic and energy realities we face today.  If we are, therefore, to take stock of what we have done thus far and what we need to do differently to create more resilient, more sustainable habitats for our society, we need to build more insightful pictures of how and why our creations and the public policies that encouraged them are performing the way they are.  We must also apply discipline to creating understandable pictures of the things we need to change in order for our urban environments to do a better job of shaping and providing shelter for the diverse and demanding range of human activity they contain today and in years hence.

The field of System Dynamics offers ways and means to understand and act upon the complex systems that form our urban environments – man made and natural.  Being able to map and simulate this complexity and draw linkages that characterize our understanding of how things seem to work based on our own experience and objective evidence is a key step toward understanding policy options, for example.  Conducting such mapping and simulation work also offers a means for testing the systemic consequences – intended and unintended  - of changes to prevailing public policies regarding such topics as development, taxation, clean air, transportation, property rights and so on1.  The art of System Dynamics is to create the simplest models that accurately portray the situations with which we are familiar and the results those situations seem to produce.  Such models also ought to enable us ask “what if?” questions where we change one or more aspects of the model to produce different results.  As an example of how to start, let us briefly examine carbon emissions and urban density.

Figure 1:  Simple model of urban density and policy choices

Figure 1 illustrates a simple model based on the high level results of recent research into carbon emissions.  The cores of older cities have much lower carbon emission footprints than their newer, lower density suburbs.  Newer cities, particularly in the South and Southwest have the highest carbon footprints because they tend to be far less dense and built around the automobile rather than public transit.2

Urban density drives carbon emissions, which in turn impact air quality and other factors that tend to increase the costs we have to bear for lifestyle-related diseases such as asthma and emphysema, both debilitating conditions that are drivers of sedentary lifestyles which in turn are driving spikes in early-onset diabetes and our current obesity epidemic.  Urban density also impacts health care costs because we know already that people tend to be fitter and healthier in walkable cities than in automobile-dependent suburban tract developments.  Finally, zoning ordnances that define density, set backs, parking requirements, mixed vs. single use, commercial vs. housing and so on, have a major impact on the resultant urban density.

The model in figure 1 is enough to prompt a wide range of questions regarding how each aspect is quantified and also which other factors external to the model drive and help define more specifically each aspect.  From a core model like the one in figure 1, it is possible to create secondary and tertiary level models comprising other knowable aspects of urban life and the issues that persistently arise as being important to the quality of life in cities.

Figure 2:  Expansion of the core policy model to track the cascading impacts of policy decisions

Figure 2 illustrates an expansion of the core model.  As with all models, it is inherently inaccurate and is not meant to be inclusive of everything possible.  This is an illustrative attempt to account for what might be some of the key drivers of sustainable employment and, hence tax receipts for funding state and local budgets.

Beginning with urban density and linking to commute times, the model explores “time with children” – a major driver of intellectual and social development, and literacy.  Impacting “literacy” is a modifier to “time with children”, namely “TV time”.  From literacy we can move to explore the drivers of the appeal of a region to employers and businesses and the resulting impacts on unemployment rates and so on also taking into account crime and its costs.  From there we can account for tax receipts, which fund state and local budgets and the ability of regional, city and local governments to invest in key elements of urban infrastructure including but not limited to schools, health and welfare, public transportation, water and waste treatment, highways and so on.  The dotted boxes are “clones” placed on the diagram to simplify the links and flows.

Each of these key aspects of urban infrastructure is amenable to being modeled and linked back into the core model either directly or indirectly.  The point to emphasize is that there are seemingly endless layers of detail possible.  A “mother of all models” actually does more to obscure the issues at stake rather than simpler ones that keep them well illuminated, hence the art of applying principles of System Dynamics11.  Such mental maps illustrated in Figures 1 and 2 are amenable to modeling using stocks and flows vocabulary in software tools such as iThink.

Humankind’s’ habitats are complex affairs.  Thus far, simplifying their design and construction through purely profit incentives and zoning ordnances has not achieved success consistently.  Where profits can be the greatest and where the greatest freedom for commercial real estate ventures exists, habitat - and all that it should entail for our species - has tended to suffer and so too, we believe has social capital and its ability to generate wealth of all kinds.

References:

1. “Using a Systems Approach to Unravel Feedback mechanisms Affecting urban Transport”, Mark Bachels, John Peet and Peter Newman, Canterbury Regional Council (and University of Canterbury) PO Box 345, Christchurch, New Zealand, 1998.  Ph.D. Dissertation.
   

2. “The Greenness of Cities” By Edward L. Glaeser, Harvard University and  Matthew Kahn, UCLA.  Policy Briefs, March 2009, The John F. Kennedy School of Government, Harvard University, Cambridge, Massachusetts.