Planning for Social Environments : Social Capital in the Context of Critical Realism and the Dynamics of Complex Systems
which we are a part . This includes the social systems and structures that arise from and shape human interactions at all scales , including meso-level neighborhood contexts in urban settings .
4.1 . Cities as Complex Systems
When Jane Jacobs wrote “ The Death and Life of Great American Cities ” in the early 1960s , complexity science as we know it today was still in its infancy . Cybernetics had been around since the 1940s when Norbert Weiner began to explore what comprised the various dynamics of purposeful systems ( Moray , 1963 ). Jacobs ( 1959 ) identified an important 1958 review essay on scientific progress and complexity by Warren Weaver that signaled growing awareness of complex systems consequences generally and what such thinking might mean in the context of cities and their functions . Lindblom ( 1959 ), writing a year or so ahead of Jacobs , reached similar conclusions about the limits of administrative planning , in principle — despite desires and efforts , accounting for and projecting all variables into the future as a way of controlling outcomes was simply not possible . Cities are not machines , Jacobs ( 1959 ) argued , because they are characterized by non-linear processes implying irreducibility . Like natural systems of energy and motion , social structures and human systems seem to hold order and chaos in tension , reflecting both predictability and novelty ( Byrne , 1998 ; Lewin , 1999 ; Simon , 1962 ; Waldrop , 1993 ).
Understanding these dynamics is an important feature of our efforts to understand social structures generally and social capital measurement in particular . There are several key features that are particularly relevant . Whether or not complex systems are deterministic ( Goldspink , 1999 ), they are certainly not linear ( Feigenbaum , 1983 ). As discovered by Lorenz while running data on weather prediction models , even where the environment is a computer operating system running algorithms , very slight variations of initial conditions can yield remarkably different outcomes ( Lorenz , 1963 ). In lab experiments and other attempts to create closed systems , this sensitivity to initial conditions can be problematic , since a failure to control even a small variable affecting a phenomenon ( e . g ., the number of decimal places used ) can lead to unexpected or erroneous outcomes . In quasi-closed systems , like networks of sensors or operating systems , these unwanted nudges make prediction ( and troubleshooting ) difficult . This sensitivity is operative even in the case of a simple push button switch that will “ bounce ” and be randomly open or closed owing to micro-states if a limit strategy is not coded into the software or built into the electronic circuits ( Margolis , 2012 ). In open systems , like societies and other human social structures , the avenues for sensitivity are numerous indeed and formal repeatability is not possible . Given the sensitivity of systems to these inputs , the deep complexity and non-linearity of an open system appears to make predictability impossible in principle :
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