“...the point about complexity is that it is useful - it helps us understand the things we are trying to understand.”
– Byrne (1998, p.7)
2.1 Introduction
This research extends from the premise that urban violence is the result of a complex, interre-lated amalgamation of underlying causal factors which manifest within similarly complex social systems.
The prime assertion in conducting research into this kind of complex problem is thus that the complexity sciences offers a body of theoretical knowledge, providing imperative concepts and tools by which these kinds of complex phenomena, their functionality and impacts can be better understood. In dealing more particularly with an empirical investigation into the de-livery of integrated violence prevention interventions the empirical elements of this research are grounded in a theoretical framework that not only recognises urban violence as a com-plex challenge but so too seeks to explore the comcom-plexity of delivering integrated prevention interventions within fragile communities. Here, the driving motivation is to make use of this knowledge base to consider how it is that such an understanding can aid in the scaling up of existing pilot projects to promote broad-based programme transfer, extending piloted successes to a significantly enlarged group of beneficiaries.
This chapter therefore focuses on outlining the key theoretical positions that have supported both the development of the research hypotheses and the pursuit of the empirical research undertakings.
The theoretical framework established here draws on two main bodies of literature. Firstly, using particular concepts derived from complexity theory, the first part of the theoretical frame-work is developed through the presentation of urban systems and violence as complex phe-nomena, which require innovative, non-standardised solutions. Complexity, as it is used here, does not intend to be merely descriptive of what some authors refer to as the obvious and non-informative portrayal of societal reality as complex (Wagenaar, 2007, p.23). Rather, complexity, is understood and presented as an epistemological approach that describes the theoretical posi-tion from which this research is conducted, based on an evolving body of literature, dealing with complexity as a determined theoretical perspective through which one views the functioning of existent physical and social realities.
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Figure 2.1.: Theoretical framework, key research concepts.
Secondly, the theoretical approach based on complexity theory is linked to prominent the-ories relating to policy and programme transfer. This research deals primarily with questions concerning not only the complexity of violence as a wicked problem but more specifically, with questions concerning the transferability of integrated violence prevention interventions from one area of intervention to another. Despite certain successes associated with violence preven-tion intervenpreven-tions, the argument is made that the move beyond the pilot project will only occur if we can better understand the nature of transferability as it is required in dealing with complex systems facing complex problems.
Overall, the dual-pronged theoretical framework established in this chapter, provides the lens through which the governance of urban violence, particularly crime prevention in South Africa, chapter 4, and the violence prevention through urban upgrading (VPUU) programme case study presented in chapter 5 are investigated and analysed. The primary focus of which is placed on the broad-based transferability of violence prevention interventions at local municipal level.
This chapter begins with a brief introduction on the origins of complexity theory and provides a working definition of the ontological and epistemological differences associated with the use of complexity as a term. Further, an explanation of the applicability of complexity theory to the social sciences and field of urban planning is presented. This applicability is supported by an overview of the opportunities that the use of such a theoretical perspective offers in exploring, understanding and dealing pragmatically with complex urban phenomena, in this instance integrated planning approaches targeting violence prevention.
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2.2 Complexity Theory
Broadly, complexity theory is the means by which we as scientists, social or otherwise, enable ourselves to understand and deal with systems, which we identify as being complex in nature.
No single, all-encompassing theory of complexity exists. Rather, the complexity sciences is characterised by a broad set of theories on the functionality of complex systems, which have been applied in a variety of fields, across a range of disciplines.
This section thus does not aim to provide an all-encompassing overview on the evolution of complexity theory, with its roots in physics and mathematics. What it does aim to do is afford a broad introduction on the evolution of complexity theory and its uses, in order to assert an understanding of complexity in its applicability to the social sciences, urban planning and most importantly to the analysis of the views and outcomes presented in the subsequent chapters of this thesis.
2.2.1 Origins of complexity theory
The notion of complexity and the evolution of theories related to complexity have evolved from scholarship in the sciences of physics, biology, mathematics and computer science. Gaining in popularity within a number of scientific and professional fields over the past two decades, complexity theory is fundamentally utilised as a means of simplifying, and thus better under-standing, an array of different processes and phenomena. This is essentially based on the increasing recognition of the world in which we all live as innately complex (Zuidema and De Roo, 2004, pp.3-4).
2.2.1.1 Defining complexity
In order to move forward with the use of complexity as a term, referring to the determinants of the complexity sciences and theories of complexity, it is important that complexity itself be adequately defined. The usage of terminology influences the way in which we define the systems that we are trying to understand and it is therefore imperative that such obstruction be dealt with in providing clarity on what it means to determine something as being complex. Many would argue, and correctly so, that based on certain dictionary definitions, the word complex may be asserted as a mere synonym for that which is complicated. This is illustrated when one undertakes a simple search for the word complex which realises the dictionary definition consisting of many different and connected parts9.
Similarly, as a noun, the word complexity is denoted as the state or quality of being intricate or complicated. It is in this light that expressions of the word complex, used merely as a descriptive term for real-world problems that are not entirely understood, abound. In thinking about com-plexity and its relationship to the discipline of planning, some criticism has been placed on the
9 See Oxford Online Dictionary – http://www.oxforddictionaries.com
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ease with which all things have become complex, as the concept itself becomes at risk of being set-aside as an overused, meaningless analogy for any situation that is not fully understood.
Within the fields of physical and social sciences research however, complexity, particularly in its reference to complex systems, has significantly evolved over recent decades to refer to something very different from that which may be merely understood to be complicated. It is therefore imperative for authors to use an interpretation of the word complex as it is refers to the usage within the scholarship of the complexity sciences, as an epistemological body of knowledge. It is this body of knowledge that provides the theories by which we are better able to identify and better understand the characteristics of the systems with which we are required to deal. A complicated system involves one where an analysis of the individual components of the system results in an accurate presentation of the complete system, regardless of the number of components. A complex system, on the other hand, refers to one where the system in its entirety cannot be fully understood through an analysis of its individual components (Reitsma, 2002, p.3).
Essentially, the concept of complexity and complex systems, as they are understood within the framework of this research, primarily asserts an acceptance of social systems as complex in nature thus stressing a rejection of reductionism to a level of simplified cause and effect relationships.
2.2.1.2 Complex systems: Non-linearity and the rejection of reductionism
The rejection of reductionism and the identification of complex systems, as those which cannot be broken down piece by piece in order to be fully understood, validates the perspective that, in contrast to complicated systems, the sum of the individual parts in a complex system is not equal to that of the entire whole. Fundamentally, all complexity theories thus concur that a complex whole is always greater than the sum of its individual parts (Senge, 1990). As Urry (2005, p.3) confirms, complex systems are irreducible to linear, simple processes and elementary laws that explain all outcomes.
Principally, complexity investigates emergent, dynamic and self-organising systems that in-teract in ways that heavily influence the probabilities of later events. Despite the rejection of simple, linear cause and effect relationships, complexity sciences thinking does rest in the as-sumption that simple causes result in complex effects, the core asas-sumption being that complexity in the world arises from simple rules (Phelan, 2001, p.130). Scientists and researchers engag-ing with complexity theory thus seek to identify these simple generative rules, determinengag-ing how agents behave within environments over time, identifying how these agents interact with one another. Unlike in the traditional sciences, however, these generative rules do not predict outcomes for identified states (ibid.).
During the time of its theoretical evolution, the complexity sciences has been significantly impacted upon by the advancement of systems thinking and, later on, by the notion of chaos and its relationship to order and disorder (Gleick, 1987). As somewhat of a precursor to complexity
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theory, systems thinking evolved as a direct critique of linear reductionist schools of thought (Flood, 2010). Systems thinking primarily cast off the linear cause-and-effect progressions previously held valuable in (read Newtonian) scientific analysis and advocated the necessity to observe processes within systems as part of their greater environment as opposed to dealing with interconnected parts of a system in isolation from the larger whole. As defined by Anderson and Johnson (1997, p.2), a system is “a group of interacting, interrelated, or interdependent components that form a unified whole”. Components can be both tangible and intangible, thus including physical objects as well as processes, policies, relationships and flows of information.
Systems thinking can, and has been, applied throughout a range of different fields and disci-plines and is recognised as a crucial component of complexity theory in that it is credited with the notion that wider systems cannot merely be understood through closed investigations of the individual component parts. Elements associated within a complex system can only be experi-enced through the combination of two or more inter-related parts. Simplistically illustrating this point, Senge (1990) famously describes the interrelatedness of complex systems using the anal-ogy that the division of an elephant, for example, does not produce two small elephants. The elephant, as a whole, is therefore not merely equal to the sum of its parts but is fully dependent on the way in which these parts are organised.
“Incidentally, sometimes people go ahead and divide an elephant in half any-way. You don’t have two small elephants then; you have a mess. By a mess, I mean a complicated problem where there is no leverage to be found because the leverage lies in interactions that cannot be seen from looking only at the piece you are holding.” – Senge (1990, p.52)
Furthermore, complex systems are open systems in the sense that they do not operate in iso-lation and are therefore inherently linked to their broader environment (e.g. a neighbourhood within a city, within a nation, within the global environment). Open systems are likewise com-plex in the sense that the individual components of the system are so numerous that causal relationships between them cannot be clearly established. These components form highly com-plex networks that include innumerable feedback channels or loops, making it all but impossible to trace cause and effect (Portugali, 2006). It is this understanding of complex interdependence, determining complex systems as interrelated and interdependent on other systems within the larger environment that resulted in the development of Bronfenbrenners (1977) ecological ap-proach to human development.
2.2.2 Interdependency in complex systems: The socio-ecological model
For all intents and purposes, ecological models demonstrate the interdependent relationship between systems, highlighting the necessity for multi-level approaches based on the fact that behaviours are affected by, and likewise affect, multiple levels of influence. Drawing on the
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Figure 2.2.: A Socio-ecological Model for Violence Prevention. Developed by Krug et al. (2002).
ecological models of interaction between organisms and their larger environment, the socio-ecological model came about as a means of understanding the dynamic interactions between humans and the environments which we inhabit.
Socio-ecological models, since Bronfenbrenner’s initial propositions evolved from a concep-tual understanding into a theoretical position and have been widely used across a variety of fields, urban planning and violence prevention included (Figure 2.2).
Based on the realisation that no single factor is determinant of why violence occurs (chap-ter 1), violence is thus recognised as the result of a complex in(chap-teraction involving individual, relationship, social, cultural and environmental factors. Socio-ecological models are thus ap-plied as a theoretical platform from which one can begin to understand how it is that the various factors associated with each level occur in combination with one another and how they interrelate dynamically to result in experiences of violence. This recognition of the complex interrelatedness of systems based on this socio-ecological theory of systemic interdependence now forms a central part of the methodologies used in the development of integrated violence prevention approaches (Krug et al., 2002).
Complexity and its recognised usefulness in understanding the interaction and interdepen-dency of complex systems, which have informed the development of theoretical perspectives such as Bronfenbrenner’s ecological model, has over the past two decades or so begun to be more readily addressed within the broader social sciences, as the potential for deeper under-standings of complexity theory in their applicability to a range of complex social problems is acknowledged.
2.3 Complexity: A Social Sciences Episteme
Urry (2005, p.2) puts it so eloquently in saying, “it is in the late 1990s that the social sciences begin to go complex”. Complexity theory is not purported as a single identifiable theory as such, which is holistically transferable. Rather, it is viewed as an emerging body of work, thus far loosely structured, which provides a set of “conceptual tools”, guiding our thinking across a range of disciplines (Walby, 2007, p.456). The “turn to complexity” in the social sciences has
2.3. Complexity: A Social Sciences Episteme 34
been arguably most comprehensively covered by Byrne (1998) in his book Complexity theory and the Social Sciences. In his positivity about the potential of complexity theories to contribute to the social sciences, Byrne advocates the necessity to develop new modes of thinking and assessment within the pursuit of social sciences research based on the fundament of complexity as an overarching means of understanding social systems as intrinsically complex. Byrne (1998) presents concepts of chaos and complexity as fundamental theories to the way in which social scientists across fields such as urban studies, education and health would approach research, using these theories as a new means of investigating the nature of social research in order to stimulate a wider appreciation for the contribution that complexity theory could, and has, made to the advancement of social sciences research.
The realisation that it is the complex system that best represents occurrences in reality fur-ther supports the emergence of complexity theory in the social sciences, as an advancement on traditional systems thinking. Social scientists are primarily concerned with human behaviour and therefore place human beings within the complexity paradigm. The relative autonomy and uniqueness of human beings in combination with an innumerable variation in the possibilities present when human beings interact with one another is what results in the complexity of out-comes (Geyer, 2004). The recognition that the complexity of systems, within which human beings are central agents, renders often unpredictable outcomes has in turn had profound im-plications for urban planning theory and practice as attempts are made to address the varying urban development challenges present in dynamic urban contexts.
2.3.1 Complexity in urban development and planning
2.3.1.1 Planning with complexity: from technical rationalism to communicative collaboration
Initially, the modernist, orderly episteme within social sciences was indeed heavily influenced by the successes of tracing cause and effect through a linear paradigm experienced within the natural sciences under Newtonian law. In viewing social interactions and urban phenomena as linear and therefore as predictable, planners aimed to create the master plan which would bring them closer to the desired “end-state”. Such linearity however is removed from reality where linear causality is far less observable. Principally, as has been outlined above, complexity pur-ports a rejection of linear reductionism in the form of identifying cause and effect relationships between system elements, their actions and resultant outcomes. Complexity thus too purports a rejection of certainty and predictability. The non-linearity and relational complexity of complex systems results in only one predictable outcome: certain uncertainty, something with which urban planners must continuously cope. In largely precluding reductionism, a more postmod-ernist episteme underpins this research approach in that uncertainty is not only accepted, but also stressed as inevitable.
2.3. Complexity: A Social Sciences Episteme 35
This brings about the discussion and presentation on the specific relevance of complexity to the discipline of urban planning and subsequently, to the manifestations of urban violence and crime as a complex development challenge. Prior to the past two decades or so, planners ap-proached their work with what authors such as Voogd (1995), cited in Zuidema and De Roo (2004, p.2) term primitive optimism. This optimism was grounded in the technical rational thought processes, which characterised this era and held the belief that government could ad-dress and adequately control all arising problems. This is of course based on the premise that a high level of certainty is attainable, something which the evolution of complexity theory and the notion of urban systems, as complex interrelated systems, dispels.
Alongside the realisation that technical rationality provided unrealistic expectations, certain shifts within planning theory emerged. Developments in planning discourses over recent years demonstrate that the reality of the complex nature of the societies and spaces we aspire to
“plan” is largely recognised. Since the 1970s, planning theories and practices have thus evolved from the implementation of traditional, technical rationalist approaches towards the realisation of more communicative and collaborative approaches. This shift from implementing planning approaches based on linear assumptions in order to “deliver” solutions to developing a focus on wider stakeholder engagement and participation rests fundamentally in the realisation that governments, and the planners they employ, can not tackle the multitude of challenges being faced alone (Healey, 1999, 2003; Innes and Booher, 2003).
A rejection of taxis-oriented approaches, which imagine the investment in abstract utopian
A rejection of taxis-oriented approaches, which imagine the investment in abstract utopian