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In this chapter, we reflect on the possibilities of purposeful community development in a non-linear understanding of society. Although the complexity and uncertainty that characterize the world put forward challenges for planning and steering, it doesn’t imply that purposive interventions are unlikely to be successful or that planning has become obsolete. It does, however, require a different understanding of how societies organize themselves and about how collective strategies sort reality-effects. Planning, as spatial planning, is a subset of strategy and provides a set of tools for others. In this chapter we highlight the importance of strategy in a world where many of the traditional planning rules and certainties have been challenged. We deepen the discussion about community development by placing it in the context of governance understood as a set of co-evolving actors, institutions, and power/knowledge configurations. Within these ever changing governance systems, forms of organization are necessarily linked to and co-evolve with narratives on identity, community, and governance itself, as the taking of collectively binding decisions. Taking into account the complexity and non-linearity that characterizes these co-evolutionary processes we discuss the links between community formation and the organization and transformation of space through planning. We explore how strategy should be understood in this context and we identify which forms of strategy can work under the structural conditions revealed through the lens of complexity theory and governance theory.

Post-contingency draws from contingency studies and their indication that the performance of alternative planning approaches is influenced by the different environmental circumstances encountered. Complexity is in these contingency studies among the criteria used to distinguish between various environmental circumstances. The performance of alternative planning approaches is subsequently considered to depend on the degree of complexity encountered. Counterintuitively, complexity is now approached from a ‘static perspective’. Rather than appreciating complexity from the perspective of a dynamically evolving world, it is then seen as but as a characteristic of a planning situation to be identified and contingently responded to in the ‘here and now’. While such a reading of complexity is arguably simplistic and limited, post-contingency argues it can nevertheless be useful. Post-contingency goes one step further than ‘classic’ contingency studies and their suggestion that the complexity of the environment directly informs planning approaches and organizational formats. Rather, post-contingency considers the role of those involved in decision-making in perceiving, interpreting and responding to complexity. The argument is that both interpreting issues’ complexity and subsequently relating a planning approach to these issues are, at least partly, socially mediated choices. Post-contingency, therefore, argues that complexity is not presented to us planners as an objectified reality, nor that it determines planning choices. Instead, post-contingency highlights notions of perception and choice as essential intervening variables in how policy approaches and organizational formats are adapted to changing conditions of complexity and uncertainty.

Using a complexity lens has not gained widespread attention for explaining why certain planning processes succeed and others fail. This chapter responds by proposing Qualitative Comparative Analysis (QCA) as a method for analyzing the performance of spatial planning processes, taking into account the perceived complexity of the contextual environments encountered. This proposal is rooted in a contingency perspective. Contingency perspectives assume that, for spatial planning approaches and organizational formats to perform well, they should be adapted to the environment in which they intend to operate; i.e., a degree of ‘fit’ is required. The perceived complexity of the contextual environments is among the key arguments contingency studies propose as informing fit. QCA follows this argument, but also responds to some of its main critiques. First, QCA can help avoid a deterministic perspective on complexity informing fit, as it assumes complexity as being conceptualized by stakeholders involved in the planning process. Second, QCA can help overcome a reductionist perspective and its simplistic reliance on a pairwise analysis of relations between isolated environmental variables and isolated organizational or decision-making variables. QCA approaches ‘fit’ from a more holistic perspective, by analyzing the performance of spatial planning processes as configurations of alternative spatial planning approaches, organizational forms, and contextual environments. The result is an understanding of ‘fit’ following what QCA considers ‘complex causality’, which is manifested in configurations of various interacting and interdependent conditions. Next to being able to analyze relationships between the performance of spatial planning processes and the perceived complexity of the contextual environment, by using the notion of ‘complex causality’ QCA also allows for analyzing spatial planning processes as complex adaptive systems.

The debate on urban complexity, and, more specifically, the application of a fractal logic in urban planning and design gained prominence in the late twentieth century. Several researchers have engaged in this theoretical debate, while others have applied this logic within spatial analyses of urban patterns, with the aim of better understanding urban planning effects. Further, some researchers have simulated ‘fractal’ cities. In this chapter, we demonstrate how a multifractal, multiscalar simulation model can be used as a tool for guiding spatial development to enable the identification of potential areas of urbanization and to protect continuous green or unbuilt areas. This simulation model can facilitate the implementation of strategic urban development plans across different scales, ranging from the neighbourhood scale to the regional scale. All scalar levels are interlinked through the application of an iterative logic underlying a multifractal pattern. We demonstrate how this multifractal, multiscalar model can be applied to resolve planning issues.

What planners should know: revolution and evolution are as real and essential to life as water and air. Revolution and evolution are expressions of change. We take the stand that spontaneous and autonomous change is a factor in the everyday environment we’re part of. In this chapter we explore the possibilities of making this kind of change part of the planners’ frame of reference. Traditionally planners’ concern is about effectively intervening in space and place, hence the desire for controlled environments. Contemporary planners also have a preference to act on the basis of consensus among the various parties involved, to create a world that is agreed upon. The message here is not that these approaches are bad or wrong, but, on the contrary, there’s more. The complexity sciences are a major source of inspiration for spatial planning, as these point out the importance of time, non-linearity, transformation and dynamics. That, together with the complexity sciences’ explaining power about co-evolutionary processes and processes of self-organization, for example within the urban environment, make the complexity sciences relevant to planners. On the other hand, this chapter also emphasizes being critical, as intentional change made by purposeful interventions remain a necessity. Also, the complexity sciences have a preference for quantitative data, while intersubjective interaction has proven its value to planning and should remain key to planning actions and the planning discipline. When it comes to mutual inspiration between planning and complexity, this chapter takes into account both opportunities and threats.

This Handbook shows the enormous impetus given to the scientific debate by linking planning as a science of purposeful interventions and complexity as a science of spontaneous change and non-linear development. Emphasising the importance of merging planning and complexity, this comprehensive Handbook also clarifies key concepts and theories, presents examples on planning and complexity and proposes new ideas and methods which emerge from synthesising the discipline of spatial planning with complexity sciences.

This chapter is about rationality as a frame of reference for choice, planning and decision-making and that is susceptible to evolutionary and revolutionary tendencies. To begin with, rationality as it is commonly used in today’s planning debate no longer holds and the debate is progressing towards new understandings. This is a rather challenging and perhaps a somewhat controversial statement, knowing that the mainstream planning debate on rationality, communicative action and intersubjective reasoning is already far beyond the traditional, but still dominant, ‘rational choice theory’. This existing planning model positions two rationalities – the technical and the communicative – as opposing and complementary extremes that frame and explain planning behaviour in the governmental domain. Between these extremes, a multitude of realities regarding public choice, planning and decision-making can be assigned, each reality representing a specific course of action. However, to relate public choice, planning and decision-making to the governmental domain only is increasingly perceived as a limitation. Consequently, developments within planning practice require additional and innovative steps outside the existing rationality model for planning behaviour. This chapter proposes a multi-level expansion of the rationality model for planning behaviour. The rise of social movements, civil initiatives and collective action, as well as the ongoing democracy crisis, inspire the idea of seeking rationalities beyond the governmental domain. These new movements, initiatives and actions no longer relate to approaches within the traditional range from command-and-control government to shared governance. Consequently, planners and decision-makers are looking for frames of reference that include processes of self-governance. Building on the concepts of dynamic patterning, co-evolution and multiple layering, taken from the complexity sciences, a new rationality model will be constructed with multiple frames of reference for public choice, planning and decision-making.

Most urban planning projects are implicitly based on the premise that people will adapt their behaviour to a new situation, be that the development of a new railroad, dedicated cycle lanes or the revitalisation of a neighbourhood. Sometimes, such behavioural changes are very smooth and quick, but usually many people are unsure about the implications of a plan and are hesitant about supporting it. Often, the support and exemplary behaviour of key actors convinces others to follow. Such behavioural dynamics add to the complexity of the planning process. Increasingly, collaboration and co-creation with communities are being recognised as more satisfactory ways to plan. Agent-based simulation models can add to this collaborative planning process by sketching out possible developments for discussion. When community behaviour is included in agent-based simulations of a plan, their behavioural responsibility (agency) is recognised and emphasised in the planning process. This chapter elaborates on the behavioural dynamics in communities and the development and use of such agent-based community simulations.