Planning within Complex Urban Systems

Shih-Kung Lai

• Publisher: Routledge
• Year: 2020
• Print ISBN: 9780367346133
• eBook ISBN: 9781000206227
• Topic: Business
• Subtopic: Business General
• Index: Business

Imagine living in a city where people could move freely and buildings could be replaced at minimal cost. Reality cannot be further from such. Despite this imperfect world in which we live, urban planning has become integral and critical especially in the face of rapid urbanization in many developing and developed countries. This book introduces the axiomatic/experimental approach to urban planning and addresses the criticism of the lack of a theoretical foundation in urban planning.

With the rise of the complexity movement, the book is timely in its depiction of cities as complex systems and explains why planning from within is useful in the face of urban complexity. It also includes policy implications for the Chinese cities in the context of axiomatic/experimental planning theory.

1 Introduction

1.1 Dilemmas in planning theory

For a long time there has been no consensus on what constitutes a useful planning theory due to the fact that the city is complex (Batty, 1995), that planning problems are “wicked” (Rittel and Webber, 1973), and that the standard for such a theory is too high to attain (Mandelbaum, 1979). Though some scholars argue for a planning theory that need not adhere to Mandelbaum’s standard but is still workable (Donaghy and Hopkins, 2006), such a theory is possible at a philosophical level at best. However, there is relatively little talk about planning theory at a scientific level that is derived from deductive logic and experiments, the two pillars of modern science. Though planning problems are conceived of as ill-defined (Hopkins, 1984), and ill-definedness is said to defy deductive logic (Arthur, 2015, pp. 5–6), I think it is useful to explain planning phenomena given an appropriate scope. In this book, I attempt to demonstrate that a scientific approach to planning is possible and that such a theory could be used to deal with the thorny issues that result from rapid urbanization. The approach I am proposing here might look abstract, but that is something I need to sacrifice in order to synthesize across both cities and plans.

Much has been said about various fragmented urban issues and how to deal with them such as housing, transportation, land use, financialization, urbanization, and property rights. With the rise of the complexity movement in science (Arthur, 2015), I think the time is ripe to look at the city as a whole through coarse graining. This might seem impossible, but if we consider all the activities in the city as interdependent decisions, we might be able to abstract these decisions from the city substance and look into and understand the patterns that emerge from the interaction of these decisions. In addition, plans are composed of interdependent decisions, and making plans connotes coordination of these decisions. As a result, the notion of decisions becomes the connecting crux between the city and the associated plans. It is based on this connection that I attempt to demonstrate how a planning theory, or at least a framework of such a theory, is possible through logical deduction and experiments (human or machine) and how we can deal with urban complexity through plans based on the insights gained from such a theoretical framework. In synthesizing the ideas presented in this book, I will propose the framework in Chapter 8 to argue for this possibility.

1.2 Cities and plans

In my view, the scope of the urban planning discipline mostly constitutes two intertwined topics: cities and plans; and these two main topics formulate four research questions: (1) How do cities actually develop? (2) How should cities develop? (3) How does planning actually take place? and (4) How should planning take place? Most, if not all, of the research topics and curriculums in an urban planning department can be roughly classified into these four categories. Here, to somewhat oversimplify, I consider the city as composed of a collection of numerous interacting decisions made by the agents residing in the city, such as where and for what to shop, where to work, where to go to school, where to sleep, where to play, where to build, what to develop, etc.

Understanding how cities work can take many perspectives such as scaling (West, 2017) and complexity (Batty, 2005). The systems perspective—in particular complex systems—is my main viewpoint. Viewed from the perspective of complex systems, the city is composed of relatively independent agents (people, families, firms, organizations, local governments) interacting with each other through decision making and communicating to come up with collective behavior. Different from the systems perspective of the last century, the emerging science of complexity considers the city as being far from equilibrium; therefore, urban modeling does not seek the solutions to equilibrium but instead seeks to discover dynamic regularities of urban development processes. This is mainly because the interdependence, irreversibility, indivisibility, and imperfect foresight (the four Is) render the city as a complex system and prevent the city from reaching equilibria, which makes plans useful. I will come back to this argument in Chapter 2.

Traditional planning theory focuses mainly on market failures and proposes solutions to these problems. For example, it suggests that through collective actions, such as governmental policies, we can provide collective goods such as infrastructure, and through regulations, as imposed by an environmental protection agency, we can eliminate externalities such as air pollution, and these issues are not independent of each other. In other words, these problems—that is, insufficient collective goods and pervading externalities—are interrelated. Lack of waste treatment plants deteriorates air quality. In addition, there is a dynamics failure that incurs transaction costs arising in the urban development process and which renders equilibrium analysis useless; there is a need for plans (Hopkins and Knaap, 2019). Plans such as signal manipulation work are needed in solving urban development problems derived from the dynamics failure caused by the four Is. In other words, plans solve urban problems arising from complexity but are not the only approach to dealing with complexity. Other approaches to urban complexity include administration, regulations, and governance as will be argued in Chapter 6, but in this book, I mainly focus on planning.

As will be argued in Chapter 8, planning and urban complexity are inseparable: they co-evolve. Therefore, plans emerge from within the urban system; they are influenced by and at the same time affect the spontaneous order that the city brings about. Industrial clustering of a city affects plans made by individual agents, which in return may give rise to industrial clustering, as evidenced by the computer simulation introduced in Section 3.4. On the one hand, what makes planning and urban complexity look independent is that we are used to the dichotomy between the observer/controller/experimenter and the observed/controlled/subject. On the other hand, technology has so empowered the planner that she or he tends to think that she or he can control the city. This was particularly true in the early stages of the development of urban planning as a discipline and later in the 1960s when control theory was introduced into the discipline. The integration of the city and plans can use the canoeing metaphor as a basis, as argued by Hopkins (2001): paddling the canoe is planning, while the river is the city. Strictly speaking, the canoeing metaphor is a representation of the stream of opportunities model in which problems, decision makers, solutions, and choice opportunities flow independently into the stream and interact with each other. The city is not controlled by one plan but interacts with a web of plans that are made by individual agents and that are interrelated with each other. In short, our current understanding of the city and plans is based on the scientific paradigm of reductionism developed in the past three centuries, but I would argue that the scientific knowledge and technology built on reductionism can only solve relatively simple problems. Urban problems are organized complexity (Jacobs, 1993); therefore, we need a shift in scientific paradigm from reductionism toward emergentism (which will be elaborated on in Chapters 2 and 3) in order to understand the city correctly and plan appropriately. Plan-based decision making is, in my view, the most cost-effective action mode in the face of urban complexity.

1.3 Planning as science

Before starting to think about what constitutes a scientific approach to planning research, perhaps we should ask whether the discipline can be considered as a science. My answer to this question is “yes,” based on the knowledge of more than 100 years of efforts made by many planning scholars, which have been derived mostly from the social sciences. What then is the science of planning? This is a difficult question, but I think a science of planning should aim at explanations and justifications of phenomena of interest, mainly cities and plans. Such explanations and justifications must abide by the scientific standard of theorizing. On the one hand, we should explain how urban phenomena emerge and justify how they should evolve, be they housing, transportation, land use, infrastructure, land and urban development, or other socio-economic processes, based on rigorous methodologies that can help us to gain a better understanding of how cities do and should work. For example, we could ask: Is the city or urban complexity solvable? That is, can the city be modeled by mathematics or computer simulations? By “rigorous” I mean that these methodologies, whether qualitative or quantitative, must be tightly logical and aim at internal depth and completeness. On the other hand, we should explain how plans for urban development are made and used and interact with each other and justify how they should be made and used and interact with each other. For example, we could ask: Is the plan solvable? That is, given a plan and a set of individual agents, does there exist a “policy” of price or rule setting that brings about the plan? In particular, can we solve urban problems that are deemed “wicked” (Rittel and Webber, 1973) completely through plans? Given this perspective, we can then identify the teaching and research scope of the science of planning as shown in Table 1.1, which is derived from Hopkins’s framework of explanations about plans (Hopkins, 2001).

Table 1.1 Teaching and research scope of the science of planning

	Urban Phenomena	Planning Phenomena
Explanations	How cities work	When and how plans are made and used
Justifications	How cities should evolve	When and how plans should be made and used

A planning science should aim at pursuing the scholarship of planning and therefore should conduct teaching and research that address all four questions shown in Table 1.1. However, in order for the science to situate itself into a particular socio-economic context, it may focus on one or several application areas, such as housing, transportation, land use, landscape architecture, and any other applications that a planning department finds important.

Given the broad framework of planning science as shown in Table 1.1, this book is aimed at addressing narrowly the question of how cities work through the lens of complexity theory and how we should take actions in the face of urban complexity based on a behavioral approach to planning. It is becoming widely recognized that cities are complex systems of many interacting, partially independent, agents that are far from equilibrium. This understanding has profound effects on how we make and use plans for urban development. For example, it is well known that planning problems in cities are “wicked” exactly because of the complexity that is derived from the interdependence, irreversibility, indivisibility, and imperfect foresights of urban development decisions. To plan for urban complexity well, we need...

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Dedication
• Table of Contents
• List of figures
• List of tables
• Foreword by Lewis D. Hopkins
• Foreword by Gert De Roo
• Preface
• Acknowledgment
• 1 Introduction
• 2 Basic concepts
• 3 Urban complexity within cities
• 4 Urban complexity across cities
• 5 Planning behavior
• 6 Planning effects
• 7 Applications
• 8 Synthesis
• Epilogue
• Notes
• Index