An Introduction to Urban Governance

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An Introduction to Urban Governance
The introduction is intended as an introductory statement of urban governance. It addresses the theoretical and methodological foundations for urban governance as well as applications of these foundations to solving real world urban governance problems. The introduction is therefore divided into three parts: Theories, Methodologies, and Applications. In the Theories part, we explain the theoretical foundation for urban governance. In particular, we depict how cities work in terms of the complexity and economic approaches to urban development, and how we can management cities through plans, governance, regulations, and administration. In the Methodologies part, we first introduce the complexity approach to explaining how cities work and based on this introduction, we then introduce analytical methods that can help city managers to cope with various urban problems, including decision analysis, policy analysis, and planning analysis. We consider city modeling as an effective way of not only understanding and gaining insights into the urban development process, but also providing a powerful tool to analyze how urban phenomena emerge. In addition, based on such understanding, a general discussion of planning support systems is provided as a basis for further developing possible technologies for city managers to cope with various urban problems. With the theoretical and methodological foundations introduced in Parts 1 and 2, in Part 3, Applications, we demonstrate how the ideas derived from these foundations can be used to deal with urban issues, spatial or non-spatial. These issues are selected so that they cover the most important application areas related to urban socio-spatial processes. Together they provide a coherent set of concrete examples of how cities should be planned, governed, regulated, and administrated.

Table of Contents

Part 1: Theories
1 Urbanization and Urban governance

2 Scope of Urban governance and Related Fields

3 How Cities Work: Property Rights and Complexity

4 Planning Cities

5 Governing Cities

6 Regulating Cities

7 Administrating Cities
Part 2: Methodologies
8 Cities and Complexity

9 Decision Analysis

10 Policy Analysis

11 Planning Analysis

12 City Modeling and Analysis

13 Planning Support Systems

Part 3: Applications
14 Transportation

15 Land Use

16 Sanitary and Infrastructure

17 Building and Constructing

18 City Design and Landscape Architecture

19 Real Estate and Housing

20 City Renewal and Regeneration

21 Ecological Environment

22 City Disaster Management

23 Slums and Homelessness

24 City Finance

25 Crime

26 Social Welfare

27 Education

28 City Institutions

29 Governmental Organization and Administration

30 Information City and Technology

31 Globalization and City Competitiveness

32 Global Climate Change and Energy

33 Comparative Studies

34 Conclusions
Part 1: Theories
For any discipline to thrive, a sound theoretical foundation is a must. This part of the introduction provides such a foundation for the discipline of urban governance. A theory is an explanation of a particular phenomenon. For example, a theory can be constructed to depict a particular phenomenon using mathematical models, verbal arguments, computer simulations, and psychological experiments. Regardless of its format, it must convey a coherent set of ideas that would not be observable through our common sense reasoning. We can observe how the bodies move in the world, but only through Newtonian mechanics can we explain why they interact in such a way that we can make sense of these movements. A theory of explanation can help us to predict what will happen and take appropriate preventive actions ahead of time. If we know how the bodies interact through gravity in the world, we know how to construct buildings and even design aircrafts that can fly.
Theories in urban governance function as both explanations and justifications for two sets of phenomena: cities and management. Explanations for cities beg objective expositions how cities do work, while those for management target at depicting how decisions are made regarding urban issues. Justifications imply value judgments and are subjective in nature. Justifications for cities prescribe subjectively how cities should work, while those for management focus on how decisions should be framed and made. Explanations and justifications for urban governance can be framed as four sets of theories as shown in Table 1.
Table 1 A Typology of Theories for Urban governance

cities management

explanations How do cities work? How are plans and decisions made?

justifications How should cities work? How should plans and decisions be made?

Management broadly defined here includes planning, governing, regulating, and administrating cities. Planning is narrowed defined as making multiple, linked decisions in face of uncertainties; governance means making and taking collective decisions and actions; regulations delineate rights in making decisions; and administration focuses on making daily decisions, routine or irregular, in organizational setting. The four modes of actions, that is, plans, governance, regulations, and administration, in urban governance constitute the activities carried out by city managers in dealing with physical and non-physical issues in cities. A useful, effective set of theories of urban governance must cover the four research questions as depicted in Table 1. More specifically, they should provide a thorough understanding of how cities do and should work as well as how city managers do and should make decisions and take actions accordingly through plans, governance, regulations, and administration.
1. Urbanization and Urban governance
How do cities come about and how they function once exist? There is no definite answer yet, but we are just beginning to understand and answer the long-lasting question. Evidence shows that large metropolitans tend to get larger, and more and more people tend to reside in cities rather than rural areas worldwide. The question of the emergence of cities is equivalent to the question of how cities grow. Increasing returns are the key factor. Put simply, increasing returns argue that the greater the number of persons adopting a particular technology, the more advantage for the newcomers to use that technology. If we replace the technology with location, we can say that the more persons residing in a particular location, the more advantage for the newcomers to reside in that location. In other words, with an increasing incoming population moving in, a city becomes more attractive due to improvement of services in public facilities and job opportunities. This phenomenon immediately leads to the question of whether there is an optimal size for cities. This question remains yet fully unanswered.
In complex systems, which cities are no doubt an example, sizes matter, as manifested by the axiom of “More is different.” Living in large cities is different from living in small ones, simply because cities are composed of interacting agents and the sizes of the amounts of agents and the intensities of the interactions between them give rise to qualitative changes in the physical and non-physical settings in cities. Large cities, such as Beijing and Shanghai, provide more job opportunities and public facilities, such as transit systems, are more accessible than smaller cities, such as Hangzhou. However, large cities cause more severe urban ills, such as air and water pollution and traffic congestion. Whether a particular size of cities is desirable depends, therefore, on pros and cons of living there, that is, the tradeoffs between gains and losses in living cities with different sizes. Because these gains and losses are difficult to measure precisely, the optimal size of cities is difficult to decide.

What is peculiar about city sizes is that there is regularity. Not only are large cities fewer than smaller ones, but also that if we take the logarithmic scales of the ranks and sizes of cities and plot these cities in a plane, they would show as a straight line. This is called the well-known rank-size rule. The rank-size rule of cities is robust in time and space. It exists in cities with histories of thousands of years and persists in many countries.

Cities are complex systems that are difficult to tame, but this difficulty enhances the usefulness of urban governance in face of complexity, rather than undermining it. The crux is that only when we have found the deep regularities of how cities function by theorizing, such as the rank-size rule, can we start to think about how to make rational choice in complex systems, such as cities, in order to survive, even better, to thrive. This introduction is targeted at such theorizing practice in hope of dealing with real urban issues based on a sound theoretical basis.
2. Scope of Urban governance and Related Fields
Urban governance is an interdisciplinary field and concerned with understanding how urban phenomena come about and what we can do about them. These phenomena can be roughly divided into physical and non-physical components. The physical component is something that can be visually perceived with a focus on urban morphology. Traditionally, it includes, but is not limited to, land/urban development, real estate investment, infrastructure construction, and ecological systems. As to the non-physical component, it has to do with structural constraints within which people behave, including, but not limited to, economic structure, sociological structure, political structure, and regulatory structure. The physical and non-physical components interact through people, or more technically agents, exchanging information and taking actions, so that the city can be viewed as a whole. Urban governance is concerned with both the physical and non-physical phenomena and seeks appropriate ways to deal with issues that emerge from the workings of cities.
Four modes of taking actions are considered: regulations, plans, governance, and administration, with different focuses. Regulations focus on rights; plans on decisions in relation to interdependence, irreversibility, indivisibility, and imperfect foresight, and governance on collective actions. More specifically, regulations deal with identification of rights within which one is allowed to take actions. Issues such as evolution, origin, and delineation of rights are considered. Plans as manifested as policies, visions, strategies, designs, and agendas are made to craft decision making in the face of interdependent, irreversible, and indivisible decisions with imperfect foresight. The urban development process is characterized by the four I’s, and thus plans are effective in coordinating decisions under such circumstances. Governance is concerned with collective actions, both formal and informal. Formal collective actions include actions taken by local governments and informal ones those taken by citizens through participation. Administration focuses on making daily decisions, routine or contingent, in organizational settings. Urban governance focuses on both physical and non-physical components of cities, and therefore, all four modes of actions, i. e., regulations, plans, governance, and administration are important in order to improve human settlement.
Urban governance is identified as a scientific pursuit to explore in depth and completeness of how to improve cities by addressing four fundamental research questions scientifically, or four H’s: 1) How do cities work? 2) How should cities work? 3) How are plans and decisions made? and 4) How should plans and decisions be made? Together, the four research questions lead to the ultimate question of how to make rational choice in complex systems, including cities. Two disciplines are closely related to urban governance: urban planning and public administration. The former focuses on the physical aspects of cities, whereas the latter on the non-physical aspects. Urban governance should deal with both the physical and non-physical aspects of cities through scientific approaches.
3. How Cities Work: Property Rights and Complexity
Cities function in a complex way with numerous actors interacting in and evolving with physical and non-physical settings. There is no satisfactory theory yet to explain all aspects of urban activities, but the property rights approach together with complexity theory provides a promising perspective to understand how cities work.

The physical settings of cities are the outcome of interacting land development decisions, whereas the non-physical, or institutional, settings, are the outcome of collective actions on regulations, formal and informal. The physical and institutional settings interact with each other and agents behave in these constraints to maximize property rights. Property rights are costly to delineate and the physical and institutional settings can be perceived as such delineation with some property rights left in the public domain. Economic agents are motivated to acquire these property rights left in the public domain. For example, why do cities grow along transit lines? A property rights approach would argue that transit development creates additional property rights of accessibility and in adjacent land, developers construct buildings exactly to acquire these property rights left in the public domain. In essence, the physical forms of cities reflect to some extent the spatial distribution of property rights.

Complexity theory deals with complex systems that are far from equilibrium. Cities are complex systems and, because of interdependence, irreversibility, indivisibility, and imperfect foresight, they are far from equilibrium as traditionally perceived by urban economists. To understand how cities work, the traditional economic theory focusing on equilibrium analysis is insufficient, a new way of looking at cities is needed that can cope with both phenomena in the equilibrium and far from equilibrium states. The main question complexity theory intends to address is: Whether complex systems with seemingly chaotic processes behave in a predetermined, regular ways. Many evidences to this date show that cities do follow some principles to function, such as the rank-size rule depicted earlier. The implication is that a general covering law of urban development might not be possible, what we need might be a set of theories explaining different aspects of the workings of cities in order to make appropriate decisions to guide urban development.
A third theme of the introduction in developing the theoretical foundation for urban governance is related to rationality. In particular, we argue for a reconsideration of the meanings of rationality and propose a new way of defining it: framed rationality. Framed rationality does not refute the traditional standard of rationality of maximization of subjective expected utility. It simply recognizes the fact that the utility maximization principle can be valid only in particular frames. With the conceptions of property rights, complexity, and framed rationality, we argue that cities work by agents interacting with each other to maximize their utilities in particular frames, constrained by physical and institutional settings, in order to acquire property rights left in the public domain.

4. Planning Cities

Urban planning has a long history at least for one hundred years. Though the scope of planning education and research becomes larger, the emphasis on physical design of cities remains as a central topic in the discipline. Urban planning can mean many things, from site planning to globalization, and the term is being used with many connotations. Urban planning defined here is simply making plans in order to influence or even guide urban development. Plans are defined narrowly here as multiple, linked decisions. Evidence shows that when faced with uncertainties, it is to the decision maker’s benefits to consider more than one decision in relation to others, rather than make these decisions independently, as argued by the strategy of divide and conquer. There are two fundamental reasons of why cities need plans. On the one hand, making plans is particularly useful when the system under consideration is complex, rather than simple. Complexity means that the elements in the system are connected with each other in a clustered, rather than random, way. Cities are complex systems; therefore, making plans is useful in dealing with urban issues. On the other hand, as argued by Hopkins (2001) depicted earlier in this introduction, plans are most effective when decisions are interdependent, irreversible, indivisible, and with imperfect foresight.
Unlike governance and regulations that focus on actions and rights, plans provide information only. Once publicized, they show the intentions of the planner as to when and where to take what actions. Plans can be formal documents and informal ideas residing in the decision maker’s head. Owners of plans share the contents strategically. In cities, many actors make plans, including developers, public officials, voluntary groups, and local governments. Plans for urban planning can be conceived as public, but they could yield benefits to local governments if these plans are secret, as exemplified in most cities in China. The traditional view of a single plan for the development of the city under consideration should be replaced by a web of plans that interact with each other because evidence shows that the latter conception about plans is closer to reality. The physical setting of cities and the web of plans for urban development interact with each other, again in a complex way.
Urban planning should be perceived in a broader context for city managers. Not only is planning concerned with both physical and institutional settings in cities, planning should also be explained and prescribed in relation to governance and regulations. We must make clear the distinction between plans, governance, and regulations and understand how they complement each other and make cities a better place to live. For example, we could plan for collective actions and regulations, and we could also regulate how to plans. Plans can be made both external and internal to organizational settings, so administrative behavior is also closely related to planning. In short, city managers must learn when to plan for urban development and socio-spatial processes in order to take appropriate actions accordingly, recognizing that plans are only one limited way of improving human settlement.
5. Governing Cities
As argued earlier, agents in cities are motivated to acquire the property rights left in the public domain. In particular, most of these property rights are collective goods, or common pool resources. Collective choices and actions must be made and taken regarding how to make use of them, the essence of city governance. City governance thus begs mechanism design through which collective choices can be made regarding collective goods provisions and common pool resources allocation. On the one hand, collective goods provision requires commitment from the participating parties. This can best be demonstrated by a two-person prisoner’s dilemma. Each of the two players can either cooperate or defect without knowing which strategy the other player would adopt. When one player cooperates and the other defects, the former will lose a significant amount of payoff, while the latter will gain. Both players are motivated to defect, and the Nash equilibrium of the game is for both players to defect. However, if both players cooperate, they would be better off than if both of them defect, thus a dilemma. One way to make sure that both would cooperate is through commitment. Collective goods provision is like the prisoner’s dilemma game in that each participant is likely to defect, that is, adopt the free-riding strategy without contributing to the provision, and thus the amount of collective goods is usually insufficient if no coercive actions are taken.
On the other hand, common pool resources allocation requires an appropriate mechanism through which these resources are effectively and efficiently allocated to the affected actors. There are traditionally three modes of allocation of such resources: governments, contracts, and markets. Common pool resources could be allocated by local governments in that local governments acquire all such resources and allocate them to the actors to enhance efficient us of such resources. This mode of allocation causes high administrative costs of managing common pool resources. Some argue that common pool resources should be allocated through market mechanisms, but this mode of allocation would incur high transaction costs. Alternatively, common pool resources could be allocated through contracts that are designed collectively by the affected actors. This mode is said to be most desirable because it would have merits of both the allocation modes by governments and markets.
A final issue concerning city governance is related to social choice. The well-known impossibility theorem originally proposed by Arrow (1965) renders any mechanism of social choice as violating democratic principles, but under some designs, such as a two-party system of representative government, indirect voting through legislators would yield the outcomes of social choices consistent with those derived from direct voting from the actors. City managers must understand alternative ways of making decisions and taking actions collectively regarding provision of collective goods and allocation common pool resources, recognizing however that we live in an imperfect world where no mechanism of social choice fulfill the principle of democracy.
6. Regulating Cities
Regulations identify rights which confine the range of choices the actors in cities can make. There are two fundamental reasons for need of regulations. On the one hand, setting regulations circumscribes individual actors’ freedom to enhance collective efficiency. If the gain of collective benefits derived from regulations outperform the loss of individual actors’ freedom of choosing, people are motivated to regulate themselves. On the other hand, setting regulations reduce transaction costs in market. Regulations delineate property rights, albeit incompletely, so that transaction cost due to incomplete delineation of rights during any exchange can be reduced. Zoning is a land use regulation by identifying land use rights to particular land, and it reduces transaction cost of information gathering in land market if the developer knows which use is designated in a parcel of land under consideration.
Regulations are a formal type of institutions of enforceable nature; therefore, regulations constitute partially the institutional settings of cities, along with the physical ones. Like informal institutions, such as cultural norms, regulations evolve with cities, meaning that they are not static, but change over time as cities grow. Due to the costly delineation of property rights, regulations can never clearly specify the range of permissible rights and need be administrated. Due to the same reason, once set, regulations usually render some property rights left in the public domain for the actors to acquire. Enacting building code is a case in point. Though building code specifies the standard of design and structure for housing and office constructions, there is always leeway in which the contractor could gain by lowering the construction cost.
Regulations are different from plans in that they are enforceable and directly affect the actors’ behaviors, while plans change these behaviors by providing information. Therefore, in some sense, regulations are irreversible, and plans are not. Both regulations and plans face interdependence, indivisibility, and imperfect foresight. Effective regulations lead to desired outcome, but this is difficult because actions (setting regulations) usually lead to uncertain outcomes due to imperfect foresight. Therefore, the technique of designing systems of regulations requires prediction of regulated behaviors, which are usually strategic in response to selected regulations and can be analyzed through game theory.
City managers must appreciate the usefulness of regulations in shaping how cities work in a broad context in relation to plans and governance. In particular, regulations, plans, and governance interact with each other, shaping and being shaped by the physical and institutional settings within cities. On the one hand, plans and governance need enforceable regulations to achieve desired outcomes. Regulations as actions are thought through and taken collectively by plans and governance. Together, they form the basis of the managerial skills for city managers to deal with complex urban issues.
7. Administrating Cities
Plans, governance, and regulations usually take place in organizational settings, which are complex systems with much smaller sizes than cities. An organization can be perceived as four independent streams of elements interact chaotically with each other within pre-specified constraints: problems, solutions, decision makers, and decision situations. Solutions may exist before problems emerge. Decision makers take part in and leave particular decision situations. Decisions may be made with no problems to bear on. In some extent, plans, governance, and regulations are decisions related to urban development and made in such chaotic, organizational settings. Organizational administration is concerned with how to make such daily decisions appropriately when faced with uncertainty and complexity.
The traditional view that organizations optimize and administrative behavior is in order is being challenged. The fact that local governments do not provide optimized services, rather they opt for votes, is not new to us. Public officials do not pursue “public interests”; rather they seek private or personal interests. In the urban governance context, a city manager acts as the principal in representation of the interests of his constituencies, or agents. This principal-agent relationship creates difficulties in urban governance administration similar to the representative government acts in representation of his constituencies. Unlike firms where the owners are the residual claimers, no one owns local governments as the residual claimers. As a result, the administrative process in local governments is more like what garbage can model depicts than as traditional view of optimizing firms.
In the stream of opportunities, the administrative behavior should be different from that as traditionally perceived. In particular, the city manger as an administrator should look for opportunities actively to make something happen. For example, solutions may be made available long before problems and decision situations, or opportunities, come about. In such a chaotic, uncertain administrative process, making plans would yield benefits, if decision situations and their outcomes are interdependent, irreversible, indivisible, and with imperfect foresight.
Organizations complement with plans in that both coordinate decisions to reduce uncertainties. Like regulations, organizations are structured to streamline decisions, but they are more apt to changes than regulations. In order to make effective decisions in relation to plans, governance, and regulations, city managers must understand how organizations work and constantly seek opportunities where solutions find themselves. Innovative techniques for making multiple, linked decisions, that is, making plans in the organizational context are also useful. Organizations are the microcosm of cities, both being complex systems, but with much different sizes. Sizes of complex systems matter, so do managerial skills for these systems. Skills for managing cities are thus different from those for administration. For large, complex systems, city managers need to seek tipping points in order to make changes.

Part 2 Methodologies

Methodologies are designs of methods to analyze and deal with particular problems. They are different from theories in that theories explain phenomena, while methodologies target at solving problems. Theories provide the underlying understanding of particular phenomena based on which methods of dealing with these phenomena are derived.
The recurring theme of the introduction is that cities are complex systems and complexity theory provides a useful framework for explaining how cities work. Section 8 discusses in depth the relationship between cities and complexity as a starting point for the ensuing discussions of the methodologies in urban governance. We do not aim at providing a general, covering law of how urban issues can be dealt with, because such law would be too abstract to be useful in solving real world problems. Alternatively, in Part 2 we address a set of analytic tools that are commonly used and related to our theme that cities are complex systems. They include, but are not limited to, decision analysis, policy analysis, planning analysis, city modeling and analysis, and planning support system.
Decision analysis aims at making single, independent decisions usually involves one decision maker. It is the simplest analytic framework of choice theory that pervades many disciplines, including, but not limited to, economics and operations research. The merit of decision analysis is that it makes crystal clear how a rational decision maker should think through preferences and uncertainties in order to choose the best alternative to maximize his or her utility. The logic is water tight, but the application of such logic is difficult in reality where complexity and uncertainty reign, rather than simplicity. Regardless, it is worth introducing decision analysis simply because it is a solid foundation from which other methodologies are built.
Policy analysis is similar to decision analysis in that it also argues for rational choice, but in a broader context. Policy analysis tends to be messier and qualitative oriented; therefore it has a wider range of applications and commonly used in the field of public administration. According our definition of plans, planning analysis focuses on making multiple, linked decisions that involve multiple actors. It is much more demanding than decision analysis in terms of cognitive and computational efforts. Decision analysis, policy analysis, and planning analysis together provide the structural framework for the problem solving methodologies of urban governance. City modeling and analysis links the ontology of urban development to observed phenomena and through the structural framework, provides a systematic way of analyzing and dealing with urban issues. Planning support system puts together the structural framework as well as city modeling and analysis onto a computational platform, as a set of automated planning tools based on which city managers can understand and cope with various urban issues. The crux of these methodologies is logic, without which city managers would be lost in the sea of methods.

8. Cities and Complexity

Cities are composed of physical and institutional settings in which numerous actors interact through information exchange to acquire property rights, mostly left in the public domain. Cities are thus complex systems characterized at least by emergence and self-organization. Emergence means two things. On the one hand, simple rules based on which the actors interact create complex outcomes. On the other hand, these complex outcomes tend to self-organize themselves from which simple rules emerge. Regardless, collectivity emerges from individuality and the former cannot be deduced directly and is qualitatively different from the latter. The behavioral rules for land development are simple in that developers maximize their utilities by acquiring property rights associated with the land under consideration and mostly left in the public domain. However, the interaction among developers shapes complex urban settings, physical and institutional, that defy any existing measures to depict precisely.
We are just beginning to look at cities in a new way of complexity. Different from the traditional top-down approach, the complexity approach looks at cities from the bottom up. It starts with the individual actors by imposing simple, interactive rules for these actors, and then looks at what collective patterns would emerge from such interaction. This new way of looking at cities has significant impact on how we should plan, govern, and regulate, that is, manage cities. The traditional, top-down approach to cities assumes an average actor ignoring differences among the actors and perceives the rules governing urban development as given. The complexity, bottom-up approach to cities takes into account the idiosyncratic characters of the actors and considers the rules on which urban development relies as emergent. The traditional, top down approach prompted an idealized way of managing cities by seeking a covering-law-like comprehensive rationality to make plans for urban development. Like decision analysis, this perfect rationality is beautiful in theory, but when put into use, it encounters difficulties. The complexity, bottom-up approach implies a less ambitious way of managing cities by looking for a coherent set of theories and methods in explaining cities and dealing with urban issues. Instead of looking for a theory of everything, the complexity, bottom-up approach seeks theories of things.
At crossroad of the paradigm shift in sciences in general, and city theories in particular, city managers must keep looking for new ways of looking at cities, while recognizing the merits of the traditional approach, such as city economics. New theories prompt new real world applications. We need innovative, effective ways of planning, governing, and regulating cities that are derived from new discoveries of how cities work. Network science, cellular automata, agent-based modeling, and fractal geometry, to name just a few, are such new discoveries of how cities work. We have a long way to go from these new theories to applications, but they shed useful lights into how we can deal with complexity.
9. Decision Analysis
Decision analysis is an interdisciplinary field that focuses on how to make appropriate decisions in face of uncertainties, drawing on work in, among others, economics, operations research, system analysis, and psychology. Decision analysis has become a specialized field with its own institutions and journals. It aims at helping decision makers to frame decision situations and select the best actions according to solid, rational procedures. Traditionally, there are three camps of decision theories: descriptive, normative, and prescriptive. Descriptive decision theories purport to explain how people actually do make decisions. Normative decision theories aim at constructing the theoretical foundation for depicting how people should make decisions. Prescriptive theories intend to help people to make decisions conforming to normative the normative standard of rationality. The standard of rationality that makes the distinction between descriptive, normative, and prescriptive decision theories is the subjective expected utility (SEU) model. The SEU model stipulates that the rational decision maker choose the best alternative in order to maximize his or her expected utility. Though the SEU model provides a sound theoretical basis for normative decision theories, it has been invalidated by numerous psychological experiments, and thus others suggest variants of the SEU model in describing how people do make decision, including prospect theory and bounded rationality.
We argue that the traditional distinction between descriptive, normative, and prescriptive decision theories enhances rather than dispels the confusion about our understanding of rationality. In this introduction, we propose an alternative view of rationality, called framed rationality. Rather than refuting the SEU model, we argue that that model is universally valid, but only subject to particular frames. Put differently, people in making decisions are rational depending on how the problems are framed. Therefore, the distinction between descriptive and normative perspectives of explaining behaviors is unnecessary because they interpret observed behaviors from different frames. We cannot conclude that if the decision maker’s choice violates the normative standard, he or she is not rational. He or she may still be rational in his or her frames of understanding the problems faced and act accordingly. Experiments show that framed rationality is valid in that drawing on the elicitation questions used in prospect theory, preference reversals can be explained by the SEU model.
Decision making is a central task for city managers, so they must understand the underlying logic of how people make decisions, not only to improve their decision making skills but also to understand the decision situations in which people interact. Decision analysis is a useful theoretical framework to make sense of rational choice behavior, but when faced with complexity, decision analysis has limitations. Rationality is a long-lasting question not yet fully answered, but framed rationality as proposed in the introduction sheds new light into our understanding of rationality and effective decision making tools can be derived from such understanding.
10. Policy Analysis
Policy analysis can be viewed as applications of decision analysis to make real world public policies. If decision analysis is the hard, deductive logic of making such policies, then policy analysis is the soft, empirical process of making these policies. Policy analysis is therefore messier and more qualitative oriented than decision analysis. Policy analysis is an art in that the policy maker needs to make clear decision situations and frame the policies in an appropriate way so that useful decision models can be applied to the situations under consideration.
Policy analysis draws on a wide range of decision tools to deal with real world situations, including, among others, linearly programming, cost-benefit analysis, multi-attribute decision making, and decision analysis. Different from planning analysis that focuses on multiple, linked decisions, policy analysis emphasizes single decisions, but sometimes in a very broad context. Different from decision analysis that can be applied to personal decisions, policy analysis aims at making policies in the public sector that affect a wide range of actors. Thus the issues faced by the policy maker are usually much more complex than the decision maker who intends to apply decision analysis to solve the problems he or she is concerned. Policies must take into account broader issues related to the society, such as social welfare, justice, equity, and esthetics.
Policy analysis is concerned with a wide range of issues related to cities, regions, states, and international affairs, from the public sector perspective. It is one of the core areas in public administration, a field that pursues excellence in managing public affairs. Policy analysis applied in urban governance can fill the gap between decision analysis dealing with simple problems and planning analysis coping with complex problems in that policy analysis applies the logic of both to a wide range of real world urban issues.
Policies, if narrowly defined as rules to follow by actions, can serve as one of five mechanisms through which plans function, that is, agendas, visions, policies, designs, and strategies. In other words, policies are decision processes through which plans influence urban development. Though understanding the logic of making decisions and plans is a must for effective city managers, how to seek opportunities to apply the logic to deal with real world urban issues is another challenge that city managers must face. Bargaining and negotiation of policies are quite common in the public arena.
City managers should know how to make effective urban policies, not just by the logic of decision analysis and planning analysis, but gain insight into how the three methodologies work together to make rational choices in complex systems of interest. Decision analysis focuses on making single decisions, planning analysis on making multiple, linked decisions, and policy analysis on applying the underlying logic of decisions and plans to a seemingly chaotic, complex urban processes.
11. Planning Analysis
Making plans, or planning, is precisely defined in this introduction as making multiple, linked decisions. Different from decision analysis that focuses on making single, independent decisions, planning analysis looks into ways of making more than one decisions that are linked to each other. The presumption is that making multiple, linked decisions yields more benefit than making these decisions independently. Though this presumption has not been proved deductively and empirically, numerical examples show that the presumption works. For example, in land development, considering both housing and infrastructure decisions at the same time yields higher net benefit to the developer; in the inventory approach to urban growth boundaries, making linked expansion decisions in time as manifested by the event-driven system gives rise to lower total cost than making these decisions independent in time as the time-driven system. It is highly plausible that making multiple, linked decisions in time and space would yield higher payoffs than making these decisions independently. In addition, city managers are faced with a complexity of urban development. The traditional choice theory emphasizing on making single decisions is insufficient in dealing with such complexity. What is needed is a way of exploring the relationship between multiple decisions with multiple actors, problems, solutions, and decision situations.
One approach to making multiple, linked decisions in face of uncertainty is Decision Network. Decision Network is derived from garbage can model, the strategic choice approach, and decision tree by focuses on contexts, relationship, and sequence of decisions. There are four elements in a decision network: problems, solutions, decision makers, and decision situations. Problems, solutions, and decision makers are associated with decision situations given certain structural constraints. For example, city managers, urban planners, and the mayor have different authorities in attending which decision situations to make decisions. Expansions of urban growth boundaries and transit systems as problems are discussed in different decision situations. The structural constraints imply the relationship between the elements in the decision network. Problems give rise to negative effects, whereas solutions and decision makers positive effects. Given the decision network of depicting the context, relationship, and sequence of decisions, and the measurement of the negative and positive effects, Decision Network requires the city manager to find an optimal solution to the network problem in order to maximize the overall positive effects.
City managers must learn to make multiple, linked decisions in face of complexity and uncertainty. The traditional choice theory of making single, independent decisions based on which decision analysis and policy analysis are developed is insufficient. Making multiple, linked decisions viewed as a design problem of determining which problem, solution, or decision maker should be related to which decision situation is insufficient in dealing with complexity. Decision Network should be developed in such a way that it can deal with complexity as strategies.
12. Urban Modeling and Analysis
Modeling is a way of understanding the ontology of phenomena under consideration. Without modeling, we are unable to represent and communicate ideas about the phenomena of interest; thus models themselves are languages for communication. City modeling has a long history and can be traced as early as 1960s. Models about cities are constructs of explaining how cities work, whether through mathematics, graphics, computer simulations, or structured experiments. Models about cities are useful if they yield insights into what actions we should take to prevent something from happening. If we anticipate population growth at particular locations in particular time, we should build sufficient infrastructure to serve that population before they come about. City modeling thus is related not only to explaining but also to forecasting.
The approach to city modeling evolves from graphics, through mathematics, to computer experiments. Graphic city models, as manifested in urban design, are iconic models that represent how cities actually look like into the future by graphics. Mathematical city models, as manifested in urban economics, mainly follow the economic approach to market systems by constructing mathematical equations to depict the workings of cities through, for example, land rents and transportation costs. Computer experiments, as manifested in complexity theory, look deep inside cities for how individual actors behave and interact, and construct computer simulations to explore into policy implications in order for city managers to take appropriate actions ahead of time.
City models are useful if they can represent the essence of how cities work. All city models suffer from simplicity because no models can represent completely how cities work. City modeling to some extent is like story-telling. Good city models should cover the essential elements of cities and depict their relationship at least in a realistic way. With the advance of the computing technology, city modeling now can be run on a computer with the city manager playing god of the city system, manipulating the parameters of the system, and observing what would happen.
Numerous computer simulations and experiments now have been conducted to emulate how cities work. Evidence has shown that underlying the seemingly chaotic process of urban development lies the deep, collective regularity of city structures, physical or institutional. We are just beginning to uncover these deep regularities and seek their implications regarding how cities should be managed. City managers must be acquainted with various modeling techniques, be they graphic, mathematical, or computational, and make use of these models in urban governance practice. Without city modeling, we might lose sight of what appropriate actions to take by delving too deep into idiosyncratic happenings. Constructing useful city models may help us to not only understand how the world works, but also how we should respond to it.
13. Planning Support Systems
Planning support system is an idealized computer system for helping city managers to make plans for urban development and take appropriate actions. No such system exists for daily use in real cities, but the idea of planning support system has been developed for at least 25 years. In essence, a planning support system is to incorporate the methodologies for urban governance we introduced so far onto a computer platform. Simply put, it includes at least a database system, a city modeling system, and a planning tools system. The database system includes the most updated data about the city under consideration, including, but not limited to, population, land use, transportation, housing, real estate, and environment. The data can be physically scattered among and managed by various government units, but integrated through the internet, such as a data warehouse system. The database system itself provides the most recent data about the city in various conditions in order for the city manager to monitor urban affairs and take appropriate daily actions. In particular, the database system should include relevant plans that are created by various entities, be they transportation, land use, infrastructure, and housing. The idea is that using these plans the city manager would realize the implication of actions in relation to these plans.
The city modeling system captures the urban process of the city of interest in order to answer the what-if type of questions. For example, what effects would a construction of new highway be on local land use patterns? To answer this question, we need to model the urban development process using the techniques depicted in the previous section, change the spatial, socio-economic configurations of the city system caused by the construction of highway, and see what happens. The city modeling can be done through agent-based modeling in that the spatial interaction of developers, the local government, landowners, households, and firms is taken into account explicitly. The planning tools system provides a set of tools derived from decision analysis, policy analysis, and planning analysis as to what actions to take, given the answers to the what-if type of questions. If the highway construction would result in deteriorated downtown development, should the city manager propose alternative routes and interchanges of the highway, or should there be a new city center in place of the old downtown? Regardless, the database, city modeling, and planning tools systems should be closely coupled in a planning support system so that the output from one system may serve as the input of another.
City managers must acquaint themselves with the cutting-edge of computation technology in order to manage cities in a timely fashion. Though the idea of planning support system has not be fully explored in practice, with the increasing need of accurate, timely decisions, the system provides a promising platform where the methodologies depicted here can be integrated efficiently. In particular, with the advance of the internet technology, public participation would be made easier through a planning support system.

Part 3: Applications

This part of applications provides a set of application areas based on the theories and methodologies depicted in Part 1 and Part 2. Each application area is introduced by the general considerations of managing the subsystem in relation to urban governance, with conclusions at the end.
14. Transportation

This section introduces general considerations of managing city transportation.

15. Land Use

This section introduces general considerations of managing city land use.

16. Sanitary and Infrastructure

This section introduces general considerations of managing city sanitary and infrastructure systems.

17. Building and Constructing

This section introduces general considerations of managing city building and constructing projects.

18. Urban Design and Landscape Architecture

This section introduces general design principles for city design and landscape architecture in urban governance.

19. Real Estate and Housing

This section introduces general principles for making real estate investment and housing policies in urban governance.

20. Urban Renewal and Regeneration

This section introduces general considerations of managing city renewal and regeneration in urban governance.

21. Ecological Environment

This section introduces general principles of ecological and environmental planning in urban governance.

22. City Disaster Management

This section introduces general principles of managing city disastrous events in urban governance.

23. Slums and Homelessness

This section focuses on slums and homelessness to eliminate poverty in urban governance.

24. City Finance

This section introduces how city finance systems work and how to manage them.

25. Crime

This section introduces city crimes and how to prevent them from happening.

26. Social Welfare

This section introduces general considerations of how to make social welfare policies in urban governance.

27. Education

This section introduces the education systems and how to manage them in urban governance.

28. City Institutions

This section introduces various city institutions and how they interact with the physical settings in urban governance.

29. Governmental Organization and Administration

This section introduces the working of governmental organizations and administrative behaviors take place in these organizations.

30. Information City and Technology

This section introduces the notion of information city and how technology affects urban development.

31. Globalization and City Competitiveness

This section introduces the trend of globalization and general principles of enhancing city competitiveness.

32. Global Climate Change and Energy

This section introduces the effects of global climate change on cities and discusses energy policies regarding the global climate change.

33. Comparative Studies

This section conducts comparative studies regarding urban governance in different cultural and socio-economic settings.

34. Conclusions

This section concludes.

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