Program Design

  • Summary
  • With rapid urbanization in Taiwan and China, numerous urban problems arise, including social, economic, and physical ones, such as traffic congestion, land acquisition, wealth distribution, social welfare, and government management. Taiwan and China need professionals with training in urban governance to apply advanced knowledge of management science to solving these problems scientifically. The establishment of the International Program on Urban Governance (IPUG) at National Taipei University (NTPU) is aimed at fostering professionals and academics to promote research and practice in urban governance.

    In China, there are more than 670 cities and about 50% of the populations live in cities, the world’s most rapidly urbanized country. This trend will continue in the foreseeable future. Though urbanization brings comfortable, convenient quality of life with many amenities, it also results in various problems, including traffic congestion, deteriorated air quality, expensive housing, slums, increased crime rate, unemployment, etc. These problems must be dealt with through systematic, scientific techniques in order to improve urban environment. Therefore, urban governance is defined as applications of systematic modern management science to plan, govern, and regulate cities, in order to solve physical and non-physical problems to improve human settlements.

    Based on this definition, urban governance includes two areas: cities and governance. For the cities area, urban governance views cities as large complex systems, and based on complexity science in association with urban development theory derived from urban economics, it attempts to understand how cities work. Complex systems are composed of many interacting agents self-organizing themselves into some patterns, and cities are no doubt complex systems. Urban economics assumes rational behavior based on neo-classic economics, and intends to explain the physical patterns resulting from human activities taking place in the space of cities. The combination of complex systems and urban economics to explain the workings of cities will transcend the traditional distinction between socio-economic and physical aspects of cities and to view them as a whole. For the governance area, urban governance focuses simultaneously on plans, governance, and regulations as governance tools to improve human settlements. In contrast to management of small systems, such as business administration, urban governance emphasizes on governance of large, complex systems; therefore, its tools are distinct. Among others, plans focus on the making, arrangement, and coordination of decisions; regulations emphasize on the restriction, expansion, and distribution of rights; and governance focuses on collective choices and actions as manifested for example by government and participation. In order for urban governance to be fully developed, the three aspects must be attended to in balance.

    As to applications, the problems that urban governance deals with can be divided roughly into physical and non-physical aspects. For the physical aspect, it includes the spatial arrangement problems that are the traditional concerns of urban planning, such as land use and management, transportation, infrastructure, architecture and construction, real estate, ecologic environment, and disaster mitigation. For the non-physical or socio-economic part, it includes the issues discussed in city economics, city sociology, city political theory, city public management, and city public finance, such as crime, taxation, social welfare, institutions, government and organization, administration, policy, and budgeting.

  • 1.   Global Trends and Social Needs: An Outlook of Urban governance
  • Cities are composed of physical settings in which people interact through information within certain structural constraints of institutions. In such settings, people make daily decisions in order to maximize their utility and these decisions interact forming a complex web of interdependent decision network. Cities can thus be characterized by networks of decisions that currently defy any modeling techniques that attempt to depict the workings of cities. This inability of modeling techniques to understand how cities work is in part due to the complexity of intertwined, interdependent decisions. In particular, the relationship between causes and effects of events in cities is far from clear cut, and cities tend not to reach equilibrium as usually assumed by economists. Put simply, cities are the containers where people, problems, choice opportunities, and solutions meet in space and time under prescribed structural constraints in an unpredictable way and something happens. The chaotic meeting of these elements gives rise to decisions that may or may not solve problems. Research shows that cities can self-organize themselves in order to survive, but the inextricable characteristics of complex urban phenomena enhance the usefulness of applying governance skills to guide urban development, rather than renders these skills useless. Therefore, urban governance focuses on applying governance skills, broadly defined, to lead how cities work to a desired direction.

    Recently, Science (2008) and Nature (2008a and 2008 b) published special issues on cities and China’s challenges in economic development and environmental protection. These issues recognize that urban governance may solve various problems in cities worldwide and that environmental challenges faced in Taiwan and China become pressing issues for which effective actions must be taken. These observations provide evidence that managing cities will be one of the major challenges faced by human beings in the twenty first century.

  • 2.   Disciplinary Framework of Urban Governance
  • Unlike city planning which focuses solely on making plans for urban physical development, urban governance intends to regulate, plan, and govern cities in order for them to work as a coherent whole. Regulations focus on rights, plans on decisions, and governance on collective actions. These three types of actions together set the stage on which cities are harnessed, with the recognition that no man-made forces can control cities. Therefore, rather than pretending to control cities, taking appropriate actions where, when, how, and by whom, becomes of paramount importance in urban governance.

    The research and educational objective of IPUG at NTPU is to help the students to understand how cities work and how to take appropriate actions to manage cities, broadly defined. In order to approach this objective, we propose a framework of enquiry that must be considered in the discipline: knowledge domain of interest, modes of taking actions, and disciplinary scope of research questions to be addressed.

  • 2.1 Urban Phenomena of Interest
  • Urban governance is 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, or institutions, 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.

    In order to understand how cities work effectively, the approach taken in the proposed department is complexity science. With numerous interdependent decisions in space and time, cities are no doubt complex systems, and complexity science is an emerging paradigm to study such systems. Complex systems are composed of numerous interacting elements, and collective patterns emerge from the interaction of individual elements. We do not know yet how individuality gives rise to collectivity through emergence, but investigating into the emergent process provides a promising research agenda of understanding how cities work. Understanding how cities work through complexity science would provide fresh insights into how we should take appropriate, effective actions in order to lead to desired outcomes.

  • 2.2 Modes of Taking Actions
  • Three modes of taking actions are considered: regulations, plans, and governance, 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 (Hopkins, 2001). 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. Urban governance focuses on both the physical and non-physical components of cities, and therefore, all three modes of actions, i. e., regulations, plans, and governance, are important in order to improve human settlements (Hopkins, 2001), the outcome being improved cities.

  • 2.3 Scope of Research Questions
  • The Program will identify the discipline of urban governance 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. Question one focuses on the descriptive workings of cities, i. e., rather than asking how cities should work, it addresses how cities work naturally. “Natural” means without human intervention. In particular, given agents and other elements of cities, how does the collective outcome of the city come about through interaction of these fundamental building blocks? Put differently, how does collectivity emerge from individuality? In contract to natural evolution of cities, Question two asks what the desired states are for cities. Cities, if let alone, may not attain the desired properties of justice, equity, sustainability, and aesthetics. Question three seeks the explanations of how plans and decisions are made descriptively in cities. It focuses on understanding where, when, how, and by whom plans and decisions regarding physical and non-physical development of cities are actually made. It intends to depict rationality underlying actual plan and decision making, explain the internal workings of decision situations, formulate networks of plans and decisions, and understand how plans and decisions relate each other. Under the presumption of limitation of rationality that plans and decisions are made imperfectly, Question four then seek improvement for making plans and decisions by providing standards of rationality to evaluate effectiveness of plans and decisions through internal and external validity. Together, these four questions lead to the ultimate question for urban governance, i. e., How should we make rational choice in complex systems, including cities? To that question, we now turn.

  • 2.4 Rational Choice in Complex Systems
  • There are many social and natural phenomena now recognized as complex systems, such as cities, economies, ecologies, political entities, and societies. The crux across many disciplines in social sciences is to ask the question of how to make rational choice in such systems. Decision making as manifested by perfectly rational choice theory of economics is insufficient in dealing with such complex systems, especially when decisions are interdependent, indivisible, irreversible, and with imperfect foresight (Hopkins, 2001). The most accepted paradigm of rationality, the subjective expected utility theory or the SEU model, has been challenged by psychologists (Hogarth and Reder, 1987) and experimental economists (Ariely, 2008) as being unable to describe how people actually make choices, at least in experimental settings . In addition, the traditional distinction among descriptive, normative, and prescriptive theories of choice provides more confusion than explanations in dispelling how people make decisions. This seemingly misconception of rational choice is mainly caused by a simplified, mechanical view of the world where the relationship between causes and effects is clear cut and systems tend to equilibrium. In particular, a covering law of the SEU approach to choice theory renders behavioral deviations as anomalies which might actually be rational in particular frames, resulting in redundancy of the descriptive, normative, and prescriptive distinction. In this proposal, we provide a fresh look at rationality, namely, framed rationality.

    Much has been said about decision making under uncertainty. Among others, subjective expected utility theory and prospect theory are both derived from decision analysis. Subjective expected utility theory considers that if the outcome of a choice is uncertain, then the traditional method of calculating expected monetary value fails to measure the decision maker’s preferences among alternatives. The concept of utility is needed and expected utility is calculated instead to measure the decision maker’s preferences. Based on subjective expected utility theory, the rational decision maker will choose the alternative that has the highest expected utility when faced with uncertain alternatives. In 1979, Kahneman and Tversky designed a set of decision questions and used them to conduct psychological experiments. They found that different ways of how questions were framed caused preference reversals. The results violate the postulation of subjective expected utility theory that the decision maker should make consistent choices. This phenomenon is called framing effects. Frames are defined as the choice conditions under which the decision maker behaves. Frames of questioning may influence the choice conditions conceived by the decision maker. The decision maker cannot penetrate the underlying logic of these decision questions if asked in different ways. Kahneman and Tversky proposed prospect theory to effectively explain this phenomenon. However, there is no explanation in prospect theory as to whether the choice made by the decision maker corresponds to the principle of utility maximization. The question remains of whether prospect theory can replace subjective expected utility theory in explaining real choice behavior. In the proposal, we argue that the decision maker is rational in the same sense as defined by subjective expected utility theory, regardless how frames are defined, and we call this explanation of choice behavior framed rationality. An experiment has been conducted that replicates the experiments that Kahneman and Tversky designed and conducted in 1979, in that subjects revealed preference reversal when questions were framed differently. However, our experiment takes one step further by measuring the subjects’ utilities in making these choices, and confirms the hypothesis of framed rationality in that by using the same questions as in Kahneman and Tversky’s experiments, we find that a statistically significant number of subjects maximize subjective expected utilities in making choices, regardless of how the elicitation questions are framed. In other words, preference reversal does not violate the SEU model; rather it validates the model subject to particular frames.

    This finding might provide a starting point to reconsider or redefine rationality by reconciling currently conflicting views of decision theories. For example, observed preference reversal phenomena might be caused by the framing effects, but they do not necessarily violate the SEU model in the framed rationality sense, as refuted by its variants, including bounded rationality (Simon, 1955) and prospect theory (.Kahneman and Tversky, 1979). The traditional distinction among descriptive, normative, and prescriptive views seems redundant from the framed rationality point of view in that these conflicting views are reconciled if we look at these theories from the corresponding frames. That is, the normative view argues for the SEU model as the standard of rationality, and it purports to describe how people should make choices. Any behavioral violation of that model is considered as an anomaly and thus falls in the descriptive view of how people do make choices. As shown in our experiment, this distinction is unsound if we consider the anomaly also as rational but in a particular frame, or framed rationality. If this logic holds, the prescriptive view that helps the decision maker to choose to conform to the standard of rationality is unnecessary because there is really no such distinction between the normative and descriptive views. Finally, unlike bounded rationality and prospect theory, framed rationality proposed here rejects the idea of comprehensive, perfect rationality that is usually assumed by neo-classic economic theory and derived from the positivist philosophy of science, and thus enhances the universality of the SEU model (or something similar) within particular frames. We argue that, the increasingly recognized conception that our world is complex and far-from-equilibrium might prompt a paradigm shift in explaining rational choice, and framed rationality might be a good starting point.

    The research on rational choice in complex systems would make significant contributions to many disciplines, including but not limited to, urban planning, urban governance, public policy, public administration, environmental design, natural resource management, transportation planning, infrastructure investment, land development, social networks, technology competition, design method, organizational theory, institutional design, ecological simulation, spatial game, game theory, computational social science, artificial society, evolutionary economics, simulated market, neural network, genetic algorithm, and social process.

  • 2.5 Specific Issues and Technologies
  • The specific issues and technologies related to a urban governance department depend on time and location where the department finds itself. For example, at Zhejiang University the Urban governance Department may focus on spatial, social, and economic development rather than on ecological preservation. Technologies change over time, so it makes more sense not to identify permanent technologies for the urban governance discipline. However, the cutting-edge technologies should be identified through time in order to take advantage of scientific innovations. Because plans and decisions are the major modes of taking actions faced with complex, uncertain environments, the technologies associated with the discipline should include, but are not limited to, database management systems, simulations, planning support systems, and geographic information systems.

    Database management systems are fundamental technologies of structurally storing and retrieving data. These data provide a basis for daily urban governance activities in monitoring and projecting events and trends in urban development. Because the complexity of the workings of cities defies any analytical formulations, computer simulations become useful tools in understanding how cities work. Through computer experiments, we can gain insights into how individual agents interact giving rise to organized patterns of urban development in cities. In order to help city managers to guide the urban development process, planning support systems provide useful plan and decision aids in making plans for urban development and acting accordingly. Geographic information systems have become powerful tools in representing spatial entities on earth, including cities, and these systems can provide spatial representation of simulation outcomes by incorporating these tools with other systems, such as planning support systems.

  • 3.   International Comparisons
  • The US News and World Report ranks the 2009 best graduate schools in the discipline of urban management and urban policy in the United States. In this section, we conduct a Web survey of the top ten graduate programs related to urban management to compare my approach to urban management with those of top U. S. programs. The Web survey includes, according to descendent order, Department of Public Administration at University of Kansas, College of Urban Affairs at Cleveland State University, Division of Public Administration of College of Liberal Arts and Sciences at Northern Illinois University, School of Policy, Planning, and Development at University of Southern California, The Master’s of Public Administration of the School of Government at University of North Carolina-Chapel Hill, School of Public Affairs at Arizona State University, Robert F. Wagner Graduate School of Public Service at New York University (tie), Department of Public Administration at University of Illinois-Chicago, Department of Public Administration at University of North Texas, Department of Public Administration at Syracuse University, and School of Urban Affairs and Public Policy at University of Delaware (tie).

    Almost all graduate programs surveyed are affiliated with certain academic institutes of public administration, showing the close relationship between publication administration and urban management, with a few programs closely linked to urban planning, such as the School of Policy, Planning, and Development at University of Southern California and the Department of Public Administration at University of Illinois-Chicago. All these programs aim at preparing students with careers in public service, including governments, non-profit organizations, and private sectors. Some focus on qualitative research, such as the Robert F. Wagner Graduate School of Public Service at New York University, whereas others on quantitative methods, such as the Department of Public Administration at Syracuse University. Without an exception, all these programs offer core courses in public administration, such as public policy analysis, organizational theory, public management, and public management. They seem quite weak in courses related to how cities work and urban management is treated as either a specialization or concentration in these programs, not an independent academic institute.

    The IPUG at NTPU, being affiliated with a public affairs college, should emphasize its strength in and commitment to urban affairs and urban studies, as a distinction from the traditional public administration programs, while maintaining a close relationship and taking advantage of being affiliated with the college, as a distinction from the traditional urban and regional planning programs. In short, the Program as an independent academic institute should serve as a platform and forum that cuts across the traditional disciplinary boundaries to explore urban phenomena and urban governance. It also should pursue a balanced scientific approach to urban governance including both quantitative methods and qualitative research as well as incorporating both physical and non-physical aspects of urban issues.

  • References
  • Ariely, Dan, (2008) Predictably Irrational: The Hidden Forces That Shape Our Decisions. New York: Harpercollins.

    Hogarth, Robin M. and Melvin W. Reder (1987) Rational Choice: The Contrast between Economics and Psychology. Chicago: The University of Chicago Press.

    Hopkins, Lewis D. (2001) Urban Development: The Logic of Making Plans. London: Island Press.

    Kahneman, Daniel, and Amos Tversky (1979) “Prospect Theory: An Analysis of Decision under Risk,” Econometrica, XLVII (1979), 263-291.

    Nature, “China’s Challenges,” 14 July 2008, Vol. 454 (7203), 367-550.

    Science, “Cities,” 8 February 2008a, Vol. 319, 693-836.

    Science, “China’s Environmental Challenges,” 1 August 2008b, Vol. 321, 597-728.

    Simon, Herbert A. (1955) “A Behavioral Model of Rational Choice,” The Quarter Journal of Economics, 69(1), 99-118.

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