Wicked Problems, Foolish Decisions: Promoting Sustainability through Urban Governance in a Complex World Symposium: Governing Wicked Problems

Why do wicked problems often give birth to bad policy choices? Put another way, why do people—in the face of complex social challenges—make misdiagnoses, ineffective decisions, or no decisions at all? Typical answers point to a plethora of suspects: impatience, myopia, political stalemate, narrow-mindedness, fear and risk aversion, hubris, greed, rational self-interest, ignorance, reliance on emotionally appealing but misleading anecdotal stories, misuse of evidence, and misunderstanding of uncertainty. Amid these divergent explanations, two classes emerge: one lies in the shortcomings and mistakes of the problem solvers, and the other lies in the nature of the problem itself. One stance is to fault the ostensible problem solvers: people are not always rational, fair, patient, thoughtful, or deliberative, but instead are myopic, selfish, greedy, power hungry, or out for revenge (among other motivations). The second stance is to point to the nature of the problem. This is the focus of this Article. In particular, we examine how the dynamics of wicked problems undermine traditional problem-solving efforts. This is not to absolve the problem solvers of responsibility for poor policy choices. It is the responsibility of policymakers to diagnose the distinctive challenges and needs of wicked problems and act accordingly. As urban planning scholars, we focus on entrenched urban problems. This focus is not accidental. Horst Rittel (an architect) and Melvin Webber (a planning theorist and transportation planner) developed the idea of “wicked problems” at the University of California, Berkeley’s College of Environmental Design in the early 1970s—an era when the optimism of solving complex social issues through technical, scientific solutions was colliding hard with the failure of such efforts to conclusively resolve urban poverty, inequality, deindustrialization, racism, white flight, and the violence of the “Urban Crisis.” In this Article, we build on previous research to demonstrate how complexity thinking can engage urban challenges at three levels: (1) describing “complexity” as a symptom of urban systems; (2) analyzing the dynamics of complex urban systems; and ultimately (3) intervening through appropriate planning strategies that account for complexity. We employ this thinking to engage the politics of sustainability at the same three levels, illustrating this at two geographic scales: the neighborhood (specifically, the challenge of ecogentrification) and the megaregion (and the resulting regional externalities and trade-offs). These scales involve actors, conflicts, and specializations within planning. Yet both represent new, hybrid patterns of urbanization that produce intractable problems of environmental unsustainability and social-spatial inequality—two core planning priorities that too often collide. Both situations also generate novel social policy challenges that conventional planning, thinking, and governance tools are ill-equipped to address. These challenges instead call for interdepartmental or intergovernmental cooperation

Eight grand challenges in socio-environmental systems modeling

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices

Generating policies for sustainable water use in complex scenarios: An integrated land -use and water -use model of Monroe County, Michigan.

Rapidly declining groundwater levels in Southeast Michigan have raised serious concern since the early 1990s. Hydrological studies suggest that land-use changes have caused this decline. The mechanisms linking land-use and groundwater dynamics are not clear, however. To examine this link, I developed the Water-Use Land-Use Model (WULUM), an agent-based model that serves as an analytical framework to understand how these processes interact to create the observed patterns of resource depletion, and to suggest policies to reverse the process. The agent-based model is empirically based on Monroe County, Michigan, and informed with land-use and survey data and expert knowledge about the case. The land-use component includes the main groundwater extractors in the county: stone quarries, golf courses, farms and households. The groundwater component includes the glacial deposits and the underlying bedrock aquifer. The behavior of water users is defined by simple rules that determine their location and consumption decisions. The dynamics of groundwater are represented through simple diffusion rules between each cell and its immediate neighbors. Scenario-based simulations provided the medium for exploratory analysis of the integrated land-use/groundwater system. Pre-testing of WULUM highlighted the importance of the glacial recharge rate of the aquifer in determining the regional hydraulic gradient, recommending reexamination of the parameter values cited in literature. Although quarries extract 75 percent of the total withdrawal, simulations showed that eliminating quarry dewatering did not entirely reverse groundwater decline. Urbanization, on the other hand, contributed significantly to long-term decline. Both low-density and high-density zoning restrictions improved aquifer conditions over medium-density development, suggesting a non-linear relationship between intensity of residential use and groundwater levels. Moreover, of all the natural and policy variables, zoning had the greatest influence on urban settlement and therefore on resource consumption. Medium to high values of hydraulic conductivity in some cases reinforced drought conditions by extending the area affected by excess withdrawals, so that land-use policies should discourage residential concentration in those areas. Thus, while quarries currently affect large areas, expanding suburbanization may lead to regional groundwater depletion in the near future, depending on the spatial distribution and the intensity of development.Ph.D.Environmental scienceGeographyHealth and Environmental SciencesSocial SciencesUrban planningUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/125557/2/3192831.pd

Generating policies for sustainable water use in complex scenarios: an integrated land-use and water-use model of Monroe County, Michigan

Rapidly declining groundwater levels since the early 1990s have raised serious concern in Monroe County, Michigan. Hydrological studies suggest that land-use changes have caused this decline. The mechanisms linking land-use and groundwater dynamics are not clear, however. In this paper I present WULUM, the Water-Use and Land-Use Model, an agent-based model that serves as an analytical framework to understand how these processes interact to create the observed patterns of resource depletion, and to suggest policies to reverse the process. The land-use component includes the main groundwater extractors in the county—stone quarries, golf courses, farms, and households. The groundwater component includes the glacial deposits and the underlying bedrock acquifer. The behavior of water users is defined by simple rules that determine their location and consumption. The dynamics of groundwater are represented through infiltration and diffusion rules between each cell and its immediate neighbors. Initial explorations with the model showed that land-use patterns contributed significantly to groundwater declines, while eliminating quarry dewatering did not entirely solve the problem. Both low-density and high-density zoning restrictions improved aquifer conditions over medium-density development, suggesting a nonlinear relationship between intensity of residential use and groundwater levels. Moreover, of all the natural and policy variables, zoning had the greatest influence on urban settlement and therefore on resource consumption.

Exploring the effectiveness of bus rapid transit a prototype agent-based model of commuting behavior

The introduction of Bus Rapid Transit (BRT), typically involving the use of exclusive bus lanes and related bus priority measures, is increasingly advocated as a flexible and cost-effective way of improving the attractiveness of public transit in congested urban areas by reducing travel times and variability. These schemes typically involve the reallocation of road space for exclusive use by buses, presenting commuters with potentially competing incentives: buses on BRT routes can run faster and more efficiently than buses running in general traffic, potentially attracting commuters to public transit and reducing congestion through modal shift from cars. However, a secondary impact may also exist; remaining car users may be presented with less congested road space, improving their journey times and simultaneously acting as an incentive for some bus-users to revert to the car. To investigate the potential for these primary and secondary impacts, we develop a prototype agent-based model to investigate the nature of these interactions and how they play out into system-wide patterns of modal share and travel times. The model allows us to test the effects of multiple assumptions about the behaviors of individual agents as they respond to different incentives introduced by BRT policy changes, such as the implementation of exclusive bus lanes, increased bus frequency, pre-boarding ticket machines and express stops, separately and together. We find that, under our assumptions, these policies can result in significant improvements in terms of individual journey times, modal shift, and length of rush hour. We see that the addition of an exclusive bus lane results in significant improvements for both car users and bus riders. Informed with appropriate empirical data relating to the behavior of individual agents, the geography and the specific policy interventions, the model has the potential to aid policymakers in examining the effectiveness of different BRT schemes, applied to broader environments

Big data and urban Informatics: innovations and challenges to urban planning and knowledge discovery

Big Data is the term being used to describe a wide spectrum of observational or “naturally-occurring” data generated through transactional, operational, planning and social activities that are not specifically designed for research. Due to the structure and access conditions associated with such data, research and analysis using such data becomes significantly complicated. New sources of Big Data are rapidly emerging as a result of technological, institutional, social, and business innovations. The objective of this background paper is to describe emerging sources of Big Data, their use in urban research, and the challenges that arise with their use. To a certain extent, Big Data in the urban context has become narrowly associated with sensor (e.g., Internet of Things) or socially generated (e.g., social media or citizen science) data. However, there are many other sources of observational data that are meaningful to different groups of urban researchers and user communities. Examples include privately held transactions data, confidential administrative micro-data, data from arts and humanities collections, and hybrid data consisting of synthetic or linked data. The emerging area of Urban Informatics focuses on the exploration and understanding of urban systems by leveraging novel sources of data. The major potential of Urban Informatics research and applications is in four areas: (1) improved strategies for dynamic urban resource management, (2) theoretical insights and knowledge discovery of urban patterns and processes, (3) strategies for urban engagement and civic participation, and (4) innovations in urban management, and planning and policy analysis. Urban Informatics utilizes urban Big Data in innovative ways by retrofitting or repurposing existing urban models and simulations that are underpinned by a wide range of theoretical traditions, as well as through data-driven modeling approaches that are largely theory agnostic, although these divergent research approaches are starting to converge in some ways. The paper surveys the kinds of urban problems being considered by going from a data-poor environment to a data-rich world and ways in which such enquiry have the potential to enhance our understanding, not only of urban systems and processes overall, but also contextual peculiarities and local experiences. The paper concludes by commenting on challenges that are likely to arise in varying degrees when using Big Data for Urban Informatics: technological, methodological, theoretical/epistemological, and the emerging political economy of Big Data

Exploring the Influence of Urban Form on Work Travel Behavior with Agent-Based Modeling

This paper examines the effect of land use regulations on travel behavior by using agent-based modeling. A simulation model for a hypothetical urban area loosely based on the Chicago, Illinois, metropolitan area was used to study the impact of six land use regulation scenarios on transit use and urban form. The key features and techniques of the model development and the scenarios tested are described. The results from the simulations showed that although the land use regulations that were designed to increase the density near the transit station or in and near the urban core were able to achieve the intended land use patterns, they did not increase the transit mode share for the region in a significant manner. More detailed examination of the output revealed that as long as the rules for mode choice, the distribution of employment, and the transit network remained unchanged, land use regulations that affect residential locations produced limited effects on transit use