Discussion Question 05: Learning and Experiments in Resilience Planning

How can we incorporate learning and experiments in designing and planning resilient cities and urban places?

Comments

  1. A key first step for incorporating leaning and experimentation in the design and planning of resilient places is to acknowledge the uncertainty inherent in urban systems and the need to experiment in order to engage and learn about that uncertainty. In its Adaptive Management Guide, The U.S. Department of the Interior reports that managers do not always acknowledge uncertainty in the development and application of environmental assessments and management strategies. This might be in part because asserting certainty is sometimes seen as a more convincing management strategy, or because it communicates confidence in decision making and acts to limit openings for potential stakeholder conflict (US DOI 2009). This kind of approach is problematic because knowledge of interconnected human-natural systems is always incomplete, and the system itself is constantly changing (Holling 1996); therefore, planners and designers need to be willing to admit uncertainty and adopt a more openly flexible and experimental approach toward (urban) ecosystem management.

    The resilience literature suggests that experimental approaches should be flexible and reversible; foster reassessment; engage stakeholders; and provide a learning experience for participants and managers (Holling 1996, Kinzig and Starrett 2003, US DOI 2009). This kind of approach requires flexibility on the part of institutions and a built-in monitoring framework for assessing results of decisions and action (Holling 1996, Schultz 2008). Based on my own experience with the City of Seattle's regulatory environment, I think that a simpler and more responsive process for the modification of existing regulations and protocols as well as greater ease of cooperation between departments could help to facilitate a more adaptive approach to the management of urban systems. As Holling suggests (1996), the creation of incentives for maintaining sustainable systems through experimental and flexible approaches could also aid in this effort.

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  2. Adaptive planning is a process that integrates learning into urban planning and design. In this process, multi-stakeholder groups examine alternative strategies, predict outcomes of these strategies, implement and monitor one or more alternatives, and alter policies based on their findings (US DOI 2009; Alberti 2003). Adaptive planning is best used in highly uncertain and controllable situations (US DOI 2009). Flexibility in institutional decision-making is essential for creating resilient cities (Kinzig 2003; Schindler 2015). In fact, small institutional changes can generate large impacts (Alberti 2016). In our fish simulation game, we could only manage the bag limit (maximum number of fish caught per person per day), but this variable dramatically altered the fish population. We reassessed the bag limit every decade in response to fish population levels and health. Similarly, planners may test and assess alternative transportation options to discourage driving, such as bolstering bike and pedestrian paths in different areas, increasing frequency of bus service, and adding routes. Planners should continue to monitor and adapt these methods as they learn about the effectiveness of different transportation alternatives in changing behavior and meeting dynamic human needs.

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  3. Learning can be incorporated in the design and planning of resilient cities and urban places through iterative processes. These iterative processes may include simulation runs or repeated experiments where the outcomes of each iteration can be analyzed to foster learning and identify trends. Emergent properties may develop through iterations which allow designers and planners to discover more variables or interaction dynamics that affect the system of study. Dewey’s educational theory has three major qualities: continuity (each subsequent experience should be influenced by prior experiences), social awareness (human contact and interpersonal communication are part of every meaningful experience), and collateral learning (learned skills from experiences are carried over to new experiences) (Susskind, L. and J. Corburn, 1999). These three principles are reflected in group simulations and experiments where learning is an iterative process that happens at each interval amongst a group of stakeholders.

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  4. The difference between science and policy was recently detailed in class and is further outline in the paper "Coping with Uncertainty" from Kinzig and Starrett. The role of uncertainty and ideology affects both fields in different manners. Science is constantly faced with uncertainty. Experiments, tests and analysis chip away at what is understood to be unknown. Ideology often has little to do with the outcome, as results are paramount. While politicians and policy makers are also faced with constant uncertainty, experiments are often difficult or impossible to utilize, as decisions must be made quickly and constituents must be appeased. For this last reason, ideology plays an enormous role in the political process. The influence of donors and other funding mechanisms further complicate the role of ideology in policy-making. Kinzig and Starrett recommend necessary changes to accommodate learning and experimentation in the policy realm, such as increased influence of science and a well-informed constituency. Though these may be helpful, they are difficult to enforce and unlikely to succeed. A strong focus must be placed on the iterative process and management/evaluation component of planning and policy making. It is often cited as a necessity but all too often ignored in reality. Appropriate funding and effort must be allocated to evaluating and reincorporating any decision.

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  5. The human environment is a learned environment: we depend on education and practice to run our households and manage political affairs. People often grasp intuitively the elements of risk and surprise inherent in these activities: a broken appliance can throw off a budget, a politician can turn out different from what is expected (even worse than expected!). But when it comes to science and scientific knowledge, many people tend to have difficulty understanding how uncertainty and risk come into play in their environment, though as Kinzig and Starrett (2003) note, risk and uncertainty are endemic to the field. Traditional teaching methods often emphasize science as a quest for ever-closer certainty about an immutable subject, such as the exact nature of the Big Bang or quantum physics. But when ecological systems behave unpredictably, it does not mean that some Newtonian model has failed; that's just a Thursday in environmental science and planning. The system changes, and we change with it. By the time we analyze the system it has changed, and our analysis itself has changed it. Too often people fail to grasp this dimension of science, the idea that it is not always about asymptotically reaching a single truth but is about treading water (and trying not to eutrophy it). This adaptive, iterative process is something that humans understand, and games, scenarios, and other techniques are tools to remind people that environmental planning relies on difficult decisions without perfect information.

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  6. Incorporating learning and experimentation in designing and planning resilient cities is to embrace uncertainty. City planning has often relied on models to predict the future and base planning policies on what is probable however we cannot fully predict the future. To become more resilient to a multitude of events that can trigger a regime shift, we must embrace uncertainty. We can learn from adaptive management practices as outlined in the Adaptive Management Guide published by the U.S Department of the Interior. The guide has a scientific approach to learning and experimentation. First, managers develop a hypotheses about how resources respond to management techniques. Then managers monitor the system to determine whether the predictors are responding well or not. Either way, we are learning about what works and what does not.

    Simulations and exercises are also a great way for planners to learn and experiment with different policy decisions. As a class we participated in the fish management simulation where we all had different roles and were able to see how our decisions affected the property values and fish stock over time. It showed us that sometimes conservation (no fishing) was not always a good option. The emissions exercise gave us experience in working collaboratively with various stakeholders to improve pollution. Collective action to reduce pollution was necessary because it is more effective and efficient to work together.

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