ware, Vensim, to build portions of a sample model as part of a problem set. We also used traditional teaching methods, r
Using a Simulation-Based Learning Environment for Teaching and Learning About Complexity in Public Policy Decision Making Minyoung Ku, Roderick H. MacDonald, Deborah L. Andersen, and David F. Andersen University at Albany, State University of New York
Michael Deegan U.S. Army Corps of Engineers, Institute for Water Resources
ABSTRACT
Public leaders and managers today are being challenged by unprecedented, complex problems. Tackling “wicked problems” requires a new way of thinking, and new methods and tools for building models that realistically account for social and natural phenomena, gather and structure convincing evidence, and predict policy outcomes accurately. To prepare future and current public decision makers for a rapidly changing, complex world, we suggest a multidimensional framework of complexity in public policy settings that encompasses both analytic and socially constructed complexity, and introduce a simulation-based learning environment (SBLE) in which the power of traditional learning environments is augmented by a computer simulation model. In this study, we report on our experimental attempt to teach students in the MPA classroom about complexity by creating and implementing a SBLE with a U.S. Gulf Coast disaster preparedness case.
KEYWORDS Complexity, simulation-based learning environment, policy analysis methods, public policy decision making
The public sector today is experiencing in creasing complexity in both natural and social environments. The unprecedented numbers and scale of natural disasters in the United States are obvious evidence of this trend. Hurricane Katrina slammed into New Orleans in 2005 and left a toll of 1,833 lives lost, 275,000 destroyed or damaged houses, and a cost to the U.S. economy of greater than $100 billion (Johnson, 2006). In 2012, Hurricane Sandy hit the East Coast with an expected cost of over $50 billion
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(Sullivan & Uccellini, 2013). The superstorm left entire communities devastated on the north eastern U.S. coast, and 6 months later, public officials, especially in New York City and New Jersey, were facing new challenges as they con templated how to recover but more importantly how to plan for a possible next storm. Planning for future events caused by extreme climate changes is a good example of the many diverse features of complex problems facing Journal of Public Affairs Education 49
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public leaders and managers. Scientific studies of climate change suggest that public leaders and managers may be facing a new long-term situation characterized by increasing temper atures, rising sea levels, droughts, flooding, and increased severity of storm surges (see Houghton, 2009; Rahmstorf, 2007). Despite advances in the science of climate change, the causal rela tionship between global and regional climate changes and the occurrence and scale of natural disasters in a specific geographic area is still hard to predict. In addition, the science of climate change di vides rather than unites the public for action. While some view climatic warming as a fact that defines a new future, others view it more as an unfounded belief (see Allegre et al., 2012). Politics appears to trump science as politically defined factions dominate the discussion about how to respond. If political will could be sum moned, implementation would have to take place within a complicated maze of state and local statutes and regulations. Government agencies in charge of planning for and preparing sites against future disasters, such as the U.S. Army Corps of Engineers, also must work within the context of a diverse set of state and local laws that result in confusion over project funding and lengthy siting delays. Local stakeholder groups are far from unified about what to do when forces favoring economic development are opposed by environmentally minded stakeholders seeking to preserve natural coastal features. Moreover, even though local groups may agree on large-scale construction projects funded by the U.S. Army Corps of Engineers and designed to protect and in some cases encourage local development, the dyna mic nature of social and environmental systems makes it difficult to guarantee the best result for the community. Here is a dilemma. Over time, the solution of building protection, which makes coastal deve lopment seem safer than it would otherwise be, can implicitly promote new construction, put ting pressure on storm-sensitive areas and ulti mately creating more construction and hence 50
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more vulnerability to future storms. That is, today’s solutions lay the foundation for tomor row’s disasters. On the other hand, the long-term consequences of less intrusive locally driven policies, such as more stringent zoning or building codes, do not effectively maximize land use but can accumulate in the long run to decrease construction density and hence vulnerability to future storms. These processes of planning, developing, and constructing both new structures and protective barriers can take decades to work themselves out. To complete the cycle, if climate change, as predicted by some climate scientists, turns out to lead to increased sea level rise and more violent storm surges 40, 50, or more years down the line, then today’s public policy planning efforts could ultimately be tested by future storms that are outside the range of any historic observations. To solve such complex problems, which resist easy solutions via traditional public policy and management analytic approaches, new perspec tives on complex systems, and new methods and skills to analyze the behavior of such systems, are being developed (e.g., Ghaffarzadegan & And ersen, 2012; Kim, Johnston, & Kang, 2011; Morçöl, 2005; Sterman, 2002, 2006; Teisman & Klijn, 2008). Teaching and learning about complexity in public policy settings has become central to increasing the capacity of public managers and policy decision makers to handle complex problems and, in turn, to enable sus tainable public management. However, the question of what approaches to teach in MPA programs, and how, is emergent. In this article, we propose traditional learning environments that are augmented with a com plex and dynamic computer simulation model, with accompanying case and other learning support materials. In the next section, we pro pose a multidimensional concept of complexity that encompasses both analytic and socially constructed complexity. In the following two sections, we introduce a simulation-based learn ing environment (SBLE) in which students can experience and learn about how to handle
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complex, “close-to-reality” problems in public policy decision-making situations. The taxonomy of complexity in public admin istration and policy provides a framework for the rest of the paper. In the fourth section, we provide a step-by-step guide to implementing, in the MPA classroom, the simulation-based learning package in policy analytic modeling methods. In the fifth section, we discuss the potential of our pilot semester-long SBLE as an enjoyable and effective method for teaching and learning about both analytic and socially constructed complexity in public policy deci sion making by reporting preliminary results and demonstrating students’ reactions to the experimental course and instructors’ experience of teaching their classes. We end by discussing the implications of our current study. MULTIPLE DIMENSIONS OF COMPLEXITY
Complexity is not a new concept in public administration and policy (Morçöl, 2005). Rather, complex features of political and gov ern mental systems have provided important epistemological background to theories and practices in the discipline. In the late 1950s, Charles E. Lindblom delineated the complexity of decision making in public policy settings in the essay “The Science of ‘Muddling Through.’” In his early work, he posited that a considerable number of factors relate to each single policy issue, but that the scopes of policy problems and the numbers of alternatives that can be bureaucratically dealt with are limited. In particular, he shed light on the complexity of policy decision making, focusing on diversity in social contexts—the politics of muddling through when decision makers emphasize different values and perspectives, and the interests of diverse policy actors. Researchers and practitioners in the field now seem to take his view of the complex, multifaceted nature of formulating policies as true whether or not they agree with Lindblom’s arguments regarding incrementalism. The difficulty in public-sector decision making, however, arises not just because of complexity in our social systems—mainly caused by the
existence and participation of policy actors with different points of view, goals, and interests— but also because of the complex nature of policy and administrative issues, which could cause analytical problems. The interconnectedness of elements of (natural and social) systems and the uncertain patterns of their behaviors as a whole and as subsystems make it much harder, if not impossible, for decision makers to make in form ed decisions about any social and environmental challenges through rigorous evidence-based analyses (Colander & Kupers, 2014). Thus, we argue that tackling complex problems requires dealing effectively with both types of complexity, socially constructed complex ity and analytic complexity. Box 1 summarizes the multiple dimensions of complexity in pub lic administration and policy. BOX 1.
Taxonomy of Complexity in Public Policy Decision Making1 1. Socially constructed complexity 1.1 Political complexity • Partisan politics • Instability of domestic and/or international politics 1.2 Stakeholder complexity • Stakeholder ambiguity • Stakeholder conflicts 1.3 Administrative complexity • Complex, diverse, and competing policy goals • Turf wars • Accountability • Authority
2. Analytic complexity 2.1 Stochastic uncertainty • Imperfectness of information • Unpredictability • Low probability with high damage 2.2 Dynamic complexity • Interconnected elements of a system • Thinking through time • Stock-and-flow thinking • Self-organization through feedback loops 2.3 Detail complexity • Multiple heterogeneous elements • Multiple alternatives satisfying different criteria
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Socially Constructed Complexity
Decisions in the public sector are made through debates over the best course of action, and through complex and dynamic interactions among interconnected governmental and non governmental actors (Allison & Zelikow, 1999; Lindblom, 1959; Sterman, 2006; Wilson, 2000). Difficulty in making policy decisions arises because various policy actors, including politicians, government managers, and other interested stakeholders, can and do view policy problems from different points of view. These differences in points of view lead to intragroup or political conflicts that make it much more difficult to reach a consensus on public admin istration and policy decisions. Moreover, when different stakeholders have different goals and interests, it can lead to intense conflicts that, in turn, can result in policy failure. Diversity in individual and organizational values can shift how individuals, or organizations that they represent, view policy options and out comes. At the macro level, trade-offs exist between personal or organizational orientation to such complex issues as equity versus effi ciency, prior preferences for market-based versus publicly planned and implemented solutions, and other such macro issues—trade-offs that make these policy systems more complex. Issues regarding this class of complexity have long been the main themes of public administration and policy research and teaching. Political com plexity (e.g., partisan conflicts and instability of domestic and/or international politics), stake holder complexity (e.g., stakeholder ambiguity and conflicts), and administrative complexity (e.g., turf wars and issues of accountability and authority between government actors and be tween government and nongovernment actors) are examples of complexity that public leaders and managers could encounter in social settings (e.g., Allison & Zelikow, 1999; Bryson, 2004; Dunleavy, 1992; Wilson, 2000). Analytic Complexity
Not only are the processes through which dif ferent policy actors reach a consensus complex, but the system being managed can have another form of complexity—analytic complexity. From 52
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this perspective, complex problems often fea ture stochastic uncertainty, detail complexity, and dynamic complexity. Stochastic uncertainty is derived from imperfect information about the state of systems. Uncertain situations occur when decision makers have no information, or when they have information that is inaccurate, unreliable, and/or incomplete (Van Asselt & Rotmans, 2002). In other words, when decision makers cannot identify the cause of policy prob lems or accurately predict the future courses of an administrative or policy action, decision making occurs under uncertainty. Stochastic uncertainty complicates future planning: it is much harder to make a decision when a poss ible event is characterized by low probability but high payoff. It is extremely hard to manage future-related issues with unaided human intuition if the pay-off for a decision about an assumption may be negative and large-scale, such as the expected damages of a natural disaster (Berke & Lyles, 2013). Dynamic complexity involves well-documented difficulties associated with stock-and-flow rea soning, conceptualizing problems in dynamic terms, managing delays in policy systems with long time horizons, and what is coming to be known as managing feedback complexity. Scholars such as Ghaffarzadegan and Andersen (2012); Kim, Johnston, and Kang (2011); Kim, McDonald, and Andersen (2013); Koliba, Zia, and Lee (2011); Sterman (2002, 2006); and Zagonel, Rohrbaugh, Richardson, and Ander sen (2004) have shed light on the importance of public decision makers’ and stakeholders’ understanding of dynamics in public policy problems in both social (agent) and/or physical (non-agent) systems in formu lating effective policies. Although the contexts of policy deci sion making and the systems models developed in or applied to the studies vary, it is common for them to pay attention to the interrelated elements of systems—thinking over time, changes in the state of system materials (i.e., stock and flow), and self-organization of the systems through feedback loops. In particular, the contexts and models focus on stock-andflow dynamics in the realm of dynamic complexity to better understand the behavior of the systems their research targets, whether
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system-level or agent-based. While stocks (i.e., accumulations) are the integration of changes over time, flows (i.e., rates) are the changes causing variations in stocks over time. Accurate decisions require that reasoning through stockand-flow analysis be undertaken. However, capturing stocks and flows is difficult with unaided human intuition (Sterman & Sweeney, 2002). This is true in that the state of the context of public policy decision making is constantly changing over time, and in that a system and agents in the system are continu ous ly adjusting themselves to their environ ment by using information from feedback loops. Therefore, due to dynamic relationships and interactions among system elements, even the directions of the change may be neither predictable nor intuitive. The delay of time— that is, the time lag between policy actions and policy results—makes handling policy prob lems more complicated (Senge, 2006; Sterman, 1994). Many policy failures result from failing to deal with dynamic complexity. Detail complexity arises from the existence of a great number of heterogeneous systems com ponents that affect system behaviors. This class of complexity in administrative and policy systems involves decisions, often repeated, in an environment characterized by a large num ber of moving parts and a need for consistent decisions to be made to assure equity and efficient outcomes. Due to the large numbers of factors, which are interconnected, it is diffi cult to identify points within a system where positive change can be made (Senge, 2006). If there are confounding or ambiguous variables in the system, the identification of the right point at which to make a policy action may be more problematic. In addition, when detail complexity is coupled with dynamic complexity, it becomes much more difficult to undertake scientific policy analysis and ultimately to make policy decisions. THE SIMULATION-BASED LEARNING ENVIRONMENT
Solving complex problems requires a new way of thinking—what Senge (2006, p. 69) calls
“a shift of mind”— and new methods and tools. Effective problem solving and public policy development begin with the under standing of (natural and social) systems as they are; “every thing is connected to everything else” (Sterman, 1994, p. 291). This shift in para digm transforms one’s view of the world and phenomena occurring in it, from static, linear, and closed systems to dynamic, nonlinear, and open sys tems, and drives the need for new methods. New tools are needed to build models that realistically account for social and natural phenomena, to gather and structure convincing evi dence, and to predict policy outcomes accurately. Teaching and learning about complexity in pub lic administration and policy should in clude both a new way of thinking and new methods through which learners can put these new ap proaches into practice. As educators, we seek approaches that augment the power of traditional learning environments. As Denhardt (2001, pp. 527–528) points out, students in MPA programs are expected to learn various useful skill sets for their future careers in the public sector and in other relevant areas, and differ ent learning mechanisms and methods may be necessary and effective in learning different skills. To teach students about dynamic systems and decision making in such complex systems, teachers need methods and tools that allow learners to gain explicit and implicit knowledge about complexity. These methods can help learners to identify points within the system where positive, meaningful outcomes can be made by human intervention; to directly make interventions in dynamic systems; and to observe how their actions influence the systems they are managing. Thus, to teach and learn about complex systems, the use of models that realistically account for social and natural phenomena with the aid of technology, such as computer simulation models, is essential (Andersen et al., 2006; Hu, Johnston, Hemp hill, Krishnamurthy, & Vinze, 2012; Sterman, 1994, 2002, 2006). Journal of Public Affairs Education 53
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Computer simulations are attracting attention as practical tools in the public and nonprofit sectors. A number of public agencies and non governmental organizations are now creating SBLEs—learning environments that use com puter simulations as educational and business tools for helping public managers and teams of interested stakeholders grapple with complex policy and managerial situations. For example, the U.S. Army Corps of Engineers has created an SBLE project called Shared Vision Planning to deal with complex projects such as planning for the next Superstorm Sandy.2 The use of SBLEs is not limited to regional planning efforts for possible natural disasters. SBLEs are also applied to the context of eliciting cross-sectional efforts to make positive changes in socioeconomic systems. For example, the Ripple Foundation has launched an SBLE to help both professionals and the public under stand dynamics in the U.S. health care system and predict the outcomes that potential policy actions will bring to society, ranging from health care costs and adequacy of self-care and clinical care to death rate and life span.3 SBLEs, whether implemented in the field or in the MPA classroom, can be defined as synthetic settings within which people learn domain con cepts by interacting with simulation models that show the expected behavior of a system. SBLEs include methods both with and without computer-based interfaces, such as role-playing games without computer models (Hu et al., 2012; Salas, Wildman, & Piccolo, 2009). A simulation model used in an SBLE for the purpose introduced above, however, refers to a computer-based model that represents impor tant aspects of a dynamic system. Such models can endogenously reproduce base-run behavior (i.e., the expected behavior of a system with no policy intervention) over the time of the problem(s) being studied. Typically, the model enables users to experience the full possible consequences of their simulated actions by allowing them to interact directly with the simulated reality. 54
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The potential and actual benefits of SBLEs in teaching and learning about how to handle complex problems have been studied in literature in various fields, such as medical training (e.g., Issenberg, McGaghie, Petrusa, Gordon, & Scalese, 2005; Kunkler, 2006), aircraft training (e.g., Hays, Jacobs, Prince, & Salas, 1992; Salas, Bowers, & Rhodenizer, 1998), military training (e.g., Taylor & Barnett, 2012), and management innovation in the forprofit and public sectors (e.g., Hu et al., 2012; Kim, MacDonald, & Andersen, 2013; Salas et al., 2009). Despite the diversity of the fields that SBLEs have been applied to, it has been commonly observed that SBLEs are effective in improving learners’ capacities not only to understand complex concepts but also to apply knowledge to practice and develop skills to handle complex problems (e.g., Hu et al., 2012; Swaak, de Jong, & van Joolingen, 1998). Existing studies highlight that the merits of SBLEs stand out when a computer simulator is used in the classroom. For example, Swaak, de Jong, and van Joolingen (1998) demonstrate that SBLEs that include running computer simulation activities are positively associated with enhancing learners’ intuitive knowledge, such as insights, rather than just helping learners to gain explicit, conceptual knowledge. According to Swaak et al. (1998), when using SBLEs, learners can experience richness of information. By running computer simulations iteratively in a context that is analogous to the real world, learners experiment with system variables. Such playful experiments are not possible in the real world because of constraints of time or ethical issues (Sterman, 2006). DISASTER PREPAREDNESS ON THE GULF COAST: A CASE STUDY
To teach current and future public decision makers about complexity, we designed and built a simulation-based learning package about coastal area protection planning for futurepossible natural disasters—the Pointe Claire Coastal Protection Planning Exercise (PCCPPE). The PCCPPE was created for use in a core
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MPA class in analytic methods as a whole package of in-class exercises, problem sets, read ings, and lectures around a simulation model.4 It is a semester-long educational program rather than a one-time simulation activity.
such as this. These are some of the aspects of complexity that are built into the simulationbased case study that forms the basis for the learning environment described in the follow ing section.
The semester-long case study is based loosely upon the U.S. Army Corps of Engineers’ Mississippi Coastal Improvements Program—a project that worked with local communities to craft comprehensive and long-term solutions to their local concerns around hurricane-related flooding. Working with the federal agency, low-lying coastal communities in the United States must decide how to be prepared for future-possible disasters involving hurricanes and other coastal storms. Prudent decisions are complicated by many factors, including the presence of a strong and coherent body of climate-warming skeptics who question whether any action at all needs to be taken in the present. As we discussed earlier, future hurricanes are stochastically uncertain, and sea level rise occurs slowly and with a long delay. Most human decision makers have been shown to exhibit significant reasoning flaws around the type of stock-and-flow decisions, such as greenhouse gas emissions, that drive much of the science of climate change (Sterman, 1994; Sterman & Sweeney, 2002).
At the center of the PCCPPE is a SBLE computer simulation model.5 Figure 1 presents a screenshot of the main user interface for the computer simulation, CoastalProtectSIM.6 Users can select from four major policy options from this main screen: (a) building protection in the form of dikes and other protective bar riers (i.e., height of protection), (b) implement ing building codes that increase the storm resistance of new construction (i.e., building code policy), (c) zoning to restrict building in areas that might be prone to flooding (i.e., zoning regulations), and (d) implementing a policy of buying out owners of parcels that have been damaged after a recent storm (i.e., buyout or relocation policy). Each policy can be implemented at differing times within the simulation and with different types of imple mentation, including especially how taxes are raised to pay for the policies. Users can also design their own future scenarios around climate change by modifying model assumptions about sea level rise, global temperature rise, and storm intensity.
Furthermore, vested interests in coastal com munities have created interest groups that favor coastal development and related job develop ment. Indeed, future projected development made possible by protective measures can create a future tax base that can help pay for the development projects. On the other hand, policy actions intended to provide protection to coastal communities can have the unintended effect of making coastal development seem safer than it would otherwise be, thereby increasing further development that, over time, will in turn increase the damage from future coastal storms. The use of discount rates in economic analysis of projects that will have impacts long into the future further complicates dynamic decision making in complex situations
Users can access output from any of the scores of variables within the model, but the front-page user screen offers six pre-programmed model outputs, all measured in cumulative dollars dis counted to net present value: (a) storm damages, (b) aggregate costs, (c) aggregate dam ages, (d) benefits and damages avoided, (e) costs and damages, and (f ) revenue benefits from taxes and desired tax rate. The model can be rerun multiple times, and a small panel in the lower middle of the main interface screen (see Figure 1) presents comparative plots of total net benefits for each run, giving a head-to-head comparison of how each policy examined is faring. The main output screen is used to dis play similar comparative plots for the selected output variable. Journal of Public Affairs Education 55
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FIGURE 1.
Screenshot of Main User Interface for the CoastalProtectSIM Model Policies You Can Change
Building Code Policy
Year of Building Policy
Buyouts or Relocation Policy
Year of Zoning Policy
Zoning Regulations
Year of Buyout Policy
Discount Rate
Automated Taxes
Key Indicators 3B Dollars
Height of Protection
Graphical Output
Percent of Maximum Tax Rate
0 2012 2020 2028 2036 2044 2052 Time (Year)
Cumulative Costs & Damages: Base Cumulative Benefits & Damages Avoided: Base
Total Net Benefit Dollars
Global Warming Scenario Temp Rise by 2052 Percent Increase in Surge per Degree Rise Percent Increase in Volatity per Degree Rise
0
–3B 2012 2020 2028 2036 2044 2052 Time (Year)
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0
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CoastalProtectSIM Version 2.0 2013
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One of the key characteristics of this computer simulation model is that it shows users a struc tural map of components in the system that are associated with the case policy issue. Figure 2 is a screenshot of a portion of the model structure, showing the dynamic relationships among the components related to a policy option (i.e., height of protection). Users can change the values of input variables and the parameters for climate change assumptions and see the six major model outputs mentioned above in the interface. Meanwhile, the original model struc ture view provides users with detailed infor mation about the causal/feedback loops between a policy action and its outcomes. Users can get information on the impact of their course of action on diverse parts of the system over time, given climate change assumptions that range from planning costs to the percentage of undeveloped land (i.e., the natural protection capacity of the region). Thus, users, by running simulations in the CoastalProtectSIM, can virtu ally experience the dynamics in the simulated system—and the uncertainty to some degree as well—and obtain empirical data to construct viable arguments and provide reasoning based on data, not just on their intuition. 56
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IMPLEMENTATION IN THE MPA CLASSROOM
To teach MPA students about diverse dimen sions of complexity in public administration and policy, we experimentally implemented the disaster preparedness SBLE learning package, PCCPPE, in three MPA classes in policy analytic modeling methods at the Rockefeller College of Public Affairs and Policy, State University of New York, in the 2012–2013 academic year. The primary goal of the classes in which our experiment was conducted was to enhance learners’ competence to deal with complex, analytic problems in public policy decision making. These classes also aimed to teach how socially constructed complexity complicates defining a policy problem, analy zing and interpreting data, and ultimately making final decisions. To achieve these goals, the PCCPPE, as a full package of learning, adopts multiple types of education methods: traditional learning methods such as readings and lectures, and simulation-based learning methods such as planning for future storms by running computer simulations. The simu lation-based learning package spanned 10 out of 16 weeks and is outlined below.
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Weeks 1–3: Preparing for the Simulation, and Stochastic Uncertainty
For the first 3 weeks, in the preparation stage, we introduced students to diverse dimensions of complexity conceptually and to the PCCPPE, excluding the CoastalProtectSIM simulator, by providing contextual information about the vir tual region, Pointe Claire, and about the Shared Vision Planning commission. In this pre-simu lation stage, we focused on providing learners with opportunities to become familiar with the context of the SBLE case (i.e., Pointe Claire coastal protection planning) in order to immerse them in the role-playing context for the term. In this stage, we mainly used tradi tional teaching methods, such as readings and lectures, and case-based role playing. Some computer technologies, such as Excel software and video clips about natural disasters and controversies over climate change, were also introduced.
Climate change issues were brought into the classroom setting to teach about stochastic un certainty and socially constructed complexity. Class exercises modeled hurricanes as uncertain events. As with hurricanes in Pointe Claire, beliefs about climate change being true or false were considered in the case materials as stochastic information. The controversy over climate change among scientists provided the impetus to further consider socially constructed complexity. Such controversy represents real istic conflict between two different perspectives on the same issue. However, to prevent polarized arguments on climate change from diverting students’ atten tion from stochastic uncertainty, during these 3 weeks we focused on decision tree analysis and applications, to discuss climate change issues and to analyze the worth of stochastic information.
FIGURE 2.
Screenshot of a Portion of the Model Structure for the CoastalProtectSIM Model
Time to Complete Siting
Project Start Time
Height of Protection
Time to Plan
Construction Progress
Finished Built Protection
Normal Siting Costs
Current Siting Costs
Current Construction Costs
Normal Construction Costs
Built Protection in Planning
Current Planning Costs
Normal Planning Costs
Built Protection Being Sited
Siting Progress
Built Protection Goal
Planning Progress
Time to Complete Construction
Total Current Costs
Current Costs Adjusted for Scope of Change
Current Maintenance Costs
Annual Maintenance Percentage
Initial Built Protection
Total Coastal Protection
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Week 4: Introducing Computer-Based Simulation Models
As an introduction to running a simulation, we introduced students to computer-based simu lation models through the role-playing game C-Learn. C-Learn is a simpler version of the C-ROADS policy simulator7 that was develop ed to give policy makers a fast and easy-to-use simulation environment that captures many of the policy-relevant details of climate changes, such as carbon dioxide (CO2) emissions and deforestation. Like C-ROADS, C-Learn is a computer-based simulator that has been con figured for easy online use by students interested in learning about climate change and how simu lators can support policy formation. However, as a simplified version, C-Learn has been built with a simple and more user-friendly interface. C-Learn was run in a role-playing context of climate change negotiation: for CO2 emissions, reduction in CO2 emissions from deforestation and land degradation, and reforestation. For this activity, we divided learners into three groups: less-developed economy negotiators, developing economy negotiators, and developed economy negotiators. In this stage, students ran simu lations with diverse sets of values for system variables, such as percent change for regional CO2 emissions, year in which CO2 emissions growth is stopped, and year in which CO2 emissions reduction begins iteratively. The C-Learn simulator provides simulated output for global sea rise, global temperature, and many other variables over a 40-year time horizon. Week 5: Connecting Analytic Complexity to Socially Constructed Complexity
A central purpose of this simulation-based learn ing package is to provide an environment in which individuals learn the domain concept, complexity with public policy decision making, through active participation. For this purpose, we used group model building (GMB)8 in a role-playing context with five different roles: (a) director of the Pointe Claire Regional Coastal Planning District as a regional policy decision maker, (b) executive director of the Pointe Claire Regional Business Association as a developer, (c) president of the Pointe Claire Environmental 58
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Coalition as an environmentalist, (d) president of the Pointe Claire Homeowner Association as a representative of residents, and (e) regional dir ector of the U.S. Army Corps of Engineers as a coastal region protection preparedness planner. Through the GMB role-playing activity, stud ents collaboratively mapped a coastal protection system reflecting the five different perspectives. An instructor who played the role of a facilitator, an assistant who categorized and rearranged system variables that learners came up with, and a modeler who sketched the map in Vensim assisted them.9 In this stage, we dealt with both types of complexity: analytic complexity (dynamic complexity) and socially constructed complexity. Weeks 6–8: Learning Dynamic Complexity
From Week 6 through Week 8, the stage of run ning the simulation, students were introduced to the CoastalProtectSIM and were provided not only basic information about the computer interface and basic operation of the simulation model, but also detailed information about simulation structure, system variables, and the technologies used to build this simulation model. For the first step of the main simulation training, students learned the basic concepts of system dynamics that are necessary to under stand dynamic complexity, such as stocks and flows, feedback loops, and change over time. In Week 7, we provided an exercise in which learners used a simplified version of the Coast alProtectSIM to build a model and figure out the relationships between or among system variables. In this period, students explored the basic functions of the system dynamics soft ware, Vensim, to build portions of a sample model as part of a problem set. We also used traditional teaching methods, readings and lectures, to deliver explicit knowledge about these concepts. The final project, writing a policy memo and presenting actionable policy recommendations regarding the case, was as signed to students during this period. Week 9: Learning Detail Complexity
Detail complexity was discussed with multiattribute utility (MAU) models. MAU models were brought into this SBLE to help learners
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make a choice among multiple alternatives that have different values in multiple criteria that people care about. With MAU models, decision makers can deal with discrete alternative multiplecriteria problems (Zeleny, 1982). That is, in the context of policy decision making, decision makers can create a policy that makes a change in single or multiple variables in policy systems.
an added educational benefit to this set of exercises, through the final presentation session and the preparation process for that session, learners had opportunities not only to present information they generated but also to build communication skills such as communicating technical ideas in ordinary language and tailor ing information to diverse audiences.
In the example of the CoastalProtectSIM, policy makers can generate a policy package to avoid damages in the Pointe Claire region due to possible storm surges or floods by (a) building a barrier; (b) clearing homes from the floodplain by implementing a buyout, relocation, or re clamation policy; (c) instituting a strict build ing code; or (d) zoning for land development. Moreover, even though multiple sets of policy packages may result in the same effect on accu mulated damage avoided, each package may have a different impact on tax increases or the regional economy, since many other modeled variables may be differentially valued by differ ent stakeholders. In this stage, learners were taught the techniques to evaluate alternatives by using MAU models. As in the stages of pre paring for simulation and decision tree analysis, we also used readings and lectures.
ASSESSMENT OF THE METHOD
Week 10: Proposing Solutions to a Complex Real World Problem
In Week 10, learners, in groups, drafted policy memoranda to answer the questions below: What, in your opinion, are the feasible policy packages that should be “on the table” during the planning process and hence mentioned in the Request for Proposals from the Shared Vision Planning unit at the U.S. Army Corps of Engineers? You will probably want to sketch several packages of policy options, not just a single option or package of options. Indicate what is your preferred package as this process is getting started. Give reasoning for why you think this policy package is a good one. The central purpose of this final stage was to look at the learning outcomes of the SBLE. As
To evaluate the potential of a SBLE, in this case the PCCPPE, as a pedagogical method of teaching and learning complexity in public administration and policy in the MPA classroom, we collected both quantitative and qualitative data from students and instructors. In particular, to measure the extent to which the SBLE was effective in increasing intrinsic motivation, yet challenging intellectually in the classroom, a survey was administered to students at the end of the semesters. In the survey questionnaire, we asked respondents to rate (a) their enjoyment levels in the PCCPPE, using a survey item “The Pointe Claire Case Study was interesting”; (b) their (intrinsic) motivation levels to learn more about complex ity, using a survey item “I would like to learn more about complexity in public policy deci sions because it is interesting”; and (c) the extent to which the case study challenged them, using a survey item “The Pointe Claire Case Study is challenging.” All three items were measured on a 7-point Likert scale ranging from 1 (not at all ) to 7 (a very great extent). To identify course topics and activities that most stimulated and challenged students in the SBLE, we asked respondents to specify the most interesting and challenging parts of the case study. Two open-ended questions were also included in the survey to hear participants’ opinions about the best and the need-to-beimproved aspects of the PCCPPE. The survey was administered to 66 students who took the course, and 64 usable/completed questionnaires (97%) were returned from 32 women (50%) and 32 men (50%). These students included 52 full-time students (81%) and 12 part-time students (19%). Fifty students (78%) had work experience before joining the Journal of Public Affairs Education 59
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MPA, and 29 of these students (45%) had work experience in the public sector. To examine the effect of the PCCPPE on participants’ learning, two of our research team members, David F. Andersen and Minyoung Ku, who participated in this experiment as instructors, closely ob served and recorded students’ learning progress during the term. However, it should be noted that this was a pilot study, and we are in the process of conducting additional evaluations of the SBLE with a different set of students in which we not only examine their reactions to the learning package but also assess students’ high-level thinking and self-awareness of competencies. In this section, we present the findings from our pilot study. The SLBE increased students’ intrinsic moti vation to learn about complexity. The PCC
PPE participants presented high levels of posi
tive learning experience. All but one of the 64 students (98%) indicated that the case exercise was interesting, and 35 students (55%) indi cated a great extent of enjoyment (i.e., from 5, a fairly great extent, to 7, a very great extent). Sixty students (94%) indicated that they were willing to learn more about com plexity in public policy decisions because they found it interesting, and 44 of these (69%) agreed to the statement to a great extent. These findings show that the PCCPPE was very effective in stimulating participants’ intrinsic motivation, and that intrinsic motivation plays a role in willingness to continue learning about the domain concept. According to literature on the relationship between intrinsic motivation and learning, not only does intrinsic motivation have a positive effect on increasing students’ learning perform
FIGURE 3.
The Most Interesting and Challenging Class Topics, by Percentage of Student Ratings
Multi-attribute models The CoastalProtectSIM Decision tree analysis Write-up and presentation of the final materials Interesting
Difference equation and systems dynamics
Challenging
C-Learn simulation Group model building Other topics
0% 20% 40% 60% 80% 100% Note. Respondents were allowed to choose more than one option for both “most interesting” and “most challenging.”
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ance, but it also enables long-term learning of cognitive knowledge and skills (Deci, Koestner, & Ryan, 2001; Deci & Ryan, 2000; Walker, Greene, & Mansell, 2006; Zimmerman, 1990). Long-term rather than short-term education is required for students to become competent in handling complex problems and reach mastery of complexity. These students’ reactions to the PCCPPE indicate that the simulation-based curriculum is a highly potent method of teach ing and learning about complexity in public administration and policy in that it can pro mote learners continuing to learn about com plexity even after the course ends. The SBLE nurtured students’ systems thinking and ability to tie together concepts and analytic skills from diverse MPA courses to solve a complex problem. Participants’ under
standing of the concept of complexity and their skills in dealing with complex problems in the case progressed throughout the term. Almost all students were taking and had taken other public administration and policy courses at the Rockefeller College or at a different institution. At the beginning of the semester, students’ contributions to class discussions did not deal with all of the dimensions of complexity pre sented in Box 1—particularly lacking were analytic difficulties of a domain issue or prob lems due to stochastic uncertainty, system dynamics, and detail complexity. Students’ argu ments tended to be based on their intuition, not on empirical evidence. We observed that in early stages of this experiment, participants ar gued for policy positions that could be directly connected to their basic values and beliefs. Toward the end of the exercise, however, par ticipants were more willing to incorporate both model-based results and diverse stakeholder positions into their final policy packages, by considering dynamics in the system consisting of a number of different variables and affected by high uncertainty. In the mock planning process for the final project, participants also showed attempts to incorporate knowledge and analytic skills gained both within and outside of the course into the problem-solving context. Our observation coin cides with students’
answers to the open-ended question about the best part of their experience with PCCPPE, in statements such as the following: • “It showed a complex and complete pro cess of public policy decision making. I was able to see the big picture of the realworld situation.” • “Best aspects were pulling the entire factors together and making a final decision that we had to justify and explain. I really liked thinking about using the simulation model to bring together the stakeholders and form minipublics. Going a step beyond just the calculated results was helpful; it made the situation seem more lifelike.” • “We had learnt [the] Pointe Claire case from different perspectives with many different analysis techniques and that’s impressive.” • “It tied together all concepts of not just this course, but our core courses as a whole.” The most challenging part of teaching and learning about complexity in the SBLE was changing from linear to dynamic thinking.
The student surveys indicated that 73% of the students found the case exercise challenging. The three most challenging parts of the case study were the CoastalProtectSIM model (58%), the write-up and presentation of final materials (41%), and the difference equations and system dynamics concepts (34%). On whether students found the exercises inter esting, our survey results indicated that the multiattribute utility models were most interesting (53%), with the CoastalProtectSIM second at 47% and the decision tree analysis third at 30%. Figure 3 shows the most interesting and challenging of all class topics based on the stu dent ratings. These results show that although it is intellectually challenging to work with the sim ulation model to learn about dynamic complexity, the CoastalProtectSIM can play an important role in motivating students to learn complexity by increasing their interest. Com pared to the number of students rating the C-Learn simulator as most interesting, more than double the number of respondents indi cated that they found the simulation model most interesting. Journal of Public Affairs Education 61
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Figure 3 demonstrates that students considered the nonlinear models (i.e., difference equations, system dynamics and the two computer simula tion models: C-Learn and CoastalProtectSIM) to be more challenging than the linear models (i.e., the decision tree analysis and multiattribute models). Our classroom observations also support this result. We consistently observ ed through the entire experiment that during classroom discussion and tests, students tended to show more confidence in handling linear models than nonlinear, dynamic models. Though most students successfully mastered the basic knowledge and analytic skills necessary for dealing with dynamic complexity as taught in class, they tended to need more time to master the dynamic models and they requested instructors’ help more often in the section on difference equations and system dynamics and the section on the CoastalProtectSIM than in other sections. When it comes to the need-to-improve aspects of the case exercise, students commented: • “I would say displaying its [the models’] complexity gradually and allowing me more time to review what factors affect what variables and how this is shown in the model would be helpful.” • “I would have liked to have more time to study with my instructor about the sim ulation model detailing what each variable is and the impacts they can have.” • “It would be helpful to show how stockand-flow thinking can make changes and what simulation models we can use in different fields, such as public health, homeland security, and nonprofit management.” CONCLUSION
Public leaders and managers today are being challenged by unprecedented complex prob lems. Teaching and learning about complexity in public administration and policy in MPA programs have become imperatives. In this study, we built a comprehensive but simple tax onomy of complexity in public policy and ad ministration, a taxonomy that encompasses 62
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both analytic and socially constructed per spectives. From an analytic point of view, the problems that are looked at in policy systems are complex because of their stochastic un certainty, detail complexity, and dynamic com plexity. Meanwhile, from a social con struc tionist point of view, decision making for public issues is difficult due to political com plexity, administrative complexity, and stake holder complexity, which stem from different perspectives and interests and from institutional constraints. We present in this paper the finding that through creating classroom materials such as the PCCPPE (Pointe Claire Coastal Protection Planning Exercise), traditional learning envir onments can be augmented with complex and dynamic computer simulation models as an innovative teaching method. Through the experimental case study about Gulf Coast disaster preparedness planning, based on actual programs used by the U.S. Army Corps of Engineers in the United States, we found the simulation-based case exercise to have high potential as a method for teaching and learning about complexity in policy decision making. Although the “proof of the pudding” for SBLEs is still under study, our findings about students’ reactions and learning progress from student surveys and instructors’ observations reveal that the curriculum we developed increased students’ intrinsic motivation, which could affect the whole learning process, and facilitated their systems thinking. In particular, we found that computer simulation models that show users the structural maps of system components behind the simple user interface, such as the CoastalProtectSIM, could promote and stimulate interest in learning about dynamic complexity in public policy settings by providing students with a challenging but enjoyable learning environment. As a fruitful byproduct of this pilot study, we also found that the SBLE stimulated students to tie together and use knowledge and skills gained from other classes to solve complex problems in this core modeling class.
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NOTES 1 We developed this framework of complexity based on “a taxonomy of complexity in public policy decisions” presented in an earlier work (Deegan et al., 2014, p. 219). 2 To learn more about the Shared Vision Planning project, visit http://www.sharedvisionplanning.us/ index.cfm. 3 For more information about the Ripple Found ation’s ReThink Health Dynamics project, please visit http://www.rippelfoundation.org/our-work/ rethink-health/.
common visions of the system structure. For more information about GMB, please see Richardson and Andersen (1995). 9 The initial development of these teaching materials occurred in a resource-rich environment. However, our goal is to standardize the materials and processes so that an instructor or an instructor and teaching assistant together may undertake these activities. In this experiment, except for the GMB (group model building) session in Week 5, all other classroom activities and lectures were managed by just the one instructor who led each class.
4 The goal is to make all materials and the simulation model publicly available on the Web at a future date. 5 System dynamics computer simulation models are developed to address a particular problem. This computer simulation model is generic enough to be used in or apply to coastal communities around the United States. The complete teaching package, with stakeholders identified, is also relevant to the majority of coastal communities. Other complex problems, such as health care reform or income inequality, would require a specific simulation model, with stakeholder roles, to be built for that problem. 6 The full details of our CoastalProtectSIM computer simulation model have previously been published in “Simulation-based learning environments to teach complexity: The missing link in teaching sustainable public management,” by Deegan et al. (2014). 7 The C-ROADS simulator is a real policy simula tion model that was used by a negotiation team, the Climate Interactive team, led by the Sustainability Institute, in Copenhagen at the United Nations Framework Convention on Climate Change’s Con ference of the Parties (COP 15) climate conference in 2009. The simulator can be down loaded at http://www.climateinteractive.org/tools/c-roads/. 8 In a classroom setting, GMB (group model build ing) provides an active role-playing context in which individuals learn about the different points of view of stakeholders and about how to encompass all different perspectives while building a model to gether. In practical settings, GMB combines tech nical modeling with group facilitation for developing
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ABOUT THE AUTHORS
is a postdoctoral associate at the Center for Technology in Government at the Uni versity at Albany, State University of New York. She holds a doctorate in public admin istration and policy from the Rockefeller College of Public Affairs and Policy at the University at Albany, State University of New York. Her research interests focus on data, information, and knowledge management; organizational behavior and theory; network governance; and social network analysis. Minyoung Ku
Michael Deegan is a policy analyst and program manager at the U.S. Army Corps of Engineers’ Institute for Water Resources. Michael received a postdoctoral fellowship through the National Academies of Science, where he completed re search on interagency coordination challenges with the implementation of floodplain manage ment recommendations. He holds a doctorate in public administration from the Rockefeller College of Public Affairs and Policy, University at Albany, State University of New York.
is director of the Initiative for System Dynamics in the Public Sector at the Rockefeller College of Public Affairs and Policy, University at Albany, State University of New York. His doctorate in public administration focused on the development of
Roderick H. MacDonald
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system dynamics simulation models as decision support tools. He has used system dynamics to address problems in various policy areas. is associate professor emerita of information studies and informatics at the College of Engineering and Applied Sciences, University at Albany, State University of New York. Her research centers on library policy, especially public libraries internationally, and on information transfer across supply chains. She teaches in the areas of library science, information policy, and research design and statistics.
Deborah L. Andersen
David F. Andersen is O’Leary Professor of Public Administration and Policy at the Rockefeller College of Public Affairs and Policy, University at Albany, State University of New York. He has served as a technical consultant to public and nonprofit agencies in the federal, state, and local sectors, as well as to corporate clients in North America and Europe. He holds a doctorate in management from the MIT Sloan School of Management.
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