Dana Meadows believed that computer simulation models and systems thinking could be powerful tools for democracy, helping make social decisions and the assumptions on which they are based more transparent and open to public debate. She also believed that people should be more involved in making conscious and informed choices about their future. In the environmental arena, pressure to increase public participation in decision-making is growing. Not only is public involvement seen as a cornerstone of democratic ideals, it is increasingly seen by decision-makers as a practical means of putting decisions into effect. Involving the public in decision-making helps avoid public obstruction of decisions and garners public resources for their implementation. However, traditional approaches to public involvement, which rely heavily on information campaigns, facilitated discussions, and public hearings for conveying information and capturing stakeholder input, frequently leave participants dissatisfied. They are often perceived as one-way communication from agency experts to stakeholders or as a mechanism for powerful special interest groups to serve their own ends. System dynamics has the potential to improve public participation in environmental decisions by providing a framework for structured deliberation when stakeholders are involved in making decisions and a more transparent and participatory educational framework to persuade stakeholders to help implement decisions. This article describes a case study using group model building to support a stakeholder advisory group examining transportation and related air quality problems. One of the most valuable effects of the approach was the information feedback it added to the advisory process. Copyright  2002 John Wiley & Sons, Ltd.Syst. Dyn. Rev. 18, 139-167, (2002) Why involve the public in environmental decisions?Almost 20 years ago Meadows and Robinson (1985:3) identified a key source of frustration in social policy-making:Even in the modern age of science and industrialization social policy decisions are based on incompletely-communicated mental models. The assumptions and reasoning behind a decision are not really examinable, even to the decider. The logic, if there is any, leading to a social policy is unclear to most people affected by the policy.Stakeholder confusion about the logic underlying social policies persists and the sense that public policy should be more accountable and responsive to the public has been growing. Pressure to involve a broader representation of the public in decision-making continues to increase (Rowe and Frewer 2000). Pressure to improve public involvement in environmental policy decisions-social decisions that influence the present or future quality of the environment (Sexton et al. 1999) or decisions about environmental resource management-is especially high. Because environmental decisions generally involve complex scientific and technical issues and a wide array of stakeholders, scientific uncertainty, value conflicts, ecos...

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Sustainable environmental management requires a decision support approach that accounts for dynamic connections between social and ecological systems, integrates stakeholder deliberation with scientific analysis, incorporates diverse stakeholder knowledge, and fosters relationships among stakeholders that can accommodate changing information and changing social and environmental conditions. Participatory system dynamics modeling provides such a framework. It supports stakeholder learning about the system and the perspectives of other stakeholders, and can help build social capital among stakeholders. Four cases of participatory system dynamics modeling, which range from no to full participant involvement in model development, support the idea that greater social capital development results from greater participation in model development, but also suggest that even the simplest use of simulation models in a group fosters stakeholder learning about the system through surprise and discovery. To maximize the learning value of simulation models, it is important to allow enough time for debriefing the “aha!” moments that lead to curiosity about system behavior. To maximize social capital development, it is important to build enough time into the problem structuring and model conceptualization phases for stakeholders to articulate their mental models and examine those of other participants

Understanding vulnerabilities in complex and interdependent modern food systems requires a wholesystem perspective. This paper demonstrates how one systems approach, system dynamics, can help conceptualize the mechanisms and pathways by which food systems can be affected by disturbances. We describe the process of creating stock-and-flow maps and causal loop diagrams from the graphical representation of a problem and illustrate their use for making links and feedback among the human health, food, and environmental health sectors visible. These mapping tools help structure thinking about where and how particular systems might be affected by different disturbances and how flows of material and information transmit the effects of disturbances throughout the system. The visual representations as well as the process of creating them can serve different purposes for different stakeholders: developing research questions, identifying policy leverage points, or building collaboration among people in different parts of the system. They can serve as a transition between mental models and formal simulation models, but they also stand on their own to support diagrammatic reasoning: clarifying assumptions, structuring a problem space, or identifying unexpected implications of an unplanned disturbance or an intentional policy intervention. The diagrams included here show that vulnerability of a national food system does not only or automatically result from exogenous shocks that might affect a country. Rather, vulnerability can be either intensified or reduced by the interaction of feedback loops in the food system, and buffered or amplified by the structure of stocks and flows. KeywordsCausal loop diagram; conceptual models; dynamic complexity; modern industrialized food systems; stock-and-flow diagram; systems mapping, structural insights. 2 IntroductionFood supply systems are richly integrated and highly dynamic social-ecological systems in which ecological factors shape the possibilities for food production and social factors govern the goals and operations of actors in the system. In low income countries, food supply systems tend to be relatively simple and closely adapted to local environmental conditions, with a small set of products and few steps between producers and consumers. Consumers are likely to participate in the system and understand how it works. By contrast, the structure and operation of modern industrialized food systems are largely invisible to consumers and policy-makers (Reisch et al. 2013). The chains of production, processing, and distribution activities that generate food supplies are long, highly differentiated, and influenced by an array of environmental, economic, social, cultural and other factors. It is hard to visualize connections in the system and even harder to see how changes in one part of the system affect other parts. Some causal connections in the system are direct and obvious, such as the effect of a drought on crop yield, but some are indirect and opaque, such as the effect of...

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While public-sector management problems are steeped in positivistic and socially constructed complexity, public management education in the management of complexity lags behind that of business schools, particularly in the application of simulation-based learning. This paper describes a Simulation-Based Learning Environment for public management education that includes a coupled case study and System Dynamics simulation surrounding flood protection, a domain where stewardship decisions regarding public infrastructure and investment have direct and indirect effects on businesses and the public. The Pointe Claire case and CoastalProtectSIM simulation provide a platform for policy experimentation under conditions of exogenous uncertainty (weather and climate change) as well as endogenous effects generated by structure. We discuss the model in some detail, and present teaching materials developed to date to support the use of our work in public administration curricula.

Ongoing professional development in system dynamics can be a challenge. Academics and practitioners often join organizations where they have few close colleagues with the same methodological background. This note describes one long-running effort to address this challenge: a peer mentoring group of system dynamics experts with various degrees of experience and different fields of application that has met weekly during academic semesters over the last four years to discuss their research, works-in-progress, and early ideas. Based on our experience with virtual meetings, we describe the history, motivation and successful practices of the group. Group members reflect on what we value and why we think it works well. We share our experiences not as a prescription for how to run such a group, but in the hopes that they motivate others to initiate and sustain their own peer mentoring groups and share their experiences with the system dynamics community.

This paper presents empirical evidence from two field studies about the effect of participatory system dynamics processes in stakeholder discussions about sustainability issues. Both studies examined paired real‐world group processes in which stakeholders were asked to provide policy recommendations to municipal decision makers. One group in each study used a general meeting facilitation approach, and the other used participatory system dynamics. One pair of groups discussed urban growth issues and met monthly for over a year. The other pair of groups discussed a proposed Zero Waste management initiative and met for one workshop session during a single‐day conference. The urban growth study was an opportunistic, post hoc comparison of two groups with similar stakeholders independently convened at the same time. The participatory system dynamics group followed a full‐group model‐building process. The Zero Waste study was a field experiment in which participants in the same 1‐day conference were divided into two groups for facilitated discussion, and the participatory system dynamics group used an existing system dynamics simulation model for analysis. Content analysis of meeting material as well as participant surveys and interviews in the longer term engagement showed the system dynamics group followed key steps of an “ideal” problem‐solving process more closely, scored higher on most group process and outcome variables, and had higher participant satisfaction than the traditionally facilitated group. In the short‐term processes, prediscussion and postdiscussion surveys showed the participatory system dynamics group produced better policy recommendations, but the traditionally facilitated group reported higher procedural satisfaction. Although the difficulty of strictly controlling for all variation in context limits our conclusions, these field comparisons support the value of participatory system dynamics for improving stakeholder engagement processes. The differences in participant satisfaction outcomes imply that special attention must be paid to satisfaction in shorter term participatory system dynamics activities.