Environmental degradation is often defined as a public goods problem, emerging when property rights are not clearly defined and costs are externalized to other parties. Proposing corrective regulation that enforces technological fixes or market-based approaches is often met with political resistance and doubts about its effectiveness. This is partly due to the complexity of interacting physical and socio-economic components that obscure the impacts of human decision-making on environmental functions. Yet, understanding the complexity of integrated human-environmental systems can help planners and stakeholders frame environmental problems, view their role in them and design effective policies to address them. This article examines the potential and limitations of agent-based models as metaphors that can contribute to the understanding of such complex systems, illustrating the argument with a hypothetical application in groundwater management.

Participatory Modeling (PM) is becoming increasingly common in environmental planning and conservation, due in part to advances in cyberinfrastructure as well as to greater recognition of the importance of engaging a diverse array of stakeholders in decision making. We provide lessons learned, based on over 200 years of the authors’ cumulative and diverse experience, about PM processes. These include successful and, perhaps more importantly, not-so-successful trials. Our collective interdisciplinary background has supported the development, testing, and evaluation of a rich range of collaborative modeling approaches. We share here what we have learned as a community of participatory modelers, within three categories of reflection: a) lessons learned about participatory modelers; b) lessons learned about the context of collaboration; and c) lessons learned about the PM process. First, successful PM teams encompass a variety of skills beyond modeling expertise. Skills include: effective relationship-building, openness to learn from local experts, awareness of personal motivations and biases, and ability to translate discussions into models and to assess success. Second, the context for collaboration necessitates a culturally appropriate process for knowledge generation and use, for involvement of community co-leads, and for understanding group power dynamics that might influence how people from different backgrounds interact. Finally, knowing when to use PM and when not to, managing expectations, and effectively and equitably addressing conflicts is essential. Managing the participation process in PM is as important as managing the model building process. We recommend that PM teams consider what skills are present within a team, while ensuring inclusive creative space for collaborative exploration and learning supported by simple yet relevant models. With a realistic view of what it entails, PM can be a powerful approach that builds collective knowledge and social capital, thus helping communities to take charge of their future and address complex social and environmental problems.