Agent-based Modeling is a computational modeling approach widely used in the study of complexity, natural sciences and increasingly social sciences, by explicitly representing interaction effects across spatial and temporal scales. These models are typically object-oriented computer programs, where the objects are actors operating at various scales (e.g., residents, farmers, businesses, units of government) making rule-based decisions in an environment characterized by various attributes (e.g., presence of natural amenities, distance to employment centers, access to water and sewerage infrastructure). Agent-based models can be spatially explicit, i.e., agents may have a specific location in space, and the strength of interactions may vary with agents’ location and with the spatial distribution of landscape attributes. Simulations with agent-based models constitute virtual experiments that are especially useful when such experiments (e.g., land-use policies) are costly and risky to run in real life (Axelrod, 1997; Epstein, 1999). The array of simulation outcomes provides useful insights on the effects of the agents’ interaction with other agents and with their environment, a unique feature of these models. As in real-world complex systems, surprises often emerge in the agent-based simulations. The simulations can provide powerful insights regarding how much variability and uncertainty the system exhibits, and how varying certain parameters matters. Thus, the focus of agent-based modeling is exploration rather than prediction, by building plausible natural and decision-making processes to expand our understanding of the range of possible consequences (Axelrod, 1997; Bankes, 1993; Batty & Torrens, 2001). By explicitly representing the characteristics of complexity, agent-based analyses allow us to investigate the role of complexity in both complicating management decisions and in supporting innovative solutions.
Fuzzy Cognitive Mapping (FCM) is a form of concept mapping that can used to understand how different individuals and groups perceive environmental and social problems. In this demonstration I present the architecture and various uses of an FCM-based software program called Mental Modeler and discuss the benefits and limitations of the tool to facilitate scenario planning and promote learning among a wide range of stakeholders. Additionally, by providing workshop participants with sample data and web-based access to the software, we will create models, run scenarios, and identify additional software functionality.
System Dynamics Modeling: Visit the System Dynamics Society website for a definition of System Dynamic Modeling.
Social Network Analysis is the study and evaluation of the structure and function of social relationships. These social relationships (or networks) can be made up of organizations, people, coalitions, or agencies working to coordinate efforts to achieve social change. Network methods aid in describing, analyzing, and modeling the flows of information and resources between people or organizations. These structures are used to understand diffusion, contagion, power, and influence.
Participants will learn social network concepts and tools in this workshop, including data collection, data management, and network visualization and description. Participants will learn how to use user-friendly software to analyze networks and consider interpreting results with community partners. They will also be engaged in how SNA can be used in collaborative modeling and participatory research contexts.