Simulation as an Approach to Social-Ecological Integration, with an Emphasis on Agent-Based Modeling
Randall B. Boone and Kathleen A. Galvin
In past decades ecological and social science research took pathways that intersected in meaningful ways only infrequently. In ecology, humans were viewed as causes of change external to the systems of interest, or as sources of variation controlled for in experiments so that human influences could be ignored. In anthropological research, ecological settings have been explored and debated as a means to understand human evolution, societal development and power over resources (e.g., Orlove 1980; Watts 1997; Boyd and Richerson 2005). Concepts were mutually borrowed by each discipline from the other (e.g., evolution, niche theory, commons theory), but active integration was uncommon and sometimes even discouraged. The roles that humans play as components of systems became a focus in the second half of the last century, and queries with humans considered as a component of ecosystems were more common (e.g., Rappaport 1967; Liverman et al. 1998; Little and Leslie 1999) (see Sect. 9.3). However this systems view of humans did not allow for agency or diversity and was highly criticized in the social sciences (e.g., Moran 2008). Today, questions regarding sustainability are so broad in scope and outcomes so important to societies that scientific fields are being invented to address new questions about linkages within systems (see Part I of this volume). An example of institutional recognition of the importance of understanding these linkages is in the Dynamics of Coupled Natural and Human Systems competition in the US National Science Foundation. Each year millions of dollars are put to increasing our understanding of linkages between humans and the ecosystems they inhabit.
A main tool to understand linkages of societies and their environments is through the use of computer simulation. Simulation is a broad term, describing “… a class of symbolic models, which are representations of particular facets of reality …” (Galvin et al. 2006). Here we confine our discussion to the kinds of simulation models often used to represent coupled natural and human systems. These are a class of models called discrete-event simulations, where analysis steps are simulated to represent the passage of time, and events are scheduled to occur at particular points in time. Simulations are often processed-based, where processes describing interactions between system elements are described mathematically in computer code, or rule-based, where thresholds and logical bifurcations are described in code and represent decision making or other system attributes. Simulations often include stochastic components. The simulations may be point-based, meaning that they represent a single element of a system such as a plant or a person, and the results from that plant or person are taken to hold for other plants or people in the area considered homogeneous by the model. Alternatively, simulations may be spatially explicit, meaning they represent real-world locations where questions of sustainability are at issue.
Simulation approaches have been used in ecological research for decades (e.g., Huston et al. 1988) and more recently in the social sciences (e.g., Brenner 1999; Kohler and Gumerman 1999). Simulation methods are transforming social sciences by adding experimentation to the toolbox of researchers. Hypotheses that may be impractical to assess in reality because of expense, complexity, or moral constraints may be assessed using computer simulations. In what follows, we describe the utility of simulation in general terms, then specific to integration of social and ecological sciences. A pathway we and others use to discovery called integrated modeling is described. Agent-based modeling (ABM) is defined and its role in scientific integration is described. Examples from our work and from the literature are then given to provide context, and we conclude.