The potential utility of agent‐based models of adaptive, spatially aware, and mobile entities in geographic and ecological research is considerable. Developing this potential, however, presents significant challenges to geographic information science. Modelling the spatio‐temporal behaviour of individuals requires new representational forms that capture how organisms store and use spatial information. New procedures must be developed that simulate how individuals produce bounded knowledge of geographical space through experiential learning, adapt this knowledge to continually changing environments, and apply it to spatial decision‐making processes. In this paper, we present a framework for the representation of adaptive, spatially aware, and mobile agents. To provide context to this research, a multiagent model is constructed to simulate the migratory behaviour of elk (Cervus elaphus) on Yellowstone's northern range. In this simulated environment, intelligent agents learn in ways that enable them to mimic real‐world behaviours and adapt to changing landscapes.

The goal of Spatial Decision Support Systems (SDSS) is to assist decision-makers as they generate alternative solutions to a variety of semi-structured geographical problems, and to evaluate these solutions with the help of applicable data and analytical models. Most existing SDSS, however, support only a limited number of decision-making environments, and are not designed to utilize web-accessible repositories of spatial data and models. These limitations are overcome through the use of ‘software agents’ within an agent-oriented modelling framework, called ‘Distributed Intelligent Geographical Modelling Environment (DIGME)’. The utility of this framework is demonstrated through the development of an SDSS to evaluate the ecological and economic impacts of agricultural policy for the Cache River watershed of southern Illinois.

Bitterman, P., and D. A. Bennett. 2016. Constructing stability landscapes to identify alternative states in coupled social-ecological agent-based models. Ecology and Society 21(3):21.http://dx.doi.org/10.5751/ES-08677-210321

Landscapes are complex adaptive spatial systems driven by biophysical and socioeconomic processes that are shaped by the cumulative behavior of interacting, but independent decision-makers. Agent-based modeling has been identified as an effective paradigm for the study of such systems because of its ability to represent behavior and interaction at the individual level. These interactions are inherently associated with cause–effect relations in the transition of land use systems through time. Elucidating specific cause and effect relations from model results, however, poses a significant challenge to the research community because of complicated model structures and the complex processes that drive system dynamics. In this article, we call for provenance-based agent-based models of land use dynamics to support the capture and tracking of complex cause–effect relations. We illustrate the importance of explicitly considering provenance in agent-based modeling through the development of a spatially explicit agent-based land use simulation framework. This framework is designed to simulate complex land use dynamics in southwest Montana, USA. Land managers are modeled as geospatial agents who interact with their spatially explicit environments. These agents exchange opinions about land use controls through social networks in an attempt to form consensus on land use policies. The heterogeneous opinion behavior of landowner agents and complicated social structural relationships produce complex dynamics. We conduct experiments to demonstrate the need to explicitly consider provenance in agent-based models of land use systems and its ability to promote our understanding of complex adaptive land use systems.

We developed an agent-based model (ABM) to simulate farmers’ decisions on crop type and fertilizer application in response to commodity and biofuel crop prices. Farm profit maximization constrained by farmers’ profit expectations for land committed to biofuel crop production was used as the decision rule. Empirical parameters characterizing farmers’ profit expectations were derived from an agricultural landowners and operators survey and integrated in the ABM. The integration of crop production cost models and the survey information in the ABM is critical to producing simulations that can provide realistic insights into agricultural land use planning and policy making. Model simulations were run with historical market prices and alternative market scenarios for corn price, soybean to corn price ratio, switchgrass price, and switchgrass to corn stover ratio. The results of the comparison between simulated cropland percentage and crop rotations with satellite-based land cover data suggest that farmers may be underestimating the effects that continuous corn production has on yields. The simulation results for alternative market scenarios based on a survey of agricultural land owners and operators in the Clear Creek Watershed in eastern Iowa show that farmers see cellulosic biofuel feedstock production in the form of perennial grasses or corn stover as a more risky enterprise than their current crop production systems, likely because of market and production risks and lock in effects. As a result farmers do not follow a simple farm-profit maximization rule.

Accurate digital representations of basin morphology are needed to support hydrologic flow and sediment transport models. The derivation of such representations from United States Geodetic Survey (USGS) digital elevation models (DEMs) has been an active area of research. The error inherent in USGS DEM data sets can limit their utility for watershed modeling, however. This article describes a new approach for extracting basin morphology from a DEM. The new approach uses inductive procedures and domain-specific knowledge to identify and correct errors. A bit-mapped classification scheme is used to place each point in a DEM into one of six hydrologic categories based on the topographic form of its local and extended neighborhood. Points with similar topographic form are aggregated into networks that represent stream channels and basin divides. These networks are extracted from the DEM and stored in vector-based data structures. Iterative examination of the collected information generates an increasingly accurate representation of basin morphology.