Simulating Wildland Recreation Use and Conflicting Spatial Interactions using Rule-Driven Intelligent Agents

Ecosystem management, in the ideal sense, gives appropriate consideration to the complex and interdependent ecological and social systems that comprise forestlands. One prominent and growing arena where ecological and social systems interact is in the recreational use of wildlands. Recreational uses of forestlands are among an extensive array of commodities and amenities that are increasingly demanded of forest managers. An in-depth understanding of the relationships between recreational and other important uses is essential to effective ecosystem management. Within the human dimension of ecosystem management, recreation and amenity uses of forestlands and the associated benefits of those uses, constitute an important component of management decisions. Forestland recreation is a special form of leisure behavior not only because it takes place outdoors, but because it depends upon a “natural” setting. Particular environmental settings are crucial to the fulfillment of forest recreation goals, because the recreationist seeks meaningful and satisfying experiences rather than simply engagement in activities. Importantly, wildland recreation takes place in settings that result from management actions of one form or another, whether the management objective is recreation opportunity, wildlife habitat improvement, or timber production, among others. The recreation opportunity spectrum (ROS) provides a conceptual framework for relating opportunities for particular behaviors and experiences to specific settings. The ROS argues that recreator's pursuits of certain activities in specific settings reveals their demand for experiences that are satisfying and that may give long-term benefits. The ROS framework describes a spectrum of recreation opportunity classes that relate a range of recreation experiences to an array of possible settings and activities. Setting structure is composed of three components: an ecological component, a social component, and a managerial component. The ecological component comprises the physical-biological conditions of the setting. These are typically delineated by the relative remoteness of the setting, its size, and evidence of human impact (number and condition of trails, structures, or roads, alteration of vegetation, etc.). The social component is typically defined by the number of users at one time (density) in the setting, delineated by the number of encounters or sightings a recreation party has with others.

Providing a Broad Spectrum of Agents in Spatially Explicit Simulation Models: The Gensim Approach

Research in natural resource management may be characterized as a search for an understanding of patterns and processes relating to a particular resource. Modeling is a crucial tool to these efforts: resource scientists use such models to help them conceptualize, understand, test, predict, or assess various aspects of the resource being studied. One central function, however, underlies all of these uses: a model simulates the way in which a real system would behave under conditions of interest to the user, and illustrates changes over time. Such a model may be used to determine the consequences of particular situations, leaving judgment of the attractiveness of those consequences to the user. Particularly in the case of complex ecosystems, such a model may also serve to clarify interactions and contribute to a deeper understanding of ecological phenomena. In recent years, computer-based models have become the most significant tool of resource managers, for two reasons. First, any model must accurately portray the real system it represents if research based on the model is to have any reliability. The use of computer technology has greatly increased the extent and the detail to which ecosystems can be modeled, and thus the accuracy of these models. The other reason for the extensive use of computer models is the flexibility that the computer as a tool brings to the modeling process. Many ecosystems are poorly understood, and complex models for such poorly understood systems are almost never completed. Rather, modeling such a system is an iterative process, with a partial understanding generating new hypotheses, which in turn generate changes to the model based on further research. Computer technology brings flexibility and ease of modification to the modeling process, naturally supporting this iterative development. In addition, as the alternatives available in resolving resource management problems become increasingly expensive, and the resources themselves become increasingly scarce and valuable, such models become vital tools not only in the direct management of resources, but in the control of expenses associated with resource management as well.

An Intelligent Agent-Based Model for Simulating and Evaluating River Trip Scenarios along the Colorado River in Grand Canyon National Park

In 1979 the National Park Service (NPS) approved a Colorado River Management Plan (CRMP) based on the Grand Canyon Wilderness Recommendation and findings from a comprehensive research program. An amendment to an Interior Appropriations Bill in 1981 prohibited the implementation of this plan and resulted in increased public use levels and continued motorized use in proposed wilderness. In the last 20 years, the demand for Whitewater experiences has increased, especially for the self-outfitted public. Today, the NPS is challenged by users and preservationists to provide accessibility while maintaining wilderness integrity. Whitewater trips along the Colorado River through the Grand Canyon National Park are an excellent example of how increasing human use is impacting a sensitive, dynamic ecosystem and threatening to degrade the quality of experience for human visitors. Although visitation of the Colorado River has remained relatively constant since the 1989 CRMP—at 20,000 to 22,000 visitors and another 3,700 guides, researchers, and park staff traveling through the Grand Canyon each year—figure 1 shows the rapid rise in visitation since 1955. Visitors travel on over 600 commercial or privately organized river trips on a variety of watercraft powered by oars, paddles, or motors for varying duration. Most of the recreational use is concentrated in the summer months, resulting in high encounter rates and congestion at riverside attraction sites. Commercially guided operations account for over 80% of the total recreational use, of which 85% is on motorized rafts. The remaining proportion of recreational river trips are undertaken by noncommercial, self-outfitted public. Nearly 60% of the self-outfitted trips occur in the summer months, with an even proportion on use in the spring and fall. Less than 1% of these trips are motorized. Major drainages and side canyons along the 277-mile river corridor in Grand Canyon National Park provide recreational activities including white water rapids, sightseeing, hiking, and swimming. Well-known attractions and destinations are regular stops for nearly every river trip that passes through the canyon. Crowding and congestion along the river at attraction sites is often extreme and has been shown to affect the character and quality of visitor experience (e.g., Shelby et a]. ).

Spatial Units as Agents: Making the Landscape an Equal Player in Agent-Based Simulations

Agent-based modeling has generated considerable interest in recent years as a tool for exploring many of the processes that can be modeled as bottom up processes. This has accelerated with the availability of software packages, such as Swarm and StarLogo, that allow for relatively complex simulations to be constructed by researchers with limited computer-programming backgrounds. A typical use of agent-based models is to simulate scenarios where large numbers of individuals are inhabiting a landscape, interacting with their landscape and each other by relatively simple rules, and observing the emergent behavior of the system (population) over time. It has been a natural extension in this sort of a study to create a landscape from a “real world” example, typically imported through a geographic information system (GIS). In most cases, the landscape is represented either as a static object, or a “stage” upon which the agents act (see Briggs et al. , Girnblett et al., and Remm). In some cases, an approximation of a dynamic landscape has been added to the simulation in a way that is completely exogenous to the population being simulated; the dynamic conditions are read from historical records, in effect “playing a tape” of conditions, to which the population reacts through time (such as Dean et al. and Kohler et al. ). There has also been many simulations where dynamic landscape processes have been modeled through “bottom up” processes, where localized processes in landscapes are simulated, and the global emergent processes are observed. Topmodel is a Fortran-based implementation of this concept for hydrologic processes; and PCRaster has used similar software constructs to simulate a variety of landscape processes, with sophisticated visualization and data-gathering tools. In both of these examples, the landscape is represented as a regular lattice or cell structure. There are also many examples of “home grown” tools (simulations created for a specific project), applying cellular automata (CA) rules to landscapes to simulate urban growth, wildfire , lava flows, and groundwater flow. There are also examples of how agent-based modeling tools were employed to model dynamic landscape processes such as forest dynamics, i.e., Arborgames. In these models the landscape was the object of the simulation, and free-roaming agents were not considered as part of the model.

Management Application of an Agent-Based Model: Control of Cowbirds at the Landscape Scale

Brood parasitism by brown-headed cowbirds (Molothrus ater) has negative impacts on a large number of songbird species. Cowbirds are obligate brood parasites, meaning that females lay their eggs in the nests of other species and do not provide care to their offspring. Parasitism by cowbirds often results in reduced reproductive success for the host, sometimes to the exclusion of fledging any of their own young. Clearly parasitism by cowbirds can have a substantial impact on the population dynamics of the host species. Over 200 species of birds are known to be parasitized by cowbirds. Cowbirds breed in shrublands and forests, and especially parasitize host nests located near ecotones, or borders between habitat types. Human land use in general may promote the success of cowbirds; landscapes with forest openings, clearcuts, small tracts of forests, and large amounts of habitat edge have higher parasitism rates than do landscapes with contiguous forest tracts. Cowbirds readily forage in feedlots, overgrazed pastures, and grasslands, and the expansion of agricultural land use over the past century has provided abundant feeding habitat for cowbirds. Large increases in the numbers of cowbirds have been documented and this increase has been implicated as one factor responsible for the decline of a large number of passerines. Compounding their impact is the fact that cowbirds can affect host populations over broad spatial scales. Because they do not protect their young or a nest, they can range large distances in search of suitable feeding areas; researchers have reported maximum daily movements from 7 to 13 km for cowbirds (Rothstein et al., Cook et al., respectively). At Fort Hood, a U.S. Army military installation located in central Texas, cowbirds parasitize the nests of numerous songbird species, including those of the black-capped vireo ( Vireo atricapillus) and the golden-cheeked warbler (Dendroica chrysoparia), two federally endangered species. The black-capped vireo appears to be particularly vulnerable to parasitism. Once her nest is parasitized, a host female often abandons it. The female may then attempt to renest but, when cowbirds are abundant, this nest is also likely to be parasitized.

Geographic Information Systems and Agent-Based Modeling

As we enter the twenty-first century, decreasing computer costs continue to result in the development of new generations of computation-based land management tools. Geographical information systems (GIS) emerged from laboratories in the mid-1970s and became, by the mid-1990s, a mainstay of the land manager’s toolbox. During this time, GIS technicians moved from a role as the sole GIS operator to one that includes the design and development of decision support systems (DSS) developed on a foundation of GIS technologies. The land manager is now provided with interactive environments that provide limited, but directed, manipulation and querying of GIS data files. GIS, now a mature technology, is an incomplete tool. It allows for the capture and analysis of landscape system state information. Historic maps, satellite and high-altitude photography, survey data, communication networks, and other sources now provide a rich record of historic and present conditions. Our culture has now embraced the idea that ideas about the state of our landscapes should be formally captured. Ideas about how landscape works, however, remain concepts in the minds of land managers. These ideas develop through formal education, continual review of the literature, and perhaps most importantly, personal experiences during one’s career. While applications of GIS technologies reflect land manager knowledge about the land processes, the GIS does not easily allow that knowledge to be formally captured. The scientific community has now developed a rich array of formalized landscape processes in the form of computer simulations. Hydrologic engineers have a wealth of groundwater, surface water, and overland water flow simulation models. Regional planners offer simulations of urban growth and traffic flow. Ecologists are developing plant and community succession models , and models of habitat responses to land use patterns. Working with scientists, land managers can apply such models to their land management decision processes. However, each model focuses on only part of the landscape system. Full use of the scientific models to affect land management decisions will not occur until the models are integrated as components of a simulation-based geographic modeling system (GMS). Over the next decade experimental systems are expected to result in the release of commercially viable land simulation modeling environments. As we currently capture our understandings of state information in GISs, we will formalize our understandings regarding the dynamics of the landscape in GMSs.

Integrating Geographic Information Systems and Agent-Based Technologies for Modeling and Simulating Social and Ecological Phenomena

To acquire a more thorough understanding of the complexity of natural systems, researchers have sought the assistance of advanced computer-based technologies in the development of integrated modeling and simulation systems. Computer simulations have been utilized in a variety of natural resource management applications from modeling animal populations, to forest fires, to hydrologic systems. Computer models may be developed to understand more about how a real system works, as when scientists develop models of ecological processes. Such models may facilitate predictions of a real system’s behavior under a variety of conditions, or a greater understanding of the structure of a real system. There are numerous advantages to developing and experimenting with models of real-system phenomena. Experimenting with the real system itself may be too costly and time consuming, or even impossible. Simulations are completely repeatable and nondestructive. The data produced by simulations is often easier to interpret than data from a real system. Geographic information systems (GIS) technology has led these developments providing powerful databases for storing and retrieving spatially referenced data. Spatial information is stored in many different themes representing quantitative, qualitative, or logical information. These data can have different resolutions that range from detailed local information to small-scale satellite imagery. GIS operators provide the means for manipulating and analyzing layers of spatial information and for generating new layers. Since it allows distributed parametrization, a GIS is useful for ecological models that need to explicitly incorporate the spatial structure and the variability of system behavior. A raster-based GIS represents spatial information as a grid of cells, and each cell corresponds to a uniform parcel of the landscape. Cells are spatially located by row and column and the cell size depends on the resolution required. GIS provides an excellent means of capturing real-world data in multiple layers (three dimensional) and resolutions (spatial scales) over time. Due to the complexity of ecosystem dynamics, interest has increased in using GIS for simulation of spatial dynamic processes.

Mobile Agents with Spatial Intelligence

Recreation behavior simulation (RBSim) is a computer program that simulates the behavior of human recreators in high-use natural environments. Specifically RBSim uses concepts from recreation research and artificial intelligence (AI) and combines them with geographic information systems (GIS) to produce an integrated system for exploring the interactions between different recreation user groups within geographic space. RBSim joins two computer technologies: • Geographic information systems to represent the environment, and • Autonomous agents to simulate human behavior within geographic space. RBSim demonstrates the potential of combining the two technologies to explore the complex interactions between humans and the environment. The implications of this technology should also be applicable to the study of wildlife populations and other systems where there are complex interactions in the environment. RBSim uses autonomous agents to simulate recreator behavior. An autonomous agent is a computer simulation that is based on concepts from artificial life research. Agent simulations are built using object-oriented programming technology. The agents are autonomous because, once they are programmed, they can move about the landscape like software robots. The agents can gather data from their environment, make decisions from this information, and change their behavior according to the situation in which they find themselves. Each individual agent has its own physical mobility, sensory, and cognitive capabilities. This results in actions that echo the behavior of real animals (in this case, humans) in the environment. The process of building an agent is iterative and combines knowledge derived from empirical data with the intuition of the programmer. By continuing to program knowledge and rules into the agent, watching the behavior resulting from these rules, and comparing it to what is known about actual behavior, a rich and complex set of behaviors emerge. What is compelling about this type of simulation is that it is impossible to predict the behavior of any single agent in the simulation and, by observing the interactions between agents, it is possible to draw conclusions that are impossible using any other analytical process. RBSim is important because, until now, there have been no tools for recreation managers and researchers to systematically investigate different recreation management options.

An Agent-Based Approach to Environmental and Urban Systems within Geographic Information Systems

Both environment and urban systems are complex systems that are intrinsically spatially and temporally organized. Geographic information systems (GIS) provide a platform to deal with such complex systems, both from modeling and visualization points of view. For a long time, cell-based GIS has been widely used for modeling urban and environment system from various perspectives such as digital terrain representation, overlay, distance mapping, etc. Recently temporal GIS (TGIS) has been challenged to model dynamic aspects of urban and environment system (e.g., Langran, Clifford and Tuzhilin, Egenhofer and Golledge), in pursuit of better understanding and perception of both spatial and temporal aspects of these systems. In regional and urban sciences, cellular automata (CA) provide useful methods and tools for studying how regional and urban systems evolve. Because of its conceptual resemblance to cell-based GIS, CA have been extensively used to integrate GIS as potentially useful qualitative forecasting models. This approach intends to look at urban and environment systems as self-organized processes; i.e., how coherent global patterns emerge from local interaction. Thus this approach differentiates it from TGIS in that there is no database support for space-time dynamics. An agent-based approach was initially developed from distributed artificial intelligence (DAI). The basic idea of agent-based approaches is that programs exhibit behaviors entirely described by their internal mechanisms. By linking an individual to a program, it is possible to simulate an artificial world inhabited by interacting processes. Thus it is possible to implement simulation by transposing the population of a real system to its artificial counterpart. Each member of population is represented as an agent who has built-in behaviors. Agent-based approaches provide a platform for modeling situations in which there are large numbers of individuals that can create complex behaviors. It is likely to be of particular interest for modeling space-time dynamics in environmental and urban systems, because it allows researchers to explore relationships between microlevel individual actions and the emergent macrolevel phenomena. An agent-based approach has great potential for modeling environmental and urban systems within GIS. Previous work has focused on modeling people environment interaction, virtual ecosystems, and integration of agent based approach and GIS.

Models of Individual Decision Making in Agent-Based Simulation of Common-Pool-Resource Management Institutions

Addressing the problems of natural resources management requires an understanding of the complex interactions between human and natural systems. Modeling and computer-based simulation has been utilized increasingly as a tool to facilitate this understanding. Numerous simulations of natural systems have been developed, from global-scale general circulation models to more localized models of watersheds or fisheries. Such simulations are useful in providing resource managers with an indication of how these systems behave under different conditions. But while considerable effort has been devoted to the simulation of natural systems, the amount of effort devoted to modeling human systems, and their interaction with natural systems, has been relatively small. Recently, researchers have outlined the importance of developing a discipline devoted to the modeling and simulation of human systems. Increased efforts are now being devoted to the simulation of social phenomena (see, for example, Doran and Gilbert). The tools now exist to develop simulations that incorporate the behavior of both a natural resource and the human individuals or institutions that interact with the resource. A considerable body of work exists devoted to understanding the behavior of the institutions that people have developed to manage natural resources. Specifically, a large number of studies have been undertaken in an effort to understand how common pool resources (CPRs) have been managed in differing natural and institutional environments. Numerous field studies and laboratory experiments using human subjects have supported the evolution of a theoretical foundation for the study of resource management institutions. But while field studies and experiments have been useful tools for exploring the management of natural resources, to date little effort has been devoted to exploring the potential role of modeling and computer-based simulation for understanding the behavior of resource management institutions. This chapter seeks to combine the theoretical foundations of research on institutions for resource management, with recent advances in human systems modeling to outline a framework for modeling individual decision making in resource management environments. Starting with a brief review of social simulation and intelligent agent-based modeling formalisms, this chapter moves on to discuss models of individual decision making in the social sciences and in simulations..