Agent-based modeling is used in fields ranging from artificial life to economy to model the emergence of complex phenomenon in groups of interacting individuals. Given their emphasis on decision making, they are particularly popular in the social sciences. Agent-based models are viewed by a growing number of scientists as being legitimate scientific instruments with which to create surrogate systems for performing virtual empirical experiments. Once appropriately validated, an agent-based model can serve to explore different what-if scenarios and to test theories relating process to pattern. In ecology, they are typically used to explore questions related to the emergence of pattern and regularity in systems where heterogeneous individuals act without the presence of central control. A simple example would be the emergence of population cycles that have periods longer than the lifetime of a single individual, or the emergence of persistent spatiotemporal patterns of population density on a landscape.
Models of ecological agents range in complexity from very simple representations of a single behavior in a relatively simple environment to detailed representations of agents interacting in a realistic spatial environment. Simple agent-based models serve to explore basic theoretical questions, whereas the more complex models serve to study questions related to ecosystem management and resource use.
Simple agent-based models have been developed to study animal and insect agglomerations such as the movement of herds, aligned bird flight, and swarming behavior. In these models, an agent advances in an arena with a known speed and direction and then adjusts its speed and direction according to simple rules that take into account other agents that fall within a specified radius. It might, for example, change its direction to align itself with a neighbor, or modify its speed to follow another agent. The choice of rules affects the type of agglomeration that is generated. The agents in these simple models usually have no memory, no methods of direct communication with other agents, and very primitive decision-making abilities. The models effectively demonstrate, however, how a collection of heterogeneous entities can give rise to organized, global-level behavior.
At the other extreme, a recent application of adaptive agents is in the fields of ecosystem and natural resource management. In these much more complex models, agents are used to represent humans that interact with other humans and with their environment in order to gain access to a natural resource. In these models, human agents may cooperate or compete for access to common pool resources such as water or recreational space. The models can be used to simulate different resource-sharing scenarios as a means of finding solutions to conflict or to minimize the impact of human activity on ecosystem functioning. Since simulations are typically run over short periods of time, mechanisms by which agent learning and decision making can be represented are emphasized and modeling agent evolution is not necessary. An example application is in the prediction of land use. Many agent-based models simulate farmer or herdsman agents who need access to fields or pastures for their livelihood. The agents will use a set of economic and other criteria to decide whether or not to clear more land, or sell land to urban developers, for example. Such models are usually initialized with real cartographic data on land use, are linked with economic or regional population growth models, and have sophisticated agent decision-making modules based on empirical data. The models are used to predict land use change over periods of several decades and to assess the impacts of different policy scenarios on future land use.
Agent-based models are based on a generative approach: they are used to 'generate' patterns or other global level phenomena from local-level interactions. Validation of such models is thus usually pattern oriented, being done at the global level. A model will be judged based on its ability to generate global, or macro-level, structures similar to those observed for the real system that it represents. The relative importance of the microlevel rules in contributing to the generation of this macro-level structure is then assessed, giving insight into the mechanisms and processes responsible for the observed patterns. Obviously, in the case where two different micro-level rules give rise to the same macro-level pattern, the validity of each rule must be judged. Agent-based models can thus be used as a means of testing competing explanations for the presence of global-level structures (group-level behaviors, landscape-level patterns, etc.) in ecological systems.
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