Agent-based models, like cellular automata (see Cellular Automata) and individual-based models (see IndividualBased Models), fall into the class of bottom-up approaches in ecological modeling in which only lower-level processes (e.g., local interactions) are explicitly modeled (Box 2). Higher-level processes (e.g., group-level behavior or population dynamics) are then allowed to emerge as the collective result of local-level dynamics. The bottom-up approach has been used in ecology to demonstrate the emergence of spatiotemporal patterns in population dynamics and is inspired from interacting particle models in physics.
Agent-based modeling in ecology has its origins in complex systems studies and artificial life. Early adaptive agents were very simple representations ofentities having animal-like characteristics and models based on these were used to explore theoretical questions related to the emergence of cooperation and other group dynamics. Adaptive agents today may contain complex learning and adaptive mechanisms and may closely mimic many characteristics of real organisms.
Classic examples of applications of agent-based models can be found in the artificial life literature. Models such as ECHO, Tierra, and Avida simulate resource-limited worlds in which abstract, yet animal-like, organisms evolve and interact. These models are typically used to study the evolution of a population over multiple generations. Individuals must compete to obtain enough resources to be able to reproduce themselves. Reproduction may be sexual or asexual, and evolution is modeled via an approach based on genetic algorithms (see Evolutionary Algorithms). These models have been used to study theoretical questions related to speciation, extinction, and competition for resources.
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