Ecological Applications of Adaptive Agents
Ecologists are constantly searching for new modelling paradigms in order to simulate realistically the distinct nature of ecosystems by computer models. The ecosystem concept as established by Forbes (1887) had the most forming influence on ecosystem modelling in the past century. It no longer bears close examination as ecosystems like lakes are known to evolve and being driven by exogenous forces rather than existing permanently and in isolation. However, the ecosystem approach resulted in valuable databases from monitoring as well as quantitative and qualitative descriptions of ecosystem dynamics and has made ecology a predictive science (Rigler and Peters 1995). Computer models resulting from the ecosystem concept were mainly based on differential equations (DE) for well-defined ecological entities and processes, adjusted by measured or estimated parameters. Radtke and Straskraba (1980) firstly tried to overcome the rigidity of such models by parameter optimization of ecological goal functions relevant to lake ecosystems as introduced by Straskraba (1977). The authors considered their results as contribution to a structural self-optimising ecosystem model but admitted that more adequate models and more suitable optimisation procedures would be needed to make it a success. In order to overcome model rigidity, Kaluzny and Swartzman (1985) suggested a library of alternative representations of ecological processes from where a simulation model picks the most relevant one for a specific ecological situation.
KeywordsArtificial Neural Network Evolutionary Computation Complex Adaptive System Evolve Role Causal Knowledge
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