Pattern-oriented modeling of agent-based complex systems: Lessons from ecology
Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity.
|Publication Subtype||Journal Article|
|Title||Pattern-oriented modeling of agent-based complex systems: Lessons from ecology|
|Contributing office(s)||Southeast Ecological Science Center|
|Online Only (Y/N)||N|
|Additional Online Files (Y/N)||N|
|Google Analytic Metrics||Metrics page|