Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Active Nonlinear Tests (Ants) of Complex Simulation Models
Management Science
Resource-aware exploration of the emergent dynamics of simulated systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
V-like formations in flocks of artificial birds
Artificial Life
Heuristics for sampling repetitions in noisy landscapes with fitness caching
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Automatic tuning of agent-based models using genetic algorithms
MABS'05 Proceedings of the 6th international conference on Multi-Agent-Based Simulation
Heuristics for sampling repetitions in noisy landscapes with fitness caching
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Automatic discovery of algorithms for multi-agent systems
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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While agent-based models (ABMs) are becoming increasingly popular for simulating complex and emergent phenomena in many fields, understanding and analyzing ABMs poses considerable challenges. ABM behavior often depends on many model parameters, and the task of exploring a model's parameter space and discovering the impact of different parameter settings can be difficult and time-consuming. Exhaustively running the model with all combinations of parameter settings is generally infeasible, but judging behavior by varying one parameter at a time risks overlooking complex nonlinear interactions between parameters. Alternatively, we present a case study in computer-aided model exploration, demonstrating how evolutionary search algorithms can be used to probe for several qualitative behaviors (convergence, non-convergence, volatility, and the formation of vee shapes) in two different flocking models. We also introduce a new software tool (BehaviorSearch) for performing parameter search on ABMs created in the NetLogo modeling environment.