Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bankes, S.: Agent-Based Modeling: A Revolution? PNAS 99(10), 7199–7200 (2002)
Brueckner, S.A., Parunak, H.V.D.: Resource-aware exploration of the emergent dynamics of simulated systems. In: AAMAS 2003: Proceedings of the Second International Joint Conference on Autonomous Agents and Multi-Agent Systems, pp. 781–788. ACM, New York (2003)
Bryson, J.J., Ando, Y., Lehmann, H.: Agent-based modelling as scientific method: a case study analysing primate social behaviour. Philosophical Transactions of the Royal Society B: Biological Sciences 362(1485), 1685–1698 (2007)
Calvez, B., Hutzler, G.: Automatic Tuning of Agent-Based Models Using Genetic Algorithms. In: Sichman, J.S., Antunes, L. (eds.) MABS 2005. LNCS (LNAI), vol. 3891, pp. 41–57. Springer, Heidelberg (2006)
Cucker, F., Smale, S.: Emergent behavior in flocks. IEEE Transactions on Automatic Control 52(5), 852–862 (2007)
Heppner, F., Convissar, J., Moonan Jr, D., Anderson, J.: Visual angle and formation flight in Canada Geese (Branta canadensis). The Auk, pp. 195–198 (1985)
Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Kennedy, J., Eberhart, R.: et al.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE, Piscataway (1995)
Miller, J.H.: Active nonlinear tests (ANTs) of complex simulation models. Management Science 44(6), 820–830 (1998)
Mitchell, M., Holland, J., Forrest, S.: When will a genetic algorithm outperform hill climbing? In: Cowan, J.D., Tesauro, G., Alspector, J. (eds.) Advances in Neural Information Processing Systems, vol. 6, pp. 51–58. Morgan Kaufmann, San Mateo (1994)
Mitchell, M., Crutchfield, J.P., Das, R.: Evolving cellular automata with genetic algorithms: A review of recent work. In: Proceedings of the First International Conference on Evolutionary Computation and Its Applications, Russian Academy of Sciences, Moscow (1996)
Nathan, A., Barbosa, V.: V-like formations in flocks of artificial birds. Artificial Life 14(2), 179–188 (2008)
Reynolds, C.W.: Flocks, herds and schools: A distributed behavioral model. In: SIGGRAPH 1987: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, pp. 25–34. ACM, New York (1987)
Sanchez, S.M., Lucas, T.W.: Exploring the world of agent-based simulations: simple models, complex analyses. In: WSC 2002: Proceedings of the 34th Conference on Winter Simulation, pp. 116–126 (2002)
Sierra, C., Sabater, J., Augusti, J., Garcia, P.: SADDE: Social agents design driven by equations. In: Methodologies and Software Engineering for Agent Systems. Kluwer Academic Publishers, Dordrecht (2004)
Stonedahl, F., Wilensky, U.: BehaviorSearch [computer software]. Center for Connected Learning and Computer Based Modeling, Northwestern University, Evanston, IL (2010), http://www.behaviorsearch.org/
Stonedahl, F., Stonedahl, S.: Heuristics for sampling repetitions in noisy landscapes with fitness caching. In: GECCO 2010: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation. ACM, New York (2010) ([in press])
Tisue, S., Wilensky, U.: NetLogo: Design and implementation of a multi-agent modeling environment. In: Proceedings of Agent 2004. pp. 7–9 (2004)
Wilensky, U., Shargel, B.: BehaviorSpace [computer software]. Center for Connected Learning and Computer Based Modeling, Northwestern University, Evanston, IL (2002), http://ccl.northwestern.edu/netlogo/behaviorspace
Wilensky, U.: NetLogo Flocking model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1998)
Wilensky, U.: NetLogo. Center for Connected Learning and Computer-based Modeling, Northwestern University, Evanston, IL (1999)
Wilensky, U., Rand, W.: An introduction to agent-based modeling: Modeling natural, social and engineered complex systems with NetLogo. MIT Press, Cambridge (in press)
Wilkerson-Jerde, M., Stonedahl, F., Wilensky, U.: NetLogo Flocking Vee Formations model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Stonedahl, F., Wilensky, U. (2011). Finding Forms of Flocking: Evolutionary Search in ABM Parameter-Spaces. In: Bosse, T., Geller, A., Jonker, C.M. (eds) Multi-Agent-Based Simulation XI. MABS 2010. Lecture Notes in Computer Science(), vol 6532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18345-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-18345-4_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18344-7
Online ISBN: 978-3-642-18345-4
eBook Packages: Computer ScienceComputer Science (R0)