Finding forms of flocking: Evolutionary search in ABM parameter-spaces

Forrest Stonedahl; Uri Wilensky

doi:10.1007/978-3-642-18345-4_5

Finding forms of flocking: Evolutionary search in ABM parameter-spaces

Forrest Stonedahl^*, Uri Wilensky

^*Corresponding author for this work

School of Education and Social Policy

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

38 Scopus citations

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.

Original language	English (US)
Title of host publication	Multi-Agent-Based Simulation XI - International Workshop, MABS 2010, Revised Selected Papers
Pages	61-75
Number of pages	15
DOIs	https://doi.org/10.1007/978-3-642-18345-4_5
State	Published - 2011
Event	11th International Workshop on Multi-Agent-Based Simulation, MABS 2010, Co-located with the 9th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2010 - Toronto, ON, Canada Duration: May 11 2010 → May 11 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6532 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	11th International Workshop on Multi-Agent-Based Simulation, MABS 2010, Co-located with the 9th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2010
Country/Territory	Canada
City	Toronto, ON
Period	5/11/10 → 5/11/10

Funding

Acknowledgments. We especially wish to thank William Rand for constructive feedback on this research, Luis Amaral for generously providing computational resources to carry out our experiments, and the National Science Foundation for supporting this work (grant IIS-0713619).

Keywords

ABM
agent-based modeling
flocking
genetic algorithms
model exploration
multi-agent simulation
parameter search

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-18345-4_5

Cite this

Stonedahl, F., & Wilensky, U. (2011). Finding forms of flocking: Evolutionary search in ABM parameter-spaces. In Multi-Agent-Based Simulation XI - International Workshop, MABS 2010, Revised Selected Papers (pp. 61-75). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6532 LNAI). https://doi.org/10.1007/978-3-642-18345-4_5

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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.",

keywords = "ABM, agent-based modeling, flocking, genetic algorithms, model exploration, multi-agent simulation, parameter search",

author = "Forrest Stonedahl and Uri Wilensky",

note = "Funding Information: Acknowledgments. We especially wish to thank William Rand for constructive feedback on this research, Luis Amaral for generously providing computational resources to carry out our experiments, and the National Science Foundation for supporting this work (grant IIS-0713619).; 11th International Workshop on Multi-Agent-Based Simulation, MABS 2010, Co-located with the 9th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2010 ; Conference date: 11-05-2010 Through 11-05-2010",

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language = "English (US)",

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Stonedahl, F & Wilensky, U 2011, Finding forms of flocking: Evolutionary search in ABM parameter-spaces. in Multi-Agent-Based Simulation XI - International Workshop, MABS 2010, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6532 LNAI, pp. 61-75, 11th International Workshop on Multi-Agent-Based Simulation, MABS 2010, Co-located with the 9th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2010, Toronto, ON, Canada, 5/11/10. https://doi.org/10.1007/978-3-642-18345-4_5

Finding forms of flocking: Evolutionary search in ABM parameter-spaces. / Stonedahl, Forrest; Wilensky, Uri.
Multi-Agent-Based Simulation XI - International Workshop, MABS 2010, Revised Selected Papers. 2011. p. 61-75 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6532 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - Funding Information: Acknowledgments. We especially wish to thank William Rand for constructive feedback on this research, Luis Amaral for generously providing computational resources to carry out our experiments, and the National Science Foundation for supporting this work (grant IIS-0713619).

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AB - 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.

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Stonedahl F, Wilensky U. Finding forms of flocking: Evolutionary search in ABM parameter-spaces. In Multi-Agent-Based Simulation XI - International Workshop, MABS 2010, Revised Selected Papers. 2011. p. 61-75. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-18345-4_5

Finding forms of flocking: Evolutionary search in ABM parameter-spaces

Abstract

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Funding

Keywords

ASJC Scopus subject areas

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