Initializing agent-based models with clustering archetypes

Samaneh Saadat; Chathika Gunaratne; Nisha Baral; Gita Sukthankar; Ivan Garibay

doi:10.1007/978-3-319-93372-6_27

Initializing agent-based models with clustering archetypes

Samaneh Saadat, Chathika Gunaratne, Nisha Baral, Gita Sukthankar, Ivan Garibay

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

9 Scopus citations

Abstract

Agent-based models are a powerful tool for predicting population level behaviors; however their performance can be sensitive to the initial simulation conditions. This paper introduces a procedure for leveraging large datasets to initialize agent-based simulations in which the population is abstracted into a set of archetypes. We show that these archetypes can be discovered using clustering and evaluate the benefits of selecting clusters based on their stability over time. Our experiments on the GitHub dataset demonstrate that simulation runs performed with the clustering archetypes are more successful at predicting large-scale activity patterns.

Original language	English
Title of host publication	Social, Cultural, and Behavioral Modeling - 11th International Conference, SBP-BRiMS 2018, Proceedings
Editors	Halil Bisgin, Robert Thomson, Ayaz Hyder, Christopher Dancy
Publisher	Springer Verlag
Pages	233-239
Number of pages	7
ISBN (Print)	9783319933719
DOIs	https://doi.org/10.1007/978-3-319-93372-6_27
State	Published - 2018
Externally published	Yes
Event	11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018 - Washington, United States Duration: Jul 10 2018 → Jul 13 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10899 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018
Country/Territory	United States
City	Washington
Period	07/10/18 → 07/13/18

Funding

This research was supported by DARPA program HR001117S0018.

Keywords

Agent-based models
GitHub archetypes
Stable clustering
Unsupervised learning

Access to Document

10.1007/978-3-319-93372-6_27

Cite this

Saadat, S., Gunaratne, C., Baral, N., Sukthankar, G., & Garibay, I. (2018). Initializing agent-based models with clustering archetypes. In H. Bisgin, R. Thomson, A. Hyder, & C. Dancy (Eds.), Social, Cultural, and Behavioral Modeling - 11th International Conference, SBP-BRiMS 2018, Proceedings (pp. 233-239). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10899 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-93372-6_27

Saadat, Samaneh ; Gunaratne, Chathika ; Baral, Nisha et al. / Initializing agent-based models with clustering archetypes. Social, Cultural, and Behavioral Modeling - 11th International Conference, SBP-BRiMS 2018, Proceedings. editor / Halil Bisgin ; Robert Thomson ; Ayaz Hyder ; Christopher Dancy. Springer Verlag, 2018. pp. 233-239 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Agent-based models are a powerful tool for predicting population level behaviors; however their performance can be sensitive to the initial simulation conditions. This paper introduces a procedure for leveraging large datasets to initialize agent-based simulations in which the population is abstracted into a set of archetypes. We show that these archetypes can be discovered using clustering and evaluate the benefits of selecting clusters based on their stability over time. Our experiments on the GitHub dataset demonstrate that simulation runs performed with the clustering archetypes are more successful at predicting large-scale activity patterns.",

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Saadat, S, Gunaratne, C, Baral, N, Sukthankar, G & Garibay, I 2018, Initializing agent-based models with clustering archetypes. in H Bisgin, R Thomson, A Hyder & C Dancy (eds), Social, Cultural, and Behavioral Modeling - 11th International Conference, SBP-BRiMS 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10899 LNCS, Springer Verlag, pp. 233-239, 11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018, Washington, United States, 07/10/18. https://doi.org/10.1007/978-3-319-93372-6_27

Initializing agent-based models with clustering archetypes. / Saadat, Samaneh; Gunaratne, Chathika; Baral, Nisha et al.
Social, Cultural, and Behavioral Modeling - 11th International Conference, SBP-BRiMS 2018, Proceedings. ed. / Halil Bisgin; Robert Thomson; Ayaz Hyder; Christopher Dancy. Springer Verlag, 2018. p. 233-239 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10899 LNCS).

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

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Saadat S, Gunaratne C, Baral N, Sukthankar G, Garibay I. Initializing agent-based models with clustering archetypes. In Bisgin H, Thomson R, Hyder A, Dancy C, editors, Social, Cultural, and Behavioral Modeling - 11th International Conference, SBP-BRiMS 2018, Proceedings. Springer Verlag. 2018. p. 233-239. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-93372-6_27

Initializing agent-based models with clustering archetypes

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Keywords

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