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Initializing Agent-Based Models with Clustering Archetypes

  • Samaneh Saadat
  • Chathika Gunaratne
  • Nisha Baral
  • Gita Sukthankar
  • Ivan Garibay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10899)

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.

Keywords

Agent-based models GitHub archetypes Unsupervised learning Stable clustering 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Samaneh Saadat
    • 1
  • Chathika Gunaratne
    • 1
  • Nisha Baral
    • 1
  • Gita Sukthankar
    • 1
  • Ivan Garibay
    • 1
  1. 1.University of Central FloridaOrlandoUSA

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