Bayesian networks for spatial learning: a workflow on using limited survey data for intelligent learning in spatial agent-based models

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Abstract

Machine learning (ML) algorithms steer agent decisions in agent-based models (ABMs), serving as a vehicle for implementing behaviour changes during simulation runs. However, when training an ML algorithm, obtaining large sets of micro-level human behaviour data is often problematic. Information on human behaviour is often collected via surveys of relatively small sample sizes. This paper presents a methodology for training a learning algorithm to guide agent behaviour in a spatial ABM using a limited survey data sample. We apply different implementation strategies using survey data and Bayesian networks (BNs). By being grounded in probabilistic directed graphical models, BNs stand out among other learning algorithms in that they can be based on expert knowledge and/or known datasets. This paper presents four alternative implementations of data-driven BNs to support agent decisions in a spatial ABM. We differentiate between training BNs prior to, or during the simulation runs, using only survey data or a combination of survey data and expert knowledge. The four different implementations are then illustrated using a spatial ABM of cholera diffusion for Kumasi, Ghana. The results indicate that a balance between expert knowledge and survey data provides the best control over the learning process of the agents and produces the most realistic agent behaviour.

Original languageEnglish
Pages (from-to)243-268
JournalGeoinformatica
Volume23
Issue number2
DOIs
Publication statusPublished - 26 Apr 2019

Fingerprint

workflow
Bayesian networks
learning
expert knowledge
Learning algorithms
human behavior
expert survey
Learning systems
simulation
cholera
micro level
Ghana
learning process
methodology

Keywords

  • UT-Hybrid-D
  • Disease modelling
  • Risk perception
  • Social survey
  • Supervised learning
  • Bayesian networks

Cite this

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title = "Bayesian networks for spatial learning: a workflow on using limited survey data for intelligent learning in spatial agent-based models",
abstract = "Machine learning (ML) algorithms steer agent decisions in agent-based models (ABMs), serving as a vehicle for implementing behaviour changes during simulation runs. However, when training an ML algorithm, obtaining large sets of micro-level human behaviour data is often problematic. Information on human behaviour is often collected via surveys of relatively small sample sizes. This paper presents a methodology for training a learning algorithm to guide agent behaviour in a spatial ABM using a limited survey data sample. We apply different implementation strategies using survey data and Bayesian networks (BNs). By being grounded in probabilistic directed graphical models, BNs stand out among other learning algorithms in that they can be based on expert knowledge and/or known datasets. This paper presents four alternative implementations of data-driven BNs to support agent decisions in a spatial ABM. We differentiate between training BNs prior to, or during the simulation runs, using only survey data or a combination of survey data and expert knowledge. The four different implementations are then illustrated using a spatial ABM of cholera diffusion for Kumasi, Ghana. The results indicate that a balance between expert knowledge and survey data provides the best control over the learning process of the agents and produces the most realistic agent behaviour.",
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Bayesian networks for spatial learning : a workflow on using limited survey data for intelligent learning in spatial agent-based models. / Abdulkareem, Shaheen A.; Mustafa, Yaseen T.; Augustijn, Ellen Wien; Filatova, Tatiana.

In: Geoinformatica, Vol. 23, No. 2, 26.04.2019, p. 243-268.

Research output: Contribution to journalArticleAcademicpeer-review

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