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Empirically-Derived Behavioral Rules in Agent-Based Models Using Decision Trees Learned from Questionnaire Data

  • N. Sánchez-MaroñoEmail author
  • A. Alonso-Betanzos
  • O. Fontenla-Romero
  • J. Gary Polhill
  • T. Craig
Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

With the increasing trend in exploring the use of agent-based models in empirical contexts, this paper reflects on the use of decision trees learned from questionnaire data as behavioral models for the agents. Decision trees are machine learning algorithms most commonly used in the data mining literature, especially for smaller datasets where other techniques such as Bayesian Networks cannot be applied. In agent-based modelling contexts, decision trees have the advantage over some other machine learning techniques in that the results are more transparent, and can be critiqued by domain experts without a background in computing or artificial intelligence. However, decision trees are sensitive to the way in which they are constructed, particularly with respect to preprocessing. We describe the processes by which the decision trees were derived in the context of a model of everyday pro-environmental behavior at work, comparing various preprocessing methods and exploring their differences.

Keywords

Decision Tree Feature Selection Feature Selection Method Decision Tree Algorithm Injunctive Norm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was funded by the European Commission Framework Programme 7, grant agreement 265155 (Low Carbon at Work: Modelling Agents and Organisations to Achieve Transition to a Low-Carbon Europe) and by the Scottish Government Rural Affairs and the Environment Portfolio Strategic Research Theme 4 (Economic Adaptation).

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • N. Sánchez-Maroño
    • 1
    Email author
  • A. Alonso-Betanzos
    • 1
  • O. Fontenla-Romero
    • 1
  • J. Gary Polhill
    • 2
  • T. Craig
    • 3
  1. 1.University of A CoruñaA CoruñaSpain
  2. 2.Information and Computational ScienceThe James Hutton InstituteAberdeenUK
  3. 3.The James Hutton InstituteCraigiebuckler, AberdeenUK

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