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

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.

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