Abstract
Agent-based modeling (ABM) is an increasingly popular technique for modeling organizations or societies. In this paper, a new approach for modeling decision-making for the environmental decisions of agents in an organization modeled using ABM is devised. The decision-making model has been constructed using data obtained by responses of individuals of the organizations to a questionnaire. As the number of responses is small, while the number of variables measured is relatively high, and obtained decision rules should be explicit, decision trees were selected to generate the model after applying different techniques to properly preprocess the data set. The results obtained for an academic organization are presented.
This work has been funded in part by the European Commission through Framework Programm 7, grant agreement number 26515, LOCAW:LOw CArbon at Work.
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References
European Commission: What is EU doing about climate change? http://ec.europa.eu/clima/policies/brief/eu/index_en.htm (last visited on January 2013)
Gilbert, N.: Agent-based models. SAGE Publications, University of Surrey (2007)
Polhill, G., Gotts, N., Sánchez-Maroño, N., Pignotti, E., Fontenla-Romero, O., Rodríguez-García, M., Alonso-Betanzos, A., Edwards, P., Craig, T.: An ontology-based design for modelling case studies of everyday proenvironmental behaviour in the workplace. In: Proc. of International Congress on Environmental Modelling and Software Managing Resources of a Limited Planet, Leipzig, Germany (2012)
Steg, L., De Groot, J.I.: Environmental values. In: The Oxford Handbook of Environmental and Conservation Psychology, Oxford University Press (2012)
Schwartz, S.H.: Universals in the content and structures of values: Theoretical advances and empirical tests in 20 countries. Advances in Experimental Psychology 25, 1–65 (1992)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. SIGKDD Explorations 11(1) (2009), http://www.cs.waikato.ac.nz/ml/weka/ (last visited on January 2013)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press (1967)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Hall, M.A.: Correlation-based Feature Selection for Machine Learning. PhD thesis, University of Waikato, Hamilton, New Zealand (1999)
Yang, Y., Webb, G.I.: Proportional k-interval discretization for naive-bayes classifiers. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 564–575. Springer, Heidelberg (2001)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc. (1993)
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Sánchez-Maroño, N., Alonso-Betanzos, A., Fontenla-Romero, Ó., Rodríguez-García, M., Polhill, G., Craig, T. (2013). A Decision-Making Model for Environmental Behavior in Agent-Based Modeling. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_14
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DOI: https://doi.org/10.1007/978-3-642-38679-4_14
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