Enhancing Agent-Based Models with Discrete Choice Experiments

Agent-based modeling is a promising method to investigate market dynamics, as it allows modeling the behavior of all market participants individually. Integrating empirical data in the agentsrsquo; decision model can improve the validity of agent-based models (ABMs). We present an approach of using discrete choice experiments (DCEs) to enhance the empirical foundation of ABMs. The DCE method is based on random utility theory and therefore has the potential to enhance the ABM approach with a well-established economic theory. Our combined approach is applied to a case study of a roundwood market in Switzerland. We conducted DCEs with roundwood suppliers to quantitatively characterize the agentsrsquo; decision model. We evaluate our approach using a fitness measure and compare two DCE evaluation methods, latent class analysis and hierarchical Bayes. Additionally, we analyze the influence of the error term of the utility function on the simulation results and present a way to estimate its probability distribution. DOI: https://doi.org/10.18564/jasss.3121 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-124895 Published Version Originally published at: Holm, Stefan; Lemm, Renato; Thees, Oliver; Hilty, Lorenz (2016). Enhancing Agent-Based Models with Discrete Choice Experiments. Journal of Artificial Societies and Social Simulation, 19(3):3. DOI: https://doi.org/10.18564/jasss.3121 Enhancing Agent-Based Models with Discrete Choice Experiments Stefan Holm1, Renato Lemm2, Oliver Thees2, Lorenz M. Hilty3 1University of Zurich (UZH) / Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Eidg. Forschungsanstalt WSL, Zürcherstr. 111, 8903 Birmensdorf, Switzerland 2Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Eidg. Forschungsanstalt WSL, Zürcherstr. 111, 8903 Birmensdorf, Switzerland 3University of Zurich (UZH) / Swiss Federal Laboratories for Materials Science and Technology (EMPA), Lerchenfeldstr. 5, 9014 St. Gallen, Switzerland Correspondence should be addressed to [email protected] Journal of Artificial Societies and Social Simulation 19(3) 3, 2016 Doi: 10.18564/jasss.3121 Url: http://jasss.soc.surrey.ac.uk/19/3/3.html Received: 05-08-2015 Accepted: 09-05-2016 Published: 30-06-2016 Abstract: Agent-basedmodeling is a promising method to investigate market dynamics, as it allows modeling thebehavior of allmarket participants individually. Integrating empirical data in theagents’ decisionmodel can improve the validity of agent-based models (ABMs). We present an approach of using discrete choice experiments (DCEs) to enhance the empirical foundation of ABMs. The DCEmethod is based on randomutility theory and therefore has the potential to enhance the ABM approach with a well-established economic theory. Our combined approach is applied to a case study of a roundwoodmarket in Switzerland. We conducted DCEswith roundwood suppliers to quantitatively characterize the agents’ decision model. We evaluate our approach using a fitness measure and compare two DCE evaluation methods, latent class analysis and hierarchical Bayes. Additionally, we analyze the influence of the error term of the utility function on the simulation results and present a way to estimate its probability distribution. Agent-basedmodeling is a promising method to investigate market dynamics, as it allows modeling thebehavior of allmarket participants individually. Integrating empirical data in theagents’ decisionmodel can improve the validity of agent-based models (ABMs). We present an approach of using discrete choice experiments (DCEs) to enhance the empirical foundation of ABMs. The DCEmethod is based on randomutility theory and therefore has the potential to enhance the ABM approach with a well-established economic theory. Our combined approach is applied to a case study of a roundwoodmarket in Switzerland. We conducted DCEswith roundwood suppliers to quantitatively characterize the agents’ decision model. We evaluate our approach using a fitness measure and compare two DCE evaluation methods, latent class analysis and hierarchical Bayes. Additionally, we analyze the influence of the error term of the utility function on the simulation results and present a way to estimate its probability distribution.