Enhancing Agent-Based Models with Discrete Choice Experiments

Stefan Holm (), Renato Lemm (), Oliver Thees () and Lorenz M. Hilty ()
Additional contact information
Stefan Holm: http://www.ifi.uzh.ch/isr/people/holm.html
Renato Lemm: http://www.wsl.ch/info/mitarbeitende/lemm/index_DE
Oliver Thees: http://www.wsl.ch/info/mitarbeitende/thees/index_EN
Lorenz M. Hilty: http://www.ifi.uzh.ch/isr/people/hilty.html

Journal of Artificial Societies and Social Simulation, 2016, vol. 19, issue 3, 3

Abstract: 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 agents’ 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 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.

Keywords: Agent-Based Modeling; Discrete Choice Experiments; Preference Elicitation; Decision Model; Market Simulation; Wood Market (search for similar items in EconPapers)
Date: 2016-06-30
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Citations: View citations in EconPapers (11)

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