Enhancing Agent-Based Models with Discrete Choice Experiments

Authored by Stefan Holm, Oliver Thees, Renato Lemm, Lorenz M Hilty

Date Published: 2016

DOI: 10.18564/jasss.3121

Sponsors: United States National Science Foundation (NSF)

Platforms: Java

Model Documentation: UML ODD Flow charts Mathematical description

Model Code URLs: Model code not found

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.

Tags

behavior Decision-Making Protocol Conjoint-analysis Experimental-designs Ranking experiments Fuel