Enhancing Agent-Based Models with Discrete Choice Experiments

Abstract

Agent-based modeling is a promising method to investigate market dynamics, as it allows modeling thebehaviorofallmarketparticipantsindividually. Integratingempiricaldataintheagents'decisionmodelcan improve the validity of agent-based models (ABMs). We present an approach of using discrete choice experi- ments(DCEs)toenhancetheempiricalfoundationofABMs. TheDCEmethodisbasedonrandomutilitytheory and therefore has the potential to enhance the ABM approach with a well-established economic theory. Our combinedapproachisappliedtoacasestudyofaroundwoodmarketinSwitzerland. WeconductedDCEswith roundwood suppliers to quantitatively characterize the agents' decision model. We evaluate our approach us- ing 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. 1.2 To further improve this situation, we present an approach where we have applied discrete choice experiments (DCEs)toelicitpreferencesofactorsthatarelaterrepresentedasagentsinourmodel. ThetermDCEisusedac- cording to the nomenclature for stated preference methods proposed by Carson & Louviere (2011). We demon- strate the potential of this approach with a case study of the Swiss wood market. The DCE was conducted with roundwood suppliers to quantify the decision model of the supplying agents. We present two approaches, la- tent class analysis and hierarchical Bayes, to evaluate the DCE data and show advantages and disadvantages of each approach to parameterize the model. The decision model is based on random utility theory and there- fore contains a deterministic component, namely utility obtained through the DCE, and a random component accounting for non-measurable factors of an individual's decision. We present a method to estimate the prob- ability distribution of this random component and analyze its influence on the simulation results.