SCIENCE@home

Enhancing Agent-Based Models with Discrete Choice Experiments

Stefan Holm,R. Lemm,O. Thees,L. Hilty

Published 2016 in Journal of Artificial Societies and Social Simulation

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.

PUBLICATION RECORD

  • Publication year

    2016

  • Venue

    Journal of Artificial Societies and Social Simulation

  • Publication date

    2016-06-30

  • Fields of study

    Business, Economics, Computer Science

  • Identifiers
    DOI 10.18564/jasss.3121
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-41 of 41 references · Page 1 of 1

CITED BY

Showing 1-29 of 29 citing papers · Page 1 of 1