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Enhancing Agent-Based Models with Discrete Choice Experiments

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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.

Suggested Citation

  • Stefan Holm & Renato Lemm & Oliver Thees & Lorenz M. Hilty, 2016. "Enhancing Agent-Based Models with Discrete Choice Experiments," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(3), pages 1-3.
    • Handle: RePEc:jas:jasssj:2015-101-3
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    1. Caussade, Sebastián & Ortúzar, Juan de Dios & Rizzi, Luis I. & Hensher, David A., 2005. "Assessing the influence of design dimensions on stated choice experiment estimates," Transportation Research Part B: Methodological, Elsevier, vol. 39(7), pages 621-640, August.
    2. Faical Akaichi & Rodolfo M. Nayga & José M. Gil, 2013. "Are Results from Non-hypothetical Choice-based Conjoint Analyses and Non-hypothetical Recoded-ranking Conjoint Analyses Similar?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(4), pages 949-963.
    3. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
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    2. Utomo, Dhanan Sarwo & Onggo, Bhakti Stephan & Eldridge, Stephen, 2018. "Applications of agent-based modelling and simulation in the agri-food supply chains," European Journal of Operational Research, Elsevier, vol. 269(3), pages 794-805.
    3. Wolbertus, Rick & van den Hoed, Robert & Kroesen, Maarten & Chorus, Caspar, 2021. "Charging infrastructure roll-out strategies for large scale introduction of electric vehicles in urban areas: An agent-based simulation study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 262-285.
    4. Khanna, Madhu & Atallah, Shadi & Kar, Saurajyoti & Sharma, Bijay & Wu, Linghui & Yu, Chengzheng, 2021. "Digital Transformation for a Sustainable Agriculture in the US: Opportunities and Challenges," 2021 Conference, August 17-31, 2021, Virtual 313799, International Association of Agricultural Economists.
    5. Chappin, Emile J.L. & Schleich, Joachim & Guetlein, Marie-Charlotte & Faure, Corinne & Bouwmans, Ivo, 2022. "Linking of a multi-country discrete choice experiment and an agent-based model to simulate the diffusion of smart thermostats," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    6. Trinh, Tra Thi & Munro, Alistair, 2023. "Integrating a choice experiment into an agent-based model to simulate climate-change induced migration: The case of the Mekong River Delta, Vietnam," Journal of choice modelling, Elsevier, vol. 48(C).
    7. Khanna, Madhu, 2021. "Digital Transformation for a Sustainable Agriculture: Opportunities and Challenges," 2021 Conference, August 17-31, 2021, Virtual 315052, International Association of Agricultural Economists.
    8. Ahmed Laatabi & Nicolas Marilleau & Tri Nguyen-Huu & Hassan Hbid & Mohamed Ait Babram, 2018. "ODD+2D: An ODD Based Protocol for Mapping Data to Empirical ABMs," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(2), pages 1-9.
    9. Holm, Stefan & Thees, Oliver & Lemm, Renato & Olschewski, Roland & Hilty, Lorenz M., 2018. "An agent-based model of wood markets: Scenario analysis," Forest Policy and Economics, Elsevier, vol. 95(C), pages 26-36.
    10. Madhu Khanna & Shady S. Atallah & Saurajyoti Kar & Bijay Sharma & Linghui Wu & Chengzheng Yu & Girish Chowdhary & Chinmay Soman & Kaiyu Guan, 2022. "Digital transformation for a sustainable agriculture in the United States: Opportunities and challenges," Agricultural Economics, International Association of Agricultural Economists, vol. 53(6), pages 924-937, November.
    11. Callum Rhys Tilbury, 2022. "Reinforcement Learning for Economic Policy: A New Frontier?," Papers 2206.08781, arXiv.org, revised Feb 2023.

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