IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v89y2019icp257-270.html
   My bibliography  Save this article

Modeling systematic technology adoption: Can one calibrated representative agent represent heterogeneous agents?

Author

Listed:
  • Chen, Huayi
  • Zhou, P.

Abstract

This paper explores whether a well-calibrated representative-agent model can represent the heterogeneous-agent model in terms of new technology's adoption. Through two types of heterogeneous-agent model, i.e., the cooperative heterogeneous-agent model and the independent heterogeneous-agent model, this paper first illustrates that cooperation among heterogeneous agents plays an important role in the adoption of new technology. After that, this paper examines whether a well-calibrated representative-agent model reacts differently from the heterogeneous-agent model in terms of new technology's adoption under different policy interventions. Compared to the heterogeneous agents’ aggregate behavior, the calibrated representative agent tends to overreact when the investment cost, risk attitude, and level of carbon tax vary. When the market size changes, however, the calibrated representative agent reacts almost the same as the heterogeneous agents’ aggregate behavior.

Suggested Citation

  • Chen, Huayi & Zhou, P., 2019. "Modeling systematic technology adoption: Can one calibrated representative agent represent heterogeneous agents?," Omega, Elsevier, vol. 89(C), pages 257-270.
    • Handle: RePEc:eee:jomega:v:89:y:2019:i:c:p:257-270
    DOI: 10.1016/j.omega.2018.10.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305048318302767
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.omega.2018.10.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Huisman, Kuno J. M. & Kort, Peter M., 2004. "Strategic technology adoption taking into account future technological improvements: A real options approach," European Journal of Operational Research, Elsevier, vol. 159(3), pages 705-728, December.
    2. William A. Barnett, 2011. "Multilateral Aggregation-Theoretic Monetary Aggregation over Heterogeneous Countries," World Scientific Book Chapters, in: Financial Aggregation And Index Number Theory, chapter 6, pages 167-206, World Scientific Publishing Co. Pte. Ltd..
    3. Martzoukos, Spiros H. & Zacharias, Eleftherios, 2013. "Real option games with R&D and learning spillovers," Omega, Elsevier, vol. 41(2), pages 236-249.
    4. Messner, S. & Golodnikov, A. & Gritsevskii, A., 1996. "A stochastic version of the dynamic linear programming model MESSAGE III," Energy, Elsevier, vol. 21(9), pages 775-784.
    5. Fredrik Hedenus, Christian Azar and Kristian Lindgren, 2006. "Induced Technological Change in a Limited Foresight Optimization Model," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 109-122.
    6. Arthur, W Brian, 1989. "Competing Technologies, Increasing Returns, and Lock-In by Historical Events," Economic Journal, Royal Economic Society, vol. 99(394), pages 116-131, March.
    7. Sungbae An & Yongsung Chang & Sun-Bin Kim, 2009. "Can a Representative-Agent Model Represent a Heterogeneous-Agent Economy," American Economic Journal: Macroeconomics, American Economic Association, vol. 1(2), pages 29-54, July.
    8. Chen, Huayi & Ma, Tieju, 2017. "Optimizing systematic technology adoption with heterogeneous agents," European Journal of Operational Research, Elsevier, vol. 257(1), pages 287-296.
    9. Azar, Christian & Lindgren, Kristian & Andersson, Bjorn A., 2003. "Global energy scenarios meeting stringent CO2 constraints--cost-effective fuel choices in the transportation sector," Energy Policy, Elsevier, vol. 31(10), pages 961-976, August.
    10. Chen, Huayi & Ma, Tieju, 2014. "Technology adoption with limited foresight and uncertain technological learning," European Journal of Operational Research, Elsevier, vol. 239(1), pages 266-275.
    11. Qiu, Yueming & Anadon, Laura D., 2012. "The price of wind power in China during its expansion: Technology adoption, learning-by-doing, economies of scale, and manufacturing localization," Energy Economics, Elsevier, vol. 34(3), pages 772-785.
    12. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    13. Yi Wen, 2009. "When does heterogeneity matter?," Working Papers 2009-024, Federal Reserve Bank of St. Louis.
    14. G.‐I. Bischi & M. Gallegati & A. Naimzada, 1999. "Symmetry‐breaking bifurcations and representativefirm in dynamic duopoly games," Annals of Operations Research, Springer, vol. 89(0), pages 252-271, January.
    15. Ma, Tieju & Nakamori, Yoshiteru, 2009. "Modeling technological change in energy systems – From optimization to agent-based modeling," Energy, Elsevier, vol. 34(7), pages 873-879.
    16. Manne, Alan S. & Barreto, Leonardo, 2004. "Learn-by-doing and carbon dioxide abatement," Energy Economics, Elsevier, vol. 26(4), pages 621-633, July.
    17. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    18. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    19. Kanudia, Amit & Loulou, Richard, 1998. "Robust responses to climate change via stochastic MARKAL: The case of Quebec," European Journal of Operational Research, Elsevier, vol. 106(1), pages 15-30, April.
    20. Sabine Messner, 1997. "Endogenized technological learning in an energy systems model," Journal of Evolutionary Economics, Springer, vol. 7(3), pages 291-313.
    21. Neij, Lena, 1997. "Use of experience curves to analyse the prospects for diffusion and adoption of renewable energy technology," Energy Policy, Elsevier, vol. 25(13), pages 1099-1107, November.
    22. Johnson, Nils & Strubegger, Manfred & McPherson, Madeleine & Parkinson, Simon C. & Krey, Volker & Sullivan, Patrick, 2017. "A reduced-form approach for representing the impacts of wind and solar PV deployment on the structure and operation of the electricity system," Energy Economics, Elsevier, vol. 64(C), pages 651-664.
    23. Martinsen, Dag & Krey, Volker & Markewitz, Peter, 2007. "Implications of high energy prices for energy system and emissions--The response from an energy model for Germany," Energy Policy, Elsevier, vol. 35(9), pages 4504-4515, September.
    24. Formaneck, Steven D. & Cozzarin, Brian P., 2013. "Technology adoption and training practices as a constrained shortest path problem," Omega, Elsevier, vol. 41(2), pages 459-472.
    25. Alan P. Kirman, 1992. "Whom or What Does the Representative Individual Represent?," Journal of Economic Perspectives, American Economic Association, vol. 6(2), pages 117-136, Spring.
    26. Ma, T. & Grubler, A. & Nakamori, Y., 2009. "Modeling technology adoptions for sustainable development under increasing returns, uncertainty, and heterogeneous agents," European Journal of Operational Research, Elsevier, vol. 195(1), pages 296-306, May.
    27. Tieju Ma, 2010. "Coping with Uncertainties in Technological Learning," Management Science, INFORMS, vol. 56(1), pages 192-201, January.
    28. Per Krusell & Anthony A. Smith & Jr., 1998. "Income and Wealth Heterogeneity in the Macroeconomy," Journal of Political Economy, University of Chicago Press, vol. 106(5), pages 867-896, October.
    29. Leonardo Barreto, Socrates Kypreos, 2002. "Multi-regional technological learning in the energysystems MARKAL model," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 17(3), pages 189-213.
    30. Gritsevskyi, Andrii & Nakicenovi, Nebojsa, 2000. "Modeling uncertainty of induced technological change," Energy Policy, Elsevier, vol. 28(13), pages 907-921, November.
    31. Leibowicz, Benjamin D. & Krey, Volker & Grubler, Arnulf, 2016. "Representing spatial technology diffusion in an energy system optimization model," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 350-363.
    32. Keppo, Ilkka & Strubegger, Manfred, 2010. "Short term decisions for long term problems – The effect of foresight on model based energy systems analysis," Energy, Elsevier, vol. 35(5), pages 2033-2042.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Huayi & Ma, Tieju, 2021. "Technology adoption and carbon emissions with dynamic trading among heterogeneous agents," Energy Economics, Elsevier, vol. 99(C).
    2. Fang, Chenhao & Ma, Tieju, 2020. "Stylized agent-based modeling on linking emission trading systems and its implications for China's practice," Energy Economics, Elsevier, vol. 92(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Huayi & Ma, Tieju, 2021. "Technology adoption and carbon emissions with dynamic trading among heterogeneous agents," Energy Economics, Elsevier, vol. 99(C).
    2. Chen, Huayi & Ma, Tieju, 2017. "Optimizing systematic technology adoption with heterogeneous agents," European Journal of Operational Research, Elsevier, vol. 257(1), pages 287-296.
    3. Chen, Huayi & Ma, Tieju, 2014. "Technology adoption with limited foresight and uncertain technological learning," European Journal of Operational Research, Elsevier, vol. 239(1), pages 266-275.
    4. Ma, Tieju & Chen, Huayi, 2015. "Adoption of an emerging infrastructure with uncertain technological learning and spatial reconfiguration," European Journal of Operational Research, Elsevier, vol. 243(3), pages 995-1003.
    5. Loschel, Andreas, 2002. "Technological change in economic models of environmental policy: a survey," Ecological Economics, Elsevier, vol. 43(2-3), pages 105-126, December.
    6. Chi, Chunjie & Ma, Tieju & Zhu, Bing, 2012. "Towards a low-carbon economy: Coping with technological bifurcations with a carbon tax," Energy Economics, Elsevier, vol. 34(6), pages 2081-2088.
    7. Chenhao Fang & Tieju Ma, 2021. "Technology adoption with carbon emission trading mechanism: modeling with heterogeneous agents and uncertain carbon price," Annals of Operations Research, Springer, vol. 300(2), pages 577-600, May.
    8. DeCarolis, Joseph & Daly, Hannah & Dodds, Paul & Keppo, Ilkka & Li, Francis & McDowall, Will & Pye, Steve & Strachan, Neil & Trutnevyte, Evelina & Usher, Will & Winning, Matthew & Yeh, Sonia & Zeyring, 2017. "Formalizing best practice for energy system optimization modelling," Applied Energy, Elsevier, vol. 194(C), pages 184-198.
    9. Karali, Nihan & Park, Won Young & McNeil, Michael, 2017. "Modeling technological change and its impact on energy savings in the U.S. iron and steel sector," Applied Energy, Elsevier, vol. 202(C), pages 447-458.
    10. Tieju Ma, 2010. "Coping with Uncertainties in Technological Learning," Management Science, INFORMS, vol. 56(1), pages 192-201, January.
    11. Keppo, Ilkka & Strubegger, Manfred, 2010. "Short term decisions for long term problems – The effect of foresight on model based energy systems analysis," Energy, Elsevier, vol. 35(5), pages 2033-2042.
    12. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    13. Berglund, Christer & Soderholm, Patrik, 2006. "Modeling technical change in energy system analysis: analyzing the introduction of learning-by-doing in bottom-up energy models," Energy Policy, Elsevier, vol. 34(12), pages 1344-1356, August.
    14. Gillingham, Kenneth & Newell, Richard G. & Pizer, William A., 2008. "Modeling endogenous technological change for climate policy analysis," Energy Economics, Elsevier, vol. 30(6), pages 2734-2753, November.
    15. Dosi, Giovanni & Nelson, Richard R., 2010. "Technical Change and Industrial Dynamics as Evolutionary Processes," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 1, chapter 0, pages 51-127, Elsevier.
    16. Ding, Suiting & Zhang, Ming & Song, Yan, 2019. "Exploring China's carbon emissions peak for different carbon tax scenarios," Energy Policy, Elsevier, vol. 129(C), pages 1245-1252.
    17. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    18. Zhang, Shuwei & Bauer, Nico & Yin, Guangzhi & Xie, Xi, 2020. "Technology learning and diffusion at the global and local scales: A modeling exercise in the REMIND model," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    19. Giovanni Dosi & Richard Nelson, 2013. "The Evolution of Technologies: An Assessment of the State-of-the-Art," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 3(1), pages 3-46, June.
    20. Ma, Tieju & Nakamori, Yoshiteru, 2009. "Modeling technological change in energy systems – From optimization to agent-based modeling," Energy, Elsevier, vol. 34(7), pages 873-879.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jomega:v:89:y:2019:i:c:p:257-270. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.