IDEAS home Printed from https://ideas.repec.org/a/bla/sysdyn/v34y2018i1-2p284-326.html
   My bibliography  Save this article

Approximating individual interactions in compartmental system dynamics models

Author

Listed:
  • P. J. Lamberson

Abstract

This paper introduces an approach known as a “pair approximation” developed in physics and mathematical biology that captures some of the effects of network structure in a compartmental system dynamics (CSD) model without substantially increasing model complexity or computational efficiency. We describe the approach using a familiar example: the standard SIS model from epidemiology that underpins many models from the marketing and innovation diffusion literature. We show that the pair approximation better approximates the results of an agent‐based simulation than the standard CSD. The model provides insights regarding the epidemic threshold, with implications for halting the spread of a disease or encouraging the spread of a product, that cannot be obtained from the standard CSD. We further illustrate the technique through the development of two new CSD models: a model of social contagion, SIS2, that allows for “social recovery,” and a model of technology adoption with local network effects. Copyright © 2018 System Dynamics Society

Suggested Citation

  • P. J. Lamberson, 2018. "Approximating individual interactions in compartmental system dynamics models," System Dynamics Review, System Dynamics Society, vol. 34(1-2), pages 284-326, January.
    • Handle: RePEc:bla:sysdyn:v:34:y:2018:i:1-2:p:284-326
    DOI: 10.1002/sdr.1599
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/sdr.1599
    Download Restriction: no
    File URL: https://libkey.io/10.1002/sdr.1599?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
    ---><---

    References listed on IDEAS

    as
    1. P.J. Lamberson & Scott E. Page, 2018. "First mover or higher quality? Optimal product strategy in markets with positive feedbacks," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 27(1), pages 40-52, March.
    2. Jackson Matthew O. & Rogers Brian W., 2007. "Relating Network Structure to Diffusion Properties through Stochastic Dominance," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 7(1), pages 1-16, February.
    3. P.J. Lamberson, 2016. "Winner-take-all or long tail? A behavioral model of markets with increasing returns," System Dynamics Review, System Dynamics Society, vol. 32(3-4), pages 233-260, July.
    4. Matthew O. Jackson & Brian W. Rogers & Yves Zenou, 2017. "The Economic Consequences of Social-Network Structure," Journal of Economic Literature, American Economic Association, vol. 55(1), pages 49-95, March.
    5. 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.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. John H. Miller & Scott E. Page, 2007. "Social Science in Between, from Complex Adaptive Systems: An Introduction to Computational Models of Social Life," Introductory Chapters, in: Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press.
    8. López-Pintado, Dunia, 2008. "Diffusion in complex social networks," Games and Economic Behavior, Elsevier, vol. 62(2), pages 573-590, March.
    9. John H. Miller & Scott E. Page, 2007. "Complexity in Social Worlds, from Complex Adaptive Systems: An Introduction to Computational Models of Social Life," Introductory Chapters, in: Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press.
    10. Hazhir Rahmandad & John Sterman, 2008. "Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models," Management Science, INFORMS, vol. 54(5), pages 998-1014, May.
    11. Mark Granovetter, 2005. "The Impact of Social Structure on Economic Outcomes," Journal of Economic Perspectives, American Economic Association, vol. 19(1), pages 33-50, Winter.
    12. Eocman Lee & Jeho Lee & Jongseok Lee, 2006. "Reconsideration of the Winner-Take-All Hypothesis: Complex Networks and Local Bias," Management Science, INFORMS, vol. 52(12), pages 1838-1848, December.
    Full references (including those not matched with items on IDEAS)

    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. Li, Francis G.N. & Trutnevyte, Evelina & Strachan, Neil, 2015. "A review of socio-technical energy transition (STET) models," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 290-305.
    2. Giovanni Pegoretti & Francesco Rentocchini & Giuseppe Vittucci Marzetti, 2012. "An agent-based model of innovation diffusion: network structure and coexistence under different information regimes," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 145-165, October.
    3. Wolfram Elsner, 2019. "Policy and state in complexity economics," Chapters, in: Nikolaos Karagiannis & John E. King (ed.), A Modern Guide to State Intervention, chapter 1, pages 13-48, Edward Elgar Publishing.
    4. P.J. Lamberson, 2016. "Winner-take-all or long tail? A behavioral model of markets with increasing returns," System Dynamics Review, System Dynamics Society, vol. 32(3-4), pages 233-260, July.
    5. Jeehong Kim & Wonchang Hur, 2013. "Diffusion of competing innovations in influence networks," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 109-124, April.
    6. Paolo Zeppini & Koen Frenken & Luis R. Izquierdo, 2013. "Innovation diffusion in networks: the microeconomics of percolation," Working Papers 13-02, Eindhoven Center for Innovation Studies, revised Feb 2013.
    7. Rand, William & Rust, Roland T., 2011. "Agent-based modeling in marketing: Guidelines for rigor," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 181-193.
    8. Chang, Sungyong & Park, Sanghyun, 2021. "Borders of Network Effects and Early Internationalization as a Latecomer Strategy," SocArXiv d74he, Center for Open Science.
    9. Amini, Mehdi & Wakolbinger, Tina & Racer, Michael & Nejad, Mohammad G., 2012. "Alternative supply chain production–sales policies for new product diffusion: An agent-based modeling and simulation approach," European Journal of Operational Research, Elsevier, vol. 216(2), pages 301-311.
    10. Elsner, Wolfram, 2015. "Policy Implications of Economic Complexity and Complexity Economics," MPRA Paper 63252, University Library of Munich, Germany.
    11. Navid Ghaffarzadegan & Richard C. Larson, 2018. "SD meets OR: a new synergy to address policy problems," System Dynamics Review, System Dynamics Society, vol. 34(1-2), pages 327-353, January.
    12. Desmarchelier, Benoît & Fang, Eddy S., 2016. "National culture and innovation diffusion. Exploratory insights from agent-based modeling," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 121-128.
    13. Michel Grabisch & Agnieszka Rusinowska & Xavier Venel, 2019. "Diffusion in countably infinite networks," Documents de travail du Centre d'Economie de la Sorbonne 19017, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    14. Gadi Fibich & Ro'i Gibori, 2010. "Aggregate Diffusion Dynamics in Agent-Based Models with a Spatial Structure," Operations Research, INFORMS, vol. 58(5), pages 1450-1468, October.
    15. Luis R. Izquierdo & Segismundo S. Izquierdo & José Manuel Galán & José Ignacio Santos, 2009. "Techniques to Understand Computer Simulations: Markov Chain Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-6.
    16. JeongWook Son, 2022. "Complexity and Dynamics in Construction Project Organizations," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    17. Vahideh Manshadi & Sidhant Misra & Scott Rodilitz, 2020. "Diffusion in Random Networks: Impact of Degree Distribution," Operations Research, INFORMS, vol. 68(6), pages 1722-1741, November.
    18. Adão, Luiz F.S. & Silveira, Douglas & Ely, Regis A. & Cajueiro, Daniel O., 2022. "The impacts of interest rates on banks’ loan portfolio risk-taking," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    19. Cerdeiro, Diego A., 2017. "Contagion exposure and protection technology," Games and Economic Behavior, Elsevier, vol. 105(C), pages 230-254.
    20. Huotari, Pontus & Järvi, Kati & Kortelainen, Samuli & Huhtamäki, Jukka, 2017. "Winner does not take all: Selective attention and local bias in platform-based markets," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 313-326.

    More about this item

    Statistics

    Access and download statistics

    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:bla:sysdyn:v:34:y:2018:i:1-2:p:284-326. 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: Wiley Content Delivery (email available below). General contact details of provider: http://onlinelibrary.wiley.com/journal/10.1111/0883-7066 .

    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.