IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v15y2013i2p497-510.html
   My bibliography  Save this article

Scientific understanding of stakeholders’ behavior in mining community

Author

Listed:
  • Masami Nakagawa
  • Kyle Bahr
  • Derek Levy

Abstract

We attempt to understand, scientifically, how different members of the mining concession, impacted communities, and government authorities behave when a conflicting situation arises. The main purpose of our effort is to start developing a framework for the scientific modeling of stakeholders’ behavior, and we create a reality-driven generic scenario of conflict. We assume that the managers and superintendants of a mining operation currently envision a problem; one that tests the limits of the commitment of the company’s mission statement, and of the spectrum of actions taken which are embedded in the “culture” of the company’s corporate social responsibility. It is an “event” that highlights the nature of an overall problem that the company would like to predict and act proactively: the integration of scientific tools, sustainability, and cultural realities within a mining framework. We adapt an agent-based modeling approach and start with a theoretical understanding of certain social behavior, build a model, and simulate “what if” scenarios to understand its dynamics to gain a better insight of the complexity of a seemingly simple social system of interest. Copyright Springer Science+Business Media B.V. 2013

Suggested Citation

  • Masami Nakagawa & Kyle Bahr & Derek Levy, 2013. "Scientific understanding of stakeholders’ behavior in mining community," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 15(2), pages 497-510, April.
    • Handle: RePEc:spr:endesu:v:15:y:2013:i:2:p:497-510
    DOI: 10.1007/s10668-012-9389-x
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10668-012-9389-x
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10668-012-9389-x?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. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, April.
    2. Nunberg, Barbara & Barma, Naazneen & Abdollahian, Mark & Green, Amanda & Perlman, Deborah, 2010. "At the frontier of practical political economy : operationalizing an agent-based stakeholder model in the World Bank's East Asia and Pacific Region," Policy Research Working Paper Series 5176, The World Bank.
    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. Luís de Sousa & Alberto Rodrigues da Silva, 2015. "Showcasing a Domain Specific Language for Spatial Simulation Scenarios with case studies," ERSA conference papers ersa15p1044, European Regional Science Association.
    2. Ross Richardson & Matteo G. Richiardi & Michael Wolfson, 2015. "We ran one billion agents. Scaling in simulation models," LABORatorio R. Revelli Working Papers Series 142, LABORatorio R. Revelli, Centre for Employment Studies.
    3. Cincotti, Silvano & Raberto, Marco & Teglio, Andrea, 2010. "Credit money and macroeconomic instability in the agent-based model and simulator Eurace," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 4, pages 1-32.
    4. Joshua M. Epstein, 2007. "Agent-Based Computational Models and Generative Social Science," Introductory Chapters, in: Generative Social Science Studies in Agent-Based Computational Modeling, Princeton University Press.
    5. Rich, Karl M. & Ross, R. Brent & Baker, A. Derek & Negassa, Asfaw, 2011. "Quantifying value chain analysis in the context of livestock systems in developing countries," Food Policy, Elsevier, vol. 36(2), pages 214-222, April.
    6. Laobing Zhang & Gabriele Landucci & Genserik Reniers & Nima Khakzad & Jianfeng Zhou, 2018. "DAMS: A Model to Assess Domino Effects by Using Agent‐Based Modeling and Simulation," Risk Analysis, John Wiley & Sons, vol. 38(8), pages 1585-1600, August.
    7. Luca Riccetti & Alberto Russo & Mauro Gallegati, 2015. "An agent based decentralized matching macroeconomic model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(2), pages 305-332, October.
    8. Yanuar Nugroho & Gindo Tampubolon, 2008. "Network Dynamics in the Transition to Democracy: Mapping Global Networks of Contemporary Indonesian Civil Society," Sociological Research Online, , vol. 13(5), pages 144-160, September.
    9. Barr, Jason & Saraceno, Francesco, 2009. "Organization, learning and cooperation," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 39-53, May.
    10. Sheri M. Markose, 2005. "Computability and Evolutionary Complexity: Markets as Complex Adaptive Systems (CAS)," Economic Journal, Royal Economic Society, vol. 115(504), pages 159-192, 06.
    11. Nannen, Volker & van den Bergh, Jeroen C. J. M. & Eiben, A. E., 2008. "Impact of Environmental Dynamics on Economic Evolution: Uncertainty, Risk Aversion, and Policy," MPRA Paper 13834, University Library of Munich, Germany.
    12. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    13. G. Fagiolo & G. Dosi & R. Gabriele, 2004. "Matching, Bargaining, And Wage Setting In An Evolutionary Model Of Labor Market And Output Dynamics," World Scientific Book Chapters, in: Roberto Leombruni & Matteo Richiardi (ed.), Industry And Labor Dynamics The Agent-Based Computational Economics Approach, chapter 5, pages 59-89, World Scientific Publishing Co. Pte. Ltd..
    14. Gräbner, Claudius, 2016. "From realism to instrumentalism - and back? Methodological implications of changes in the epistemology of economics," MPRA Paper 71933, University Library of Munich, Germany.
    15. Loet Leydesdorff, 2015. "Can intellectual processes in the sciences also be simulated? The anticipation and visualization of possible future states," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2197-2214, December.
    16. Tamotsu Onozaki, 2018. "Nonlinearity, Bounded Rationality, and Heterogeneity," Springer Books, Springer, number 978-4-431-54971-0, February.
    17. Ricetti, Luca & Russo, Alberto & Gallegati, Mauro, 2013. "Unemployment benefits and financial leverage in an agent based macroeconomic model," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 7, pages 1-44.
    18. Setsuya Kurahashi & Takao Terano, 2008. "Historical Simulation: A Study Of Civil Service Examinations, The Family Line And Cultural Capital In China," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 187-198.
    19. Stefan Gold & Thomas Chesney & Tim Gruchmann & Alexander Trautrims, 2020. "Diffusion of labor standards through supplier–subcontractor networks: An agent‐based model," Journal of Industrial Ecology, Yale University, vol. 24(6), pages 1274-1286, December.
    20. Giannoccaro, Ilaria, 2015. "Adaptive supply chains in industrial districts: A complexity science approach focused on learning," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 576-589.

    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:spr:endesu:v:15:y:2013:i:2:p:497-510. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.