IDEAS home Printed from https://ideas.repec.org/a/zna/indecs/v5y2007i2p138-150.html
   My bibliography  Save this article

Modeling local monetary flows in poor regions: A research setup to simulate the multiplier effect in local economies

Author

Listed:
  • Rinke C. Hoekstra

    (STRO Foundation)

  • Henk van Arkel

    (STRO Foundation)

  • Bas Leurs

    (STRO Foundation)

Abstract

In poor regions, lack of local monetary circulation is one of the key elements causing underdevelopment. The more incoming money is passed from hand to hand, the more the local economy will be stimulated. However, in most poor areas money is spent outside the community before circulating locally, reducing the effectiveness of money inflow dramatically. Development programs would increase their effectiveness if knowledge was available on how spending money could lead to optimized and prolonged local circulation. To gain this knowledge a simulation tool will be created, which is able to analyze financial flows, to evaluate the potency of specific actions aimed on local development, and to monitor a development scheme during the execution phase. The basic model will be developed through a multi-agent approach, where each agent represents one (or more) family/households belonging to one of several socio-economic groups. A Social Accounting Matrix (SAM) of the local economy will be used as a basis to set up a spendings matrix for each agent, defining its spending priorities. Artificial Intelligence techniques will be used to give the agent the possibility to make decisions on how to satisfy these spending priorities. Also, social dynamics, the simulation of strategic planning behavior, learning, and exchange in limited networks will be addressed. The simulation application will consist of a common user interface allowing the user to "play" the simulation. This user interface layer will be "pluggable" with the underlying programming layer responsible for the calculations on the simulation, so that different plug-ins may be used for different simulation techniques. Classification-ACM-1998: J.4

Suggested Citation

  • Rinke C. Hoekstra & Henk van Arkel & Bas Leurs, 2007. "Modeling local monetary flows in poor regions: A research setup to simulate the multiplier effect in local economies," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 5(2), pages 138-150.
  • Handle: RePEc:zna:indecs:v:5:y:2007:i:2:p:138-150
    as

    Download full text from publisher

    File URL: http://indecs.eu/2007/indecs2007-pp138-150.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    2. James B. Davies, 2004. "Microsimulation, CGE and Macro Modelling for Transition and Developing Economies," WIDER Working Paper Series DP2004-08, World Institute for Development Economic Research (UNU-WIDER).
    3. Wilhite, Allen, 2006. "Economic Activity on Fixed Networks," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 20, pages 1013-1045, Elsevier.
    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. João Bernardino & Tanya Araújo, 2013. "On positional consumption and technological innovation: an agent-based model," Journal of Evolutionary Economics, Springer, vol. 23(5), pages 1047-1071, November.
    2. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046, November.
    3. De Kamps, Marc & Ladley, Daniel & Simaitis, Aistis, 2014. "Heterogeneous beliefs in over-the-counter markets," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 50-68.
    4. Jason Barr & Troy Tassier, 2010. "Endogenous Neighborhood Selection and the Attainment of Cooperation in a Spatial Prisoner’s Dilemma Game," Computational Economics, Springer;Society for Computational Economics, vol. 35(3), pages 211-234, March.
    5. Simon Gemkow & Michael Neugart, 2011. "Referral hiring, endogenous social networks, and inequality: an agent-based analysis," Journal of Evolutionary Economics, Springer, vol. 21(4), pages 703-719, October.
    6. Dan Ladley & Seth Bullock, 2008. "The Strategic Exploitation of Limited Information and Opportunity in Networked Markets," Computational Economics, Springer;Society for Computational Economics, vol. 32(3), pages 295-315, October.
    7. Wilhite, Allen, 2014. "Network structure, games, and agent dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 47(C), pages 225-238.
    8. Jackson, Matthew O. & Zenou, Yves, 2015. "Games on Networks," Handbook of Game Theory with Economic Applications,, Elsevier.
    9. Shu-Heng Chen & Umberto Gostoli, 2017. "Coordination in the El Farol Bar problem: The role of social preferences and social networks," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(1), pages 59-93, April.
    10. Bargigli, Leonardo & Tedeschi, Gabriele, 2014. "Interaction in agent-based economics: A survey on the network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 1-15.
    11. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    12. Popoyan, Lilit & Napoletano, Mauro & Roventini, Andrea, 2017. "Taming macroeconomic instability: Monetary and macro-prudential policy interactions in an agent-based model," Journal of Economic Behavior & Organization, Elsevier, vol. 134(C), pages 117-140.
    13. Kubin, Ingrid & Zörner, Thomas O. & Gardini, Laura & Commendatore, Pasquale, 2019. "A credit cycle model with market sentiments," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 159-174.
    14. Klaus Jaffe, 2015. "Agent based simulations visualize Adam Smith's invisible hand by solving Friedrich Hayek's Economic Calculus," Papers 1509.04264, arXiv.org, revised Nov 2015.
    15. repec:hal:spmain:info:hdl:2441/5bnglqth5987gaq6dhju3psjn3 is not listed on IDEAS
    16. Zhang, Hui & Cao, Libin & Zhang, Bing, 2017. "Emissions trading and technology adoption: An adaptive agent-based analysis of thermal power plants in China," Resources, Conservation & Recycling, Elsevier, vol. 121(C), pages 23-32.
    17. 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.
    18. Paul L. Borrill & Leigh Tesfatsion, 2011. "Agent-based Modeling: The Right Mathematics for the Social Sciences?," Chapters, in: John B. Davis & D. Wade Hands (ed.), The Elgar Companion to Recent Economic Methodology, chapter 11, Edward Elgar Publishing.
    19. Liu, Beibei & He, Pan & Zhang, Bing & Bi, Jun, 2012. "Impacts of alternative allowance allocation methods under a cap-and-trade program in power sector," Energy Policy, Elsevier, vol. 47(C), pages 405-415.
    20. 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.
    21. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.

    More about this item

    Keywords

    multiplier effect; simulation; multi-agent based simulation; social accounting matrix; artificial intelligence techniques;
    All these keywords.

    JEL classification:

    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

    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:zna:indecs:v:5:y:2007:i:2:p:138-150. 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: Josip Stepanic (email available below). General contact details of provider: .

    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.