IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i16p9370-d618448.html
   My bibliography  Save this article

Adding Emergence and Spatiality to a Public Bad Game for Studying Dynamics in Socio-Ecological Systems (Part I): The Design of Musa-Game for Integrative Analysis of Collective Action in Banana Disease Management

Author

Listed:
  • Julissa Alexandra Galarza-Villamar

    (Knowledge, Technology and Innovation Group, Wageningen University & Research, Hollandseweg 1, 6706 KN Wageningen, The Netherlands
    Escuela Superior Politécnica del Litoral, ESPOL Polytechnic University, Guayaquil 090150, Ecuador)

  • Mariette McCampbell

    (Knowledge, Technology and Innovation Group, Wageningen University & Research, Hollandseweg 1, 6706 KN Wageningen, The Netherlands)

  • Cees Leeuwis

    (Knowledge, Technology and Innovation Group, Wageningen University & Research, Hollandseweg 1, 6706 KN Wageningen, The Netherlands)

  • Francesco Cecchi

    (Development Economics Group, Wageningen University & Research, Hollandseweg 1, 6706 KN Wageningen, The Netherlands)

Abstract

Human decision-making plays a critical and challenging role in the prevention and control of public bads within socio-ecological systems. Farmers daily confront dilemmas regarding public bad management, such as infectious diseases in their crops. Their decisions interplay with multiple factors and may create the risk conditions in which a public bad can occur (e.g., a disease outbreak). This article presents an experimental board game method (DySE) and its contextualized version (Musa-game) to study the effect of individual and collective human actions on creating or preventing a public bad. The DySE method and the Musa-game add emergence and spatiality (both attributes of SES) to the study of public bads and collective action problems. This methodological proposal allows us to build a contextual understanding of how individual and collective actions of various entities lead to typical system outcomes, i.e., conditions that are (un)favourable to pathogens, and individual decisions about infectious disease management. To conceptualize our method, we used the case of Banana Xanthomonas Wilt disease in Rwanda. This research is published as a diptych. Part I (this article) covers the conceptualization and design of Musa-game. Part II presents empirical findings from testing Musa-game with farmers in Rwanda and recommendations for using the method.

Suggested Citation

  • Julissa Alexandra Galarza-Villamar & Mariette McCampbell & Cees Leeuwis & Francesco Cecchi, 2021. "Adding Emergence and Spatiality to a Public Bad Game for Studying Dynamics in Socio-Ecological Systems (Part I): The Design of Musa-Game for Integrative Analysis of Collective Action in Banana Disease," Sustainability, MDPI, vol. 13(16), pages 1-21, August.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9370-:d:618448
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/16/9370/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/16/9370/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marta G. Rivera-Ferre & Miguel Ortega-Cerdà & Johann Baumgärtner, 2013. "Rethinking Study and Management of Agricultural Systems for Policy Design," Sustainability, MDPI, vol. 5(9), pages 1-18, September.
    2. Thomas Tanner & David Lewis & David Wrathall & Robin Bronen & Nick Cradock-Henry & Saleemul Huq & Chris Lawless & Raphael Nawrotzki & Vivek Prasad & Md. Ashiqur Rahman & Ryan Alaniz & Katherine King &, 2015. "Livelihood resilience in the face of climate change," Nature Climate Change, Nature, vol. 5(1), pages 23-26, January.
    3. Sonnemans, Joep & Schram, Arthur & Offerman, Theo, 1998. "Public good provision and public bad prevention: The effect of framing," Journal of Economic Behavior & Organization, Elsevier, vol. 34(1), pages 143-161, January.
    4. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    5. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    6. Chater, Nick & Loewenstein, George, 2016. "The under-appreciated drive for sense-making," Journal of Economic Behavior & Organization, Elsevier, vol. 126(PB), pages 137-154.
    7. E. Ostrom, 2010. "A Behavioral Approach to the Rational Choice Theory of Collective Action Presidential Address, American political Science Association, 1997," Public administration issues, Higher School of Economics, issue 1, pages 5-52.
    8. Joshua C. Hall & Sara Harper (ed.), 2019. "Economic and Political Institutions and Development," Springer Books, Springer, number 978-3-030-06049-7, September.
    9. Gisela Wachinger & Ortwin Renn & Chloe Begg & Christian Kuhlicke, 2013. "The Risk Perception Paradox—Implications for Governance and Communication of Natural Hazards," Risk Analysis, John Wiley & Sons, vol. 33(6), pages 1049-1065, June.
    10. Stefano Balbi & Carlo Giupponi, 2009. "Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability," Working Papers 2009_15, Department of Economics, University of Venice "Ca' Foscari".
    11. Hossein Sabzian & Mohammad Ali Shafia & Ali Maleki & Seyeed Mostapha Seyeed Hashemi & Ali Baghaei & Hossein Gharib, 2019. "Theories and Practice of Agent based Modeling: Some practical Implications for Economic Planners," Papers 1901.08932, arXiv.org.
    12. Meinzen-Dick, Ruth & DiGregorio, Monica & McCarthy, Nancy, 2004. "Methods for studying collective action in rural development," Agricultural Systems, Elsevier, vol. 82(3), pages 197-214, December.
    13. Ostrom, Elinor, 2006. "The value-added of laboratory experiments for the study of institutions and common-pool resources," Journal of Economic Behavior & Organization, Elsevier, vol. 61(2), pages 149-163, October.
    14. Castillo, Daniel & Bousquet, François & Janssen, Marco A. & Worrapimphong, Kobchai & Cardenas, Juan Camillo, 2011. "Context matters to explain field experiments: Results from Colombian and Thai fishing villages," Ecological Economics, Elsevier, vol. 70(9), pages 1609-1620, July.
    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. Julissa Alexandra Galarza-Villamar & Mariette McCampbell & Andres Galarza-Villamar & Cees Leeuwis & Francesco Cecchi & John Galarza-Rodrigo, 2021. "A Public Bad Game Method to Study Dynamics in Socio-Ecological Systems (Part II): Results of Testing Musa-Game in Rwanda and Adding Emergence and Spatiality to the Analysis," Sustainability, MDPI, vol. 13(16), pages 1-27, August.

    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. Röttgers, Dirk, 2016. "Conditional cooperation, context and why strong rules work — A Namibian common-pool resource experiment," Ecological Economics, Elsevier, vol. 129(C), pages 21-31.
    2. Speelman, E.N. & García-Barrios, L.E. & Groot, J.C.J. & Tittonell, P., 2014. "Gaming for smallholder participation in the design of more sustainable agricultural landscapes," Agricultural Systems, Elsevier, vol. 126(C), pages 62-75.
    3. Yoo, Seung Han, 2014. "Learning a population distribution," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 188-201.
    4. Therese Lindahl & Anne-Sophie Crépin & Caroline Schill, 2016. "Potential Disasters can Turn the Tragedy into Success," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 65(3), pages 657-676, November.
    5. Sarah Wolf & Steffen Fürst & Antoine Mandel & Wiebke Lass & Daniel Lincke & Federico Pablo-Marti & Carlo Jaeger, 2013. "A multi-agent model of several economic regions," PSE - Labex "OSE-Ouvrir la Science Economique" halshs-00825217, HAL.
    6. Moulet, Sonia & Rouchier, Juliette, 2008. "The influence of seller learning and time constraints on sequential bargaining in an artificial perishable goods market," Journal of Economic Dynamics and Control, Elsevier, vol. 32(7), pages 2322-2348, July.
    7. Giuseppe Attanasi & Samuele Centorrino & Ivan Moscati, 2011. "Double Auction Equilibrium and Efficiency in a Classroom Experimental Search Market," LERNA Working Papers 11.03.337, LERNA, University of Toulouse.
    8. Michael Pickhardt, "undated". "A few can do – Ethical behavior and the provision of public goods in an agent-based model," Working Papers 201037, Institute of Spatial and Housing Economics, Munster Universitary.
    9. Michael Neugart & Matteo G. Richiardi, 2012. "Agent-based models of the labor market," LABORatorio R. Revelli Working Papers Series 125, LABORatorio R. Revelli, Centre for Employment Studies.
    10. Olivier Brandouy & Angelo Corelli & Iryna Veryzhenko & Roger Waldeck, 2012. "A re-examination of the “zero is enough” hypothesis in the emergence of financial stylized facts," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 223-248, October.
    11. Mikhail Anufriev & Jasmina Arifovic & John Ledyard & Valentyn Panchenko, 2013. "Efficiency of continuous double auctions under individual evolutionary learning with full or limited information," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 539-573, July.
    12. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    13. Shu‐Heng Chen & Shu G. Wang, 2011. "Emergent Complexity In Agent‐Based Computational Economics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(3), pages 527-546, July.
    14. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    15. Daniel A. DeCaro & Marco A. Janssen & Allen Lee, 2015. "Synergistic effects of voting and enforcement on internalized motivation to cooperate in a resource dilemma," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 10(6), pages 511-537, November.
    16. 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.
    17. Hommes, Cars, 2011. "The heterogeneous expectations hypothesis: Some evidence from the lab," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 1-24, January.
    18. Takahiro Ezaki & Yutaka Horita & Masanori Takezawa & Naoki Masuda, 2016. "Reinforcement Learning Explains Conditional Cooperation and Its Moody Cousin," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-13, July.
    19. repec:wyi:journl:002151 is not listed on IDEAS
    20. Frank M. A. Klingert & Matthias Meyer, 2012. "Effectively combining experimental economics and multi-agent simulation: suggestions for a procedural integration with an example from prediction markets research," Computational and Mathematical Organization Theory, Springer, vol. 18(1), pages 63-90, March.
    21. Torres-Guevara, Luz Elba & Schlüter, Achim, 2016. "External validity of artefactual field experiments: A study on cooperation, impatience and sustainability in an artisanal fishery in Colombia," Ecological Economics, Elsevier, vol. 128(C), pages 187-201.

    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:gam:jsusta:v:13:y:2021:i:16:p:9370-:d:618448. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.