IDEAS home Printed from https://ideas.repec.org/p/uto/dipeco/201429.html
   My bibliography  Save this paper

Transitivity matters. Norms Enforcement and diffusion using different neighborhoods in CAs

Author

Listed:

Abstract

The study of norms' self-enforcement and diffusion is one of the most acknowledged application of ABMs. Peer pressure, limited knowledge and communication channels are some of the most accounted elements in this kind of modelization, and for these same reasons, cellular automata models are very popular for the subject. A very interesting model in this ambit is Centola, et al. (2005). "The Emperor's New Clothes", the popular fable, is used as example of a society where stable compliance to a norm that the majority does not want to observe is made possible by the presence of a committed minority that triggers compliance cascades through peer-pressure. This paper, starting from the original code, unfolds the concept of "cascade” phenomena. Changing the order of procedure and especially the neighborhood structure is not only a way to test results robustness; the transitivity structure of two different neighborhoods (Von Neumann and Moore neighborhood), on which the local rule is constructed, develops completely different emergent results, under similar initial conditions. Results from this work give insights on how code design strongly changes outcomes interpretation, in particular the concepts of “cascade” and “diffusion”.

Suggested Citation

  • Bertazzi, Ilaria, 2014. "Transitivity matters. Norms Enforcement and diffusion using different neighborhoods in CAs," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201429, University of Turin.
    • Handle: RePEc:uto:dipeco:201429
    as

    Download full text from publisher

    File URL: http://www.est.unito.it/do/home.pl/Download?doc=/allegati/wp2014dip/wp_29_2014.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. J. Gareth Polhill, 2010. "ODD Updated," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(4), pages 1-9.
    2. Robert Axelrod, 1997. "Advancing the Art of Simulation in the Social Sciences," Working Papers 97-05-048, Santa Fe Institute.
    3. Andreas Flache & Rainer Hegselmann, 2001. "Do Irregular Grids Make a Difference? Relaxing the Spatial Regularity Assumption in Cellular Models of Social Dynamics," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 4(4), pages 1-6.
    4. David F. Batten, 2001. "Complex landscapes of spatial interaction," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 35(1), pages 81-111.
    5. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    6. Arvid Oskar Ivar Hoffmann & Wander Jager & J. H. Von Eije, 2007. "Social Simulation of Stock Markets: Taking It to the Next Level," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-7.
    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. Ulfia A. Lenfers & Julius Weyl & Thomas Clemen, 2018. "Firewood Collection in South Africa: Adaptive Behavior in Social-Ecological Models," Land, MDPI, vol. 7(3), pages 1-17, August.
    2. 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.
    3. Sina Hocke & Matthias Meyer & Iris Lorscheid, 2015. "Improving simulation model analysis and communication via design of experiment principles: an example from the simulation-based design of cost accounting systems," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 26(2), pages 131-155, August.
    4. Rianne Duinen & Tatiana Filatova & Wander Jager & Anne Veen, 2016. "Going beyond perfect rationality: drought risk, economic choices and the influence of social networks," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 57(2), pages 335-369, November.
    5. Francis Tseng & Fei Liu & Bernardo Alves Furtado, 2017. "Humans of Simulated New York (HOSNY): an exploratory comprehensive model of city life," Papers 1703.05240, arXiv.org, revised Mar 2017.
    6. Hassani-Mahmooei, Behrooz & Parris, Brett W., 2013. "Resource scarcity, effort allocation and environmental security: An agent-based theoretical approach," Economic Modelling, Elsevier, vol. 30(C), pages 183-192.
    7. Matthew Oldham, 2019. "Understanding How Short-Termism and a Dynamic Investor Network Affects Investor Returns: An Agent-Based Perspective," Complexity, Hindawi, vol. 2019, pages 1-21, July.
    8. Tardy, Olivia & Lenglos, Christophe & Lai, Sandra & Berteaux, Dominique & Leighton, Patrick A., 2023. "Rabies transmission in the Arctic: An agent-based model reveals the effects of broad-scale movement strategies on contact risk between Arctic foxes," Ecological Modelling, Elsevier, vol. 476(C).
    9. Matteo Richiardi, 2003. "The Promises and Perils of Agent-Based Computational Economics," LABORatorio R. Revelli Working Papers Series 29, LABORatorio R. Revelli, Centre for Employment Studies.
    10. Vimercati, Giovanni & Hui, Cang & Davies, Sarah J. & Measey, G. John, 2017. "Integrating age structured and landscape resistance models to disentangle invasion dynamics of a pond-breeding anuran," Ecological Modelling, Elsevier, vol. 356(C), pages 104-116.
    11. Lovric, M. & Kaymak, U. & Spronk, J., 2008. "A Conceptual Model of Investor Behavior," ERIM Report Series Research in Management ERS-2008-030-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    12. Jagadish, Arundhati & Dwivedi, Puneet & McEntire, Kira D. & Chandar, Mamta, 2019. "Agent-based modeling of “cleaner” cookstove adoption and woodfuel use: An integrative empirical approach," Forest Policy and Economics, Elsevier, vol. 106(C), pages 1-1.
    13. Hinker, Jonas & Hemkendreis, Christian & Drewing, Emily & März, Steven & Hidalgo Rodríguez, Diego I. & Myrzik, Johanna M.A., 2017. "A novel conceptual model facilitating the derivation of agent-based models for analyzing socio-technical optimality gaps in the energy domain," Energy, Elsevier, vol. 137(C), pages 1219-1230.
    14. Tianran Ding & Wouter Achten, 2023. "Coupling agent-based modeling with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/359527, ULB -- Universite Libre de Bruxelles.
    15. Jascha-Alexander Koch & Jens Lausen & Moritz Kohlhase, 2021. "Internalizing the externalities of overfunding: an agent-based model approach for analyzing the market dynamics on crowdfunding platforms," Journal of Business Economics, Springer, vol. 91(9), pages 1387-1430, November.
    16. Crevier, Lucas Phillip & Salkeld, Joseph H & Marley, Jessa & Parrott, Lael, 2021. "Making the best possible choice: Using agent-based modelling to inform wildlife management in small communities," Ecological Modelling, Elsevier, vol. 446(C).
    17. Günter Küppers & Johannes Lenhard, 2005. "Validation of Simulation: Patterns in the Social and Natural Sciences," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-3.
    18. Andrew W. Bausch, 2014. "Evolving intergroup cooperation," Computational and Mathematical Organization Theory, Springer, vol. 20(4), pages 369-393, December.
    19. David, Viviane & Joachim, Sandrine & Tebby, Cleo & Porcher, Jean-Marc & Beaudouin, Rémy, 2019. "Modelling population dynamics in mesocosms using an individual-based model coupled to a bioenergetics model," Ecological Modelling, Elsevier, vol. 398(C), pages 55-66.
    20. Lorscheid, Iris & Meyer, Matthias, 2016. "Divide and conquer: Configuring submodels for valid and efficient analyses of complex simulation models," Ecological Modelling, Elsevier, vol. 326(C), pages 152-161.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:uto:dipeco:201429. 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: Laura Ballestra or Cinzia Carlevaris (email available below). General contact details of provider: https://edirc.repec.org/data/detorit.html .

    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.