IDEAS home Printed from https://ideas.repec.org/a/ijm/journl/v4y2011i1p21-34.html
   My bibliography  Save this article

Microsimulation of Virtual Encounters: A New Methodology for the Analysis of Socio-Cultural Cleavages

Author

Listed:
  • Georg Mueller

    (University of Fribourg, Faculty of Economics and Social Science, Blvd de Perolles 90, CH-1700 Fribourg / Switzerland)

Abstract

This paper describes a new methodology for the analysis of socio-cultural conflicts in situations where actual conflict information is lacking, but survey data on socio-cultural opinions are available. The methodology is based on an innovative combination of three different approaches; Social network analysis, microsimulation, and inferential statistics. Virtual encounters within and across the borders of countries are simulated by randomly matching pairs of persons who answered the same interview questions in the European Values Study 1999/2000, but may be supposed to never have met in real life. The results of these encounters are stored as a new type of dyadic data record. Among other things, each of these dyadic records contains information about the degree of dissent between citizens with regard to various types of work-related values such as obedience to superiors, meritocratism, or work ethos. By aggregation of these simulated value conflicts it becomes possible to anticipate future conflicts within and between groups of natives and immigrants. If conflicts between natives and immigrants are stronger than the corresponding conflicts within each of the two groups, a cleavage situation i s predicted, which often results in a ghettoization of immigrated minorities. By focusing on certain categories of immigrants with similar socioeconomic status or age, the analysis can further be refined such that it may also be instrumental for conceptualizing new immigration policies. This is illustrated through an exploration of the potential consequences of Polish migration to Germany.

Suggested Citation

  • Georg Mueller, 2011. "Microsimulation of Virtual Encounters: A New Methodology for the Analysis of Socio-Cultural Cleavages," International Journal of Microsimulation, International Microsimulation Association, vol. 4(1), pages 21-34.
  • Handle: RePEc:ijm:journl:v:4:y:2011:i:1:p:21-34
    as

    Download full text from publisher

    File URL: http://ima.natsem.canberra.edu.au/IJM/V4_1/Volume%204%20Issue%201/2_IJM_2009_4_Final%20Mueller.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lynne Hamill & Nigel Gilbert, 2009. "Social Circles: A Simple Structure for Agent-Based Social Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(2), pages 1-3.
    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. Mueller, Georg P., 2016. "On the use of interview data for the microsimulation of ideological conflicts : an analysis of the political cleavages of the European left," FSES Working Papers 471, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.

    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. Marcel Ausloos & Herbert Dawid & Ugo Merlone, 2015. "Spatial Interactions in Agent-Based Modeling," Dynamic Modeling and Econometrics in Economics and Finance, in: Pasquale Commendatore & Saime Kayam & Ingrid Kubin (ed.), Complexity and Geographical Economics, edition 127, pages 353-377, Springer.
    2. Bale, Catherine S.E. & McCullen, Nicholas J. & Foxon, Timothy J. & Rucklidge, Alastair M. & Gale, William F., 2013. "Harnessing social networks for promoting adoption of energy technologies in the domestic sector," Energy Policy, Elsevier, vol. 63(C), pages 833-844.
    3. Somayeh Koohborfardhaghighi & Jorn Altmann, 2016. "How Strategic Networking Impacts the Networking Outcome: A Complex Adaptive System Approach," TEMEP Discussion Papers 2016131, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Aug 2016.
    4. Albertini, Marco & Sage, Lucas & Scherer, Stefani, 2020. "Intergenerational contacts and Covid-19 spread: Omnipresent grannies or bowling together?," SocArXiv exym8, Center for Open Science.
    5. 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.
    6. Per Block & Marion Hoffman & Isabel J. Raabe & Jennifer Beam Dowd & Charles Rahal & Ridhi Kashyap & Melinda C. Mills, 2020. "Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world," Nature Human Behaviour, Nature, vol. 4(6), pages 588-596, June.
    7. Silvia Leoni, 2022. "An Agent-Based Model for Tertiary Educational Choices in Italy," Research in Higher Education, Springer;Association for Institutional Research, vol. 63(5), pages 797-824, August.
    8. Maggi,Elena & Vallino,Elena, 2017. "An Agent-Based Simulation of Urban Passenger Mobility and Related Policies. The Case Study of an Italian Small City," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201708, University of Turin.
    9. Sascha Holzhauer & Friedrich Krebs & Andreas Ernst, 2013. "Considering baseline homophily when generating spatial social networks for agent-based modelling," Computational and Mathematical Organization Theory, Springer, vol. 19(2), pages 128-150, June.
    10. Meysam Alizadeh & Claudio Cioffi-Revilla & Andrew Crooks, 2017. "Generating and analyzing spatial social networks," Computational and Mathematical Organization Theory, Springer, vol. 23(3), pages 362-390, September.
    11. Telcs, András & Csernai, Márton & Gulyás, András, 2013. "Load balanced diffusive capture process on homophilic scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(3), pages 510-519.
    12. Jaap Sok & Egil A J Fischer, 2020. "Farmers' heterogeneous motives, voluntary vaccination and disease spread: an agent-based model," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 47(3), pages 1201-1222.
    13. Somayeh Koohborfardhaghighi & Jorn Altmann, 2016. "How Network Visibility and Strategic Networking Leads to the Emergence of Certain Network Characteristics: A Complex Adaptive System Approach," TEMEP Discussion Papers 2016130, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Aug 2016.
    14. Alistair Sutcliffe & Di Wang, 2012. "Computational Modelling of Trust and Social Relationships," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(1), pages 1-3.
    15. Dan Farhat, 2013. "An Agent-based Model of Interdisciplinary Science and the Evolution of Scientific Research Networks," Working Papers 1302, University of Otago, Department of Economics, revised Jan 2013.
    16. Ronald, Nicole & Arentze, Theo & Timmermans, Harry, 2012. "Modeling social interactions between individuals for joint activity scheduling," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 276-290.
    17. Davide Natalini & Giangiacomo Bravo, 2013. "Encouraging Sustainable Transport Choices in American Households: Results from an Empirically Grounded Agent-Based Model," Sustainability, MDPI, vol. 6(1), pages 1-20, December.
    18. Fontana, Magda & Terna, Pietro, 2015. "From Agent-based models to network analysis (and return): the policy-making perspective," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201507, University of Turin.
    19. Laura Schmitt Olabisi & Ryan Qi Wang & Arika Ligmann-Zielinska, 2015. "Why Don’t More Farmers Go Organic? Using A Stakeholder-Informed Exploratory Agent-Based Model to Represent the Dynamics of Farming Practices in the Philippines," Land, MDPI, vol. 4(4), pages 1-24, October.
    20. Wolf, Ingo & Schröder, Tobias & Neumann, Jochen & de Haan, Gerhard, 2015. "Changing minds about electric cars: An empirically grounded agent-based modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 269-285.

    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:ijm:journl:v:4:y:2011:i:1:p:21-34. 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: Jinjing Li (email available below). General contact details of provider: http://www.microsimulation.org/ijm/ .

    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.