Micro-Cultural Preferences and Macro-Percolation of New Ideas: A NetLogo Simulation

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Abstract

This paper provides an extension of the Schelling agent-based model (ABM) of segregation which is augmented here with a mechanism for the percolation of new ideas. The main objective of the paper is to demonstrate that individual segregation preferences affect not only the intensity of aggregate segregation, but also the aggregate efficiency from crucial decision-making processes, such as the decision to invest in new ideas. To perform our research, we implement a NetLogo simulation in two steps by (i) obtaining three sets, each composed of 500 random segregation patterns, generated through a one-step simulation of a Schelling ABM for three different levels of segregation preference: namely, 20, 25 and 30%; and (ii) using the obtained level of segregation, we set the porosity level in a model for the percolation of new ideas and record the observed speed of percolation of new ideas for the first 100 steps. We find that levels of segregation due to 20 and 25% individual preference for homophily produce a difference of 3.4% in their effect on the speed of the percolation of new ideas. The levels of segregation of 25 and 30% individual preference for homophily, however, produce a difference of 12.8% in their effect on the percolation of new ideas. This means that the increase of the individual preference for segregation increases the intensity with which segregation acts as a barrier for new ideas to percolate successfully in the world of R&D investment. The segregation-percolation model used can be extended with further dynamics and developed as a code to be added to the NetLogo library. The main implication of our findings is that small changes in segregation preferences as in the Schelling ABM model produces increasingly negative on aggregate level spillover effects on other socio-economic processes, such as percolation of new ideas, which depend on the connectivity between people in the local society.

Keywords

Segregation Percolation Cultural preferences Ideas R&D investment 

JEL Classifications

Z10 C79 C99 L26 R11 

References

  1. Aghion, P., & Howitt, P. (1992). A model of growth through creative destruction. Econometrica, 60, 323–351.CrossRefGoogle Scholar
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.CrossRefGoogle Scholar
  3. Ajzen, I. (1985). From intentions to actions: a theory of planned behavior. In J. Kuhl & J. Beckman (Eds.), Action-control: from cognition to behavior (pp. 11–39). Heidelberg: Springer.CrossRefGoogle Scholar
  4. Arribas-Bel, D., Nijkamp, P., Poot, J. 2014. How diverse can spatial measures of cultural diversity be? Results from Monte Carlo simulations of an agent-based model. Centre for Research and Analysis of Migration, Discussion Paper Series, CPD 22/14.Google Scholar
  5. Autant-Bernard, C., Fadairo, M., & Massard, N. (2013). Knowledge diffusion and innovation policies within the European regions: challenges based on recent empirical evidence. Research Policy., 42(1), 196–210.CrossRefGoogle Scholar
  6. Barro, R., & Sala-i-Martin, X. (1995). Economic growth. New York: McGraw-Hill.Google Scholar
  7. Boh, W. F., Evaristo, R., & Ouderkirk, A. (2014). Balancing breadth and depth of expertise for innovation: a 3M story. Research Policy, 43(2), 349–366.CrossRefGoogle Scholar
  8. Chadha, J. S., & Holly, S. (2010). Macroeconomic models and the yield curve: an assessment of the fit. Journal of Economic Dynamics and Control, 34(8), 1343–1358.CrossRefGoogle Scholar
  9. Chattoe-Brown, E. (2014). Sociological research online in its journal. Sociological Research Online, 19(1), 16.CrossRefGoogle Scholar
  10. Collard, M., Collard, P., & Stattner, E. (2012). Mobility and information flow: percolation in a multi-agent model. Procedia Computer Science, 10, 22–29.CrossRefGoogle Scholar
  11. Dalmazzo, A., Pin, P., & Scalise, D. (2014). Communities and social inefficiency with heterogeneous groups. Journal of Economic Dynamics and Control, 48, 410–427.CrossRefGoogle Scholar
  12. Damaceanu, R. (2011). An agent-based computational study of wealth distribution in function of technological progress using Netlogo. American Journal of Economics., 1(1), 15–20.CrossRefGoogle Scholar
  13. Dawson, J. (2010). Diversity faultlines, shared objectives, and top management team performance. Human Relations, 64, 307–336.Google Scholar
  14. Fabrizio, K., & Hawn, O. (2013). Enabling diffusion: how complementary inputs moderate the response to environmental policy. Research Policy., 42(5), 1099–1111.CrossRefGoogle Scholar
  15. Gulden, T. (2013). Agent-based modeling as a tool for trade and development theory. Journal of Artificial Societies and Social Simulation., 16(2), 1.CrossRefGoogle Scholar
  16. Hofstede, G. (1989). The cultural relativity of the quality of life concept. The Academy of Management Review., 9(3), 389–398.Google Scholar
  17. Hofstede, G. (2001). Culture’s consequences: comparing values, behaviors, institutions, and organizations across nations (2nd ed.). Thousand Oaks: Sage Publications, Inc..Google Scholar
  18. Hong, L., & Page, S. E. (2001). Problem solving by heterogeneous agents. Journal of Economic Theory., 97(1), 123–163.CrossRefGoogle Scholar
  19. Iozzi, F. 2008. A simple implementation of Schelling’s segregation model in NetLogo. University of Bocconi, Dondena Working Paper 015.Google Scholar
  20. Jackson, M., Lopez-Pintado, D. 2012. Diffusion and contagion in networks with heterogeneous agents and homophily. CORE Discussion Papers 2012012, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).Google Scholar
  21. Jacquemet, N., Yannelis, C., 2012. Indiscriminate discrimination: a correspondence test for ethnic homophily in the Chicago Labor Market. PSE—Labex "OSE-Ouvrir la Science Economique" hal-00745109, HA.Google Scholar
  22. Janssen, M. (1990). Micro and macro in economics: an inquiry into their relation (Groningen theses in economics, management and organization). Groningen: Wolters-Noordhof.Google Scholar
  23. Jensen, P., Palangkaraya, A., Webster, E. 2014. Trust and the market for technology. Corrected Proof, Available online 5 November 2014.Google Scholar
  24. Joyce, D., Kennison, J., Densmore, O., Guerin, S., Barr, S., Charles, E., & Thompson, N. (2006). My way or the highway: a more naturalistic model of altruism tested in an iterative prisoners’ dilemma, journal of artificial societies and social. Simulation., 9(2).Google Scholar
  25. Lozares, C., Verd, J., Cruz, I., & Barranco, O. (2014). Homophily and heterophily in personal networks. From mutual acquaintance to relationship intensity. Quality & Quantity, 48(5), 2657–2670.CrossRefGoogle Scholar
  26. Montes, G. (2012). Using artificial societies to understand the impact of teacher student match on academic performance: the case of same race effects, journal of artificial societies and social. Simulation., 15(4), 8.Google Scholar
  27. Shackle, G. L. S. (1949). Expectation in economics. Cambridge: University Press.Google Scholar
  28. Schelling, T. (1969). Models of segregation. American Economic Review., 59(2), 488–493.Google Scholar
  29. Schelling, T. (1978). Micromotives and Macrobehavior. New York: Norton.Google Scholar
  30. Suárez, J., & Sancho, F. (2011). A virtual laboratory for the study of history and cultural dynamics. Journal of Artificial Societies and Social Simulation, 14(4), 19.CrossRefGoogle Scholar
  31. Schumpeter, J. (1942). Capitalism, socialism and democracy. New York: Harper.Google Scholar
  32. Talke, K., Salomo, S., & Rost, K. (2010). How top management team diversity affects innovativeness and performance via the strategic choice to focus on innovation fields. Research Policy, 39(7), 907–918.CrossRefGoogle Scholar
  33. Terna, P. (2009). The epidemic of innovation—playing around with an agent-based model. Economics of Innovation and New Technology, 18(7), 707–728.CrossRefGoogle Scholar
  34. Tiebout, C. (1956). A pure theory of local expenditures. Journal of Political Economy, 64(5), 416–424.CrossRefGoogle Scholar
  35. Tubadji, A., Angelis, V., & Nijkamp, P. (2015). Endogenous intangible resources and their place in the institutional hierarchy, review of regional research. Jahrbuch für Regionalwissenschaft, 36(1), 1–28.CrossRefGoogle Scholar
  36. Tubadji, A., Nijkamp, P. 2015a. The cultural percolation of new knowledge: a regional analysis for cultural impact on knowledge creation in the EU27. In J. Bakens, J. Poot and P. Nijkamp (eds.) The Economics of Cultural Diversity, Edward Elgar Publishing, 2015. Forthcoming, pp. 297–326.Google Scholar
  37. Tubadji, A., & Nijkamp, P. (2015b). Cultural gravity effects among migrants: a comparative analysis of the EU15. Economic Geography, 91(3), 344–380.CrossRefGoogle Scholar
  38. Van Leeuwen, E. (2010). The effects of future retail deveopments on the local economy: combining micro and macro approaches. Regional Science, 89(4), 691–710.CrossRefGoogle Scholar
  39. Van Leeuwen, E., Hagens, J., & Nijkamp, P. (2007). Multi-agent systems: a tool in spatial planning. The Planning Review, 43(170), 19–32.CrossRefGoogle Scholar
  40. Wilensky, U. 1997. NetLogo Segregation model. http://ccl.northwestern.edu/netlogo/models/Segregation. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
  41. Wilensky, U. 1998. NetLogo Percolation model. http://ccl.northwestern.edu/netlogo/models/Percolation. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
  42. Wilensky, U. 1999. NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
  43. Wilhite, A. (2014). Network structure, games, and agent dynamics. Journal of Economic Dynamics and Control, 47, 225–238.CrossRefGoogle Scholar
  44. Yang, H., & Kevin Steensma, H. (2014). When do firms rely on their knowledge spillover recipients for guidance in exploring unfamiliar knowledge? Research Policy, 43(9), 1496–1507.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Annie TUBADJI
    • 1
  • Vassilis ANGELIS
    • 2
  • Peter NIJKAMP
    • 3
    • 4
  1. 1.Department of EconomicsUniversity of BolognaRiminiItaly
  2. 2.University of the AegeanChiosGreece
  3. 3.Tinbergen InstituteAmsterdamThe Netherlands
  4. 4.A. Mickiewicz UniversityPoznanPoland

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