Abstract
The complexity approach to science highlights the importance of communication between different areas of knowledge. The present chapter offers a discussion about the contributions of bridging social network analysis and behavior analysis in explaining behavior in systems. Social network analysis provides graphical representations and tools to describe structural features of complex webs of interactions in social groups. Networks may be described as the architecture of a complex system. Network analysis plays an important role in mapping the flow of information communication processes and cooperation in social groups. Networks are here understood as the structures of social systems. However, recent work attempts to move from static descriptions to incorporate a dynamic perspective that focuses on change and system resilience. There is an increasing recognition of the importance of reinforcement in order to understand to spread of behavior in complex systems. In this respect, much can be learned from the selectionist perspective that permeates behavior analysis. From a behavioral perspective, there is an interest in understanding interlocking behavioral contingencies, to the extent that interdependent individuals produce an aggregate product. The description of the interdependencies in a system may help identify the interlocking behavioral contingencies. In turn, this may provide a basis for prediction and influencing the system. Network analysis has been used in several contexts, including the spread of viruses and diffusion of innovations. The philosophical distinction between Being and Becoming is used to illustrate the differences and space for communication between network analysis and behavior analysis.
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Notes
- 1.
Macrobehavior, as Glenn et al. (2016) used the term, is different from macrocontingency insofar as it results from large-scale individual behavioral change that is the aggregate product and not the sum of behaviors. This may be tangible as in the case of donation to charity or intangible as for political or other preferences. Although it has not been specified what is meant by large scale, the main point is that macrobehavior has some societal or cultural consequences.
References
Abernathy, W. B. (2008). Implications and applications of a behavior systems perspective. Journal of Organizational Behavior Management, 28(2), 123–138. https://doi.org/10.1080/01608060802100980
Albert, R., & Barabási, A. L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47–97. https://doi.org/10.1103/RevModPhys.74.47
Axelrod, R., & Cohen, M. D. (2001). Harnessing complexity. New York, NY: Basic Books.
Barabási, A. L. (2005). Taming complexity. Nature Physics, 1(2), 68–70. https://doi.org/10.1038/nphys162
Barabási, A. L. (2016). Network science. Cambridge, England: Cambridge University Press.
Baum, W. M. (1974). On two types of deviation from the matching law: Bias and undermatching. Journal of the Experimental Analysis of Behavior, 22(1), 231–242. https://doi.org/10.1901/jeab.1974.22-231
Benartzi, S., Peleg, E., & Thaler, R. H. (2013). Choice architecture and retirement saving plans. In E. Shafir (Ed.), The behavioral foundations of public policy (pp. 245–263). Princeton, NJ: Princeton University Press.
Birchler, G. R., Weiss, R. L., & Vincent, J. P. (1975). Multimethod analysis of social reinforcement exchange between maritally distressed and nondistressed spouse and stranger dyads. Journal of Personality and Social Psychology, 31(2), 349–360. https://doi.org/10.1037/h0076280
Borgatti, S., Everett, M., & Johnson, J. (2018). Analyzing social networks. Thousand Oaks, CA: Sage.
Borgatti, S. P., Brass, D. J., & Halgin, D. S. (2014). Social network research: Confusions, criticisms, and controversies. In D. J. Brass, G. Labianca, A. Mehra, D. S. Halgin, & S. P. Borgatti (Eds.), Research in sociology of organizations: Contemporary perspectives on organizational social networks (Vol. 40, pp. 1–29). Bingley, England: Emerald Group Publishing Limited.
Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892–895. https://doi.org/10.1126/science.1165821
Brethower, D. M. (1972). Behavioral analysis in business and industry: A total performance system. Kalamazoo, MI: Behaviordelia.
Brethower, D. M. (2004). Understanding behavior of organizations to improve behavior in organizations. The Behavior Analyst Today, 5(2), 170–181. https://doi.org/10.1037/h0100028
Byrne, D., & Callaghan, G. (2014). Complexity theory and the social sciences. The State of the Art. London, England: Routledge.
Caldarelli, G., & Catanzaro, M. (2012). Networks: A very short introduction. Oxford, England: Oxford University Press.
Catania, A. C., & Harnad, S. R. (1988). The selection of behavior: The operant behaviorism of BF Skinner: Comments and consequences. Cambridge, England: Cambridge University Press.
Centola, D. (2018). How behavior spreads: The science of complex contagions (Vol. 3). Princeton, NJ: Princeton University Press.
Cooper, C. L., Liu, Y., & Tarba, S. Y. (2014). Resilience, HRM practices and impact on organizational performance and employee well-being. The International Journal of Human Resource Management, 25(17), 2466–2471. https://doi.org/10.1080/09585192.2014.926688
Cross, R., Laseter, T., Parker, A., & Velasquez, G. (2006). Using social network analysis to improve communities of practice. California Management Review, 49(1), 32–60. https://doi.org/10.2307/41166370
Cross, R., Parise, S., & Weiss, L. M. (2007). The role of networks in organizational change. The McKinsey Quarterly, 3, 28–41. Retrieved from https://www.mckinsey.com/business-functions/organization/our-insights/the-role-of-networks-in-organizational-change
Cross, R., Parker, A., & Sasson, L. (2003). Introduction. In R. Cross, A. Parker, & L. Sasson (Eds.), Networks in the knowledge economy (pp. 3–10). Oxford, England: Oxford University Press.
de Carvalho, L., & Sandaker, I. (2016). Interlocking behavior and cultural selection. Norsk Tidsskrift for Atferdsanalyse, 43(1), 19–25. Retrieved from www.nta.atferd.no/loadfile.aspx?IdFile=1376
Diener, L. H., McGee, H. M., & Miguel, C. F. (2009). An integrated approach for conducting a Behavioral Systems Analysis. Journal of Organizational Behavior Management, 29(2), 108–135. https://doi.org/10.1080/01608060902874534
Glenn, S. S. (1988). Contingencies and metacontingencies: Toward a synthesis of behavior analysis and cultural materialism. The Behavior Analyst, 11(2), 161–179. https://doi.org/10.1007/bf03392470
Glenn, S. S. (2004). Individual behavior, culture, and social change. The Behavior Analyst, 27(2), 133–151. https://doi.org/10.1007/bf03393175
Glenn, S. S., & Malott, M. E. (2004). Complexity and selection: Implications for organizational change. Behavior and Social Issues, 13(2), 89–106. https://doi.org/10.5210/bsi.v13i2.378
Glenn, S. S., Malott, M. E., Andery Benvenuti, M., Houmanfar, R., Sandaker, I., Todorov, J. C., … Vasconcelos, L. (2016). Toward consistent terminology in a behaviorist approach to cultural analysis. Behavior & Social Issues, 25, 11–17. https://doi.org/10.5210/bsi.v25i0.6634
Herrnstein, R. J. (1961). Relative and absolute strength of response as a function of frequency of reinforcement. Journal of the Experimental Analysis of Behavior, 4(3), 267–272. https://doi.org/10.1901/jeab.1961.4-267
Herrnstein, R. J. (1990). Rational choice theory: Necessary but not sufficient. American Psychologist, 45(3), 356–367. https://doi.org/10.1037/0003-066x.45.3.356
Houmanfar, R. A., & Rodrigues, N. J. (2006). The metacontingency and the behavioral contingency: Points of contact and departure. Behavior and Social Issues, 15(1), 13–30. https://doi.org/10.5210/bsi.v15i1.342
Houmanfar, R. A., Rodrigues, N. J., & Smith, G. S. (2009). Role of communication networks in behavioral systems analysis. Journal of Organizational Behavior Management, 29(3–4), 257–275. https://doi.org/10.1080/01608060903092102
Hull, D. L., Langman, R., & Glenn, S. S. (2001). A general account of selection: Biology, immunology, and behavior. The Behavioral & Brain Sciences, 24, 511–528. Retrieved from https://www.researchgate.net/publication/11669815_A_general_account_of_selection_Biology_immunology_and_behavior
Hunter, C. S. (2012). Analyzing behavioral and cultural selection contingencies. Revista Lationomericana de Psicología, 44, 43–54. Retrieved from http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-05342012000100005&lng=en&nrm=iso
Johnston, J. M., & Pennypacker, H. S. (1993). Readings for strategies and tactics of behavioral research. Hillsdale, NJ: Lawrence Erlbaum Associates.
Kohler, H. P. (1997). Learning in social networks and contraceptive choice. Demography, 34(3), 369–383. https://doi.org/10.2307/3038290
Krispin, J. V. (2017). Positive feedback loops of metacontingencies: A new conceptualization of cultural-level selection. Behavior and Social Issues, 26, 95–110. https://doi.org/10.5210/bsi.v26i0.7397
Lattal, K. A. (1995). Contingency and behavior analysis. The Behavior Analyst, 18(2), 209–224. https://doi.org/10.1007/bf03392709
Lin, N. (2017). Building a network theory of social capital. In N. Lin, K. Cook, & R. Burt (Eds.), Social capital (pp. 3–28). London, England: Routledge.
Malott, M. E. (2016). Selection of business practices in the midst of evolving complexity. Journal of Organizational Behavior Management, 36(2–3), 103–122. https://doi.org/10.1080/01608061.2016.1200511
McDowell, J. J., Calvin, O. L., Hackett, R., & Klapes, B. (2017). Falsification of matching theory and confirmation of an evolutionary theory of behavior dynamics in a critical experiment. Behavioural Processes, 140, 61–68. https://doi.org/10.1016/j.beproc.2017.03.025
Mobus, G. E., & Kalton, M. C. (2015). Principles of systems science. New York, NY: Springer.
Moreno, J. L. (1978). Who shall survive? Beacon, NY: Beacon House.
Naug, D. (2009). Structure and resilience of the social network in an insect colony as a function of colony size. Behavioral Ecology and Sociobiology, 63(7), 1023–1028. Retrieved from https://www.jstor.org/stable/40295391
Parise, S. (2007). Knowledge management and human resource development: An application in social network analysis methods. Advances in Developing Human Resources, 9(3), 359–383. https://doi.org/10.1177/1523422307304106
Parise, S., Cross, R., & Davenport, T. H. (2006). Strategies for preventing a knowledge-loss crisis. MIT Sloan Management Review, 47(4), 31–38. Retrieved from https://sloanreview.mit.edu/article/strategies-for-preventing-a-knowledgeloss-crisis/
Prigogine, I. (1980). From being to becoming: Time and complexity in the physical sciences. New York, NY: W. H. Freeman and Company.
Putnam, R. D. (Ed.). (2002). Democracies in flux: The evolution of social capital in contemporary society. Oxford, England: Oxford University Press.
Ribeiro, H. V., Alves, L. G., Martins, A. F., Lenzi, E. K., & Perc, M. (2018). The dynamical structure of political corruption networks. Journal of Complex Networks, 6(6), 989–1003. https://doi.org/10.1093/comnet/cny002
Robins, G. (2015). Doing social network research: Network-based research design for social scientists. London, England: Sage.
Rogers, E. (2003). The diffusion of innovation. New York, NY: Free Press.
Rogers, E., & Kincaid, D. (1981). Communication networks: Toward a new paradigm for research. New York, NY: Free Press.
Rosenquist, J. N., Fowler, J. H., & Christakis, N. A. (2011). Social network determinants of depression. Molecular Psychiatry, 16(3), 273–281. https://doi.org/10.1038/mp.2010.13
Sandaker, I. (2006). How should behavior analysis interact effectively with the social sciences? Behavior and Social Issues, 15(1), 81–91. https://search.proquest.com/docview/229305016?accountid=26439.
Sandaker, I. (2009). A selectionist perspective on systemic and behavioral change in organizations. Journal of Organizational Behavior Management, 29(3–4), 276–293. https://doi.org/10.1080/01608060903092128
Sandaker, I., Couto, K. C., & de Carvalho, L. C. (2019). Selection at three levels of organization: Does structure matter? Behavior and Social Issues, 28, 221–228. https://doi.org/10.1007/s42822-019-00020-z
Scott, J. (2011). Social network analysis: Developments, advances, and prospects. Social Network Analysis and Mining, 1(1), 21–26. https://doi.org/10.1007/s13278-010-0012-6
Scott, J. (2013). Social network analysis. London, England: Sage.
Siisiäinen, M. (2000). Two concepts of social capital: Bourdieu vs. Putnam. Paper presented at the ISTR Fourth International Conference, The Third Sector: For What and for Whom? Trinity College, Dublin, Ireland. Retrieved from http://hdl.handle.net/10535/7661
Skinner, B. F. (1938). The behavior of organisms: An experimental analysis. New York, NY: Appleton-Century.
Skinner, B. F. (1953). Science and human behavior. New York, NY: Free Press.
Skinner, B. F. (1981). Selection by consequences. Science, 213(4507), 501–504. https://doi.org/10.1126/science.7244649
Stacey, R. (2009). Complexity and organizational reality: Uncertainty and the need to rethink management after the collapse of investment capitalism. London, England: Routledge.
Sunstein, C. R., Reisch, L. A., & Rauber, J. (2017). Behavioral insights all over the world? Public attitudes toward nudging in a multi-country study. Harvard John M. Olin Discussion Paper, 1-31. https://doi.org/10.2139/ssrn.2921217.
Todorov, J. C. (2006). The metacontingency as a conceptual tool. Behavior and Social Issues, 15(1), 92–94. https://doi.org/10.5210/bsi.v15i1.347
Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. https://doi.org/10.1126/science.aap9559
Wenger, E., McDermott, R. A., & Snyder, W. (2002). Cultivating communities of practice: A guide to managing knowledge. Boston, MA: Harvard Business Press.
Wilson, E. O. (1998). Consilience. New York, NY: Knopf.
Zheng, M., Lü, L., & Zhao, M. (2013). Spreading in online social networks: The role of social reinforcement. Physical Review E, 88(1), 012818. https://doi.org/10.1103/PhysRevE.88.012818
Zilio, D. (2019). On the function of science: An overview of 30 years of publications on metacontingency. Behavior and Social Issues, 28, 46–76. https://doi.org/10.1007/s42822-019-00006-x
Zweg, K. (2016). Network analysis literacy: A practical approach to the analysis of networks. New York, NY: Springer.
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Bento, F., Tagliabue, M., Sandaker, I. (2020). Complex Systems and Social Behavior: Bridging Social Networks and Behavior Analysis. In: Cihon, T.M., Mattaini, M.A. (eds) Behavior Science Perspectives on Culture and Community. Behavior Analysis: Theory, Research, and Practice. Springer, Cham. https://doi.org/10.1007/978-3-030-45421-0_4
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