Advertisement

Procedural Influence on Consensus Formation in Social Networks

  • Kathrin Eismann
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)

Abstract

How do the rules of interaction influence consensus formation in a social network? In this paper, I analyse procedural influence – a construct that is well-established within the group decision-making research tradition – in the context of networked consensus formation. I argue that interaction procedures regulate the flow of social influence among actors, which, in turn, potentially affects collective outcomes. Based on this, I explain how procedural influence can be integrated into a formal model of social influence. I then utilise an agent-based simulation (ABS) to quantify the effects of three exemplary interaction rules on the formation of consensus in a social network. My findings indicate that applying these rules to regulate interactions has mixed effects on the overall consensus outcomes, but consistently negative effects on the efficiency of consensus formation.

Keywords

Social network Social influence Consensus formation Belief dynamics Procedural influence Agent-based simulation 

References

  1. 1.
    Acemoglu, D., Ozdaglar, A.: Opinion dynamics and learning in social networks. Dyn. Games Appl. 1(1), 3–49 (2011)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Cialdini, R., Goldstein, N.: Social influence: compliance and conformity. Ann. Rev. Psychol. 55, 591–621 (2004)CrossRefGoogle Scholar
  3. 3.
    Davis, J.: Some compelling intuitions about group consensus decisions, theoretical and empirical research, and interpersonal aggregation phenomena: selected examples, 1950 - 1990. Organ. Behav. Hum. Dec. 52(1), 3–38 (1992)Google Scholar
  4. 4.
    Davis, J., Hulbert, L., Au, W.: Procedural influence on group decision making: the case of straw polls – observation and simulation. In: Hirokawa, R., Poole, M. (eds.) Communication and Group Decision Making, pp. 384–425. Sage, London (1996)Google Scholar
  5. 5.
    Davis, J., Kameda, T., Parks, C., Stasson, M., Zimmerman, Z.: Some social mechanics of group decision making: the distribution of opinion, polling sequence, and implications for consensus. J. Pers. Soc. Psychol. 59(6), 1000–1012 (1989)CrossRefGoogle Scholar
  6. 6.
    Davis, J., Stasson, M., Ono, K., Zimmerman, Z.: Effects of straw polls on group decision making: sequential voting pattern, timing, and local majorities. J. Pers. Soc. Psychol. 55(6), 918–926 (1988)CrossRefGoogle Scholar
  7. 7.
    Davis, J., Tindale, R., Naggao, D., Hinsz, V., Robertson, B.: Order effects in multiple decisions by groups: a demonstration with mock juries and trial procedures. J. Pers. Soc. Psychol. 47(5), 1003–1012 (1984)CrossRefGoogle Scholar
  8. 8.
    Endriss, U., Grandi, U., Porello, D.: Complexity of judgment aggregation. J. Artif. Intell. Res. 45, 481–514 (2012)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Friedkin, N.: A Structural Theory of Social Influence. Cambridge University Press, Cambridge (2006)Google Scholar
  10. 10.
    Friedkin, N., Jia, P., Bullo, F.: A theory of the evolution of social power: natural trajectories of interpersonal influence systems along issue sequences. Sociol. Sci. 3, 444–472 (2016)CrossRefGoogle Scholar
  11. 11.
    Friedkin, N., Johnsen, E.: Social influence and opinions. J. Math. Sociol. 15(3–4), 193–206 (1990)CrossRefGoogle Scholar
  12. 12.
    Friedkin, N., Johnsen, E.: Social influence networks and opinion change. Adv. Group Process 16, 1–29 (1999)Google Scholar
  13. 13.
    Friedkin, N., Proskurnikov, A., Tempo, R., Parsegov, S.: Network science on belief system dynamics under logic constraints. Science 354(6310), 321–326 (2016)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Grandi, U.: Social choice and social networks. In: Endriss, U. (ed.) Trends in Computational Social Choice, pp. 169–184. AI Access (2017)Google Scholar
  15. 15.
    Greenberg, J., Williams, K., O’Brien, M.: Considering the harshest verdict first: biasing effects on mock juror verdicts. Pers. Soc. Psychol. Rev. 12(1), 45–50 (1980)Google Scholar
  16. 16.
    Jia, P., MirTabatabaei, A., Friedkin, N., Bullo, F.: Opinion dynamics and the evolution of social power in influence networks. SIAM Rev 57(3), 367–397 (2015)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Kameda, T.: Procedural influence in consensus formation: evaluating group decision making from a social choice perspective. In: Witte, E., Davis, J. (eds.) Understanding Group Behavior: Consensual Action by Small Groups, pp. 137–161. Lawrence Erlbaum, New York (1996)Google Scholar
  18. 18.
    Kameda, T., Sugimori, S.: Procedural influence in two-step group decision making: power of local majorities in consensus formation. J. Pers. Soc. Psychol. 69(5), 865–876 (1995)CrossRefGoogle Scholar
  19. 19.
    Kerr, N., Tindale, R.: Group performance and decision making. Ann. Rev. Psychol. 55, 623–655 (2004)CrossRefGoogle Scholar
  20. 20.
    Klein, D., Marx, J., Fischbach, K.: Agent-based modeling in social science, history, and philosophy: an introduction. Hist. Soc. Res. 43(1), 7–27 (2018)Google Scholar
  21. 21.
    Levine, J., Moreland, R.: Progress in small group research. Ann. Rev. Psychol. 41, 585–634 (1990)CrossRefGoogle Scholar
  22. 22.
    Lorenz, J.: Continuous opinion dynamics of multidimensional allocation problems under bounded confidence: more dimensions lead to better chances for consensus. EJESS 19(2), 213–227 (2006)Google Scholar
  23. 23.
    Marino, S., Hogue, I., Ray, C., Kirschner, D.: A methodology for performing global uncertainty and sensitivity analysis in systems biology. J. Theor. Biol. 254(1), 178–196 (2008)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Mason, W., Conrey, F., Smith, E.: Situating social influence processes: dynamic, multidirectional flows of influence within social networks. Pers. Soc. Psychol. Rev. 11(3), 279–300 (2007)CrossRefGoogle Scholar
  25. 25.
    McGrath, J., Arrow, H., Berdahl, J.: The study of groups: past, present, and future. Pers. Soc. Psychol. Rev. 4(1), 95–105 (2000)CrossRefGoogle Scholar
  26. 26.
    McKay, M., Beckman, R., Conover, W.: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2), 239–245 (1979)MathSciNetzbMATHGoogle Scholar
  27. 27.
    Newman, M.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  28. 28.
    Page, S.: Agent-based models. In: Ltd, M.P. (ed.) The New Palgrave Dictionary of Economics, pp. 107–113. Palgrave Macmillan, Basingstoke (2018)CrossRefGoogle Scholar
  29. 29.
    Parsegov, S., Proskurnikov, A., Tempo, R., Friedkin, N.: Novel multidimensional models of opinion dynamics in social networks. IEEE Trans. Autom. Control 62(5), 2270–2285 (2017)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Saltelli, A., Marivoet, J.: Non-parametric statistics in sensitivity analysis for model output: a comparison of selected techniques. Reliab. Eng. Syst. Safe. 28(2), 229–253 (1990)CrossRefGoogle Scholar
  31. 31.
    Stasser, G., Davis, J.: Group decision making and social influence: a social interaction sequence model. Psychol. Rev. 88(6), 523–551 (1981)CrossRefGoogle Scholar
  32. 32.
    Thiele, J., Kurth, W., Grimm, V.: Facilitating parameter estimation and sensitivity analysis of agent-based models: a cookbook using netlogo and R. JASSS 17(3), 2 (2014)CrossRefGoogle Scholar
  33. 33.
    Thompson, L., Mannix, E., Bazerman, M.: Group negotiation: effects of decision rule, agenda, and aspiration. J. Pers. Soc. Psychol. 54(1), 86–95 (1988)CrossRefGoogle Scholar
  34. 34.
    Tindale, R., Kameda, T., Hinsz, V.: Group decision making. In: Hogg, M., Cooper, J. (eds.) The SAGE Handbook of Social Psychology, pp. 381–403. Sage, London (2003)Google Scholar
  35. 35.
    Uzzi, B., Amaral, L., Reed-Tsochas, F.: Small-World networks and management science research: a review. Eur. Manag. Rev. 4(2), 77–91 (2007)CrossRefGoogle Scholar
  36. 36.
    Watts, D., Strogatz, S.: Collective dynamics of “small-World” networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  37. 37.
    Wilensky, U.: NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston (1999). http://ccl.northwestern.edu/netlogo/
  38. 38.
    Xia, H., Wang, H., Xuan, Z.: Opinion dynamics: a multidisciplinary review and perspective on future research. Int. J. Knowl. Syst. Sci. 2(4), 72–91 (2011)CrossRefGoogle Scholar
  39. 39.
    Xiong, F., Liu, Y., Wang, L., Wang, X.: Analysis and application of opinion model with multiple topic interactions. Chaos 27(8) (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Information Systems and Social NetworksUniversity of BambergBambergGermany

Personalised recommendations