The influence of random interactions and decision heuristics on norm evolution in social networks

Article

Abstract

In this paper we explore the effect that random social interactions have on the emergence and evolution of social norms in a simulated population of agents. In our model agents observe the behaviour of others and update their norms based on these observations. An agent’s norm is influenced by both their own fixed social network plus a second random network that is composed of a subset of the remaining population. Random interactions are based on a weighted selection algorithm that uses an individual’s path distance on the network to determine their chance of meeting a stranger. This means that friends-of-friends are more likely to randomly interact with one another than agents with a higher degree of separation. We then contrast the cases where agents make highest utility based rational decisions about which norm to adopt versus using a Markov Decision process that associates a weight with the best choice. Finally we examine the effect that these random interactions have on the evolution of a more complex social norm as it propagates throughout the population. We discover that increasing the frequency and weighting of random interactions results in higher levels of norm convergence and in a quicker time when agents have the choice between two competing alternatives. This can be attributed to more information passing through the population thereby allowing for quicker convergence. When the norm is allowed to evolve we observe both global consensus formation and group splintering depending on the cognitive agent model used.

Keywords

Social networks Norms Agent based modeling Random dynamic interactions 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Axelrod R (1997) The dissemination of culture: a model with local convergence and global polarization. J Confl Resolut 41(2):203–226 CrossRefGoogle Scholar
  2. Barrat BKA (2008) Consensus formation on adaptive networks. Phys Rev E, Stat Nonlinear Soft Matter Phys 77(1):016102 CrossRefGoogle Scholar
  3. Baum JAC, Shipilov AV, Rowley TJ (2003) Where do small worlds come from? Ind Corp Change 12(4):697–725 CrossRefGoogle Scholar
  4. Becker R, Zilberstein S, Lesser V, Goldman CV (2003) Transition-independent decentralized Markov decision processes. In: AAMAS ’03: Proceedings of the second international joint conference on autonomous agents and multiagent systems. ACM, New York, pp 41–48 CrossRefGoogle Scholar
  5. Bikhchandani S, Hirshleifer D, Welch I (1992) A theory of fads, fashion, custom, and cultural change as informational cascades. J Polit Econ 100(5):992 Google Scholar
  6. Bikhchandani S, Hirshleifer D, Welch I (1998) Learning from the behavior of others: Conformity, fads, and informational cascades. J Econ Perspect 12(3):151–170 Google Scholar
  7. Boutilier C (1999) Sequential optimality and coordination in multiagent systems. In: IJCAI ’99: Proceedings of the sixteenth international joint conference on artificial intelligence. Morgan Kaufmann, San Francisco, pp 478–485 Google Scholar
  8. Brockmann D, Hufnagel L, Geisel T (2006) The scaling laws of human travel. Nature 439(7075):462–465 CrossRefGoogle Scholar
  9. Carley KM (2009) Computational modeling for reasoning about the social behavior of humans. Comput Math Organ Theory 15(1):47–59 CrossRefGoogle Scholar
  10. Centola D, Willer R, Macy M (2005) The emperor’s dilemma: a computational model of self-enforcing norms. Am J Sociol 110(4):1009–1040 CrossRefGoogle Scholar
  11. Centola D, Gonzalez-Avella JC, Eguiluz VM, San Miguel M (2007) Homophily, cultural drift, and the co-evolution of cultural groups. J Confl Resolut 51(6):905–929 CrossRefGoogle Scholar
  12. Conte R, Falcone R, Sartor G (1999) Introduction: agents and norms: how to fill the gap? Artif Intell Law 7(1):1–15 CrossRefGoogle Scholar
  13. Davis GF, Yoo M, Baker WE (2003) The small world of the American corporate elite, 1982–2001. Strateg Organ 1(3):301–326 CrossRefGoogle Scholar
  14. Deffuant G, Neau D, Amblard F, Weisbuch G (2001) Mixing beliefs among interacting agents. Adv Complex Syst 3:87–98 CrossRefGoogle Scholar
  15. Denes-Raj V, Epstein S (1987) Conflict between intuitive and rational processing: when people behave against their better judgment. J Pers Soc Psychol 66(5):819 CrossRefGoogle Scholar
  16. Eagle N, Pentland A (2009) Eigenbehaviors: identifying structure in routine. Behav Ecol Sociobiol 63:1057–1066. doi: 10.1007/s00265-009-0739-0 CrossRefGoogle Scholar
  17. Ehrlich PR, Levin SA (2005) The evolution of norms. PLoS Biol 3(6):e194 CrossRefGoogle Scholar
  18. Ellickson R (1989) Bringing culture and human frailty to rational actors: a critique of classical law and economics. 65 CHI-KENT L REV, 23 Google Scholar
  19. Epstein JM (2008) Why model? J Artif Soc Soc Simul 11(4):12 Google Scholar
  20. Erdos P, Reyni A (1961) On the evolution of random graphs. Publ Math Inst Hung Acad Sci 4(5):17–61 Google Scholar
  21. Fenner T, Levene M, Loizou G, Roussos G (2007) A stochastic evolutionary growth model for social networks. Comput Netw 51(16):4586–4595 CrossRefGoogle Scholar
  22. Fronczak A, Fronczak P, Holyst JA (2002) Average path length in random networks Google Scholar
  23. Giddens A (1984) The constitution of society: outline of the theory of structuration. Polity, Cambridge Google Scholar
  24. Goldfarb B, Henrekson M (2003) Bottom-up versus top-down policies towards the commercialization of university intellectual property. Res Policy 32(4):639–658 CrossRefGoogle Scholar
  25. González MC, Hidalgo CA, Barabási A (2008) Understanding individual human mobility patterns. Nature 453(7196):779–782 CrossRefGoogle Scholar
  26. Gonzalez-Avella JC, Cosenza MG, Tucci K (2005) Nonequilibrium transition induced by mass media in a model for social influence Google Scholar
  27. Granovetter MS (1973) The strength of weak ties. Am J Sociol 78(6):1360 CrossRefGoogle Scholar
  28. Henrick J, Boyd R (2001) Why people punish defectors: weak conformist transmission can stabilize costly enforcement of norms in cooperative dilemmas. J Theor Biol 208(1):79–89 CrossRefGoogle Scholar
  29. Horne C (2007) Explaining norm enforcement. Ration Soc 19(2):139–170 CrossRefGoogle Scholar
  30. Izquierdo LR, Izquierdo SS, Galán JM, Santos JI (2009) Techniques to understand computer simulations: Markov chain analysis. J Artif Soc Soc Simul 12(1):6 Google Scholar
  31. Jackson MO, Rogers BW (2007) Meeting strangers and friends of friends: how random are social networks? Am Econ Rev 97(3):890–915 CrossRefGoogle Scholar
  32. Johnson DB (1973) A note on Dijkstra’s shortest path algorithm. J ACM 20(3):385–388 CrossRefGoogle Scholar
  33. Kittock J (1995) Emergent conventions and the structure of multi–agent systems. In: Lectures in complex systems: the proceedings of the 1993 complex systems summer school. Santa Fe Institute studies in the sciences of complexity lecture, vol VI. Addison-Wesley, Reading, pp 507–521 Google Scholar
  34. Klein RG (1999) The human career: human biological and cultural origins. University of Chicago Press, Chicago Google Scholar
  35. Kuperman MN (2006) Cultural propagation on social networks. Phys Rev E, Stat Nonlinear Soft Matter Phys 73(4):046139 CrossRefGoogle Scholar
  36. Lee E, Lee J, Lee J (2006) Reconsideration of the winner-take-all hypothesis: complex networks and local bias. Manag Sci 52(12):1838–1848 CrossRefGoogle Scholar
  37. Liefbroer AC, Billari FC (2009) Bringing norms back in: a theoretical and empirical discussion of their importance for understanding demographic behaviour. Popul Space Place 16:287–305 CrossRefGoogle Scholar
  38. Lopez-Pintado D, Watts DJ (2008) Social influence, binary decisions and collective dynamics. Ration Soc 20(4):399–443 CrossRefGoogle Scholar
  39. Lopez y Lopez F, Luck M, d’Inverno M (2006) A normative framework for agent-based systems. Comput Math Organ Theory 12(2):227–250 CrossRefGoogle Scholar
  40. Mckeown G, Sheehy N (2006) Mass media and polarisation processes in the bounded confidence model of opinion dynamics. J Artif Soc Soc Simul, 9 Google Scholar
  41. McPherson M, Smith-Lovin L, Cook J (2001) Birds of a feather: homophily in social networks. Ann Rev Sociol 27:415–444 CrossRefGoogle Scholar
  42. Milgram S (1967) The small world. Psychol Today 2:60–67 Google Scholar
  43. Mukherjee P, Sen S, Airiau S (2008) Norm emergence under constrained interactions in diverse societies. In: AAMAS ’08: Proceedings of the 7th international joint conference on autonomous agents and multiagent systems, Richland, SC. International foundation for autonomous agents and multiagent systems, pp 779–786 Google Scholar
  44. Newman MEJ (2002) Random graphs as models of networks Google Scholar
  45. Ostrom E (2000) Collective action and the evolution of social norms. J Econ Perspect 14(3):137–158 CrossRefGoogle Scholar
  46. Papadimitriou CH, Tsitsiklis JN (1987) The complexity of Markov decision processes. Math Oper Res 12(3):441–450 CrossRefGoogle Scholar
  47. Rahmandad H, Sterman J (2008) Heterogeneity and network structure in the dynamics of diffusion: comparing agent-based and differential equation models. Manag Sci 54(5):998–1014 CrossRefGoogle Scholar
  48. Ruef M, Aldrich HE, Carter NM (2003) The structure of founding teams: homophily, strong ties, and isolation among US entrepreneurs. Am Sociol Rev 68(2):195–222 CrossRefGoogle Scholar
  49. Savarimuthu BTR, Cranefield S, Purvis M, Purvis M (2007) Norm emergence in agent societies formed by dynamically changing networks. In: IAT ’07: Proceedings of the 2007 IEEE/WIC/ACM international conference on intelligent agent technology. IEEE Computer Society, Washington, pp 464–470 CrossRefGoogle Scholar
  50. Shilling C (1999) Towards an embodied understanding of the structure/agency relationship. Br J Sociol 50(4):543–562 CrossRefGoogle Scholar
  51. Shoham Y, Tennenholtz M (1995) On social laws for artificial agent societies: Off-line design. Artif Intell 73:231–252 CrossRefGoogle Scholar
  52. Verspagen B, Duysters G (2004) The small worlds of strategic technology alliances. Technovation 24(7):563–571 CrossRefGoogle Scholar
  53. Villatoro D, Malone N, Sen S (2009) Effects of interaction history and network topology on rate of convention emergence. In: Proceedings of 3rd international workshop on emergent intelligence on networked agents Google Scholar
  54. Walker A, Woolridge M (1995) Understanding the emergence of convensions in multi agent systems. In: Proceedings of the first international conference on multi-agent systems (ICMAS ’95), vol 1, pp 384–389 Google Scholar
  55. Watts D (1999a) Small worlds: the dynamics of networks between order and randomness. Princeton University Press, Princeton Google Scholar
  56. Watts DJ (1999b) Networks, dynamics, and the small-world phenomenon. Am J Sociol 105(2):493–527 CrossRefGoogle Scholar
  57. Watts DJ, Dodds PS (2007) Influentials, networks, and public opinion formation. J Consum Res 34(4):441–458 CrossRefGoogle Scholar
  58. Watts D, Strogatz S (1998) Collective dynamics of ‘small-world’ networks. Nature, 440–442 Google Scholar
  59. White CC, White DJ (1989) Markov decision processes. Eur J Oper Res 39(1):1–16 CrossRefGoogle Scholar
  60. Zhan FB, Noon CE (1998) Shortest path algorithms: an evaluation using real road networks. Transp Sci 32(1):65–73 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.I.T. DepartmentNational University of IrelandGalwayIreland

Personalised recommendations