Abstract
Agent Based Modelling (ABM) is a methodology used to study the behaviour of norms in complex systems. Agent based simulations are capable of generating populations of heterogeneous, self-interested agents that interact with one another. Emergent norm behaviour in the system may then be understood as a result of these individual interactions. Agents observe the behaviour of their group and update their belief based on those of others. Social networks have been shown to play an important role in norm convergence. In this model agents interact on a fixed social network with members of their own social group 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. This means that friends-of-friends are more likely to randomly interact with one another than agents with a higher degree of separation. Using this method we investigate the effect that random interactions have on the dissemination of social norms when agents are primarily influenced by their social network. We discover that increasing the frequency and quality of random interactions results in an increase in the rate of norm convergence.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baum, J.A.C., Shipilov, A.V., Rowley, T.J.: Where do small worlds come from? Ind. Corp. Change 12(4), 697–725 (2003)
Conte, R., Falcone, R., Sartor, G.: Introduction: Agents and norms: How to fill the gap? Artificial Intelligence and Law 7(1), 1–15 (1999)
Davis, G.F., Yoo, M., Baker, W.E.: The Small World of the American Corporate Elite, 1982-2001. Strategic Organization 1(3), 301–326 (2003)
Erdos, P., Reyni, A.: On the evolution of random graphs. PubI. Math. Inst. Hungar. Acad. Sci. 4(5), 17–61 (1961)
Luck, M., López, F.L.y., d’Inverno, M.: A normative framework for agent-based systems. Computational & Mathematical Organization Theory 12(2), 227–250 (2006)
Fenner, T., Levene, M., Loizou, G., Roussos, G.: A stochastic evolutionary growth model for social networks. Comput. Netw. 51(16), 4586–4595 (2007)
Fronczak, A., Fronczak, P., Holyst, J.A.: Average path length in random networks (2002)
Gabaix, X.: Zipf’s law for cities: An explanation*. Quarterly Journal of Economics 114(3), 739–767 (1999)
Goldfarb, B., Henrekson, M.: Bottom-up versus top-down policies towards the commercialization of university intellectual property. Research Policy 32(4), 639–658 (2003)
Izquierdo, L.R., Izquierdo, S.S., Galán, J.M., Santos, J.I.: Techniques to understand computer simulations: Markov chain analysis. Journal of Artificial Societies and Social Simulation 12(1), 6 (2009)
Johnson, D.B.: A note on dijkstra’s shortest path algorithm. J. ACM 20(3), 385–388 (1973)
Kittock, J.: 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 Volume VI, Santa Fe Institute, pp. 507–521. Addison-Wesley, Reading (1995)
Lee, E., Lee, J., Lee, J.: Reconsideration of the Winner-Take-All Hypothesis: Complex Networks and Local Bias. Management Science 52(12), 1838–1848 (2006)
Milgram, S.: The small world. Psychology Today 2, 60–67 (1967)
Mukherjee, P., Sen, S., Airiau, S.: Norm emergence under constrained interactions in diverse societies. In: AAMAS 2008: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, pp. 779–786 (2008)
Newman, M.E.J.: Random graphs as models of networks (2002)
Rahmandad, H., Sterman, J.: Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Management Science 54(5), 998–1014 (2008)
Savarimuthu, B.T.R., Cranefield, S., Purvis, M., Purvis, M.: Norm emergence in agent societies formed by dynamically changing networks. In: IAT 2007: Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Washington, DC, USA, pp. 464–470. IEEE Computer Society, Los Alamitos (2007)
Shoham, Y., Tennenholtz, M.: On social laws for artificial agent societies: Off-line design. Artificial Intelligence 73, 231–252 (1995)
Verspagen, B., Duysters, G.: The small worlds of strategic technology alliances. Technovation 24(7), 563–571 (2004)
Villatoro, D., Malone, N., Sen, S.: Effects of interaction history and network topology on rate of convention emergence. In: Proceedings of 3rd International Workshop on Emergent Intelligence on Networked Agents (2009)
Walker, A., Woolridge, M.: Understanding the emergence of convensions in multi agent systems. In: Proceedings of the First International Conference on Multi-Agent Systems (ICMAS 1995), vol. (1), pp. 384–389 (1995)
Watts, D.J.: Small worlds: The dynamics of networks between order and randomness. Princeton University Press, Princeton (1999)
Watts, D.J., Strogatz, S.: Collective dynamics of ‘small-world’ networks. Nature, 440–442 (1998)
Watts, D.J.: Networks, dynamics, and the small-world phenomenon. American Journal of Sociology 105(2), 493–527 (1999)
Benjamin Zhan, F., Noon, C.E.: Shortest Path Algorithms: An Evaluation Using Real Road Networks. Transportation Science 32(1), 65–73 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mungovan, D., Howley, E., Duggan, J. (2010). Norm Convergence in Populations of Dynamically Interacting Agents. In: Coyle, L., Freyne, J. (eds) Artificial Intelligence and Cognitive Science. AICS 2009. Lecture Notes in Computer Science(), vol 6206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17080-5_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-17080-5_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17079-9
Online ISBN: 978-3-642-17080-5
eBook Packages: Computer ScienceComputer Science (R0)