Hostname: page-component-669899f699-8p65j Total loading time: 0 Render date: 2025-05-05T03:45:27.143Z Has data issue: false hasContentIssue false
  • English
  • Français

Engineering the emergence of norms: a review

Published online by Cambridge University Press:  22 August 2017

Chris Haynes ,
Michael Luck ,
Peter McBurney ,
Samhar Mahmoud ,
Tomáš Vítek  and
Simon Miles
Chris Haynes
Affiliation:
Department of Informatics, King’s College London, Strand, London, WC2R 2LS e-mail: christopher.haynes@kc.ac.uk, michael.luck@kcl.ac.uk, peter.mcburney@kcl.ac.uk, tomas.vitek@kcl.ac.uk, simon.miles@kcl.ac.uk
Michael Luck
Affiliation:
Department of Informatics, King’s College London, Strand, London, WC2R 2LS e-mail: christopher.haynes@kc.ac.uk, michael.luck@kcl.ac.uk, peter.mcburney@kcl.ac.uk, tomas.vitek@kcl.ac.uk, simon.miles@kcl.ac.uk
Peter McBurney
Affiliation:
Department of Informatics, King’s College London, Strand, London, WC2R 2LS e-mail: christopher.haynes@kc.ac.uk, michael.luck@kcl.ac.uk, peter.mcburney@kcl.ac.uk, tomas.vitek@kcl.ac.uk, simon.miles@kcl.ac.uk
Samhar Mahmoud
Affiliation:
Department of Informatics, King’s College London, Strand, London, WC2R 2LS e-mail: christopher.haynes@kc.ac.uk, michael.luck@kcl.ac.uk, peter.mcburney@kcl.ac.uk, tomas.vitek@kcl.ac.uk, simon.miles@kcl.ac.uk
Tomáš Vítek
Affiliation:
Department of Informatics, King’s College London, Strand, London, WC2R 2LS e-mail: christopher.haynes@kc.ac.uk, michael.luck@kcl.ac.uk, peter.mcburney@kcl.ac.uk, tomas.vitek@kcl.ac.uk, simon.miles@kcl.ac.uk
Simon Miles
Affiliation:
Department of Informatics, King’s College London, Strand, London, WC2R 2LS e-mail: christopher.haynes@kc.ac.uk, michael.luck@kcl.ac.uk, peter.mcburney@kcl.ac.uk, tomas.vitek@kcl.ac.uk, simon.miles@kcl.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

Complex systems often exhibit emergent behaviour, unexpected macro-level behaviour caused by the interaction of micro-level components. In multiagent systems, these micro-level components may be autonomous agents and the emergent behaviour may be expressed as norms—patterns of behaviour that arise among the agents in response to their environment and each other. These emergent norms may be beneficial (e.g. by encouraging cooperative behaviour), or detrimental, but in either case it is useful to recognize these norms as they emerge and either encourage or discourage their establishment. We term this process engineering the emergence of norms and have identified three steps: the identification of a possible norm, evaluation of its benefit and its encouragement (or discouragement). This paper is an attempt to provide a survey of existing research related to these steps. We also provide an analysis of the approaches based upon their suitability for a variety of normative systems: we examine the requirements for agents to have autonomy over their choice of norms, the degree of observability required in the system, and the norm enforcement methods. The paper concludes with an discussion of open issues.

Type
Articles
Information
The Knowledge Engineering Review , Volume 32 , 2017 , e18
Copyright
© Cambridge University Press, 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Andrighetto, G., Campenni, M., Cecconi, F. & Conte, R. 2010a. The complex loop of norm emergence: a simulation model. In Simulating Interacting Agents and Social Phenomena, Agent-Based Social Systems 7, Takadama K., Cioffi-Revilla C., Deffuant G. (eds). Springer, 19–35.Google Scholar
Andrighetto, G., Villatoro, D. & Conte, R. 2010b. Norm internalization in artificial societies. AI Communications 23(4), 325–339.Google Scholar
Artikis, A. 2009. Formalising dynamic protocols for open agent systems. In Proceedings of the Twelth International Conference on Artificial Intelligence and Law, ICAIL ’09, 68–77. ACM.Google Scholar
Artikis, A. 2012. Dynamic specification of open agent systems. Journal of Logic and Computation 22(6), 1301–1334.Google Scholar
Atzori, L., Iera, A. & Morabito, G. 2010. The internet of things: a survey. Computer Networks 54(15), 2787–2805.CrossRefGoogle Scholar
Axelrod, R. 1986. An evolutionary approach to norms. The American Political Science Review 80(4), 1095–1111.Google Scholar
Balke, T., De Vos, M. & Padget, J. 2012. Normative run-time reasoning for institutionally-situated BDI agents. In Coordination, Organizations, Institutions, and Norms in Agent System VII, Cranefield S., van Riemsdijk M.B., Vázquez-Salceda J., Noriega P. (eds). Springer, 129–148.Google Scholar
Bedau, M. A. 1997. Weak emergence. Noûs 31(s11), 375–399.Google Scholar
Berger, T., Schreinemachers, P. & Woelcke, J. 2006. Multi-agent simulation for the targeting of development policies in less-favored areas. Agricultural Systems 88(1), 28–43.CrossRefGoogle Scholar
Bettenhausen, K. & Murnighan, J. K. 1985. The emergence of norms in competitive decision-making groups. Administrative Science Quarterly 25(4), 350–372.Google Scholar
Bicchieri, C. 2006. The Grammar of Society: The Nature and Dynamics of Social Norms. Cambridge University Press.Google Scholar
Bou, E., López-Sánchez, M. & Rodrguez-Aguilar, J. A. 2007. Towards self-configuration in autonomic electronic institutions. In Coordination, Organizations, Institutions, and Norms in Agent Systems II, Lecture Notes in Computer Science 4386, 229–244. Springer.CrossRefGoogle Scholar
Broughton, J. 1990. Restraint Use by Car Occupants. Transport and Road Research Laboratory Research Report, 289.Google Scholar
Bryant, V. 1985. Metric Spaces. Cambridge University Press.Google Scholar
Bunn, D. W. & Oliveira, F. S. 2001. Agent-based simulation: an application to the new electricity trading arrangements of England and Wales. IEEE Transactions on Evolutionary Computation 5(5), 493–503.Google Scholar
Campillo-Sanchez, P. & Gomez-Sanz, J. J. 2015. A framework for developing multi-agent systems in ambient intelligence scenarios. In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, 1949–1950. International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
Casari, M. & Luini, L. 2009. Cooperation under alternative punishment institutions: an experiment. Journal of Economic Behavior & Organization 71(2), 273–282.Google Scholar
Castelfranchi, C., Conte, R. & Paolucci, M. 1998. Normative reputation and the costs of compliance. Journal of Artificial Societies and Social Simulation 1(3), 3.Google Scholar
Castelfranchi, C. & Falcone, R. 1998. Principles of trust for MAS: cognitive anatomy, social importance, and quantification. In The Proceedings of the International Conference on Multi Agent Systems, 72–79. IEEE.Google Scholar
Castelfranchi, C. & Falcone, R. 2003. Founding autonomy: the dialectics between (social) environment and agent’s architecture and powers. In Agents and Computational Autonomy, Lecture Notes in Computer Science 2969, 40–54. Springer.Google Scholar
Chen, C.-C., Nagl, S. B. & Clack, C. D. 2007. Specifying, detecting and analysing emergent behaviours in multi-level agent-based simulations. In Proceedings of the 2007 Summer Computer Simulation Conference, 969–976. Society for Computer Simulation International.Google Scholar
Chen, C.-C., Nagl, S. B. & Clack, C. D. 2009. A formalism for multi-level emergent behaviours in designed component-based systems and agent-based simulations. In From System Complexity to Emergent Properties, Aziz-Alaoui M.A., Bertelle C. (eds). Springer, 101–114.Google Scholar
Conte, R. & Castelfranchi, C. 1999. From conventions to prescriptions. Towards an integrated view of norms. Artificial Intelligence and Law 7(4), 323–340.Google Scholar
Cranefield, S., Savarimuthu, B., Meneguzzi, F. & Oren, N. 2015. A Bayesian approach to norm identification. In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, 1743–1744. International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
Criado, N., Argente, E. & Botti, V. 2011. Open issues for normative multi-agent systems. AI Communications 24(3), 233–264.Google Scholar
Criado, N., Argente, E., Noriega, P. & Botti, V. 2010. Towards a normative BDI architecture for norm compliance. In Proceedings of the Multi-Agent Logics, Languages, and Organisations Federated Workshops, 2010, 65–81.Google Scholar
Deguet, J., Demazeau, Y. & Magnin, L. 2006. Elements about the emergence issue: a survey of emergence definitions. Complexus 3(1–3), 24–31.Google Scholar
Delgado, J. 2002. Emergence of social conventions in complex networks. Artificial Intelligence 141(1), 171–185.Google Scholar
Delgado, J., Pujol, J. M. & Sangüesa, R. 2003. Emergence of coordination in scale-free networks. Web Intelligence and Agent Systems 1, 131–138.Google Scholar
De Pelsmacker, P. & Janssens, W. 2007. The effect of norms, attitudes and habits on speeding behavior: scale development and model building and estimation. Accident Analysis & Prevention 39(1), 6–15.Google Scholar
Dignum, F., Dignum, V. & Jonker, C. M. 2009. Towards agents for policy making. In Multi-Agent-Based Simulation IX, Lecture Notes in Computer Science 5269, 141–153. Springer.Google Scholar
dos Santos Neto, B. F., da Silva, V. T. & de Lucena, C. J. P. 2012. An architectural model for autonomous normative agents. In Advances in Artificial Intelligence-SBIA 2012, Barros L. N., Finger M., Pozo A. T., Gimenénez-Lugo G. A, Castilho M. (eds). Springer, 152–161.Google Scholar
Elhag, A. A., Breuker, J. A. & Brouwer, P. W. 2000. On the formal analysis of normative conflicts. Information & Communications Technology Law 9(3), 207–217.Google Scholar
Emons, W. 2007. Escalating penalties for repeat offenders. International Review of Law and Economics 27(2), 170–178.Google Scholar
Faillo, M., Grieco, D. & Zarri, L. 2013. Legitimate punishment, feedback, and the enforcement of cooperation. Games and Economic Behavior 77(1), 271–283.Google Scholar
Finnemore, M. & Sikkink, K. 1998. International norm dynamics and political change. International Organization 52(04), 887–917.Google Scholar
Fitzek, F. H. P. & Katz, M. D. 2007. Cellular controlled peer to peer communications: overview and potentials. In Cognitive Wireless Networks, Fitzek F.H.P., Katz M.D. (eds). Springer, 31–59.Google Scholar
Franks, H., Griffiths, N. & Jhumka, A. 2013. Manipulating convention emergence using influencer agents. Autonomous Agents and Multi-Agent Systems 26(3), 315–353.CrossRefGoogle Scholar
Galán, J. M. & Izquierdo, L. R. 2005. Appearances can be deceiving: lessons learned re-implementing Axelrod’s ‘evolutionary approach to norms’. Journal of Artificial Societies and Social Simulation 8(3), 2.Google Scholar
Garlick, M. & Chli, M. 2009. The effect of social influence and curfews on civil violence. In Proceedings of the Eighth International Conference on Autonomous Agents and Multiagent Systems – Volume 2, 1335–1336. International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
Gibbs, J. P. 1965. Norms: the problem of definition and classification. American Journal of Sociology 70(5), 586–594.Google Scholar
Gneezy, U., Meier, S. & Rey-Biel, P. 2011. When and why incentives (don’t) work to modify behavior. The Journal of Economic Perspectives 25(4), 191–209.Google Scholar
Griffiths, N. & Luck, M. 2010. Changing neighbours: improving tag-based cooperation. In Proceedings of the Ninth International Conference on Autonomous Agents and Multiagent Systems: Volume 1, 249–256. International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
Günay, A. & Yolum, P. 2013. Constraint satisfaction as a tool for modeling and checking feasibility of multiagent commitments. Applied Intelligence 39(3), 489–509.Google Scholar
Hart, H. L. A. 2012. The Concept of Law. Oxford University Press.Google Scholar
Haynes, C., Miles, S. & Luck, M. 2014. Monitoring the impact of norms upon organisational performance: a simulation approach. In Coordination, Organizations, Institutions, and Norms in Agent Systems IX, Lecture Notes in Computer Science 8386, 103–119. Springer.Google Scholar
Hoffmann, M. J. 2005. Self-organized criticality and norm avalanches. In Proceedings of the Symposium on Normative Multi-Agent Systems, AISB05: Social Intelligence and Interaction in Animals, Robots and Agents, 117–125.Google Scholar
Holland, J. H. 1992. Complex adaptive systems. Daedalus 121(1), 17–30.Google Scholar
Hollander, C. D. & Wu, A. S. 2011. The current state of normative agent-based systems. Journal of Artificial Societies and Social Simulation 14(2), 6.Google Scholar
Itaiwi, A. M. K., Ahmad, M. S., Tang, A. Y. C. & Mahmoud, M. A. 2014. A proposed norms’ benefits awareness framework for norms adoption. In International Conference on Information Technology and Multimedia, pages 287–292.Google Scholar
Joseph, S., Sierra, C. & Schorlemmer, M. 2008. A coherence based framework for institutional agents. In Coordination, Organizations, Institutions, and Norms in Agent Systems III, Lecture Notes in Computer Science 4870, 287–300. Springer.Google Scholar
Kittock, J. E. 1994. 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, Nadel L. & Stein D. (eds). Addison-Wesley Publishing Company, 1–14.Google Scholar
Kotsiantis, S. & Kanellopoulos, D. 2006. Association rules mining: a recent overview. GESTS International Transactions on Computer Science and Engineering 32(1), 71–82.Google Scholar
Kubík, A. 2003. Toward a formalization of emergence. Artificial Life 9(1), 41–65.Google Scholar
Mahmoud, M. A., Ahmad, M. S., Ahmad, A., Yusoff, M. Z. M. & Mustapha, A. 2012a. A norms mining approach to norms detection in multi-agent systems. In International Conference on Computer Information Science, Volume 1, 458–463.Google Scholar
Mahmoud, S., Barakat, L., Miles, S., Taweel, A., Delaney, B. & Luck, M. 2014. Information-based incentivisation when rewards are inadequate. In ECAI, 591–596.Google Scholar
Mahmoud, S., Griffiths, N., Keppens, J. & Luck, M. 2010. An analysis of norm emergence in Axelrod’s model. In Proceedings of 8th European Workshop on Multi-Agent Systems.Google Scholar
Mahmoud, S., Griffiths, N., Keppens, J. & Luck, M. 2012b. Efficient norm emergence through experiential dynamic punishment. In Proceedings of the Twentieth European Conference on Artificial Intelligence, Volume 12, 576–581.Google Scholar
Mahmoud, S., Griffiths, N., Keppens, J. & Luck, M. 2013. Norm emergence through dynamic policy adaptation in scale free networks. In Coordination, Organizations, Institutions, and Norms in Agent Systems VIII, Aldewereld H. & Sichman J. (eds), Lecture Notes in Computer Science 7756, 123–140. Springer Berlin Heidelberg.Google Scholar
Mahmoud, S., Griffiths, N., Keppens, J., Taweel, A., Bench-Capon, T. J. & Luck, M. 2015a. Establishing norms with metanorms in distributed computational systems. Artificial Intelligence and Law 23(4), 367–407.CrossRefGoogle Scholar
Mahmoud, S., Miles, S., Taweel, A., Delaney, B. & Luck, M. 2015b. Norm establishment constrained by limited resources. In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, 1819–1820. International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
Miceli, T. J. & Bucci, C. 2005. A simple theory of increasing penalties for repeat offenders. Review of Law & Economics 1(1), 71–80.Google Scholar
Mill, J. S. 1863. Utilitarianism. Parker, Son and Bourn.Google Scholar
Mintzberg, H. & Waters, J. A. 1985. Of strategies, deliberate and emergent. Strategic Management Journal 6(3), 257–272.Google Scholar
Modgil, S., Oren, N., Faci, N., Meneguzzi, F., Miles, S. & Luck, M. 2015. Monitoring compliance with e-contracts and norms. Artificial Intelligence and Law 23(2), 161–196.Google Scholar
Mogul, J. C. 2006. Emergent (mis) behavior vs. complex software systems. ACM SIGOPS Operating Systems Review 40(4), 293–304.Google Scholar
Morales, J., Lopez-Sanchez, M., Rodriguez-Aguilar, J. A., Vasconcelos, W. & Wooldridge, M. 2015. Online automated synthesis of compact normative systems. ACM Transactions on Autonomous and Adaptive Systems 10(1), 2.CrossRefGoogle Scholar
Mungovan, D., Howley, E. & Duggan, J. 2011. The influence of random interactions and decision heuristics on norm evolution in social networks. Computational and Mathematical Organization Theory 17(2), 152–178.Google Scholar
Oren, N. & Meneguzzi, F. 2013. Norm identification through plan recognition. In Proceedings of the Workshop on Coordination, Organization, Institutions and Norms in Agent Systems (COIN 2013@ AAMAS).Google Scholar
Parunak, H. V. D. & VanderBok, R. S. 1997. Managing emergent behavior in distributed control systems. In Proceedings of ISA Tech'97, Instrument Society of America, 1–8.Google Scholar
Riveret, R., Artikis, A., Busquets, D. & Pitt, J. 2014. Self-governance by transfiguration: from learning to prescriptions. In International Conference on Deontic Logic in Computer Science, 177–191. Springer.CrossRefGoogle Scholar
Savarimuthu, B. & Cranefield, S. 2011. Norm creation, spreading and emergence: a survey of simulation models of norms in multi-agent systems. Multiagent and Grid Systems–An International Journal 7(1), 21–54.Google Scholar
Savarimuthu, B., Cranefield, S., Purvis, M. & Purvis, M. 2008. Role model based mechanism for norm emergence in artificial agent societies. Coordination, Organizations, Institutions, and Norms in Agent Systems III 487, 203–217.Google Scholar
Savarimuthu, B., Cranefield, S., Purvis, M. & Purvis, M. 2010. A data mining approach to identify obligation norms in agent societies. In Agents and Data Mining Interaction, Lecture Notes in Computer Science 5980, 43–58. Springer.Google Scholar
Savarimuthu, B., Cranefield, S., Purvis, M. & Purvis, M. 2013a. Identifying prohibition norms in agent societies. Artificial Intelligence and Law 21(1), 1–46.Google Scholar
Savarimuthu, B. & Dam, H. K. 2013. Towards mining norms in open source software repositories. In International Workshop on Agents and Data Mining Interaction, 26–39. Springer.Google Scholar
Savarimuthu, B., Padget, J. & Purvis, M. 2013b. Social norm recommendation for virtual agent societies. In International Conference on Principles and Practice of Multi-Agent Systems, 308–323. Springer.Google Scholar
Savarimuthu, B. T. R., Cranefield, S., Purvis, M. & Purvis, M. 2007. Norm emergence in agent societies formed by dynamically changing networks. In Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology, 464–470.Google Scholar
Sen, O. & Sen, S. 2010. Effects of social network topology and options on norm emergence. In Coordination, Organizations, Institutions and Norms in Agent Systems V, Lecture Notes in Computer Science 6069, 211–222. Springer.Google Scholar
Sen, S. & Airiau, S. 2007. Emergence of norms through social learning. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, 1507–1512.Google Scholar
Sensoy, M., Norman, T. J., Vasconcelos, W. W. & Sycara, K. 2012. OWL-POLAR: a framework for semantic policy representation and reasoning. Web Semantics: Science, Services and Agents on the World Wide Web 12, 148–160.Google Scholar
Seth, A. K. 2008. Measuring emergence via nonlinear Granger causality. Artificial Life 11, 545–552.Google Scholar
Shoham, Y. & Tennenholtz, M. 1995. On social laws for artificial agent societies: off-line design. Artificial Intelligence 73(1–2), 231–252.Google Scholar
Shoham, Y. & Tennenholtz, M. 1997. On the emergence of social conventions: modeling, analysis, and simulations. Artificial Intelligence 94(1), 139–166.Google Scholar
Singh, M. P. 2013. Norms as a basis for governing sociotechnical systems. ACM Transactions on Intelligent Systems and Technology (TIST) 5(1), 21.Google Scholar
Singh, M. P., Arrott, M., Balke, T., Chopra, A. K., Christiaanse, R., Cranefield, S., Dignum, F., Eynard, D., Farcas, E., Fornara, N. & Gandon, F. 2013. The uses of norms. In Normative Multi-Agent Systems, Volume 4, Andrighetto G., Governatori G., Noriega P. & van der Torre L. (eds). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 191–229.Google Scholar
Sirin, E., Parsia, B., Grau, B. C., Kalyanpur, A. & Katz, Y. 2007. Pellet: a practical OWL-DL reasoner. Web Semantics: Science, Services and Agents on the World Wide Web 5(2), 51–53.Google Scholar
Sycara, K. P. 1998. Multiagent systems. AI magazine 19(2), 79–92.Google Scholar
Tinnemeier, N., Dastani, M. & Meyer, J.-J. 2010. Programming norm change. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: Volume 1, 957–964. International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
Van der Aalst, W. M. P., van Dongen, B. F., Herbst, J., Maruster, L., Schimm, G. & Weijters, A. J. M. M. 2003. Workflow mining: a survey of issues and approaches. Data & Knowledge Engineering 47(2), 237–267.Google Scholar
Vasconcelos, W., Kollingbaum, M. J. & Norman, T. J. 2007. Resolving conflict and inconsistency in norm-regulated virtual organizations. In Proceedings of the Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, 632–639. ACM.Google Scholar
Vasconcelos, W. W., Kollingbaum, M. J. & Norman, T. J. 2009. Normative conflict resolution in multi-agent systems. Autonomous Agents and Multi-Agent Systems 19(2), 124–152.Google Scholar
Villatoro, D., Andrighetto, G., Sabater-Mir, J. & Conte, R. 2011a. Dynamic sanctioning for robust and cost-efficient norm compliance. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Volume 11, 414–419.Google Scholar
Villatoro, D., Sabater-Mir, J. & Sen, S. 2011b. Social instruments for robust convention emergence. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Volume 11, 420–425.Google Scholar
Villatoro, D., Sen, S. & Sabater-Mir, J. 2009. Topology and memory effect on convention emergence. In Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, 233–240. IEEE Computer Society.Google Scholar
Walker, A. & Wooldridge, M. 1995. Understanding the emergence of conventions in multi-agent systems. In Proceedings of the First International Conference on Multiagent Systems, 384–389.Google Scholar
Wolf, T. D. & Holvoet, T. 2005. Emergence versus self-organisation: different concepts but promising when combined. In Engineering Self-Organising Systems, Brueckner S., Serugendo G. D. M., Karageorgos A. & Nagpal R. (eds), Lecture Notes in Computer Science 3464, 77–91. Springer.Google Scholar
y López, F. L. & Luck, M. 2004. A model of normative multi-agent systems and dynamic relationships. In Regulated Agent-Based Social Systems, Lindemann G., Moldt D. & Paolucci M. (eds), Lecture Notes in Computer Science 2934, 259–280. Springer.Google Scholar
Zhang, Y. & Leezer, J. 2009. Emergence of social norms in complex networks. In Proceedings of the International Conference on Computational Science and Engineering, Volume 4, 549–555. IEEE.Google Scholar
Zimmermann, M. G. & Eguluz, V. M. 2005. Cooperation, social networks, and the emergence of leadership in a prisoner’s dilemma with adaptive local interactions. Physical Review E 72(5), 056118.Google Scholar