Search
  • All Journals
Advanced Search
Search articles by Title, Author, Keywords, DOI
7.7CiteScore
2.0Impact Factor
Advanced Search
MAP  >  The Knowledge Engineering Review
2017 Volume 32
Article Contents
Next Previous
RESEARCH ARTICLE   Open Access    

Engineering the emergence of norms: a review

  • Department of Informatics

More Information
  • 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.
  • 加载中
  • 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.

    Google 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.

    Google 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.

    Google 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.

    Google 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.

    Google 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.

    Google 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.

    Google 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

  • Cite this article

    Chris Haynes, Michael Luck, Peter McBurney, Samhar Mahmoud, Tomáš Vítek, Simon Miles. 2017. Engineering the emergence of norms: a review. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888917000169
    Chris Haynes, Michael Luck, Peter McBurney, Samhar Mahmoud, Tomáš Vítek, Simon Miles. 2017. Engineering the emergence of norms: a review. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888917000169
    shu
Download PDF

Article Metrics

Article views(32) PDF downloads(43)

Other Articles By Authors

RESEARCH ARTICLE   Open Access    

Engineering the emergence of norms: a review

  • Department of Informatics

  • Published online: 22 August 2017
The Knowledge Engineering Review  32,  2017, 32(1)  |  Cite this article

Abstract: 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.

    HTML

    Acknowledgments

    • This material is based upon work supported by the Air Force Office of Scientific Research, Air Force Materiel Command, USAF under Award No. FA9550-15-1-0092.

    • Fortunately, the lack of accepted definitions leads most authors to make their semantics clear in each paper.

    • Of course, there are examples of laws that do change behaviour rapidly, such as the seat-belt laws we describe in Section 8.

    • Norms concerned with how agents should react to norm violations are referred to as metanorms within the literature (Axelrod, 1986). We examine research on using metanorms to influence norm emergence in Section 8.1.

    • Bedau terms the emergent phenomena we are concerned with as weak emergence to distinguish it from a metaphysically inconsistent version he terms strong emergence.

    • Mintzberg and Waters (1985) make this explicit in their definition of emergent organizational strategies.

    • Such an agent is known as a norm entrepreneur (Finnemore & Sikkink, 1998).

    • Within the literature it is also referred to as norm recognition or norm learning.

    • For example, if there is a norm obliging vehicles to travel under 70 miles/hour, and a norm obliging trucks to travel under 50 miles/hour, the latter is a more specialized norm since a truck is a type of vehicle.

    Rights and permissions
    • © Cambridge University Press, 2017 2017Cambridge University Press
References (100)
  • About this article
    Cite this article
    Chris Haynes, Michael Luck, Peter McBurney, Samhar Mahmoud, Tomáš Vítek, Simon Miles. 2017. Engineering the emergence of norms: a review. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888917000169
    Chris Haynes, Michael Luck, Peter McBurney, Samhar Mahmoud, Tomáš Vítek, Simon Miles. 2017. Engineering the emergence of norms: a review. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888917000169
    shu

    • Catalog

      /

      DownLoad:  Full-Size Img  PowerPoint
      Return
      Return