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The cognitive legacy of norm simulation

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Abstract

The comprehension of norms in complex social systems is one of the most active fields of research in agent-based modelling. This is faced with the challenge to comprehend the recursive interaction between inter- and intra-agent processes. In this article, a comparative analysis of selected cases of normative agent architectures will be given based on a review of theories of norms in the social sciences. This allows to identify the prerequisites for a representation of the cognitive processes of norm recognition. As yet, there is no unequivocal concept for the design of normative agents. Different approaches are compared along the line of different theoretical accounts.

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Notes

  1. Such co-ordination processes have already been implemented in simulation models (comp. e.g. Sen and Airiau 2007). These models are characterised by the fact that a certain co-ordination of agents action emerge in course of the simulation run because this is in the benefit of all agents. If learning agents compute their utilities, gradually their behaviour becomes co-ordinated since this maximises expected utilities. However, the agents need not ‘know’ the norm. The norm does not become incorporated in the agent’s mind. Such models contribute to a comprehension of an important aspect of self-organised systems. However, they do not capture an important aspect of human societies, namely the recognition of emergent macro-properties.

  2. The cases that have been considered are: Norms in Multi-Agent Systems: From theory to practice (Vazquez-Salceda et al. 2005), On the synthesis of useful social laws for artificial agent societies (Shoham and Tennenholtz 1992), Normative KGP Agents (Sadri et al. 2006), An architecture for autonomous normative agents (Lopez and Marquez 2004), Norm-oriented programming of electronic institutions (Garcia-Camino et al. 2006), From desires, obligations and norms to goals (Dignum et al. 2002), From Social Monitoring to Normative Influence (Conte and Dignum 2001), From conventions to prescriptions (Conte and Castelfranchi 1999), Deliberative normative agents (Castelfranchi et al. 2000), Norms in Artificial Decision Making (Boman 1999), The BOID Architecture (Broersen et al. 2001), An architecture of a normative System (Boella and van der Torre 2006), Norm Governed Multiagent Systems (Boella and van der Torre 2003), Norm internalization in Artificial Societies (Andrighetto et al. 2010).

  3. This is a crucial difference to both game theoretical models as well as, for instance, Goffman’s interaction order. Game theoretical agents have a certain dynamical probability to defect but they do not have access to or deliberately manipulate this probability themselves. On the other hand, Goffman’s interaction order depends also on habits. These include ways of performing, which can be described as tacit knowledge, i.e. a certain ‘know-how’ that need not be described by a symbolic representation.

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  1. Institute for Sociology, RWTH Aachen University, Eilfschornsteinstr. 7, 52062, Aachen, Germany

    Martin Neumann

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Neumann, M. The cognitive legacy of norm simulation. Artif Intell Law 20, 339–357 (2012). https://doi.org/10.1007/s10506-012-9129-4

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