A Peer-to-Peer Normative System to Achieve Social Order

Abstract

Abstract. Social order in distributed descentralised systems is claimed to be obtained by using social norms and social control. This paper presents a normative P2P architecture to obtain social order in multiagent systems. We propose the use of two types of norms that coexist: rules and conventions. Rules describe the global normative constraints on autonomous agents, whilst conventions are local norms. Social control is obtained by providing a non-intrusive control infrastructure that helps the agents build reputation values based on their respect of norms. Some experiments are presented that show how communities are dynamically formed and how bad agents are socially excluded.

An Approach to Operationalize Regulative Norms in Multiagent Systems

“…The application of norms in MAS is not a straightforward task, since heterogeneity and autonomy rule out any assumption concerning the way that heterogeneous agents are implemented and behave in MAS [Grizard et al, 2006]. In [Gaertner et al 2007], the authors of the paper propose to extend the coordination level of a MAS with a normative level, so that, norms can be integrated during the design and execution time of the system.…”

Organisational adaptation of multi-agent systems in a peer-to-peer scenario

“…As for MAS, the work in [24] focuses on adaptation of two types of norms: rules (global and mandatory) and conventions (local and optional). Rules are enforced by restricting interaction, thanks to a reputation service oered by a meta-level.…”

Distributed Norm Enforcement Via Ostracism

“…Two enforcement strategies have been studied to attain norm compliance: the use of power to change the utilities through sanctions or rewards [2,3,8,14], and the spread of normative reputation in order to avoid interaction with violators [4,6,7,12,15]. Both strategies have the goal of making norm adopters better off than norm violators.…”

“…Norm enforcement models in [2,6] show how violating the norm becomes an irrational strategy when punishment is possible. But these models assume the following: (i) agents are able to monitor other agents' activities; and (ii) agents have the ability to influence the resulting utility of interactions.…”

“…The second scales because no agent is the enforcement bottleneck, but it is less efficient since in a distributed environment not all violations can be detected. Assumption (ii) can be carried out through third-party enforcement [2], or self-enforcement [6] in which each agent carries out sanctions to agents it interacts with. Third party does not scale since it can easily be overwhelmed in a large system.…”