Open and decentralised multi-agent systems (ODMAS) are particularly vulnerable to the introduction of buggy or malevolent agents. It is therefore very important to protect these systems from such intrusions. In this article, we propose the L.I.A.R. model to control the agents' interactions. This model is issued from the social control approach, which consists in developing an adaptive and autoorganised control, that is set up by the agents themselves. As being intrinsically decentralised and non intrusive to the agents' internal functioning, it is more adapted to ODMAS than other approaches, like cryptographic security or centralised institutions. To implement such a social control, agents should be able to characterise interaction they observe and to sanction them. The L.I.A.R. model is composed of a social commitment model that enables agents to represent and reason about the interactions they perceive and models for social norms and social policies that allow agents to define and to evaluate the acceptability of the interactions. Also, L.I.A.R. contains a reputation model that enables any agent to apply a sanction to its peers. The L.I.A.R. model has been tested in an agentified peer-to-peer framework. The experiments show that this model is able to compute reputation levels quickly, precisely and efficiently. Moreover, these reputation levels are adaptive and enable agents to identify and isolate harmful agents. These reputation levels also enable agents to identify good peers, with which to pursue their interactions.