| Summary: | Norms provide a means to regulate the behaviour of the members of a society, organisa¬tion or system. While much work has been done on various aspects of norms, normative systems and normative behaviour, this work has been limited in several respects. In particular, the problems of norm emergence have only recently begun to be considered, with existing work adopting only simple structural models. This relates to two crucial issues that have not adequately been addressed. First, existing models of norms assume that sanctions are static, and do not change in relation to relevant information about the violator, the situation or the history. Yet, typically there is information available that can significantly impact on the nature of such sanctions, and can even allow so¬phisticated sanctioning structures that achieve more effective regulation. Second, work on norm emergence has typically assumed simple topological structures of agents, if any at all, yet real computational systems, in which norms are relevant, such as peer-to-peer systems and wireless sensor networks, may have topologies of varying degrees of sophistication. These topologies constrain potential relationships between agents, limiting the observation of violations, and possibly also limiting the kind of sanctions than may be imposed. In this thesis, therefore, we seek to address these problems in support of more effective norm-regulated systems, by developing mechanisms that can incentivise cooperative behaviour in societies of self-interested agents.
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