Convention emergence solves the problem of choosing, in a decentralized way and among all equally beneficial conventions, the same convention for the entire population in the system for their own benefit. Our previous work has shown that reaching 100% agreement is not as straighforward as assumed by previous researchers, that, in order to save computational resources fixed the convergence rate to 90% (measuring the time it takes for 90% of the population to coordinate on the same action). In this article we present the notion of social instruments as a set of mechanisms that facilitate and accelerate the emergence of norms from repeated interactions between members of a society, only accessing local and public information and thus ensuring agents' privacy and anonymity. Specifically, we focus on two social instruments: rewiring and observation. Our main goal is to provide agents with tools that allow them to leverage their social network of interactions while effectively addressing coordination and learning problems, paying special attention to dissolving metastable subconventions. The first experimental results show that even with the usage of the proposed instruments, convergence is not accelerated or even obtained in irregular networks. This result leads us to perform an exhaustive analysis of irregular networks discovering what we have defined as Self-Reinforcing Structures (SRS). The SRS are topological configurations of nodes that promote the establishment and persistence of subconventions by producing a continuous reinforcing effect on the frontier agents. Finally, we propose a more sophisticated composed social instrument (observation + rewiring) for robust resolution of subconventions , which works by the dissolution of the stable frontiers caused by the Self-Reinforcing Substructures (SRS) within the social network.

As explained by Axelrod in his seminal work An Evolutionary Approach to Norms, punishment is a key mechanism to achieve the necessary social con-trol and to impose social norms in a self-regulated society. In this paper, we distinguish between two enforcing mechanisms. i.e. punishment and sanc-tion, focusing on the specific ways in which they fa-vor the emergence and maintenance of cooperation. The key research question is to find more stable and cheaper mechanisms for norm compliance in hybrid social environments (populated by humans and computational agents). To achieve this task, we have developed a normative agent able to punish and sanction defectors and to dynamically choose the right amount of punishment and sanction to impose on them (Dynamic Adaptation Heuristic). The results obtained through agent-based simula-tion show us that sanction is more effective and less costly than punishment in the achievement and maintenance of cooperation and it makes the pop-ulation more resilient to sudden changes than if it were enforced only by mere punishment. 1

We present the notion of Social Instruments as mechanisms that facilitate the emergence of conventions from repeated interactions between members of a society. Specifically, we focus on two social instruments: rewiring and observation. Our main goal is to provide agents with tools that allow them to leverage their social network of interactions when effectively addressing coordination and learning problems, paying special attention to dissolving metastable subconventions. Our initial experiments throw some light on how Self-Reinforcing Substructures (SRS) in the network prevent full convergence to society-wide conventions, resulting in reduced convergence rates. The use of an effective composed social instrument, observation + rewiring, allow agents to achieve convergence by eliminating the subconventions that otherwise remained meta-stable.

This paper examines the decentralized recognition of groups within a multiagent normative society in dynamic environments. In our case, a social group is defined based on the set of social norms used by its members. These social norms regulate interactions under certain situations, and situations are determined by the environmental conditions. Environmental conditions might change unexpectedly, and so should the notion of social group for each agent. Consequently, agents need mechanisms to adjust their notion of group dynamically and accordingly the agents with whom it is socially related. In this work we analyze how different algorithms (whitelisting, blacklisting, labelling), that allow agents to recognize the others as members of a certain social group, behave in these dynamic environments. Simulation results are shown, confirming that the limited memory approach reacts better against environmental changes. Moreover we compare two approaches that regulate the adaptation of the relevance of norms and the notion of group: the unlimited normative memory and the limited memory.