Norms are known to be a major factor determining humans behavior. It's also shown that norms can be quite effective tool for building agent-based societies. Various normative architectures have been proposed for designing normative multi-agent systems (NorMAS). Due to human nature of the concept norms, many of these architectures are built based on theories in social sciences. Tipping point theory, as is briefly discussed in this paper, seems to have a great potential to be used for designing normative architectures. This theory deals with the factors that affect social epidemics that arise in human societies. In this paper, we try to apply the main concepts of this theory to agent-based normative architectures. We show several ways to implement these concepts, and study their effects in an agent-based normative scenario.
As explained by Axelrod in his seminal work An Evolutionary Approach to Norms, punishment is a key mechanism to achieve the necessary social control and to impose social norms in a self-regulated society. In this paper, we distinguish between two enforcing mechanisms. i.e. punishment and sanction, focusing on the specific ways in which they favor 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 simulation show us that sanction is more effective and less costly than punishment in the achievement and maintenance of cooperation and it makes the population more resilient to sudden changes than if it were enforced only by mere punishment. Agent-based models are a useful technique for rapidly prototyping complex social systems, they are widely used in a number of disciplines and can yield theoretical insights that are different from those produced by a variable based analysis. However, it remains difficult to compare the results of two models and to validate the performance of an agent-based simulation. In this paper, we present a case study on how to analyze the relationship between agent-based models using category theory. Category theory is a powerful mathematical methodology that was originally introduced to organize mathematical ideas according to their shared structure. It has been successfully employed in abstract mathematical domains, but has also enjoyed some success as a formalism for software engineering. Here we present a procedure for analyzing agent-based models using category theory and a case study in its usage at analyzing two different types of simulations. Holonic multi-agent systems (HOMAS) have their own properties that make them distinct from general multi-agent systems (MAS). They are neither like competitive multi-agent systems nor cooperative, and they have features from both of these categories. There are many circumstances that holonic agents need to negotiate. Agents involved in negotiations try to maximize their utility as well as their holon’s utility. In addition, holon’s Head can overrule the negotiation whenever it wants. These differences make defining a specific negotiation mechanism for holonic multi-agent systems more significant. In this work, holonic systems are introduced at the beginning; and then different aspects of negotiation in these systems are studied. We especially try to introduce the idea of holonic negotiations. A specific negotiation mechanism for holonic multi-agent systems is proposed which is consistent with the challenges of HOMAS.