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.
Within the second category, social impact, norms are considered in terms of cost provided to or imposed on the parties involved in a social interaction @cite_23 . For instance, punishment and sanctions are introduced as two enforcement mechanisms used to achieve the necessary social control required to impose social norms @cite_0 . Here they demonstrate a normative agent that can punish and sanction defectors and also dynamically choose the right amount of punishment and sanction to impose @cite_27 .
@cite_0 @cite_27 @cite_23
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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. Norms are an important part of human social systems, governing many aspects of group decision-making. Yet many popularly used social models neglect to model normative effects on human behavior, relying on simple probabilistic and majority voting models of influence diffusion. Within the multi-agent research community, the study of norm emergence, compliance, and adoption has resulted in new architectures and standards for normative agents; however few of these models have been successfully applied to real-world public policy problems. In this paper, we propose a new lightweight architecture for constructing normative agents to model human social systems; the aim of our research is to be able to study the effects of different public policy decisions on a community. Here we present a case study showing the usage of our architecture for predicting trends in smoking cessation resulting from a smoke-free campus initiative. Our agent-based model combines social, environmental, and personal factors to accurately predict smoking trends and attitudes. The performance of both the whole and ablated model is evaluated against statistics from an independent source. The right framework for studying normative issues in infosociety and MAS is that of deliberate or spontaneous social order, and intended or unintended, centralised or decentralised forms of social control. For effectively supporting human cooperation it is necessary to "incorporate" social and normative knowledge in intelligent technology; computers should deal with--and thus partially "understand"--permissions, obligations, power, roles, commitments, trust, etc. Here only one facet of this problem is considered: the spontaneous and decentralised norm creation, and normative monitoring and intervention. Cognitive aspects of spontaneous conventions, implicit commitments, tacit agreements, and the bottom-up issuing and spreading of norms are discussed. The transition from "face to face" normative relationships to some stronger constraints on agents' action, and to institutions and authority, and the possibility of a consequent increase of trust, are explored. In particular, I focus on the transition from two party trust, right, permission, and commitment, to three party relationships, where some witness or some enforcing authority is introduced. In this perspective of 'formalising the informal', i.e., the interpersonal unofficial normative matter, I discuss (also in order to stress dangers of computer-based formalisation and enforcement of rules) the important phenomenon of functional (collaborative) systematic violation of rules in organisation and cooperation, and the possible emergence of a "convention to violate". In a multiagent system where norms are used to regulate the actions agents ought to execute, some agents may decide not to abide by the norms if this can benefit them. Norm enforcement mechanisms are designed to counteract these benefits and thus the motives for not abiding by the norms. In this work we propose a distributed mechanism through which agents in the multiagent system that do not abide by the norms can be ostracised by their peers. An ostracised agent cannot interact anymore and looses all benefits from future interactions. We describe a model for multiagent systems structured as networks of agents, and a behavioural model for the agents in such systems. Furthermore, we provide analytical results which show that there exists an upper bound to the number of potential norm violations when all the agents exhibit certain behaviours. We also provide experimental results showing that both stricter enforcement behaviours and larger percentage of agents exhibiting these behaviours reduce the number of norm violations, and that the network topology influences the number of norm violations. These experiments have been executed under varying scenarios with different values for the number of agents, percentage of enforcers, percentage of violators, network topology, and agent behaviours. Finally, we give examples of applications where the enforcement techniques we provide could be used.