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.
Hollander and Wu @cite_8 refer to three categories of normative studies in the social sciences: 1) social function of norms @cite_31 , 2) impact of social norms @cite_7 , 3) mechanisms leading to the emergence and creation of norms @cite_35 . In the context of social function, norms are often concerned with the and of agent behavior; where oughtness refers to the condition where an agent should or should not perform an action, and expectation refers to the behavior that other agents expect to observe from that agent @cite_37 @cite_9 . An example of work belonging to this category is Boella and Torre's architecture containing separate subsystems for counts-as conditionals, conditional obligations, and conditional permissions @cite_15 .
@cite_35 @cite_37 @cite_7 @cite_8 @cite_15 @cite_9 @cite_31
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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. Social norms play a pivotal role in sustaining social order by regulating individual behaviors in a society. In normative multiagent systems, social norms have been used as an efficient mechanism to govern virtual agent societies towards cooperation and coordination. In this paper, we study the emergence of social norms via learning from repeated local interactions in networked agent societies. We propose a collective learning framework, which imitates the opinion aggregation process in human decision making, to study the impact of agent local collective behaviors on norm emergence in different situations. In the framework, each agent interacts repeatedly with all of its neighbors. At each step, an agent first takes a best-response action towards each of its neighbors and then combines all of these actions into a final action using ensemble learning methods. We conduct extensive experiments to evaluate the framework with respect to different network topologies, learning strategies, numbers of actions, and so on. Experimental results reveal some significant insights into norm emergence in networked agent societies achieved through local collective behaviors. 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". When entering a system, an agent should be aware of the obligations and prohibitions (collectively norms) that will aect it. Several solutions to this norm identication problem have been proposed, which make use of observations of either other’s norm compliant, or norm violating, behaviour. These solutions fail in situations where norms are typically violated, or complied with, respectively. In this paper we propose a Bayesian approach to norm identication which operates by learning from both norm compliant and norm violating behaviour. By utilising both types of behaviour, our work not only overcomes a major limitation of existing approaches, but also yields improved performance over the state-of-the-art. We evaluate its eectiveness empirically, showing, under certain conditions, high accuracy scores.