Recent years have seen an increase in the application of ideas from the social sciences to computational systems. Nowhere has this been more pronounced than in the domain of multiagent systems. Because multiagent systems are composed of multiple individual agents interacting with each other many parallels can be drawn to human and animal societies. One of the main challenges currently faced in multiagent systems research is that of social control. In particular, how can open multiagent systems be configured and organized given their constantly changing structure? One leading solution is to employ the use of social norms. In human societies, social norms are essential to regulation, coordination, and cooperation. The current trend of thinking is that these same principles can be applied to agent societies, of which multiagent systems are one type. In this article, we provide an introduction to and present a holistic viewpoint of the state of normative computing (computational solutions that employ ideas based on social norms.) To accomplish this, we (1) introduce social norms and their application to agent-based systems; (2) identify and describe a normative process abstracted from the existing research; and (3) discuss future directions for research in normative multiagent computing. The intent of this paper is to introduce new researchers to the ideas that underlie normative computing and survey the existing state of the art, as well as provide direction for future research.

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The Current State of Normative Agent-Based Systems

Complex systems often exhibit emergent behaviour, unexpected macro-level behaviour caused by the interaction of micro-level components. In multiagent systems, these micro-level components may be autonomous agents and the emergent behaviour may be expressed as norms—patterns of behaviour that arise among the agents in response to their environment and each other. These emergent norms may be beneficial (e.g. by encouraging cooperative behaviour), or detrimental, but in either case it is useful to recognize these norms as they emerge and either encourage or discourage their establishment. We term this process engineering the emergence of norms and have identified three steps: the identification of a possible norm, evaluation of its benefit and its encouragement (or discouragement). This paper is an attempt to provide a survey of existing research related to these steps. We also provide an analysis of the approaches based upon their suitability for a variety of normative systems: we examine the requirements for agents to have autonomy over their choice of norms, the degree of observability required in the system, and the norm enforcement methods. The paper concludes with an discussion of open issues.

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Engineering the emergence of norms: a review

This chapter is concerned with the dynamics of social norms, that is the way that such norms change. In particular this chapter concentrates on the lifecycle that social norms go through, focusing on the generation of norms, the way that norms spread and stabilize, and the way that norms evolve. We also discuss the cognitive mechanisms behind norm compliance, the role of culture in norm dynamics, and the way that trust affects norm dynamics.

Social) Norm Dynamics

Computational social science grows from several research traditions with roots in The Enlightenment and earlier origins in Aristotle’s comparative analysis of social systems. Extant standards of scientific quality and excellence have been inherited through the history and philosophy of science in terms of basic principles, such as formalization, testing, replication, and dissemination. More specifically, the properties of Truth, Beauty, and Justice proposed by C.A. Lave and J.G. March for mathematical social science are equally valid criteria for assessing quality in social simulation models. Helpful as such classic standards of quality may be, social computing adds new scientific features (complex systems, object-oriented simulations, network models, emergent dynamics) that require development as additional standards for judging quality. Social simulation models in particular (e.g., agent-based modeling) contribute further specific requirements for assessing quality. This paper proposes and discusses a set of dimensions for discerning quality in social simulations, especially agent-based models, beyond the traditional standards of verification and validation.

Matthew J. Hoffmann

Papers

Self-Organized Criticality and Norm Avalanches.