This article introduces the research issues related to and definition of normative multiagent systems. It also describes the papers selected from NorMAS05 that are part of this double special issue and relates the papers to each other. Keywords Norms . Multiagent systems . Normative multiagent systemsNormative multiagent systems as a research area can be defined as the intersection of normative systems and multiagent systems. Since the use of norms is a key element of human social intelligence, norms may be essential too for artificial agents that collaborate with humans, or that are to display behavior comparable to human intelligent behavior. By integrating norms and individual intelligence normative multiagent systems provide a promising model for human and artificial agent cooperation and co-ordination, group decision making, multiagent organizations, regulated societies, electronic institutions, secure multiagent systems, and so on.With 'normative' we mean 'conforming to or based on norms', as in normative behavior or normative judgments. According to the Merriam-Webster Online (2005) Dictionary, other meanings of normative not considered here are 'of, relating to, or determining norms or standards', as in normative tests, or 'prescribing norms', as in normative rules of ethics or normative grammar. With 'norm' we mean 'a principle

The COVID-pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers' demands for advice despite the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing specific local conditions and the social reactions of individuals. While experts have built simulation models to estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic crisis. Modelling that has such a large potential impact upon people's lives is a great responsibility. This paper calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls on stakeholders to improve the rapidity with which data from trusted sources are released to the community (in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and the social/economic value of research.

This special issue contains four selected and revised papers from the second international workshop on normative multiagent systems, for short NorMAS07 (Boella et al. In this editorial we discuss the shift, examples, and 10 new challenges in this more dynamic setting, which we use to introduce the papers of this special issue.Keywords Norms · Multiagent Systems · Normative multiagent systems · Social mechanism design · Artifical social systems Towards a more dynamic interactionist viewTraditionally normative systems have been studied in philosophy, sociology, law, and ethics, and during the past two decades they have been studied in deontic logic in computer science ( EON). Normative multiagent systems is a research area where the traditional normative systems and EON research fields meet agent research. The proposed solutions to the EON research problems are changing, and solutions based on multiagent systems are increasing. Gradually the EON research focus changes from logical relations among norms, to, for

During the COVID-19 crisis there have been many difficult decisions governments and other decision makers had to make. E.g. do we go for a total lock down or keep schools open? How many people and which people should be tested? Although there are many good models from e.g. epidemiologists on the spread of the virus under certain conditions, these models do not directly translate into the interventions that can be taken by government. Neither can these models contribute to understand the economic and/or social consequences of the interventions. However, effective and sustainable solutions need to take into account this combination of factors. In this paper, we propose an agent-based social simulation tool, ASSOCC, that supports decision makers understand possible consequences of policy interventions, but exploring the combined social, health and economic consequences of these interventions.

The Delphi methodology allowed for international consensus on a new procedure specific global rating scale for assessment of competence in EVAR. The resulting scale, EndoVascular Aortic Repair Assessment of Technical Expertise (EVARATE), represents key elements in the procedure. EVARATE constitutes an assessment tool for providing structured feedback to endovascular operators in training.

Multiagent system research tries to obtain predictability of social systems while preserving autonomy at the level of the individual agents. In this article, social theory is called upon to propose a solution to this version of the micro-macro problem. The use of norms and learning of norms is one possible solution. An implementation of norms and normative learning is proposed and evaluated by using simulation studies and measures for the internalizing and spreading of norms. The results of the simulations provide food for thought and further research. Modeling the norm set of the group in accordance with the individual mind-set in absence of other information (i.e., at the beginning of the group-forming process) proves to be a more fruitful starting point than a random set.

In this study we aim to describe in what ways the behaviour of non-player characters (NPCs) affects to what extent the player finds the game experience to be believable. To this end, we have conducted an online survey, where respondents were asked to classify and describe NPCs. Furthermore, we also examined recordings of NPCs in games. These data sources were analysed using a model for NPC social believability in order to describe the effects of NPC behaviour in relation to how different types of NPCs are perceived as being believable. Based on this we were able to construct a model of NPC believability, which describes the NPC’s level of complexity and ability to handle a mutable social context. As described by the model, NPCs are currently less capable of handling changing social contexts. They do, however, show promise, and given current emerging technologies it is feasible that new types of more socially capable NPCs will arise within the near future.

Collective dilemmas have attracted widespread interest in several social sciences and the humanities including economics, sociology and philosophy. Since Hardin's intuitive example of the Tragedy of the Commons, many real-world public goods dilemmas have been analysed with a wide ranging set of possible and actual solutions. The plethora of solutions to these dilemmas suggests that people make different kinds of decision in different situations. Rather than trying to find a unifying kind of reasoning to capture all situations, as the paradigm of rationality has done, this article develops a framework of agent decision-making for social simulation, that takes seriously both different kinds of decision making as well as different interpretations of situations. The Contextual Action Framework for Computational Agents (CAFCA) allows for the modelling of complex social phenomena, like dilemma situations, with relatively simple agents by shifting complexity from an agent's cognition to an agent's context.