When designing an agent-based simulation, an important question to answer is how to model the decision making processes of the agents in the system. A large number of agent decision making models can be found in the literature, each inspired by different aims and research questions. In this paper we provide a review of 14 agent decision making architectures that have attracted interest. They range from production-rule systems to psychologically-and neurologically-inspired approaches. For each of the architectures we give an overview of its design, highlight research questions that have been answered with its help and outline the reasons for the choice of the decision making model provided by the originators. Our goal is to provide guidelines about what kind of agent decision making model, with which level of simplicity or complexity, to use for which kind of research question.

Today’s politicians are confronted with new information technologies to\ud tackle complex decision-making problems. In order to make sustainable decisions,\ud a profound analysis of societal problems and possible solutions (policy options)\ud needs to be performed. In this policy-analysis process, different stakeholders are\ud involved. Besides internal direct advisors of the policy makers (policy analysts),\ud external experts from different scientific disciplines can support evidence-based decision making. Despite the alleged importance of scientific advice in the policy-making\ud process, it is observed that scientific results are often not used. In this work, a concept\ud is described that supports the collaboration between scientists and politicians. We propose a science–policy interface that is realized by including information visualization in the policy-analysis process. Therefore, we identify synergy effects between\ud both fields and introduce a methodology for addressing the current challenges of\ud science–policy interfaces with visualization. Finally, we describe three exemplary\ud case studies carried out in European research projects that instantiate the concept of\ud this approach

Conflicts between laws can readily arise in situations governed by different laws, a case in point being when the context of an inferior law (or set of regulations) is altered through revision of a superior law. Being able to detect these conflicts automatically and resolve them, for example by proposing revisions to one of the modelled laws or policies, would be highly beneficial for legislators, legal departments of organizations or anybody having to incorporate legal requirements into their own procedures. In this paper we present a model based approach for detecting and finding legal conflicts through a combination of a formal model of legal specifications and a computational model based on answer set programming and inductive logic programming. Given specific scenarios (descriptions of courses of action), our model-based approach can automatically detect whether these scenarios could lead to contradictory outcomes in the different legal specifications. Using these conflicts as use cases, we apply inductive logic programming (ILP) to learn revisions to the legal component that is the source of the conflict. We illustrate our approach using a case-study where a university has to change its studentship programme after the government brings in new immigration regulations .

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