Towards Agents for Policy Making

  • Frank Dignum
  • Virginia Dignum
  • Catholijn M. Jonker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5269)


The process of introducing new public policies is a complex one in the sense that the behavior of society at the macro-level depends directly on the individual behavior of the people in that society and ongoing dynamics of the environment. It is at the micro-level that change is initiated, that policies effectively change the behavior of individuals. Since macro-models do not suffice, science has turned to develop and study agent-based simulations, i.e., micro-level models. In correspondence with the good scientific practice of parsimony, current ABSS models are based on agents with simple cognitive capabilities. However, the societies being modeled in policy making relate to real people with real needs and personalities, often of a multi-cultural composition. Those circumstances require the agents to be diversified to accommodate these facts.

In this positioning paper, we propose an incrementally complex model for agent reasoning that can describe the influence of policies or comparable external influences on the behavior of agents. Starting from the BDI model for agent reasoning, we discuss the effect when personality and Maslow’s hierarchy of needs are added to the model. Finally, we extend the model with a component that captures the cultural background and normative constitution of the agent.

In the paper we show how these extensions affect the filtering of the desires and intentions of the agent and the willingness of the agent to modify its behavior in face of a new policy. This way, simulations can be made that support the differentiation of behaviors in multi-cultural societies, and thus can be made to support policy makers in their decisions.


Meso Level Social Simulation Agent Reasoning Agent Belief Support Policy Maker 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Frank Dignum
    • 1
  • Virginia Dignum
    • 1
  • Catholijn M. Jonker
    • 2
  1. 1.Dept. Information and Computing SciencesUtrecht UniversityThe Netherlands
  2. 2.Technical University DelftThe Netherlands

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