A key problem in agent-based simulation is that integrating qualitative insights from multiple discipline experts is extremely hard. In most simulations, agent capabilities and corresponding behaviour needs to be programmed into the agent. We report on the architecture of a tool that disconnects the programmed functions of the agent, from the acquisition of capability and displayed behaviour. This allows multiple different domain experts to represent qualitative insights, without the need for code to be changed. It also allows a continuous integration, or even change, of qualitative behaviour processes, as more insights are gained. The consequent behaviour observed in the model is both, more faithful to the expert’s insight as well as able to be contrasted against other models representing other insights.

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Different Facets for Different Experts: A Framework for the Integration of Qualitative Insights into Agent-Based Model Development

  • Vivek Nallur,
  • Pedram Aghaei,
  • Graham Finlay

摘要

A key problem in agent-based simulation is that integrating qualitative insights from multiple discipline experts is extremely hard. In most simulations, agent capabilities and corresponding behaviour needs to be programmed into the agent. We report on the architecture of a tool that disconnects the programmed functions of the agent, from the acquisition of capability and displayed behaviour. This allows multiple different domain experts to represent qualitative insights, without the need for code to be changed. It also allows a continuous integration, or even change, of qualitative behaviour processes, as more insights are gained. The consequent behaviour observed in the model is both, more faithful to the expert’s insight as well as able to be contrasted against other models representing other insights.