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.

This paper demonstrates a disconnected ABM architecture that enables domain experts, and non-programmers to add qualitative insights into the ABM model without the intervention of the programmer. This role separation within the architecture allows policy-makers to systematically experiment with multiple policy interventions, different starting conditions, and visualizations to interrogate their ABM. Keywords: BehaviourFlow Multiple Experts Policy Validation Domain Expertise. 1 Introduction The ideal that agent-based modelling (ABM) in social simulation strives to achieve, in many cases, is a true representation of the'society-of-agents' under study, so that we may gain insight into (or even generate) surprising interactions, emergent behaviour, and some level of explainability in an otherwise complex scenario. This promise has led ABM to be used in many and varied domains, e.g., GIS and socio-ecological modelling [3][2], migration networks [13][6], epidemiological and crisis simulation [12][7], computer games [8], pedestrian dynamics [1][5], self-adaptive software [10][14], modelling emergence[11], emotion modelling [4][9]. Unfortunately, agent-based modelling mechanisms are rarely built to accommodate multiple different experts.