(Demo) Systematic Experimentation Using Scenarios in Agent Simulation: Going Beyond Parameter Space

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.