Designing agent-based models is a difficult task. Some guidelines exist to aid modelers in designing their models, but they generally do not include specific details on how the behavior of agents can be defined. This paper therefore proposes the AbCDe methodology, which uses causal discovery algorithms to specify agent behavior. The methodology combines important expert insights with causal graphs generated by causal discovery algorithms based on real-world data. This causal graph represents the causal structure among agent-related variables, which is then translated to behavioral properties in the agent-based model. To demonstrate the AbCDe methodology, it is applied to a case study in the airport security domain. In this case study, we explore a new concept of operations, using a service lane, to improve the efficiency of the security checkpoint. Results show that the models generated with the AbCDe methodology have a closer resemblance with the validation data than a model defined by experts alone.
|Title of host publication||Multi-Agent-Based Simulation|
|Editors||Koen H. Van Dam, Nicolas Verstaevel|
|Number of pages||14|
|Publication status||Published - 2022|
|Event||22nd International Workshop, Multi-Agent-Based Simulation - virtual event|
Duration: 3 May 2021 → 7 May 2021
|Name||Lecture Notes in Computer Science|
|Workshop||22nd International Workshop, Multi-Agent-Based Simulation|
|Abbreviated title||MABS 2021|
|Period||3/05/21 → 7/05/21|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- Causal Discovery
- Airport Security
- Agent-based Modelling