This paper focuses on defining and simulating behavioural outcomes of the by-stander effect. These insights were modeled by temporal-causal networks. Typi-cal patterns of bystander behaviour were translated into three requirements and seven simulated scenarios of the bystander eﬀect. All scenarios were simulated to showcase the main bystander eﬀect dynamics and its accordance with the litera-ture. Unknown parameters of the eﬀect were further estimated by a Simulated Annealing algorithm. In the end, the created model shows the potential to simu-late the bystander eﬀect in diﬀerent and new scenarios.
|Title of host publication||Proceedings of the 11th Annual International Conference on Brain-Inspired Cognitive Architectures for AI, BICA*AI'20|
|Publisher||Springer Nature Switzerland AG|
|Publication status||Accepted/In press - 28 Jul 2020|
|Name||Advances in Intelligent Systems and Computing|