Abstract
Augmented reality is well known for extending the real world by adding computer-generated perceptual information and overlaid sensory information. In contrast, simulation worlds are commonly closed and rely on artificial social behaviour and synthetic sensory information generated by the simulator program or using data collected off-line by surveys. Agent-based modelling used for investigation and evaluation of social interaction and networking relies on parameterisable models. Finding accurate and representative parameter settings can be a challenge. In this work, a new simulation paradigm is introduced, providing augmented virtuality by coupling crowd sensing and social data mining with simulation worlds in real-time by using mobile agents in an unified way. A simple social network analysis case-study based on the Sakoda social interaction model and mobile crowd sensing demonstrates the capabilities of the new hybrid simulation method and the impact of collected real-world data on social simulation.
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Bosse, S., Engel, U.H. (2021). Combining Crowd Sensing and Social Data Mining with Agent-Based Simulation Using Mobile Agents Towards Augmented Virtuality. In: Ahrweiler, P., Neumann, M. (eds) Advances in Social Simulation. ESSA 2019. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-61503-1_12
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DOI: https://doi.org/10.1007/978-3-030-61503-1_12
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