Abstract
Agent-based simulation is an alternative approach to traditional analytical methods for understanding and capturing different types of complex, dynamic interactive processes. However, the application of these models is currently not common in the field of socio-economical science and many researchers still consider them as intransparent, unreliable and unsuitable for prediction. One of the main reasons is that these models are often built on architectures derived from computational concepts, and hence do not speak to the selected domain’s ontologies. Using Triandis’ Theory of Interpersonal Behaviour, we are developing a new agent architecture for the choice model simulation that is capable of combining a diverse number of determinants in human decision-making and being enhanced by empirical data. It also aims to promote communication between technical scientists and other disciplines in a collaborative environment. This paper illustrates an overview of this architecture and its implementation in creating an agent population for the simulation of mobility demands in Switzerland.
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Acknowledgement
This project is part of the activities of SCCER CREST, which is financially supported by the Swiss Commission for Technology and Innovation (Innosuisse). The current version also utilises data from the Mobility and Transport Microcensus - 2015 edition, which provided by the Federal Office for Spatial Development (ARE) in October 2017.
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Nguyen, K., Schumann, R. (2020). On Developing a More Comprehensive Decision-Making Architecture for Empirical Social Research: Agent-Based Simulation of Mobility Demands in Switzerland. In: Paolucci, M., Sichman, J.S., Verhagen, H. (eds) Multi-Agent-Based Simulation XX. MABS 2019. Lecture Notes in Computer Science(), vol 12025. Springer, Cham. https://doi.org/10.1007/978-3-030-60843-9_4
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