Digital twin as risk-free experimentation aid for techno-socio-economic systems

Barat, Souvik, Kulkarni, Vinay, Clark, Tony and Barn, Balbir ORCID logoORCID: https://orcid.org/0000-0002-7251-5033 (2022) Digital twin as risk-free experimentation aid for techno-socio-economic systems. MODELS '22: Proceedings of the 25th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems. In: ACM / IEEE 25th International Conference on Model Driven Engineering Languages and Systems (MODELS), 23-28 Oct 2022, Montreal, Canada. . [Conference or Workshop Item] (Accepted/In press)

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Abstract

Environmental uncertainties and hyperconnectivity force techno-socio-economic systems to introspect and adapt to succeed and survive. Current practice is chiefly intuition-driven which is inconsistent with the need for precision and rigor. We propose that this can be addressed through the use of digital twins by combining results from Modelling & Simulation, Artificial Intelligence, and Control Theory to create a risk free ‘in silico’ experimentation aid to help: (i) understand why system is the way it is, (ii) be prepared for possible outlier conditions, and (iii) identify plausible solutions for mitigating the outlier conditions in an evidence-backed manner. We use reinforcement learning to systematically explore the digital twin solution space. Our proposal is significant because it advances the effective use of digital twins to new problem domains that have greater impact potential. Our novel approach contributes a meta model for simulatable digital twin of industry scale techno-socio-economic systems, agent-based implementation of the digital twin, and an architecture that serves as a risk-free experimentation aid to support simulation-based evidence-backed decision-making. We also discuss validation of this approach, associated technology infrastructure, and architecture through a representative sample of industry-scale real-world use cases.

Item Type: Conference or Workshop Item (Paper)
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Research Areas: A. > School of Science and Technology > Computer Science > Foundations of Computing group
Item ID: 35416
Notes on copyright: © {Owner/Author | ACM} 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in MODELS '22: Proceedings of the 25th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, http://dx.doi.org/10.1145/{number}
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Depositing User: Balbir Barn
Date Deposited: 15 Jul 2022 14:41
Last Modified: 23 Oct 2022 03:04
URI: https://eprints.mdx.ac.uk/id/eprint/35416

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