Infonomics of Autonomous Digital Twins

Abstract

High autonomy is challenging to achieve in digital twins. This is due to the lack of understanding of the socio-technical challenges and the information needs of digital twin autonomy. In this paper, we contextualize digital twin autonomy in terms of human and technical factors, identify novel socio-technical classes of digital twins with varying levels of autonomy, and define strategies that help improve autonomy across these classes. Our strategies are governed by information valuation models we developed specifically for digital twins. Our approach fosters a systematic top-down technique to improve the autonomy of digital twins.