Quantum Imprecise Bayesian Networks (QIBN) for Modelling Socio-Ecological-Technical Systems under Uncertainty

Socio-ecological-technical systems are dynamic ecosystems composed of natural and man-made elements, whose interactions are affected not only by the natural environment, but also culture, personal behaviour, politics, economics and social organisation. These systems must be resilient to respond systemically to shocks and stresses. To cope with the inherent uncertainty of load effects and resistances, safety analyses are often performed through risk-based methods. An interesting tool is represented from Bayesian Network (BN), a probabilistic graphical model useful for risk-informed decision making. A notable example of socio-ecological-technical systems is represented by urban ecosystems. They rely on infrastructures that are not stand-alone and are dependent on other systems (environmental, cyber, physical, and human) to which they are interconnected, i.e., forming a system of systems. Moreover, empirical findings show that humans tend to violate the expected utility metric and consequently violate the laws of Classical Probability (CP). Thus, novel methodologies are necessary to model, understand, and predict their dynamic responses. (ii) hidden interdependencies, where the variables of the BN can be dependent even if they are not linked nor sharing a common parent node, and (iii) human decision making under uncertainty. The promising features of QIBN are demonstrated through three benchmark examples of different nature: (a) two-stage gambling as an example of human decision making showing violation of the total probability theorem, (b) Structural Health Monitoring (SHM), and (c) damage monitoring through risk-informed digital twin technologies.