“…Already today we may mention that the following refined classes of techniques can be naturally suggested for our new class of networks under uncertainty, for their identification, optimization and extension: (i) Tchebychev Approximation [53], (ii) Semi-Infinite Optimization [29], (iii) Generalized Semi-Infinite Optimization [48,49,56], (iv) Bi-level and Multilevel Optimization [36], (v) Disjunctive Optimization [2], (vi) Robust Optimization [24,39,42], (vii) Conic Optimization [4], (viii) Optimal Control [1], and (ix) Stochastic Optimal Control [33]. Concerning classes of future real-world applications we would like to recommend emerging challenges of, for example, (a) Collaborative Games under Ellipsoidal Uncertainty or (per inner or outer approximations) Hypercube Uncertainty [11,52], (b) Transportation ("Piano Mover's" and many more) problems [32,40], (c) Supply Chain and Inventory Management [12,20,30,41,43,57] Production Planning [38], various kinds of (d) Design problems [46], (e) Artificial Intelligence and Machine Learning (e.g., "Infinite Kernel Learning") [6,13,28], and (f ) Finance, Actuarial Sciences and Pension Fund Systems [14,19,55]. 10.…”