  Near-optimality robustness extends multilevel optimization with a limited
deviation of a lower level from its optimal solution, anticipated by higher
levels. We analyze the complexity of near-optimal robust multilevel problems,
where near-optimal robustness is modelled through additional adversarial
decision-makers. Near-optimal robust versions of multilevel problems are shown
to remain in the same complexity class as the problem without near-optimality
robustness under general conditions.
