The paper defines the ambiguity premium Δ_ε(x) as the gap between pessimistic and optimistic upper-level values over ε-optimal follower responses and provides bounds plus a screening workflow to trace robustness-efficiency frontiers in bilevel problems.
A survey of network interdiction models and algorithms
2 Pith papers cite this work. Polarity classification is still indexing.
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Multipartite GNN learns MILP formulations of network interdiction to outperform baselines on bi-level combinatorial tasks.
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A Diagnostic Framework for Implementation Risk in Bilevel Decision Problems: The Ambiguity Premium and the Robustness--Efficiency Frontier
The paper defines the ambiguity premium Δ_ε(x) as the gap between pessimistic and optimistic upper-level values over ε-optimal follower responses and provides bounds plus a screening workflow to trace robustness-efficiency frontiers in bilevel problems.
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Network Interdiction Goes Neural
Multipartite GNN learns MILP formulations of network interdiction to outperform baselines on bi-level combinatorial tasks.