Abstract
We study the aggregation of user preferences in the network flow model. These users can be referred to the followers in the sequential decision model. We transform the preference data revealed by the followers into a bundle of linear constraints to represent the strategic norms of the followers in the centralized decision model. We also show that the revealed preference theory is a useful foundation to construct such a multiple users’ rational norm without losing much the richness of preference by our simplification. Although the aggregated preference results in a set of ambiguous decision norms of the representative follower, in our case a convex polyhedron, we can apply this framework to the bilevel optimization and formulate this sequential decision problem as a maximin problem which is the centralized decision problem of the leader in our network flow model.
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Sariddichainunta, P., Inuiguchi, M. (2019). Revealed Preference for Network Design in Bilevel Linear Programming. In: Seki, H., Nguyen, C., Huynh, VN., Inuiguchi, M. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2019. Lecture Notes in Computer Science(), vol 11471. Springer, Cham. https://doi.org/10.1007/978-3-030-14815-7_7
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