Multi-parametric approach for multilevel multi-leader-multi-follower games using equivalent reformulations

Addis Belete Zewde, Semu Mitiku Kassa

Abstract


Multilevel multi-leader multi-follower games address compromises among multiple interacting decision agents within a hierarchical system in which multiple followers are involved at each lower-level unit and more than one decision maker (multiple leaders) are involved in the upper-level. The leaders' decisions are affected not only by reactions of the followers but also by various relationships among the leaders themselves. In general, multiple-leaders multiple-followers (MLMF) game serve as an important modeling tool in game theory with many applications in economics, engineering, operations research and other fields. In this paper, we have reformulated a multilevel-MLMF game into an equivalent multilevel single-leader multi-follower (SLMF) game by introducing a suppositional (or dummy) leader, and hence the multiple leaders in the original problem become followers in the second level. If the resulting multilevel-SLMF game consists of separable terms and parameterized common terms across all the followers, then the problem is further transformed into equivalent multilevel programs having a single leader and single follower at each level of the hierarchy. The proposed solution approach can solve multilevel multi-leader multi-follower problems whose objective values at all levels have common but having different positive weights of non-separable terms. This result improves the work of Kulkarni and Shanbhag (2015).


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Published: 2021-04-19

How to Cite this Article:

Addis Belete Zewde, Semu Mitiku Kassa, Multi-parametric approach for multilevel multi-leader-multi-follower games using equivalent reformulations, J. Math. Comput. Sci., 11 (2021), 2955-2980

Copyright © 2021 Addis Belete Zewde, Semu Mitiku Kassa. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

 

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