Optimization of non-stationary Stackelberg models using a self-adaptive evolutionary algorithm
Journal Title: TecnoLógicas - Year 2017, Vol 20, Issue 39
Abstract
Stackelberg’sgame models involve an important family of Game Theory problems with direct application on economics scenarios. Their main goal is to find an optimal equilibrium between the decisions from two actors that are related one to each other hierarchically. In general, these models are complex to solve due to their hierarchical structure and intractability from an analytical viewpoint. Another reason for such a complexity comes from the presence of uncertainty, which often occurs because of the variability over time of market conditions, adversary strategies, among others aspects. Despite their importance, related literature reflects a few works addressing this kind of non-stationary optimization problems. So, in order to contribute to this research area, the present work proposes a self-adaptive meta-heuristic method for solving online Stackelberg’s games. Experiment results show a significant improvement over an existing method
Authors and Affiliations
Olga P. Cedeño-Fuentes, Lorena Arboleda-Castro, Iván Jacho-Sánchez, Pavel Novoa-Hernández
Keywords
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- EP ID EP348411
- DOI 10.22430/22565337.715
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