An evolutionary computational approach for the dynamic Stackelberg competition problems

Abstract

Stackelberg competition models are an important family of economical decision problems from game theory, in which the main goal is to find optimal strategies between two competitors taking into account their hierarchy relationship. Although these models have been widely studied in the past, it is important to note that very few works deal with uncertainty scenarios, especially those that vary over time. In this regard, the present research studies this topic and proposes a computational method for solving efficiently dynamic Stackelberg competition models. The computational experiments suggest that the proposed approach is effective for problems of this nature.