The question whether linear programs can be solved in strongly polynomial time is a major open problem in the field of optimization.One promising candidate for an algorithm that potentially guarantees to solve any linear program in such time is the simplex algorithm of George Dantzig.This algorithm can be parameterized by a pivot rule, and providing a pivot rule guaranteeing a polynomial number of iterations in the worst case would resolve this open problem.For all known classical natural pivot rules, superpolynomial lower bounds have been developed.Starting with the famous Klee-Minty cube, a series of exponential lower bound constructions have been developed for a majority of pivot rules.There were, however, two classes of pivot rules whose worst-case behavior remained unclear for a long timerandomized and memorizing rules.Only in the 2010s, the works of Fearnley, Friedmann, Hansen and their colleagues provided superpolynomial bounds for those rules, starting a second series of lower bounds.The arguably most remarkable of these bounds was Friedmann's construction for which Zadeh's L E pivot rule requires at least a subexponential number of iterations.This pivot rule is the main focus of this thesis.Following the work of Friedmann, we introduce parity games, Markov decision processes and linear programs and investigate certain subclasses of the first two structures.We discuss connections between these three frameworks, generalize previous definitions and provide a clean framework for working with so-called sink games and weakly unichain Markov decision processes.We then revisit Friedmann's subexponential lower bound and discuss several of its technical aspects in full detail and exhibit several flaws in his analysis.The most severe is that the sequence of steps performed by Friedmann does not consistently obey Zadeh's pivot rule.We resolve this issue by providing a more sophisticated sequence of steps, which is in accordance with the pivot rule, without changing the macroscopic structure of Friedmann's construction.The main contribution of this thesis is the newest member of the second wave of lower bound examples -the first exponential lower bound for Zadeh's pivot rule.This closes a long-standing open problem by ruling out this pivot rule as a candidate for a deterministic, subexponential pivot rule in several areas of linear optimization and game theory.iii First of all, I want to thank Prof. Dr. Yann Disser for supervising me during my studies and giving me the opportunity to obtain a doctoral degree.