Although competition is a dynamic phenomenon, currently used competition models do not take price dynamics into account, and some models are not even quantitative. This poses a problem for regulators or hedge funds and M&A departments who rely on these models to either quantify price and demand movements, or to determine the cost of competition.

This thesis solves that problem by using Economic Engineering to build on the existing game-theoretic models of competition to include price dynamics. A bond-graph model of a competitive market is developed, from which the price dynamics are derived. Model-predictive controllers are used to model profit-maximizing companies within this model, and to simulate competitive behavior and its effects on prices and demand flows.

Finally, this thesis shows how control engineering tools in both the time and the frequency domain can then be exploited by regulators and hedge funds. Time-domain simulations enable regulators to quantify the effects of competition on prices and demands, and analyses in the frequency domain enable hedge funds to determine the change in company valuations due to changes in competition, i.e., the cost of competition.