Abstract
The design of large scale complex engineering systems requires interaction and communication between multiple disciplines and decentralized subsystems. Game theory has been used previously to model interactions between distributed multidisciplinary design subsystems and predict convergence and equilibrium solutions. These game theoretic models assume that designers make perfectly rational decisions by selecting solutions from their rational reaction set. For convergent decentralized design problems, the intersection of the designers' rational reaction set results in a Nash equilibrium solution where designers may converge if they were making rational choices. However, this equilibrium solution is rarely optimal from a multi-objective optimization perspective. Further, empirical studies reject the claim that decision makers always make rational choices and the concept of bounded rationality is used to explain such behavior. In this paper, a framework is proposed that uses the idea of bounded rationality in conjunction with set-based design, metamodeling, and multi-objective optimization techniques to improve solutions for convergent decentralized design problems. Through the use of this framework convergent decentralized design problems converge to solutions that are superior to the Nash equilibrium.
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