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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.

As the complexity of system design has substantially increased, so, too, has the need for incorporating tradeoffs into the design process. When such tradeoffs are incorporated, the optimal system performance of each objective is sacrificed to increase the overall range of the system's functionality. Reconfigurable systems have been defined as systems designed to maintain a high level of performance through real-time change in their configuration when operating conditions or requirements change in a predictable or unpredictable way. When effectively applied, these reconfigurable systems have the ability to achieve the optimal performance for all system objectives, while also meeting the different constraints of each objective. A method of designing effective reconfigurable systems is presented that focuses on determining how the design variables of a system change, as well as investigating the stability of a reconfigurable system through the application of a state-feedback controller. This method is then applied to a study involving the design of a reconfigurable Formula One race car traversing a predefined racetrack where the design variables change values to achieve maximum performance.

The design of complex systems can involve the selection of several subsystem designs. We investigate the problem of selecting discrete concepts from multiple, coupled subsystems. This problem is one where measures of merit for both subsystem (local) and system (global) levels are present. An approach is developed to obtain the sets of preferred subsystem design concepts. Graph theory is used to represent the coupled selection problem where the nodes of the graph are the subsystem design choices and the arcs connecting the nodes indicate the relationships between the subsystems. Optimization techniques from graph theory and physical programming are combined to form an approach to model and solve this problem. This approach can be used to identify a given number of successful, or feasible, subsystem combinations that represent design alternatives. Once the promising subsystem designs are obtained at the conceptual design stage, focus can be restricted to these chosen design alternatives for further testing and refinement at a later embodiment design stage. Although the examples presented in this paper involve conceptual design, the presented approach can be used with any coupled discrete selection problem.

A design methodology that combines the robust design complex with the game theoretic approach to multidisciplinary design has been developed. Ranges of solutions were derived for variables coupled between multiple disciplines using the Stackelberg leader/follower rules. This methodology provided the flexibility that proved helpful in resolving the disputes and conflicts of rationality that were present between the varying interests of multiple disciplines.

Multiple decision makers, design teams, or organizations were cited as essential components in designing multidisciplinary systems. The game theory approach was used to model decision-making processes facilitated by concurrent engineering principles and was established useful in many other decision-making sectors such as economics, politics, and strategic warfare.