Increasing the probability of developing affordable systems by maximizing and adapting the solution space

The present research suggests that the size of the solution space, which for a given set of stakeholder needs is delimited by system requirements, relates to the probability of finding affordable solutions. As a result, the effectiveness of tradespace exploration techniques is limited by its size and internal ordering. Therefore, we suggest that there exist models to elicit and use requirements that, for a given set older needs, could facilitate the maximization of the solutionof stakehspace so that the probability of finding more affordable solutions during tradespace exploration is also maximized.The present research proposes a mathematical model of the requirements elicitation process that facilitates defining performance objectives for the requirements elicitation process in striving for system affordability. The uniqueness of this research lays on two elements. First, the requirements elicitation process is mathematically modeled so that their objectives with respect to the effects on the solution space can be mathematically, and thus rigorously, described. Second, the system of interest focuses on the definition of the solutionspace as a driver for system affordability instead of on its actual exploration. The present research closes therefore the loop between stakeholder needs, system requirements, solutionspaces, and system affordability. The results of the present research are generalized to discrete requirements, fuzzy requirements, and continuous requirements or value functions.