Principal-Agent Models of Decision Delegation During Systems Design: Integrating Modeling and Behavioral Approaches Grant uri icon


  • Modern systems engineering projects can involve tens of thousands of engineers spread across thousands of independent organizations. For example, the Air Force''s F-35 Lightning II consists of more than 300,000 individual parts delivered by more than 1,400 suppliers. Similarly, development of the Boeing 787 Dreamliner involved over 2,000 suppliers organized into three tiers of responsibility. Decisions about how to delegate design authority within and among these organizations can have significant impact on project metrics (schedule and budget) as well as the performance of the resulting engineered systems. Poor design delegation decisions have been cited as an explanation for problems on large systems engineering projects. However, the impact of different approaches to design delegation in systems engineering is not well understood at a fundamental level. Improved understanding of design delegation in systems engineering will improve the efficiency and effectiveness of systems engineering projects and result in better-performing engineering systems. The objective of this research is to create a new behaviorally-informed modeling framework for decisions that occur during the design stages of a systems engineering project. The research will combine the mathematics of principal-agent theory with new empirical results about human judgment and decision-making behavior. This will enable the analysis of various decision delegation strategies in a manner that is supported by data about how engineering decision makers behave but at a scale impractical via human studies. It is anticipated that the new modeling framework will enable researchers to answer important questions about design delegation approaches, such as whether one method is superior to another under particular circumstances and the degree to which the performance of a method is sensitive to problem context. The research will contribute new knowledge about how engineers interpret and respond to incentives in their decision making and about how well-known methods for design delegation compare to one another.

date/time interval

  • 2016 - 2020