Lessons for Decision-Analysis Practice from the Automotive Industry

Abstract

Decision analysis is widely used in making decisions involving low-probability, high-consequence events (e.g., drug discovery, oil and gas drilling, risk reduction). This paper focuses on the automotive industries in which more intermediate uncertainties are important. As in any large organization, different members of the organization have different information and different incentives. In this setting, influence diagrams proved invaluable in identifying the information creditable models need, discovering new distinctions of high value, developing win/win compromises, and enabling higher-value technology transfer. However, these examples also highlight the need for more research on addressing motivational biases within organizations. History: This paper was refereed. This paper was accepted for the Special Issue of INFORMS Journal on Applied Analytics—Decision Analysis