Defining the Path to a Decision, featuring Marie Ivanco
“When you have to study potential science missions in the pre-formulation phase, it’s a very complex exercise.” Marie Ivanco, an aerospace engineer and decision science expert with the Space Mission Analysis Branch at the NASA Langley Research Center, explains how she tackles the challenges of designing new science mission architectures in a way that meets objectives from interested parties around the world.
In 2017, the National Academy of Sciences published a decadal survey for Earth Science, which included recommendations for targeted observables to study from space, including aerosols and their connections with clouds, atmospheric convection, and precipitation. After reading the document, Ivanco noticed that something was missing—a defined process to go from considering the desired study topics to actually designing missions and selecting architectures. She advocated at Langley and across the Agency for the inclusion of a Value Framework to bridge this gap, leading the Aerosol, Cloud, Convection and Precipitation (ACCP) study team to recognize the importance of implementing decision science as part of their process.
Leading the team that developed and implemented the Value Framework, Ivanco coordinated with the ACCP study management team to determine what was needed. With stakeholders across six NASA centers, four international partners, and many academic institutions, the ACCP study included many objectives to satisfy and alternatives to trade, and the study team needed to define potential architectures and assess them to make a recommendation to NASA Headquarters. Ivanco recognized that the “decision was too complex to be made using heuristics, so [she] developed a process that was structured, transparent, and traceable”. Working with the teams within the ACCP study that totaled over one hundred people, Ivanco’s Value Framework team offered a structured process and methodologies rooted in decision science for the scientific communities to conduct data-driven evaluations of the candidate architectures.