A Multiattribute Decision Model to Evaluate Potential Investments in Near-Earth Object Detection Technologies
Presented by: Thomas Palley
Co-authored by: Victor Richmond R Jose (Georgetown University - McDonough School of Business), Asa Palley (Indiana University - Kelley School of Business - Department of Operation & Decision Technologies), Ralph Keeney (Duke University - Fuqua School of Business), Mario Juric (University of Washington - Department of Astronomy)
Asteroids and other near-earth objects (NEOs) pose a significant ongoing threat to our planet, with the potential to catastrophically disrupt life on Earth. Advance detection is essential to be able to respond to any object on a collision course, but detection and tracking technologies require substantial financial commitments. In this paper, we provide a multiattribute utility framework to analyze whether and which NEO detection technologies are worthwhile. This framework enables rigorous and systematic understanding of the uncertainties, multiple objectives, and tradeoffs inherent to advance decisions involving low-probability, high-consequence events. Using some reasonable baseline parameter estimates, the model shows that the detection technology investment decision is driven more by the abundant population of small undiscovered NEOs than any other size group. We subsequently extend the framework to consider how a decision maker might evaluate alternatives when the risk affects a larger group of people, but the economic cost of investment is shouldered by a smaller subset of stakeholders.