Daniel Sarewitz, Professor of Science and Society, Arizona State University
“The Science of Modeling Through”

Policy relevant scientific models are not independent of the policy and political contexts that they are meant to inform.

Zoom https://utah.zoom.us/j/94237271485?pwd=OXF4d2tnVXNucFNta1pleE9GQjNpQT09

Meeting ID 942 3727 1485  Passcode 816762

Daniel Sarewitz is Professor of Science and Society at Arizona State University and Co-Director of the Consortium for Science, Policy, & Outcomes.  He is interested in relationships among knowledge, technology, uncertainty, disagreement, policy, and social outcomes.  He is editor-in-chief of Issues in Science and Technology, and was a regular columnist for Nature from 2009-2017.  “Saving Science,” his analysis of the multiple challenges facing the scientific enterprise, appeared in The New Atlantis in August 2016.

Abstract:  All decisions are future-oriented, and policy relevant predictive modeling has become an important tool in many realms of public-policy decision making over the past several decades.  However, all models are not the same, and all decision contexts are not the same, and the relationship between the two is often complex and laden with political difficulties.  I’ll discuss three cases of how models are used in decision processes (hurricane forecasts; monetary policy; and geoengineering) in an effort to understand the origin of these difficulties and hopefully say something useful about how and when predictive modeling can support successful policy making, and why such efforts often become politically controversial.