Innovation & Technology
Dynamic Benefit-Cost Analysis for Uncertain Futures
September 17, 2019
By: Susan E. Dudley, Daniel R. Pérez, Brian F. Mannix, & Christopher Carrigan
Policymakers face demands to act today to protect against a wide range of future risks, and to do so without impeding economic growth. Yet traditional analytical tools may not be adequate to frame the relevant uncertainties and tradeoffs. Challenges such as climate change, nuclear war, and widespread natural disasters don't lend themselves to decision rules designed for discrete policy questions and marginal analyses. We refer to such issues as "uncertain futures."
Muddling-Through and Deep Learning for Managing Large-Scale Uncertain Risks
September 17, 2019
By: Tony Cox
Building on Charles Lindblom's research on the limits of rational-comprehensive decision-making, Tony Cox provides insights on how machine learning can help individuals and institutions make better informed decisions - improving society's experience of 'muddling through' policymaking under uncertainty.
Privacy Research: The Need for Evidence in the Design of U.S. Privacy Policy
July 03, 2019
By: Daniel R. Pérez
This regulatory policy insight details the importance of using evidence to inform the development of U.S. privacy policy and identifies the kinds of evidence that would be particularly useful for policymakers to consider.
NTIA's Approach to Consumer Privacy
November 12, 2018
By Daniel R. Pérez
The National Telecommunications and Information Administration issued a request for public comments on developing the administration's approach to consumer privacy. This public interest comment provides an overview of the agency's proposed approach to guide federal policymaking, and provides three core recommendations (1) Privacy regulation should be based on consumers' value, (2) the benefits of regulation should exceed social costs, and (3) future research should be focused on improving benefit-cost analysis of privacy regulations.