Applying ideas developed in research on decision-making under ambiguity, Manski recommends that the planner or policymaker first determine which vaccination rates are inferior and eliminate them. Focusing on the remaining possibilities, Manski then shows what vaccination rate the planner should choose if he applies one of two decision criteria. Both criteria protect the planner from poor outcomes, but in different ways.

Manski became interested in vaccination policy in light of H1N1. However, he said his paper is more relatable to a disease that occurs in the same way every year, like the regular seasonal flu, measles or mumps. Not enough is known about H1N1 to have long-term observable data. "If we get H1N1 next year, what I did in this paper would become more relevant," he said.

Manski said the issue of making public policy with limited information applies to many other issues that are currently in the headlines.

"Take one that's very controversial today - global warming," he said. "We have limited information on what exactly is happening. There's a lot of disagreement, and yet we have to make some decisions now."

As a new member of the National Academy of Sciences, Manski was invited to submit an article to PNAS. "Vaccination With Partial Knowledge of External Effectiveness" will be published in print in an upcoming issue of PNAS.

Source: Northwestern University