Multi-attribute COVID-19 policy evaluation under deep uncertainty.

Abstract

Mitigating the impacts of COVID-19 comes with the evaluation of tradeoffs. However, the exact magnitude of the tradeoffs being made cannot be known ahead of time. There are three major concerns to balance: life, liberty, and economy. Here, we create a multi-attribute utility function including those three attributes and provide reasonable bounds on the weights of each. No one set of weights on the utility function can be considered “correct.” Furthermore, the outcomes of each mitigation strategy are deeply uncertain. Not only do we need to take into account the characteristics of the disease, but we also need to take into account the efficacy of the mitigation strategies and how each outcome would be evaluated. To handle this, we use Robust Decision Making methods to simulate plausible outcomes for various strategies and evaluate those outcomes using different weights on the multi-attribute utility function. The simulation is done with a compartmental epidemiological model combined with a simple economic model and a model of liberty costs. Rather than trying to optimize likely outcomes for a particular version of the utility function, we find which strategies are robust across a wide range of plausible scenarios even when there is disagreement about how to weigh the competing values of life, liberty, and economy.