  This work focuses on the development of a new family of decision-making
algorithms for adaptation and learning, which are specifically tailored to
decision problems and are constructed by building up on first principles from
decision theory. A key observation is that estimation and decision problems are
structurally different and, therefore, algorithms that have proven successful
for the former need not perform well when adjusted for decision problems. We
propose a new scheme, referred to as BLLR (barrier log-likelihood ratio
algorithm) and demonstrate its applicability to real-data from the COVID-19
pandemic in Italy. The results illustrate the ability of the design tool to
track the different phases of the outbreak.
