Decision-Making Algorithms for Learning and Adaptation with Application to COVID-19 Data

TL;DR

This paper introduces a novel decision-making algorithm, BLLR, tailored for adaptive learning in decision problems, demonstrated on COVID-19 data to effectively track outbreak phases.

Contribution

It develops a new decision-making algorithm based on decision theory principles, addressing the gap between estimation and decision problem algorithms.

Findings

BLLR effectively tracks COVID-19 outbreak phases

Algorithm outperforms traditional methods in decision tasks

Demonstrated on real COVID-19 data from Italy

Abstract

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.