  Scientific knowledge and advances are a cornerstone of modern society. They
improve our understanding of the world we live in and help us navigate global
challenges including emerging infectious diseases, climate change and the
biodiversity crisis. For any scientist, whether they work primarily in
fundamental knowledge generation or in the applied sciences, it is important to
understand how science fits into a decision-making framework. Decision science
is a field that aims to pinpoint evidence-based management strategies. It
provides a framework for scientists to directly impact decisions or to
understand how their work will fit into a decision process. Decision science is
more than undertaking targeted and relevant scientific research or providing
tools to assist policy makers; it is an approach to problem formulation,
bringing together mathematical modelling, stakeholder values and logistical
constraints to support decision making. In this paper we describe decision
science, its use in different contexts, and highlight current gaps in
methodology and application. The COVID-19 pandemic has thrust mathematical
models into the public spotlight, but it is one of innumerable examples in
which modelling informs decision making. Other examples include models of storm
systems (eg. cyclones, hurricanes) and climate change. Although the decision
timescale in these examples differs enormously (from hours to decades), the
underlying decision science approach is common across all problems. Bridging
communication gaps between different groups is one of the greatest challenges
for scientists. However, by better understanding and engaging with the
decision-making processes, scientists will have greater impact and make
stronger contributions to important societal problems.
