From Climate Change to Pandemics: Decision Science Can Help Scientists Have Impact

Abstract

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. However, there is a perpetual challenge in translating scientific insight into policy. Many articles explain how to better bridge the gap through improved communication and engagement, but we believe that communication and engagement are only one part of the puzzle. There is a fundamental tension between science and policy because scientific endeavors are rightfully grounded in discovery, but policymakers formulate problems in terms of objectives, actions and outcomes. Decision science provides a solution by framing scientific questions in a way that is beneficial to policy development, facilitating scientists’ contribution to public discussion and policy. At its core, decision science is a field that aims to pinpoint evidence-based management strategies by focussing on those objectives, actions, and outcomes defined through the policy process. The importance of scientific discovery here is in linking actions to outcomes, helping decision-makers determine which actions best meet their objectives. In this paper we explain how problems can be formulated through the structured decision-making process. We give our vision for what decision science may grow to be, describing current gaps in methodology and application. By better understanding and engaging with the decision-making processes, scientists can have greater impact and make stronger contributions to important societal problems.

The decision maker’s lament: If I only had some science!

“…Fortunately, many decision makers welcome the contributions that scientific information, tools, and services offer to help them mitigate risk, uncertainty, and speculation (Morrison-Saunders and Bailey 2003 ; White et al 2019 ; Thomas-Walters et al 2021 ; Cooke et al 2023 ). Their use of science to improve decision making is, in fact, consistent with the intended central role of science contemplated within the field of decision analysis (e.g., Murphy and Weiland 2014 ; Baker et al 2022 ; Hemming et al 2022 ). Moreover, in the United States and many other countries, the legal, administrative, and institutional standard for the application of science to decision making instructs regulatory natural resource agencies to include the best available science in the formulation of public policies and planning directives (Bisbal 2002 ; Sullivan et al 2006 ; Ryder et al 2010 ; Charnley et al 2017 ).…”

“…Second, contextualizing the decision also helps refine the disciplinary perspectives and categories of knowledge (Raymond et al 2010 ) that may provide the most useful evidence to inform the decision at hand (Cooke et al 2023 ). Third, it highlights key uncertainties and critical variables in need of quantification (Runge et al 2020 ), and offers valuable details to recognize configurations of the decision space such as time frames, geographic scale, and governance scope (Baker et al 2022 ). The fourth reason reveals important legal, economic, political, cultural, social, historic, and even religious and moral dimensions that have crucial influence over the implementation of decision options.…”

Context-dependent representation of within- and between-model uncertainty: aggregating probabilistic predictions in infectious disease epidemiology

“…Accurate estimates of future outcomes and related uncertainty is important for effective infectious disease management, including the integration of formal decision theory into infectious disease applications [65][66][67][68]. Aggregating predictions from multiple experts or models has proven to yield better calibrated estimates of future outcomes both for infectious disease dynamics (e.g.…”

Rapid prototyping of models for COVID-19 outbreak detection in workplaces

“…7 Murdoch Children's Research Institute, The Royal Children's Hospital, Melbourne, Australia. 8 Centre of Excellence for Biosecurity Risk Analysis, The University of Melbourne, Melbourne, Australia.…”

“…Rapid prototype modelling is a model development approach that aims to provide rapid insights while laying the foundations for more detailed modelling [7]. Models developed should be simple yet still convey complexities and subtleties of a problem to decision makers [8]. Problem identification at each step drives prototyping, and as questions are revised and improved, models are updated to reflect new scenarios [7, 9].…”