(A full list of authors can be found at the end of this piece.)
Many scientists are keen to communicate research they believe can help inform the decisions people make, from public opinion to the policy of our governments.
But the will of scientists to abandon intellectual “ivory towers” does not in itself ensure a more prominent role for science in any decision-making.
Does this signify a prioritisation of emotions, personal beliefs and social media savviness above facts? If so, then ensuring a role for research evidence in decision-making may be one of the greatest challenges facing the science community.
A risky and uncertain world
One aim was to better understand and improve on how scientists from many disciplines can communicate their research to decision makers, including any risk and uncertainty.
Which evidence to consider?
All scientific research is subject to varying degrees of uncertainty. This can arise from a number of issues such as incomplete knowledge or variability in the phenomena being researched.
A goal of research is to reduce any uncertainties through study and experimentation, and to improve the accuracy by which uncertainties are defined.
Even the most scientifically-informed decision-making contains positive and negative risks resulting from the uncertainty.
The extent to which this uncertainty influences decision-making is often unclear and difficult to evaluate.
Reporting on the status of the Great Barrier Reef in the past has omitted any form of uncertainty.
The importance of including an uncertainty assessment has now been recognised in advice to the Queensland Government. But it remains unclear how best to quantify the uncertainty and communicate it in a way that helps decision making.
The challenge for scientists
Scientists see it as best-practice to characterise and include any uncertainties in their research when publishing in peer-reviewed journals. But the scientific community lacks consensus about the most effective way to communicate science and uncertainty to decision-makers.
For example, are absolute or relative probabilities more effective when publicly communicating risk? Should uncertainties be included in weather forecasts, bushfire trajectories or tsunami inundation predictions?
Our discussions revealed that our risk communication experiences and perspectives varied across our diverse fields of expertise.
This included our use of language, our target audiences, the types of risks we communicate (economic vs life and death) and the cultures and protocols of our host institutions.
But we also found consensus. We do not live in a “post-truth” world where science evidence is offered but not considered. Nor do we live in an “ivory tower” world where science evidence is needed but not offered.
Rather, we live in a world with increasing diversity and complexity in decision-making. This world offers real challenges.
However it also provides opportunities for scientists with diverse skills and priorities to communicate and engage with decision-makers. This includes those who acquire, interpret and communicate scientific data, through to those who engage in science arbitration and advocacy.
How to improve communication with decision-makers
In our report we recommend a new plan for scientists to adopt when doing any evidence-based communication with decision-makers.
A key element of this plan is to develop a common language on risk and uncertainty communication. This will make sure lessons learned may be more easily translated across distinct scientific disciplines.
We recommend that scientists explicitly state the motivations that underlie their scientific experimentation and modelling processes. That way decision-makers can better understand the role of the science in assisting with any decision they make.
We also recommend that both scientists and decision-makers keep a record of how research evidence and uncertainty was considered in any decision-making scenarios. This should include whether the research was asked for or offered, how the evidence and uncertainties were communicated, and how all this was received and considered.
The need for feedback
If the research did influence any decision, then it will be important to know how. If the research was not used in the decision-making process, it will be important to understand why.
Was it because uncertainties were not understood, inadequately represented, or exceeded tolerable thresholds?
Perhaps the models themselves were not easy for decision-makers to understand? This could mean modifications are needed to increase their utility.
Were other societal, political or fiscal factors prioritised? Are all of these factors able to be objectively analysed and justified?
And what approaches are available to scientists who conclude that research has been unjustly used by decision-makers?
In our experience there is a large variability in the way decision-makers provide documentation on how scientific advice they received actually informed the decision making process.
Both the public and the media have a role to play in encouraging these forms of documentation.
The uptake of any science evidence and the understanding of scientific uncertainty by decision-makers remains sparsely documented. This includes any influence of public and media communications, structured science communication workshops, involvement in science advisory panels, and other science engagement strategies.
So hopefully our plan for a more unifying language across the science community, and a concerted effort to document communication experiences, should help scientists who want to contribute their work to any decision-making processes that may guide future policies.
Mark Quigley, Associate professor, University of Melbourne; Adrien Ickowicz, Research scientist, Data61; Antonio Verdejo-Garcia, Associate Professor on Addiction Studies, Monash University; Ben Galton-Fenzi, Senior Scientist; Christopher J White, Senior Lecturer in Environmental Engineering, University of Tasmania; Gery Geenens, Senior Lecturer in Statistics, UNSW Australia; Keith Gerard Pembleton, Senior Research Fellow in Agricultural Systems Modelling, University of Southern Queensland; Kirsty Kitto, Visiting Research Fellow in Data Science, Queensland University of Technology; Kyra Hamilton, Senior Lecturer in Health and Applied Psychology, Griffith University; Luke Bennetts, Lecturer in applied mathematics, University of Adelaide; Madhura Killedar, Adjunct Lecturer, Monash University; Mark Lindsay, Research Fellow in Geoscientific Modelling, University of Western Australia; Melanie Roberts, Research Scientist in Applied Mathematical Modelling, University of Melbourne; Patricia Durance, Adjunct Research Associate, Monash University, and Petra Kuhnert, Statistician, Data61