Change Needs People, Not Papers

COVID-19 has brought together a wide range of scientists, policymakers, and businesses in an unprecedented fashion. Just like neighbors who barely responded to one another’s greetings in the past, with the surge of the pandemic, they have begun to exchange far more than minimal gestures of empathy. The shared awareness of the ubiquitous presence of the virus has fostered unusual partnerships. It has opened multidimensional dialogue, giving us in all probability the greatest lesson in decades on agility in the decision-making process. The dust of the pandemic’s outbreak is slowly falling, revealing a looming question—how to preserve the business-academia-policy triangle?

Data can’t make it on their own (yet)

In April 2020, half of the world’s population had been asked or ordered by their governments to stay home. The lockdown may have brought back country borders. At the same time, it has united us in an incredible feeling of uncertainty. And this is something both extraordinarily usual and unbearable for politicians. 

At that time, humankind wondered “what would happen,” and the answer partially came from mathematical epidemiologists. The mathematical models they developed were supposed to predict the virus’ spread and solve the biggest question for policymakers. If introduced, how might the safety measures limit the pandemic outbreak? 

While we were all trying to understand what “the basic reproduction number” is (you probably recall the abbreviation: R0), similarly in governmental offices, the temperature was rising. Decision-makers desperately needed facts to back up their decisions.

To make an accurate model, you need solid entry data, which reflects the reality as much as possible. And it has been tough to get such data at that moment, not to mention that in the spring 2020, we were only about to start learning about the COVID-19 disease. On top of this, scientists must have anticipated and assumed a number of aspects of other people’s behavior, such as acceptance for safety measures and mobility. 

Back at the beginning of 2020, was it all new to scientists? No. Was it new for most policy and decision-makers? On that scaleyes. 

Most politicians were about to discover an obvious fact for academia. Even the best model is only a simplified representation of reality, not a crystal ball. And data cannot make it on its own. To be able to make the most of these models, decision-makers needed to acknowledge the findings.

Possessing a 200 page report means nothing. Change may be based on this paper, but there are people who make it. Someone has to be skilled in communication and often brave enough to share this information. Governmental officials also have to be open and have the empathy and willingness to understand the message.

Policymakers in the optimism trap

After a few months of complete lockdown and a somehow too-long-winter, Europe sighed with relief with the first summerish days of 2020. Almost everywhere, the numbers of positive tested  patients were falling as quickly as our masks. Everyone was eager for good news and ready to receive “the new normal” rhythm back in their lives. 

The future was about to prove us horribly wrong, with the second, third and fourth waves of the pandemic just around the corner. But what could be heard from many politicians reflected people’s hopes: “the pandemic is over.” 

At the same time, many scientists warned about abandoning all the safety measures too soon. With this, the great breakdown in academia and policy worlds became clearly visible. What had been only the beginning of a great war for one, the others claimed as their success and declared a winner. 

Back in the summer of 2020, there was a great deal of magical thinking in politics. “If everything goes right…”, “if we will have a vaccine…”, “if…”. Numerous statements about the combatted virus have been the consequence of this thinking. This is a reflection of something called the “optimism trap” or “optimism bias.”. It has been defined as a cognitive bias that makes someone think that they are less likely to fail than others.

In many countries, the consequences of becoming trapped in optimism were devastating, leaving healthcare systems unprepared for the second surge of the pandemic. 

But does it mean that scientists are bulletproof from positive thinking? Certainly not.

Let’s make science and politics

What has been unique for many of us was the fact that suddenly we became more than familiar with tens of healthcare experts. And I am positive that for many of those experts, this has also been a unique experience. If it weren’t for COVID-19, they wouldn’t have been exposed to the media all that much. 

Many of them would not have been exposed to politics as well. And there is more than an “optimism trap” that lays a shadow on this cooperation. 

You may recall that having data does not mean being able to make use of it. There is a significant difference between the “evidence for science making” portfolio and the needed evidence for policy-making.

The major obstacle to tackle seems to be a completely different perception of time. Over the year, something incredibly slow for politicians might progress with breakneck speed in the eyes of scientists. Businesses may have already started to prepare for the change based upon this evidence.

If this time machine gets faster, as is still the case during the COVID-19 pandemic, another dimension comes into the game—quality. 

Gathering data for the work of science is a structured and well-documented process that requires a great deal of time and a complex methodological approach. When these resources are at stake, the quality of the data becomes poorer from the scientific point of view. They may still, however, provide insights into trends and light the road for policymakers. They can initiate change, which in many cases provides society with access to the newest scientific achievements and the best-in-class technologies. 

Are time, quality, and policy-making relationships doomed to failure? Not necessarily. But it is highly dependent on understanding each other’s needs and priorities, acknowledging the mutual borders of compromise and expectations.

Go green when fueling the discussion

“Every two weeks, one of the world’s languages disappears, along with the human history and cultural heritage that accompanies it,” Audrey Azoulay, Director-General of the UN Educational, Scientific and Cultural Organization (UNESCO) stated in 2018 on International Mother Language Day. Language apparently holds that much information about ourselves, and apparently, the business-academia-policy triangle often does not speak a common language. Why not look for what we do have in common?

This was a shared vision of the pandemic’s end, which has fostered many multisectoral initiatives and out-of-the-box collaborations over the last few months. Tech giants shared mobility data with governments to track peoples’ traffic in cities better, FMCG companies introduced know-how on communication to a mass audience via apps, and the MedTech industry was the one to co-develop with authorities testing strategies. They all represented different businesses but sat for months in the same strategy room, being somehow dependent on one another’s input. 

Many of these projects were far from being perfect. Some even painfully exposed the darkest sides of relations between business and politics. Although some might scoff at the utopian vision of harmonious, cross-sectoral cooperation in the business-academia-policy triangle, oriented purely on problem-solving, but one can also try to make it happen, at least more frequently. 

A range of personal, cognitive, and even logistic skills are needed on each side of the table to make it work. This will require stupendous effort. The business-academia-policy triangle is dependent, however, on a great deal of the emotional and behavioral fuel which comes in. So likewise, when someone chooses green energy for its homeland and business, it might also go green when fueling the discussion. 

And this goes far beyond speech. Although the pandemic proved the power of science, it also revealed how vulnerable it could be. Reflecting on multiple conspiracy theories budding around the vaccines against COVID-19 brings to the spotlight the shredded trust which people have in science. 

Breaking the ripple effect of ubiquitous fake news may be one of the utmost challenges that the business-academia-policy triangle faces. As long as the massive emotional impact of any kind of change will not be accepted as an indispensable upshot of every decision- or policy-making process, it will make the road even more rocky.


Sylwia Piekarska

works as a Health Economics & Market Access Manager at Boston Scientific. Before that, she worked as a Public Policy Manager CEE in Becton Dickinson (BD). She oversees stakeholders’ relations and addresses public burdens of high societal relevance in the MedTech sector. She engages with several industry organizations and NGOs to strengthen transparent partnerships in business & policymaking. Prior to that she managed a number of Public Affairs and Public Relations projects as a consultant and worked in public administration. A graduate of the University of Warsaw, Institute of Political Science (2013). Sylwia is an alumna of the ‘Aspen Young Leaders Program 2018′ by Aspen Institute and the ’30 under 30’ program by AmCham.

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