Combining research and values to support democracy

Fortunately, a large majority of people support evidence-based decision making. So, why does this happen so slowly? In this text, Kausal Chief Science Officer, Jouni Tuomisto analyses some key aspects and offers novel solutions.

We are facing a concerning forecast for the coming decades, with expectations of the most significant decline in biodiversity since the extinction of dinosaurs 66 million years ago. As a result, the design, development, and biodiversity strategies of future cities — as well as the transformation of existing ones — will play a pivotal role in shaping the health and well-being of billions of humans.

Historical aspects to science and democracy

Printing machine revolutionised the information flow in Europe in the 1500's. This facilitated the spread of both false and true information and ideas. The 1600's saw the rise of both witch hunts and the development of the scientific method.

Scientific method is one of the great human innovations. Some innovations are intuitive, like the wheel and the lever. Once you get the idea, it sticks and helps you in all mechanical tasks. In contrast, the scientific method is counterintuitive, and it takes a lot of learning and training to not fail in its implementation. Why?

Humans are typically fast, intuitive thinkers. This is called type 1 thinking, and in everyday life it mostly works great. However, scientific thinking is analytical and slow and focussed on identifying flaws in one's own reasoning. This is called type 2 thinking, and it is hard and tiring.

Humans also have a strong confirmation bias. We pay attention to information that confirms what we already believe and ignore what is against it.

In science, there are practices that fight against these natural instincts. Intuition is a good start, but you will not be believed until you have strong data that supports your case and leaves little room for alternative explanations. You must publish your hypotheses and data for anyone to criticise and evaluate in such a way that someone could repeat and verify your work.

All hypotheses together must form a coherent set of theories, which are general explanations of the world. If your hypothesis is in conflict with a theory, you must revise the whole theory to get your hypothesis accepted. This is extremely difficult and happens rarely — and in practice never unless you happen to be right.

Democratic principles started to take shape in the late 18th century. Most democratic institutions developed during the next 150 years. Little has changed since women got the right to vote during the 20th century. Democracy is based on a few key ideas. Societal decision-making is about people, so people should decide. When people come together to discuss, they learn from each other (Parliament comes from the French word parler, to talk). Decisions are better when nobody can dictate; rather, people must find a common ground and consider everyone's interests.

The Planner system for democracy support

The current climate crisis demonstrates that mankind as a collective is failing to make decisions that consider everyone's interests. I am not saying that climate negotiations or parliaments are not important; they are vital. However, we could do better.

Let's imagine that we can start again from scratch. What would our democratic, evidence-based decision making system look like? For simplicity, let's call it the Planner.

The Planner would contain all of the key principles of science and democracy listed above. And importantly, it would not give room for practices that are against these principles.

For example, we know that false tweets spread faster and wider in X than true tweets. This systematic bias must not be accepted.

The system should use good modern information technology methods that did not exist when parliaments and scientific journals were innovated during previous centuries.

Because the key challenge is to make good decisions, the system should specifically focus on describing and evaluating possible decision options and their consequences based on the available scientific evidence.

Scientific practices are much more powerful in rejecting poor hypotheses than proving that a particular hypothesis is true. Therefore, the Planner should focus on rejecting destructive policies on scientific grounds rather than optimising between several good ones.

Also, the system should describe people's values and priorities, giving the most precious things more value than those that occupy people's minds during the day in a hectic social media environment.

For those who are technically oriented, it may be helpful to describe the system as a digital twin of the decision space.

Importantly, the system must not be a shouting exercise where everyone tries to manipulate the algorithm to make their message go viral. Instead, the conclusions must be made using careful, analytic, empathetic type 2 thinking. This takes time, but on the other hand, many decision situations repeat over and over again in various municipalities and other contexts. This improves the scalability of the Planner.

It may sound counterintuitive, but everyone's explicated and revealed preferences and actions should be described. Especially the stupid ones. Why? The natural instinct is to shout louder to make more room for one's own good aspirations. But like in science, the noise volume is irrelevant. You win if and only if you can demonstrate that the opponent's hypothesis, according to data, does not support the theory of good life.

Therefore, we must especially focus on describing the bad ideas in as good light as possible. If those ideas are truly destructive, they appear bad even in good light. The sun is the best disinfectant.

The purpose of this exercise is not to distinguish between two good ideas. The purpose is to identify the most destructive ones and make sure that all of the emperor's golden misinformation clothes are stripped away demonstrating that the emperor was, indeed, naked from the start.

How to make the change happen?

Now, there is an idea how things could have been done. But are we already too far on the track to climate hell with our outdated decision processes? I don't think so.

What makes me optimistic is that we don't need to replace anything existing to get this new online democratic system up and running. The system just starts to inform all the existing parties and stakeholders about what we know, what we could do and what we should not do.

Irrespective of the current political or negotiation systems, this Planner does offer important and relevant information for climate negotiators, prime ministers, citizens, and dictators. Not all of them will like that. But because the system is built in an open, scalable, and copyable way, there are no good methods to prevent the spread of this information.

In this short text, I have no space to convincingly tell how we are planning to implement the Planner. But it is half-way ready now. We can describe and analyse decisions and their impacts on our open-source impact assessment tool. We can add weights for different values and preferences.

It is built in a scalable way, and new users can start running their own instances with their own decision analyses.

There is still a lot of work to be done with describing disagreements and uncertainties in the system. Also, the participatory functionalities still need work.

But I believe that there are a lot of knowledgeable and enthusiastic people out there, who want to promote open democracy and evidence-based decision making related to climate change.

Contact us if you are interested. We are happy to tell you more and work together.