From mystic prophecies to sci-fi novels, people have always tried to foresee the future. Their aspirations varied widely, from the pursuit of profit and the dream of an alternative social order to practical needs like having a sufficient harvest or preparing before the flood. Modern integrated decision-support tools have gotten us far in such pursuits, and for a good reason.
Andreas Budiman and Marie-Amelie Brun report.
Today, we are trying to learn to live on a planet altered by environmental and social crises. Models like WILIAM are created to help us find our way through, resembling how the world operates across space and time.
In theory, such models can help us make better choices or prepare better for what comes next. The reality is more puzzling. Can we trust a model to tell us something inherently unknown? And what if the model we have trusted to guide us misses a decisive point? We need to understand how integrated models work, what they can do, and their limits to get the most out of them.
The clockwork multiverse
Harald Ulrik Sverdrup, a Professor of Systems Thinking at the Inland Norway University of Applied Sciences, suggests that models are somewhat like clocks:
“Imagine opening a clock. You can see the clockwork, with the wheels pushing each other in perfect synchrony. Now try to imagine countless clockworks interacting in one huge mechanism. This a gross simplification of a model at work.”
Of course, the clockwork analogy is just a departure point. The world is full of sudden events and processes more intricate than a spin of wheels.
Observing the world for decades and capturing its trends, scientists learned to make sense of them. Researchers have been able to figure out what each model needs and what it doesn’t need by going from simple models that show how predators and prey interact to complex models that show how global processes work.
Today, integrated models often consist of smaller models representing specific systems in great detail, such as energy and transport systems, demographics, economy and natural processes. Researchers often collaborate to make those smaller models compatible with each other so that they can study interactions between them.
Learning by running
Modellers are never interested in a single version of the future. Instead, they want to see what the future might hold depending on our choices. Or, vice versa, what choices can we make today to arrive at a future given specific constraints, such as limits on GDP growth or planetary boundaries?
The WILIAM model was specifically designed to run simulations of possible futures. Running hundreds of simulations and comparing different scenarios, researchers can gain new insights, such as when nickel scarcities might kick in if we all switch to EVs (around 2030) or possible drivers of a shift to sustainable diets (global vegan diet would require 76% less land to grow food).
Many similar insights might be impossible without modelling, and increasingly, we can also test some newer models to analyse uncertainty and unconventional patterns, which helps us see how the results we look for might differ if the future deviates from the expected trends.
Crucially, models increasingly draw attention to the fact that we can’t continue with the business-as-usual. We need new creative pathways to allow people and nature to thrive together.
Researchers believe it’s just the dawn of possibilities for using models to address sustainability challenges. And yet, rising sea levels and rising temperatures will not wait for us to figure it all out. We need to start making better use of the models we already have.
Models as tools for change
Models in use today already inform climate and energy policies. They help us prepare for floods and develop more resilient agricultural practices. They give insights into the future of inequality and steps we might take for a more just and inclusive transition. Yet no model will do the job for us on its own.
Today’s policymaking still often happens as if we live in a predictable world of gradual progress towards universal wellbeing. Policies take years to develop and often gloss over the surface when radical change is needed, such as in the recent EU economic governance framework orientation paper.
One thing the models tell us for sure: we don’t have much time to wait. As we need democratic ways to get people involved in the transition, models can help them learn more about what drives social and ecological processes and what the possible outcomes of decisions made by policymakers are. This can help them demand and implement the changes that are needed.
We also need new bridges between science and policy, allowing policymakers to understand the options in front of them better. Imagine policy makers who know how fewer working hours could help deal with the cost of living crisis or why both responsible resource use and systems change are necessary to prevent material scarcity. Tools to learn this are available at their fingertips.
Models like WILIAM with user-friendly interfaces make modelling feel like a game while yielding deep and actionable insights. We need policy makers to learn the rules of this game and put them into action.
Listen to our third podcast from the META series to learn more about how the models work and what they can teach us about addressing the world’s grand challenges.
Learn more about the LOCOMOTION project, designing a next-generation WILIAM model to help develop future-fit sustainability policies for a truly green and just transition.