Welcome to our interview series with a twist — where researchers choose from a pool of questions which ones they want to answer. It’s a chance to get to know them and their work from a more personal angle.
Today in the spotlight: Guillermo Prieto Viertel, who is a PhD Candidate at CSH investigating Human Migration.
In this interview, he talks about why combining different approaches is so powerful and why path dependence is his favorite concept in complexity science — but also how cooking serves as his personal thermometer and why he doesn’t drink coffee.
What are you currently working on, and why is it exciting to you?
I am currently working on forecasting the use of public services such as healthcare and education by both the natives and the migrant population in Austria.
In today’s political climate, where claims about migrants overwhelming public services are widespread, it is crucial to understand how the system as a whole operates in order to better inform the public and guide policymaking.
Complexity science offers tools to build models that capture the essential elements needed to answer this question. By simulating the country’s population dynamics and the behaviour of people according to their characteristics, we can estimate how demand for these services will evolve in the coming years.
What problem would you like to have solved in 10 years?
I would like to put my efforts into bridging science with policymaking. Large efforts have already been put towards science-based policy making, and there are several institutions currently working on it. Still, in the current context of polarisation and disinformation, there is a lot to do to put academia and policy makers together.
Which tools or methods do you use most in your work?
Some craftspeople are masters of a single tool, like a carpenter who can do wonders with a hammer. Others may not rely on just one instrument but carry a well-stocked toolbox, knowing how and when to use each tool. I see myself in this second group.
My strength lies not in perfecting one method to its extreme, but in combining different approaches, adapting to the task at hand, and selecting the right tool for each moment. This versatility allows me to tackle complex problems and create solutions that a single tool alone might not achieve.
What brought you to science in the first place, and how did you end up at CSH?
My path to science was anything but straightforward. I had always been drawn to research, but it wasn’t always clear that I would pursue it as a career. After completing my bachelor’s degree, I explored different directions: working in various private-sector roles, in international development, and in the public sector. In the end, my drive to ground my work in strong, evidence-based approaches naturally led me to research.
At CSH, I found the opportunity to conduct research while still engaging with pressing societal issues, bringing together both of my core motivations.
What’s the most unexpected place your work has taken you—intellectually or geographically?
A bit metaphorical, but down a rabbit hole. Working in research offers the chance to read and learn about an incredible range of topics, a dream come true for me. But it also comes with the risk of disappearing down intellectual rabbit holes.
As a self-acknowledged dilettante, I often find myself diving deep into subjects that catch my interest simply because they come up during my current work. Doing a PhD has been a tough but valuable lesson in knowing when to stop, when to prioritize, and where my efforts can bring the greatest value to the research.
If you could invite any historical figure to a research meeting at CSH, who would it be—and what would you talk about?
One of the main and most exciting topics of discussion at CSH is the validation of models. As complexity scientists, we model reality by creating quantitative abstractions that aim to capture it as closely as possible. The question then becomes: to what extent is your model a valid representation of reality?
If I could invite a famous person to join this discussion, it would be Paul Feyerabend.
He would remind us that the idea of a single, universally “correct” way to assess a model is itself a construct shaped by culture, history, and perspective. In his view, different approaches to knowledge can be equally valuable, even if they conflict. I imagine him challenging us to see model validation not as the pursuit of a single objective truth, but as a conversation between many possible truths.
What’s one concept in complexity science you think everyone should know—and why?
Path dependence is my favorite idea in complexity science because it captures the way that history shapes the present and the future.
In complex systems, small, seemingly insignificant events can set processes on drastically different trajectories. Path dependence teaches that the past matters, that context matters, and that in complexity, no change happens in isolation.
What’s your go-to strategy when you’re stuck on a problem?
Walking! There’s nothing better than stretching your muscles and pumping blood to the brain to clear your thoughts.
I feel especially inspired by the French flâneurs wandering through the streets, getting lost not only in your own reflections but also in the little surprises and entertainments that the city offers. Each walk becomes a mix of introspection and a way to connect with both your inner thoughts and the surrounding life.
What’s something in your daily routine that might surprise your colleagues?
No coffee. Being tired is often a sign of dehydration that can be fixed with a glass of water.
What do you enjoy doing when you're not thinking about complexity?
Cooking is my thermometer. I enjoy it as a way to relax and of course, because I love a good meal! But it’s also my gauge for balance: when I don’t feel like cooking, I know something is off and that it’s time to pause and reorder my priorities.
Complete the sentence: “Complexity is …”
… to a rainforest as complicated is to a jet engine. A rainforest changes, adapts, and organizes itself in ways you might not be able to fully predict, i.e., there’s no manual for it. A jet engine is complicated: it has many parts, but they work in predictable ways, and if you follow the instructions, you can fix it.
What’s your favorite place in Vienna so far?
Riesenflohmarkt Wienerberg is one of my favorite spots in Vienna. You can find all sorts of hidden gems, and it’s also a great place for a morning Sunday walk.
