Mrs. Wolf, Mr. Nagel, in the MATH+ campaign motif, a math agent sits on the historic traffic lights at Potsdamer Platz and has to choose between different modes of transport. Why not an agent?

Sarah Wolf: For me, agent is a neutral word, like table. Because our agents are mathematical elements or objects in the computer. They can take on very different roles.

Kai Nagel: In mathematical language, an agent is the description of an acting person who has an internal state and follows certain rules of behavior. With a large number of such agents, we can ultimately simulate a society in the computer, within certain limits of course.

What properties can such an agent have?

Wolf: A colleague always explains this to school classes: "Shoe size and your favorite movie are not important when it comes to mobility, but age, income, place of residence and work, health and even political views are.

Do you also work with school classes?

Wolf: Yes, we combine the agent simulations with an element of citizen participation, for example for schools. The technical term for this is transdisciplinarity, but the BUA campaign's catchphrase “open knowledge lab” would also be very apt.

Nagel: We organize workshops in which people can make political or administrative decisions themselves and then immediately see the effects of these decisions through our simulations. Our topic here is transportation planning.

The aim of these workshops can be to get ideas, for example for new measures. Another goal can be to provide the actual political decision-makers with packages of measures that a large majority of people in our workshops have endorsed.

How exactly do your workshops work?

Wolf: Kai Nagel and I use slightly different formats. I use what is known as Decision Theater, which was originally developed in the US. In our case, 15 to 30 people discuss ten different options for a mobility transition in Germany in small groups. These include purely political measures such as bans, but also possible investments and assessments of future developments.

Specifically, participants must decide, for example, whether to make public transportation more affordable or invest in charging stations for electric cars. And what do we think: Will they soon become cheaper? Can we more easily combine public transportation and rental vehicles through digitalization?

The groups must make a selection by answering questions on a tablet. We then look at the corresponding results of our agent simulations with these different inputs and can discuss the effects in a plenary session.

So we take a look at various possible futures that reveal some surprising consequences of the participants' decisions. For example, when subsidies for electric cars mean that public transport is no longer used because even environmentally conscious people are now buying cars.

Shouldn't such workshops be made mandatory in the work of the Bundestag? Wouldn't that prevent many mistakes?

Wolf: That would be exciting, of course. But I must emphasize that we cannot predict the future like a crystal ball. The discussions in the plenary session are more about helping people recognize that things are complexly interrelated and that there are no easy solutions. In addition, everyone has to put their arguments on the table, and it often becomes clear that we actually want the same thing, but have different ideas about how the world works.

What characteristics and interests do you give the synthetic humans in the simulation?

Wolf: Our agents want to maximize the benefits of their mobility choices, taking into account factors such as cost and convenience. They exchange information with other agents about the benefits of different modes of transport.

Nagel: At our company, every agent receives a daily schedule with goals they want to achieve. So they drive to work, go shopping, go to the gym in the evening, or meet up with friends—just like real people. These agents are then placed on virtual city maps and assigned work locations and so on. They choose their means of transportation based on criteria such as time and cost, and the means of transportation themselves also play an active role. For example, there may be traffic jams in the model if there are too many vehicles on a road.

Where does all the data on how people behave come from?

Nagel: In fact, traffic planning consists largely of empirical social research. Data has been collected for decades, including surveys of tens of thousands of people, traffic counts, population and company registers, and psychological studies. We can test our models by creating the conditions for well-researched effects in their synthetic world and seeing whether these effects are reproduced.

You are both members of the MATH+ Cluster of Excellence. Where do you encounter particularly tricky mathematical problems in your research?

Wolf: Simulating the behavior of millions of agents takes time, and we don't have that in a one-day workshop. To solve this problem, you can keep the number of options to choose from small and simply simulate all their combinations in advance. But if you really want to be live, you have to simplify the models. One way to do this is to use molecular dynamics methods and approximate the behavior of the agents using equations that can be calculated much faster. This also allows you to respond flexibly to complex compromise proposals from participants, such as when they suggest that the parking fee should not be tripled but only doubled, and in return the bus should only run every 15 minutes instead of every 10 minutes.

Nagel: The much-maligned compromise is, in fact, the norm when it comes to democratic decisions.

Wolf: Yes, ideally, our workshops are scientifically sound compromise machines!

Ms. Wolf, Mr. Nagel, thank you very much for talking to us!

Interview: Wolfgang Richter

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Unusual science communication from MATH+: The comic “Ida and the Math Agent” →