Uri Wilensky is a professor of Learning Sciences, Computer Science and Complex Systems at Northwestern University; co-founder of the Northwestern Institute on Complex systems; and the founder and director of Northwestern’s Center for Connected Learning and Computer-Based Modeling. Around the world, he is also known as the author of NetLogo – a modeling environment used by teachers, students, and researchers. NetLogo is used to simulate and explore complex systems and counterintuitive phenomena in natural and social sciences, from pandemics’ dynamics, climate change and ecosystems to economics and voting. The NetLogo design was influenced by Wilensky’s Ph.D. advisor Seymour Papert, one of the founders of artificial intelligence and constructionist learning theories (which advocate student-centered, discovery-based learning).
Julia Brodsky: Why is complexity education so critical for us these days?
Uri Wilensky: The world has grown very complex, and to adapt to it, we need to recognize how complex systems change over time. In complex systems, there’s often a tipping point, and when a critical parameter changes just a little bit, the state of the whole system transforms, and we get phase transitions between the states of matter (such as water turning into ice), epidemics, and uncontrollable wildfires. It is, therefore, crucial for us as a society to educate students on recognizing and dealing with complexity.
Brodsky: Why do you consider representing complexity in agent-based models so important?
Wilensky: Finding a working representation is a significant step toward making a concept accessible to everyone. Once, I took a class taught by philosopher Thomas Kuhn, who was interested in the evolution of scientific ideas. As an example, he used the adoption of Hindu-Arabic numerals on the European continent around the turn of the first millennium. Until then, Europeans who used Roman numerals had a very hard time with multiplication and division. (Try multiplying in Roman numerals yourself!) But once they switched to the new representation of numerals, every child became capable of doing multiplication. This transition completely changed the world.
Many educators nowadays say it is very difficult for a human brain to understand emerging, non-linear processes. As I was interested in how patterns arise in the world from an early age, I spent much of my academic life working on the representations of complexity and concluded that one of the most promising ways to do so is through agent-based models. The core idea behind agent-based modeling is that almost every situation involves interactions of individual actors called agents (atoms, trees, cars, viruses, people, animals, etc). Each agent has a simple set of rules describing their goals, actions, and responses to other agents. Those simple rules lead to the emergence of the system’s behaviors. Our new agent-based educational tools, such as NetLogo, introduce complexity to everyone, from preschoolers to college students.
Brodsky: Could you provide an example of how NetLogo is used?
Wilensky: NetLogo helps students to visualize non-intuitive, complex systems. Take a forest wildfire model, for example, represented from a perspective of a tree. In the model, an unburned tree looks to the north, to the east, to the south, and the west. If it sees a burning tree next to it, it ignites. Very simple rule. If we run the model at 50% forest density, we’ll see the fire will die out soon. At 57% density, too, the picture will be similar. But if I raise the density to 63%, the whole forest is burning. Most of the time, our unaided intuition would suggest that a small change of density should lead only to a bit more burn. It is important to recognize that our intuitions about complex systems are often misleading.
NetLogo curricula help students and researchers build computational models to experiment with complexity. The models can be made accessible to young children as well. And there is no need for mathematical formalisms (such as differential equations). Kids observe something happening in nature or society, and play with a corresponding model that shows the unfolding of multi-step, self-organizing processes. Our research shows that teaching students to model complex systems leads to a deeper understanding, compared to traditional science instruction, and helps transfer knowledge between different content areas.
Brodsky: Is NetLogo mostly used for natural sciences, or can it be applied to social sciences and politics?
Wilensky: In fact, I find those to be the most exciting areas of growth. However, it takes a little more work to recruit social science teachers. We’ve been working hard to put more social scenarios into our units, such as cooperation among animals, conflict resolution, ethnocentrism, voting, housing segregation, urban sprawl, taxation systems, and more. In addition to education, which is our main mission, we also conduct applied research. For example, in Chicago, we work with the Department of Public Health to test various strategies for dealing with infections in the most cost-effective way, to inform public health policies.
Brodsky: How would you communicate the urgency of introducing new ways of teaching complexity?
Wilensky: We are at a major inflection point. The computation has gotten so powerful that it can allow us to imagine, analyze, and play games with things that we just couldn’t do before. Fortunately, we now have an understanding of how to do that. A curriculum for teaching complex systems doesn’t have to do so by directly hitting the students over the head with the systems thinking concepts. Instead, those could be inserted into a variety of courses as a new perspective for approaching the material.
It took Europeans about 500 years to adopt Hindu-Arabic numerals – and we don’t have 500 years to spare. All we need is the political will to bring the issue of complexity education into the mainstream educational discourse.