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Course Instructor

Instructor Bio

Computational Modelling in the Natural and Social Sciences

"Learning by doing, peer-to-peer teaching, and computer simulation are all part of the same equation."

Nicholas Negroponte


Open to all students entering Grades 8,9,10 in 2022-23

Understanding how things work is at the heart of human curiosity. And humans have found ways to understand phenomena around them. Most of these ways include some form of simulations of the phenomena in controlled conditions of laboratories or controlled field sites. We understand the laws of motion because of numerous theories and experiments done in laboratories. 

However, some systems are complex, expensive or dangerous and these systems cannot be simulated in any controlled environment. For example, it would be expensive and ecologically damaging to see how a forest fire spreads in nature, but this can be cheaply and safely simulated on the computer. In recent years, through advances in computing power, we can simulate some of these phenomena. 

In this course we will build and conduct computational simulations of complex phenomena like forest fires, climate change, and drug design/delivery. We will dive into understanding the anatomy of computer simulations by comparing simulations of different phenomena, using tools such as netlogo. Forest fires, viral diseases and information all spread from one individual to another. Through such comparisons we will explore the similarities and differences in the spread of forest fires, viral diseases and information. 

This course is designed for future scientists, engineers, researchers, business entrepreneurs, and financial analysts so that they get introduced to this new way of computationally thinking and understanding natural and social phenomena in the world.

By employing computational simulations from natural and social sciences in this course, we will develop an understanding of the basics of game theory, effects of individual behaviour on populations, and probabilistic thinking. This experience will not only introduce students to such new areas that are not typically covered in the school curriculum, but will also give them a tool to gain a deeper understanding of concepts in the mainstream curriculum. For example, simulations can be a powerful way to learn about chemical equilibrium or how electron-ion collisions are responsible for the phenomenon of electrical resistance. Students can continue to use the simulation tools on their own to further their learning, beyond the course.

The course draws upon important research in the learning sciences from top organisations including Harvard Project Zero's "Understandings of Consequence" Project, Northwestern University's Learning Sciences Department, and the Santa Fe Institute.


1. No prior coding experience needed for the course.

2. Students will need to carry a laptop with them.

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