Max Stauffer

Complexity Science & Computational Modelling: Expanding Our Analytical Toolbox

My name is Max Stauffer. I am co-founder and co-director of Effective Altruism Geneva. I am also a part-time research assistant at the Graduate Institute in Geneva. I recently co-founded the Social Complexity Lab Geneva. I have a degree in international relations and learned about complexity science / computational modelling in online courses. I apply this knowledge to my work in the three aforementioned entities.

The world is a system composed of subsystems - be them physical, biological, or social. Few systems are simple. Few systems are chaotic. Most of them are complex. A complex system can be defined as many, heterogenous moving agents, components, and variables in interaction that generate emerging, non-linear properties. They are hard to predict. Until the 1980s, the scientific method had a reductionist approach: separating the systems in different parts and trying to understand these parts. In the 80s, a group of scientists created the Santa Fe Institute together with the field of complexity science: literally the study of complexity. In a nutshell, the idea is the following: to understand complexity, we can use more sophisticated tools and languages rather than being reductionist. One of these tools is computational modelling.

Effective Altruism increasingly deals with very complex issues (global catastrophic risks, long-term future, improving institutional decision-making, community-building (!), ... ). Yet, only very few people within the EA community talk about complexity science (or even know about it). Is it because EA mainly relies on mainstream methods?

In this talk, I would like to present the field of complexity science, how to construct computational models, and present some examples that may be applicable to EA contexts (perhaps with a strong focus on risks and policy).

We all have a model of reality in our minds - be them implicit or explicit. Taking the lens of complexity science and computational modelling opens new ways to navigate a complex world more clearly, seek truth more systematically, and guide our decision-making in uncertain situations.