A new offering from SFI’s online education resource, Complexity Explorer, gives complexity enthusiasts quantitative tools for distinguishing the "complex" aspects of a system from the merely "complicated."

The mathematics tutorial, “Maximum Entropy Methods,” teaches data analysis techniques for identifying simple sets of principles that describe complex systems such as a brains, or ecosystems.

The tutorial's instructor Simon DeDeo explains that “what MaxEnt does is allow you to disregard what is only ‘complicated’ in the system…if you’re able to come up with a satisfying account of the system using MaxEnt principles, it implicitly is telling you that a bunch of stuff doesn’t matter. It looks like it’s meaningful but it’s not.” DeDeo is an SFI External Professor and an alum of the Omidyar Postdoctoral Fellowship.

The Maximum Entropy Methods tutorial is available through SFI’s Complexity Explorer website.

High-school-level calculus is prerequisite for understanding the course, and the workload is self-paced. Because it is a mathematics tutorial, Maximum Entropy Methods will always be available online, unlike some other online courses in the Complexity Explorer roster.

Read Simon DeDeo’s Q&A about the ‘MaxEnt’ tutorial on Complexity Explorer (June 10, 2015)