Complicated systems have many parts, or take many steps, or have many rules; complex systems are complicated systems connected to and interdependent with other systems (likely also complex).

The associated complexity of a seemingly simple resolution generally shows up in unintended or unexpected consequences; complicated interconnections cannot be cut without repercussions.

The only way to reduce and resolve the complexity of a given situation is to reduce its level of interconnection with other systems; doing so, however, can undermine the value or power of the given system, and will alter the systems to which it was once connected.

In other words, the opposite of "complex" is not "simple," the opposite of "complex" is "isolated."

From the comments:

There's a solution in computer engineering known as loosely-coupled systems. Basically, the notion is that each subsystem should have the minimum number of connections to other systems. This probably somehow applies to the future somehow.

"At Continuum, we use deliberative discourse--or what we fondly call “Argue. Discuss. Argue. Discuss.” Deliberative discourse was originally articulated in Aristotle’s Rhetoric. It refers to participative and collaborative (but not critique-free) communication. Multiple positions and views are expressed with a shared understanding that everyone is focused on a common goal. There is no hierarchy. It’s not debate because there are no opposing sides trying to “win.” Rather, it’s about working together to solve a problem and create new ideas. "

Questions to gather context and collect details
Questions to probe on what’s unsaid
Questions that create contrasts in order uncover frameworks and mental models

The interactive course is designed to be fully automated, with successful students receiving a certificate.

In this prototype stage, the online assessment will depend on an "honour code" in which home students will commit to honest behaviour. But in future, the university says, there will be mechanisms for checking identity and verifying work.

"Combining models full of nuance and obscurity increases complexity. Organizations that plan complex uses of Big Data and the algorithms that analyze the data need to think about continuity and succession planning in order to maintain the accuracy and relevance of their models over time, and they need to be very cautious about the time it will take to integrate, and the value of results achieved, from data and models that border on the cryptic."