A game, action or sport that is complex generally shifts into those that require continuous team-based effort and action. Moving beyond complicated baton-passing, we move into team sports such as soccer and basketball—where the players dance across a field, loosely deciding to send the ball to any other team-mate or the goal at any moment. If soccer were only complicated, then each player would have to stand in line and pass only to the next. They could run in a well-demarcated zone of the field, yet not leave that zone or never pass when out of that zone.

Such a game, we believe, would be tedious, so the full engagement of complexity makes soccer and basketball two of the most watched and revered sports in the world. The thought of an IBM Deep Blue taking on an entire soccer team seems a bit ridiculous, with the computer sitting stationary on the field, blinking and vibrating its powerful memory-discs, requiring a long extension cord and being in a tent if it rains. It might successfully predict the winning team in a soccer match—yet is no match for the soccer team of humans. Thus, in the realm of complexity in the physical realm, computers are no match for humans.

The requirement for human movement, loosely coupled, dynamic interactions of multiple agents far surpass the computer’s capacity for intricate movement and interaction. To date, no AI or computer has even attempted to form a soccer team or basketball team and a collection of current-state robots would likely provide humorous entertainment—yet lose badly to a human team. It is true that in a virtual realm, a computer could take on multi-player roles and begin to outwit human teams—yet, to date, we see teams incorporating artificial intelligence to inform and guide their human teams.

There is some evidence that highly skilled experts can be out-performed by a less expert team that is using a computer AI system to assist them, what is now being called crowd-sourcing or collaborative intelligence. For example, at the University of Washington, biologists used crowds of external contributors to map the structure of an AIDS-related virus that had stumped academic and industry experts for more than 15 years.

It would appear, then, that in the complex quadrant, collaboration between computer-AI and human actions, games, and performance may present promising future directions, yet we do not face any immediate risk of being surpassed by computer-AI, especially in complex environments that involve complex human interactions, emotions, and social engagement like playing soccer or team-based healthcare.

Snowden also makes clear that there is a cliff between Clear and Chaotic—that can happen suddenly due to external and internal threats or disruptions to the organization. It seems plausible as well that an organization that is acting as if it is in a Clear state, is really in a complex one and may not be appropriately adapting to that reality—and is thus thrust into Chaos which can rapidly spiral into total collapse or, if well led, an inflection point or vital re-orientation that likely will require more complexity-responsive leadership, teamwork, and actions.

Chaotic Systems

The state of chaos goes beyond the scope of this essay, so we will leave further clarifications around chaos to future essays that we write as well as to other experts in that field.