As engineers, we’re used to thinking in algorithms where causality is clear and the same set of actions always produces the same results. When we apply algorithmic thinking to complex systems, however, our well-meaning actions often result in unintended consequences. Whether driving organizational change or fixing a small bug in your monitoring system, it’s not enough to consider the most immediate and direct result.

In this talk, I’ll go over examples of simple changes causing large scale unintended consequences, explain how to recognize when your actions could impact more than you wish for, share techniques for anticipating ripple effects so you can use them to your advantage, and help you become more adept at reasoning about complex systems.