“I would always do a Retrospective.”
“I would never again do estimates.”
We Agilists tend to be a passionate bunch. We ground ourselves in the principles and often too, the practices. We can get pretty rooted in our beliefs and lose sight of our biases. As coaches and mentors, we owe it to ourselves and those around us to stay mindful of the positions from which we operate and work to practice active inquiry.
Never Would I Ever! is an adaptation of the traditional drinking game, and (according to actual participants) provides just as much fun! From newbies to veterans, game players arrive at new insights and clarity around Agile principles and practices.
In the game, a volunteer nominates a strongly-held position, and for 4 minutes, others ask questions in service to understanding the reasons for the particular belief (but not to refute it). In the subsequent 4 minutes, players then suggest scenarios in which the volunteer might change his or her mind.
There are multiple learning outcomes to the game. Volunteers have the space to clarify their beliefs and re-examine any biases. All participants experience the value of powerful questions and active listening. The emphasis is on humble inquiry (the art of asking, not telling) and recognition that stated positions don’t need to be binary or polarizing.
A couple of brain-based learning methods are in operation in this Agile18 workshop.
When we engage in game playing, our brain chemistry is a little different. Fun and laughter typically mean that emotional stakes are low and our defenses drop. We can make mistakes in a risk-free setting and through experimentation, we actively learn and use new skills. We can practice behaviors and thought processes that are transferable from the simulated environment to real life.
Double-loop learning allows us to examine underlying actions and thoughts. Where single-loop learning involves changing methods and improving efficiency to reach established objectives, double-loop learning involves changing the objectives themselves. Double-loop learning is helpful when we want to examine the mental model on which a decision depends.