Our superior intellect places us at the very top of the animal kingdom. However, we are notoriously slow learners compared to our more primitive cousins. As it turns out, our enhanced capability to process greater amounts of information comes with an unparalleled ability to confuse, deny, and rationalize away information that doesn’t fit with our stories. That makes change difficult.
Leveraging Simulation to Tell Better Stories
As an Experience Designer with a substantial stake in effective change management, Monte Carlo simulation is my friend. This form of simulation helps us model uncertainty—using random numbers conforming to defined probability distributions—to assess the ripple effects of decisions in a complex and dynamic environment. We can then simulate the outcomes of our decisions thousands of times. The formal objective is often better outcomes with lower risk. The real power of these simulations, however, comes from their form. They can be presented to look like real life stories.
People often tell stories to fill in the gaps between events that are too complex to fully understand. Presented correctly, Monte Carlo simulations can tell interesting stories while incorporating many more relevant factors than we can consider. Simulation should be honored for its ability to tell a story as much if not more than the solutions it delivers.
Three Reasons Our Analyses Fail to Persuade (and How Simulation Helps)
For a practical running example, let’s consider a physician’s practice that is reconsidering how they schedule appointments. Their primary objective is to minimize patient wait times and physician idle time. Ideally, they would factor in no-show rates, late patient arrivals, the duration of individual appointments, staff call-outs, and other attributes that impact their day-to-day.
• Reason 1: “It’s Complicated.”
We tend to feel our stories have nuances that numbers (“data”) just can’t capture. For instance, Dr. Jones may see an analysis based on an average late patient arrival of 5 minutes, but he knows that the really late arrivals(watered down by an average) have a disproportionate effect on how his clinic will operate on any given day. He has plenty of stories of late evenings that kept him from spending quality time with his kids. He’s not buying into this “average” so he’s not persuaded to change.
How Simulation Helps: Through Monte Carlo simulation, you can show Dr. Jones that you utilized the full historical distribution of his patients’ late arrivals. After a few tweaks to his schedule, he’s now going to be home in time to throw the ball around with his children.That’s a great, new story!
"Numbers can tell a story, but often not the full story. If we want to persuade people to change, our analyses have to tell better stories than the stories people tell themselves. Simulation can help with that"
• Reason 2: “Patients ‘always’…”
We HATE uncertainty. Psychologically it’s better to write uncertainty completely out of our personal stories altogether. People feel better in a world with clear causes and effects, so Dr. Williams psychs herself up with stories that her clinic is “never” sufficiently staffed and her patients are “always” running late. Because these factors are somewhat or fully outside of her control, changing her schedule won’t do any good.
How Simulation Helps: If humans play a part in your work, then your reality is based on a series of interacting probabilities. Monte Carlo simulations can reflect any number of probabilities you want to model. We can help reshape Dr. Williams’ story by showing her the actual percentages of late arrivals and staff call-ins over the last 6 months. Then we can show her the effects of a change in her scheduling even if no other factors change. Simulations provide the story for change when people see what their individual contributions can do.
• Reason 3: “I want my cake and to eat it too.”
We want to do what we want and have everything turn out just as we want it. Dr. Richards may want to double-book her appointments AND not have dissatisfied patients, but her patients get to choose how they feel about those delays. She double-books because “patients don’t show up.” However, when those patients do show up, every double-booking creates waiting for those patients and the patients following them.
How Simulation Helps: If you can’t make decisions AND choose your consequences, the next best thing is to see your consequences BEFORE you make your decisions. Dr. Richards knows that if she doesn’t double-book, she may reduce patient waiting but she will also see her idle time increase. However, our simulation showed her that a few small changes to her schedule could save patients a combined 3.5 hours of waiting each day while only adding 7 additional minutes of idle time for her. That’s a story she would love to tell!
Numbers can tell a story, but often not the full story. If we want to persuade people to change, our analyses have to tell better stories than the stories people tell themselves. Simulation can help with that.