ShadowBox Training for Making Better Decisions

The need for better decision-making

How can we improve decision-making skills? My colleagues and I kept asking ourselves that question after we published our findings on the Recognition-Primed Decision (RPD) model in 1986. Frustratingly, we never could find an answer. Decades passed.

Then in 2008, I got lucky. I was in New York City for a conference, and some FDNY firefighters asked if we could meet for lunch. One member of the group was a Battalion Chief, Neil Hintze. The lunch went well, and we all exchanged contact information with no real expectation that we would collaborate on anything.

But a few months later, I was invited to put on a two-day decision-making workshop for the Seattle Fire Department. I decided to ask Hintze to help me develop the materials and to accompany me to Seattle. After we had arrived at an overall design for the workshop, Hintze asked if he could have a slot, perhaps just a half-hour, to try out an exercise based on the Master’s thesis he had just completed. I didn’t fully understand how the exercise would work but I agreed to let him try.

His exercise was the origin of ShadowBox.

ShadowBox: Training for better decision-making under pressure

In the exercise, Hintze presented a challenging scenario with a series of multiple-choice decision points interspersed throughout. The firefighters in the workshop had to rank the options for each decision point and write down the reasons for their ranking, and then Hintze would tell them what a panel of experts had chosen and why. He had found that this training—specifically, the reflection on the expert feedback after three practice scenarios—resulted in about an 18 percent closer match in responses between the trainees and the experts compared to a control group, who did not receive expert feedback.

Because Hintze was running the exercise, I could sit back and watch the group. I saw how captivating this exercise was—everyone was involved, everyone wanted their rankings to match the experts. I also realized that Hintze’s method looked like a way to improve decision-making skills—the approach I had been seeking for so long.

Plus Hintze was capturing the judgment of the panel of experts without bringing in the panel itself. He had shown the experts the scenario and recorded their choices and reasons and synthesized all of this so that the experts were no longer needed. This was significant because one of the bottlenecks of training is getting access to the experts when you need them, and Hintze had eliminated that bottleneck. He had the experts’ responses, so he didn’t have to involve the experts any longer.

In the following years, as my team and I used and modified Hintze’s method, we worried about the effort and expense needed to build the scenarios. My colleague (and daughter) Devorah Klein came up with a streamlined approach. Instead of a scenario, she just prepared statements and had trainees and experts identify what they agreed with and what aspects of the statements they didn’t accept. We call this the “snapshot” version to contrast it with the “scenario” version Hintze had developed. And we expanded it to cover different types of snapshots: a single spreadsheet in financial settings or a chart of vital signs in medical settings. We contrast what the trainees notice in these snapshots versus what the experts noticed.

We also worked out a third ShadowBox format: the Expert-Eyes [TM] approach that we have patented. Here, we show trainees a video and let the trainees click on anything they find noteworthy or relevant to a mission. The trainees record their reasons for clicking on any specific cue. Then they see what the experts clicked on and why. So it’s the same format as the scenario version and the snapshot version, using a video instead of a text scenario or a single page with a statement or chart. With this open-ended format, the trainees truly are learning to see the world through the eyes of experts.

We have developed software versions of ShadowBox, and these have come in handy during the pandemic to allow us to conduct online training without requiring face-to-face interaction. We currently are using the software version of ShadowBox to train child protective services workers in Ohio and law enforcement officers in California.

Regardless of the format, the ShadowBox work has shown its effectiveness in bringing people up to speed more quickly and strengthening decision-making (Klein & Borders, 2015). We are still discovering new types of applications for it.