The Judgement in Crisis simulation thrust us into the product manager’s position at Mannerhorn Health, during a crisis involving the company’s newly released premiere medical device, the Glucogauge. The Glucogauge is a device used to measure blood glucose levels in patients. Yet, quickly after its release, accuracy problems began to get reported about the device. The FDA allows devices of this nature to have up to a 20% range of error, whereas the American Diabetes Association recommends no more than a 10% range of error. Mannerhorn Health had been aiming for the ADA’s target, which is why it was super alarming when the device started reporting margins of error near 30%. In this simulation, we had to make several decisions that involved addressing this inaccuracy problem, and we had to decide how we would manage the crisis on a customer level.

Cognitive bias played a large role in this simulation. We learned that there were four major types of bias affecting how we responded to the simulation. Those were, confirmation bias, sunk-cost bias, framing bias, and anchoring bias. I wasn’t highly affected by confirmation bias, since I was successfully able to alter my response to the crisis as new information was presented. That being said, I was affected by sunk-cost bias, framing bias, and anchoring bias. Sunk-cost bias caused me to continually invest money into strategies that yielded little returns. Framing bias prompted me to choose riskier options, rather than avoiding uncertainty. Anchoring bias caused me to estimate the percentage of patients using the Glucogauge app to be a very high percentage, where it could very possibly be lower. My susceptibility to each of these biases taught me a valuable lesson. I need a way to combat bias. On this point, I can think of two useful solutions that we discussed. Firstly, I want to remind myself of the different types of cognitive bias that exist before I approach a big decision. Even acknowledging the existence of bias goes a long way to combat it. Secondly, I should run decisions of consequence by non-interested parties. This allows for a second opinion, free of many biases. Hopefully, these strategies will help me avoid falling victim to cognitive bias in the future.

In this simulation, the areas that I found most challenging were the areas that I did not have data to work off of. This stood out in two major areas. The first budgetary decision was one of them. I had no idea what strategy would work and what wouldn’t, so my first guess was a shot in the dark. At least during the second budgetary decision, I had the ineffectiveness of my first action to go off of. I also struggled significantly with the decision of whether or not to lay off 400 people or try to save everyone at risk of laying off 600 people. A lack of context or outside information made these decisions especially difficult. This level of ambiguity caused some stress. Therefore I had to come up with a way of dealing with said stress. I elected to use ‘what if’ scenarios, as a way of justifying my answers. For example, in the employee decision situation, I thought about what I wanted my overall strategy to be, and then I imagined ‘what if I went with option A vs option B, how does this align with my overall strategy?’ This allowed me to feel as though every decision I made had some justification, which lowered my stress levels.

Incidents like the one in this situation happen all the time. In fact, my group briefly discussed one during our group activity today. We discussed the situation with Boeing and the failure of their new 737 MAX model. After a deadly crash in Ethiopia, many of the world’s airlines lost faith in Boeing’s new 737 MAX and canceled their orders. The plains that were already in circulation were soon grounded. Boeing tried to mitigate the problem, by emphasizing repair efforts. Yet, their strategy proved to be relatively ineffective, as the plains stayed grounded. Unable to declare with certainty that the problem was solved, Boeing’s new cunning edge consumer aircraft flopped. There are many lessons that can be learned from Boeing’s failure.