Decisions, Decisions

Hello Dear Marshmallow Peeps,

The holiday season is upon us and everything is still upside down and backwards due to the pandemic raging on. And on, and on…but I am hopeful, there are two good vaccines on the horizon and more in the pipeline, so there is a tiny light at the end of the tunnel. Thank you science community, and– thank you Dolly Parton who gave a million dollars for COVID-19 vaccine research which helped fund one of the two upcoming vaccines. Go figure. Now, if Dolly could have only helped me make a decision I had not wanted to make regarding school re-opening.

While I wait for Dolly to impart her sage advice, I have had to make the tough choice many parents around the world have had to make. Once schools re-open, do I send my child back before a COVID vaccine is distributed en masse? It is a personal and tough call to make, and like many difficult decisions, there are no good choices, just a choice between the lesser of two proverbial evils. During the time frame our family had to make the choice “to return or not to return,” I came across a very timely episode of the podcast Planet Money  (you know how I love podcasts) which focused on making data driven decisions when presented with seemingly impossible choices. In the episode, the host spoke with Emily Oster, professor of Economics at Brown University regarding how to best approach difficult decisions.

She breaks it down in this way: many times we don’t know all the consequences that will follow any given choice. This is due to incomplete data. Despite having incomplete date, we still have to make a decision. To help wade through the difficulties of the decision, Emily Oster created a 5 step system to help solidify as many known data points as possible which, in turn, helps point to the best solution, even when there are no good choices. The steps are as follows:

  1. Frame the question. Often times we fail to fully articulate our choices. Instead of a yes/ no question, frame the question to fully identify what we know about the problem at hand.
  2. Mitigate risk. Ask how do we make this choice as low risk as possible.
  3. Evaluate the risks.
  4. Evaluate the benefits. If the benefits outweigh the risks then your choice becomes a little clearer.
  5. Make the decision and commit to it. Be confident that you made the choice in the best way you can and move forward.

This system can be applied to many different types of tough choices. And as you probably guessed, I used her system when I had to decide whether our daughter would go back to a hybrid schedule once schools re-opened in the New Year, or whether she would stay distance learning for the rest of 2020/2021 academic calendar. Her system works really well. The 5 steps forced me to concretize as many aspects of the problem as I could and working from those knowable points, our family came to a collective decision with which we were comfortable. Of course, no sooner than we came to said decision 2020 chaos struck again. Three days before we had to submit our decision to the school district, our governor placed most of the counties in our state under the strictest re-opening tier due to a spike in COVID-19 cases. So, as it turns out, our schools may not be re-opening any time soon, and at some point, it will be moot as to whether the kids go back this school year or not. If our COVID numbers stay elevated, all the kids will all stay on the distance learning schedule regardless of our decision of whether to go back or not.

So there you have it. Our decision is precluded with a big “Whomp whomp” from the fates. But, at least there is this. As we all wait to see what happens, I can take heart in the fact that our family made our decision in the best way we could have made it. Which, for now, is a small comfort. I’ll take that for the time being, while I wait for Dolly Parton to swoop in and save the day.

Wishing you a happy and safe holiday season from our family to yours, take good care of one another.

In gratitude,

Anouck

Take a listen to the full episode of Planet Money:

 

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