How the 2016 Presidential Election Teaches Us Not to Ignore Decision Science

November 16, 2016

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With the election now in our rear view mirror, and the reactions swirling all around us, it’s really interesting to take a look at what happened.  The outcome was surprising to nearly everyone, regardless of your political leaning. It was a surprise, of course, because of the personality involved and the last year of the campaign. But really, it was a surprise because of the amount of polling and predictions that surrounded this election, and how “comfortable” everyone got with the “highest percentage result”. So what happened? There will be plenty of analysis of the polls, the errors, and the statistics, and I’m not here to claim I know what happened there, but I do think there is a really interesting side of decision science that is worth paying attention to and can be used to our advantage in the workplace.

What you’ll hear is that the polls were flawed for any number of reasons, but I don’t think that the polls were a problem. They were guesses at patterns. And in reality, showed us pretty low-confidence pictures of scenarios that could happen. And they were statistically correct in many cases. I’d argue they weren’t “flawed” as scenario builders, we just focused on the wrong scenarios. What was flawed was our ASSUMPTIONS about what people would actually value when making such an important choice and the factors they actually cared about when it mattered. It became trite and easy to assume we knew the factors they would care about – the social issues, the candidate qualifications, history of actions, etc. And we weighted those factors we assumed were most important, higher in our mental scenarios. Reality showed a very different story. We truly “mis-weighted” the actual factors that people cared about – personal comfort, fear & mistrust, anti-elite sentiment, desire for change, etc. It’s clear those factors weighed a lot higher in the decision, and it led to a different outcome than many thought.

And while it’s easy to see how this can affect something like an election, what do we read into this for our organizations? For the big decisions and funding choices we make every day? For the strategic direction we try to implement at our organizations? I think it paints us a clear picture of how complicated things get when many people are involved, but why it is still important to GET people involved. People bring biases to a decision. People bring emotions to a decision. People also bring data to a decision. How do you, as a strategic leader, ensure that you can sift through the data, emotions, and biases to get an actual picture of the right path forward? You need to find your way PAST the assumptions and seek out the hidden priorities. You need to be certain you’ve weighted the factors, the data, and the actual values of the people and organization involved to ensure the decision is the best one. The most informed one. The most strategically-directional one. And then, hopefully, the right one.

But how do you do that? You certainly don’t do it manually. You certainly don’t do it with a spreadsheet. You certainly don’t do it with more meetings. Advocacy-based decision making based on who-yells-the-loudest will do nothing to bring out the hidden values and hidden strategic needs. You need the analytic power to identify and give a voice to those things that may not be the most obvious. You need the ability to run scenarios of EVERY different value system and weighted priorities to truly identify what factors matter most and what direction you want to take. You need to seek it out, bring it to life, and use it to find the truth and the smartest direction. You need to get past the assumptions into the analytics of your decision. ALL the factors.  ALL the values.

So no matter your feelings on the outcome of the election, let’s learn from how many values and factors were underestimated. And let’s be sure we put the power into our organizations to be sure it doesn’t happen there too.­­­

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