Nudging and Data Science

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I’ve recently been reading a great book on how people make decisions and what organizations can do to help folks make better choices. That book is Nudge.

What is a nudge?

The authors describe a nudge as anything that can influence the way we make decisions. Take the primacy affect, for instance, namely the idea that order matters in a series of items. We’re more likely to recall the first or last option in a list of items simply because of their positions. This would be a nudge if you later chose the first movie from a list that a friend had recommended mostly because it was the first one to come to mind in the store.

The fact that humans have these biases is in indicator that we don’t always act rationally. In cases where we haven’t had enough experience to learn from our decisions, we need a bit of help finding the most appropriate option for our needs. Most people only decide what type of health care plan they need or at what rate to contribute to their retirements plans a few times in their life, so there isn’t much opportunity to learn at all.

All in all, the book is a great read, and much of it is an explanation of how proper nudges have excellent applications in areas like health care and making financial decisions.

How does Data Science fit in?

So, why bring in Data Science? Well, lately companies have been looking to the field of Machine Learning and Statistics to determine how to make better business decisions and these methods can play an important role in helping define the right nudges to use.

The authors emphasize that proper nudges should a) offer a default option that is stacked in the favor of most people and b) make it easy to stray from the default option as needed.

When I think about those two, a few things come to mind. In Machine Learning, a mathematical optimization takes data about outcomes and selects the best set of choices. And recommender systems are designed to, when given a few hints, offer up suggestions of similar or like items.

In the case of deciding on the most favorable default option, that decision should be made based off of the available data. The authors talk about health-care and Medicare Part D and the fact that the government randomly assigned plans, thereby leaving most people in a sub-optimal situation. An approach to solve this problem given the available data would have been to make a survey of citizens and their prescription needs, and then selected a default plan from every option in a way that minimize some variable, such as the median cost per participant.

Additionally, the authors describe a tool for Medicare Part D that allowed someone to input their prescriptions and assigned someone a plan to choose from. One of the difficulties with this system was that it rarely gave the same answer, even with the same inputs, because the plans would change over time. This gave people a false sense of which plan was good for them. A better approach would have been to give recommendations of appropriate plans, by taking the drug information and matching it to available plans. When presented with 100s of options, people have a difficult time making a choice that will work, but if those 100 could be winnowed down to the 3-5 most appropriate ones, people will have an easier time weighing the pros and cons.

Obviously, there is still plenty of constructive work to be done in supporting any nudge. And I believe that the tools that Data Scientists use day-to-day are valuable to keep in mind in these efforts.

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