Thinking, Decisions and Data: Chapter 9 — Answering an Easier Question

Simon Jackson here with “Thinking, Decisions and Data”, a blog post series where I summarise the psychological insights of Daniel Kahneman’s, “Thinking, Fast and Slow”, and discuss the applications to decision and data science.

We unknowingly answer difficult questions (which path should I take?) with simpler ones (which path makes me feel safer?). Photo by Vladislav Babienko on Unsplash

CHAPTER 9: ANSWERING AN EASIER QUESTION

This chapter builds on the previous ones to explain that we have intuitions for complex problems because we unknowingly substitute for easier questions. This lets us operate in a complex world but also to make serious errors. My practical takeaway is that you should let data give answers while you focus on the more difficult job: defining questions and the data needed to answer them. Read on to learn about the psychology behind this!

Chapter Summary

With rare exceptions like Chapter 1’s, “12 x 17 = ?”, have you noticed that you’re rarely stumped? We live in a highly complex world but have intuitive feelings and opinions about almost everything. How is this? Spoiler alert, it’s not because we’re hyper intelligent.

Kahenman proposes that when we can’t answer a difficult question, our mind will think fast (thanks to System 1) and substitute it for something simpler. The simpler question is called a heuristic, which technically means “a simple procedure that helps find adequate, though often imperfect, answers to difficult questions.” To demonstrate, answer the target questions in the left column below.

How did you answer these? You probably didn’t think much even though they’re actually complex questions. This can be explained by how System 1 forms basic assessments (Chapter 8). As you read the questions, your mental shotgun answered many simpler, heuristic questions like those shown in the right column. Then, these answers are mapped by System 1’s single intensity scale to a value that makes sense. For example, System 1 will map the level of anger you feel when thinking of financial predators to what it considers to be a punishment of similar intensity. The mental shotgun often evokes feelings or intuitive responses to simpler, heuristic questions, and their intensity is mapped to a response that fits the original question.

Kahneman discusses three heuristics that demonstrate this substitution. One is that our visual system substitutes 2-D questions for 3-D ones (leading to the likes of the Müller-Lyer illusion in Chapter 1). Another is that we substitute questions about our overall happiness for an assessment of our current mood. Finally, we tend to form complex opinions by substituting with our feelings. For example, my opinion about the potential success of a new product will likely be driven by how much I like or dislike it.

Decisions and Data Takeaways

This is an amazing insight to have about ourselves and one that leads me to propose that you:

Let data give answers while you focus on the more difficult job: defining questions and the data needed to answer them.

I often think that decision makers, data scientists, and analysts fall prey to the same belief: that it’s our job to answer questions. Yet we talk about being “data-driven”, which means that the answer comes from data. Resolve this and shift your mindset. When an important question comes your way, don’t answer it! That’s not your job. Put that responsibility on data. You have a different and (in my opinion) far more difficult responsibility: to define the data needed to answer the question.

Why? We’re blissfully unaware that most of our judgements and decisions are driven by an over-simplified model of the world. For example, say a colleague presents you an idea and asks, “is it worth investing in?” Whether you realize it or, more likely, not, your answer will have more to do with how much you like your colleague than anything else. Your mind will often produce intuitive but erroneous feelings for new and complex problems about which you have no expertise. But you can avoid these mistakes by committing to let data answer questions for you and focussing on defining exactly what data you need. For example, think about the data and evidence you need to assess whether your colleague's idea is worth investing in. This keeps you focused on the target problem instead of a simple substitute. It also forces you to consider all the potentially important factors that you probably would have ignored otherwise. Finally, it helps you to work out if the data you need is available or if you need to act without. Once you’ve defined and gathered the necessary data (if you can), there’s no need for you to answer the question. Let the data speak for itself and drive the decisions it needs to.

Favourite quotes

“The normal state of your mind is that you have intuitive feelings and opinions about almost everything that comes your way”

“…you often have answers to questions that you do not completely understand, relying on evidence that you can neither explain nor defend.”

“…heuristic questions provide an off-the-shelf answer to each of the difficult target questions.”

“…System 2 is more of an apologist for the emotions of System 1 than a critic of those emotions–an endorser rather than an enforcer.”

Sign off

Thanks for reading and I hope that you’ll share your learnings and advice below! Watch for new posts by following me on Medium, Twitter or LinkedIn.

Data Science Manager at Facebook, empowering people to make good decisions. Love to talk stats, psych, decision making, machine learning