Category: Reading

For almost as long as I have had a job, I have watched people use metrics to gauge performance. Many bosses live by the adage, “If you don’t measure it, you can’t improve it.” For almost as long as I have had a job, I have struggled with the notion that everything of value can be measured and thus improved.

Muller’s book puts that sentiment into writing. Sourcing detailed accounts of how articulate measurements often go awry from their intention. Muller quotes Marilyn Strathern’s paraphrase of Goodhart’s law: “When a measure becomes a target, it ceases to be a good measure.”

In our race for efficiency, we often expect metrics to manage things rather than people, assuming metrics never lie but neglecting the fact metrics also only tell a very narrow piece of the story.

Spending her career researching the brain in general, and emotions in particular, Barrett is able to illustrate how the classical framework of emotion is different from how things actually work. For example, in classical theory, the brain produces a universal emotion in response to a particular input. Barrett’s research shows quite a different story: different brains react differently to the same input, indicating that different people have different experiences even though we use the word to describe the emotion.

Barrett reminds readers that the brain itself never experiences reality. Instead, it experiences life through a variety of sensory input (nerves that feel, eyes that see, ears that hear) and spends its existence bringing meaning to the signals it receives. All the world we know is a simulation based on sensory inputs and our predictions about what will happen next. For any given set of inputs, we run a number of simulations, discarding the ones that seem less likely.

To efficiently handle these constant simulations the brain develops guides, Barrett calls “concepts”. Concepts allow the brain to quickly match a set of input patterns to responses. This allows the body to prepare, in advance of recognition, for the needed action.

We start developing these concepts from infancy and continue to develop them as we age. Concepts are strongly influenced by our environment and how we see other people responding their simulations. This is in part what emotional outputs have so many different forms. Some people clench their fists in anger while others blush and turn silent. Some people cry with joy others only cry in pain.

Concepts also inform our ability to identify and distinguish between different emotional states. Much like colors, people who were raised with a little emotional vocabulary tend to express themselves in few emotional states. People raised with a larger emotional vocabulary tend to express themselves with a larger range of emotions.

I recently learned Schadenfreude (“…the experience of pleasure, joy, or self-satisfaction that comes from learning of or witnessing the troubles, failures, or humiliation of another.” Wikipedia). After having a word for the emotion, I find that I am more aware of the emotion than I was before. Barrett suggests this is not because I am actually experiencing the emotion more, rather that I recognize it when I do because I now have a word for it.

Plutchik wheel

In her research, Barrett found this was not representative what actually happens in the brain. Instead, people feel emotions in two emotional spectrums: arousal and valence (or pleasantness). An emotion like “anger” is unpleasant and may be arousing, if you are driven to action, or not arousing if you retreat within yourself.

Circumplex model

The decision process, something that we do hundreds of times a day, seems so straight-forward and natural but involves some really complex internal processes. Making a decision is a delightful orchestration of mental processing, short-term memory, long-term memory and some far reaching chemical processes.

Amazon has had a long, notable rise that has been turbulent and often controversial. Under the driven leadership of Jeff Bezos, the company has managed to succeed better than their competitors to dominate the internet not just as a retailer but as an internet company.

Stone lays out the company’s course with a good blend of stories pulled from many sources to illustrate the Amazon story. Not all rosy or gloom, Stone’s rendition of history feel fair and gives enough context to grasp the difficult decisions that often led to controversy.

Interesting tidbit: Amazon launch Amazon Web Service losing money so that competitor would be less interested in competing. It ended up giving them a massive advantage.

It was not until I was halfway through this way thick book that I realized something important: the book’s title is incredibly clever. Reading the title, I assumed that in explaining how not to be wrong that it would explain how to be correct. In reality, there is a lot of ground between not being wrong and being correct. Some interesting points:

• All lines are curves so stop drawing straight lines all the time. Basically, anytime you are using a linear graph, with a straight line, you can be very confident that your chart is wrong.
• Be careful with regression calculations. They are easy to do but often make up a story that is far different than reality.
• Beware of the Gamblers fallacy. Each instance has an equal chance of occurrence regardless of how many instances there have been before.
• Don’t talk about percentages of numbers when the numbers might be negative. For example, percent of sales is a bad practice because it deludes the story: if sales are down 50%, is everything down 50% or is a single category doing so poorly it drags everything else down? Expenses, revenue and populations, however, are rarely negative (an expense that pays you?) so they are acceptably expressed in percentages.
• Small data sets are susceptible sample volatility. Beware.
• Inference is a tricky sport that can often lead to dangerous conclusions.
• Things are not usually equal and the inequalities often matter a lot.
• Lead with data, it will show who should win.
• Instead of asking what the chances of getting into a slot are, ask what are the chances that something in the slot is wrong. The chances you got something wrong are often the scary numbers we should be concerned about.