Smarter Faster Better
The followings are sections in the book that I like.
On motivation
When we realise that replying to an email or helping a coworker, on its own, might be relatively unimportant. But it is a part of a bigger project that we believe in, that we want to achieve, that we have chosen to do. Self motivation, in other words, is a choice that we make because it is part of something bigger and more emotionally rewarding than the immediate task that needs doing.
Sometimes we lose sight of the big picture or the end game because we are so overwhelmed by the menial tasks at hand. Stop and think of our goals might put things in perspectives and give another energy boost to march through the mind-numbing chores that are not meaningful in themselves. Leaders could utilise this concept and remind their team of the big picture, motivate their team by discussing why we are doing what we are doing. We are going through these mind-numbing banalities to get to those mind-blowing goals.
On a good manager
Project oxygen was part of an in-house human resource study at Google-a wildly successful, scientifically and data heavily driven company, It found characteristics of a good manager as follows:1 is a good coach; 2 empowers and does not micromanage; 3 expresses interest and concern to subordinates’ success and well-being; 4 is result-oriented; 5 listens and shares information; 6 helps with career development; 7 has a clear vision and strategy; 8 has key technical skills.
Five key norms of effective teams
Teams need to believe that their work is important.
Teams need to feel that their work is personally meaningful.
Teams need clear goals and defined roles.
Team members need to know they can depend on one another.
Most importantly, teams need psychological safety. For psychological safety to emerge among group, teammates don’t have to be friends. They do, however, need to be socially sensitive (i.e. having empathy) and ensure everyone feels heard. The best tactic for establishing psychological safety is demonstration by a team leader.
The following are Google-designed checklists leaders could use to create psychological safety.
Leaders should not interrupting teammates during conversation, because that will establish an interrupting norm.
They should demonstrate they are listening by summarising what people say after they said it.
They should admit what they don’t know.
They shouldn’t end a meeting until all team members have spoken at least once.
They should encourage people who are upset to express their frustrations, and encourage teammates to respond in non-judgemental ways.
They should call out intergroup conflict and resolve them through open discussion.
To me this reads pretty much like a characteristic of a decent human being with a particularly good listening skill.
On learning
When we encounter new information and want to learn from it, we should force ourselves to do something with the data. There are several psychological studies that looked into information retention rate. Subjects who did something with the information tend to retain more compared to those who just read through it. If you read a book filled with new ideas, force yourself to put it down and explain the concepts to someone sitting next to you and you will be more likely to apply them in your life. When you find a new piece of information, force yourself to engage with it, to use it in an experiment or describe it to a friend and then you will start building a mental folders that are at the core of learning.
The postgrad seminar session we learn the most, I find, is when there are a lot of discussions, and debates whereby postgrads hold different views and logically and scientifically argue their cases strongly and fiercely. I guess this is doing something with the information.
On making decision
Making good decisions relies on forecasting the future, the forecasting is an imprecise, often terrifying, science because it forces us to confront how much we don’t know. The paradox of learning how to make better decisions is that it requires developing comfort with doubt.
The future isn’t one thing. Rather, it is a multitude of possibilities that often contradict one another until one of them comes true. And those futures can be combined in order for someone to predict which one is more likely to occur. This is probabilistic thinking. It is the ability to hold multiple, conflicting outcomes in your mind and estimate their relative likelihoods. We are not accustomed to thinking about multiple futures.
Bayes’ rule, which was first postulated by the Reverend Bayes in a posthumously published 1763 manuscript, can be so computationally complex that for century most statisticians essentially ignored the work because they lacked tools to perform their calculation it demanded. Starting in the 1950s however as computer became more powerful, scientists found they could use bayesian approaches to forecast events that were previously thought unpredictable, such as the likelihood of a war, or the odds that a drug will be broadly effective even if it has only been tested on a handful of people. Even today, though, calculating a bayesian probability curve can, in some cases tie up a computer for hours.
At the core of Bayes’ rule is the principle: even if we have very little data, we can still forecast the future by making assumptions and then skewing them based on what we observe about the world. For instance, suppose your brother said he’s meeting a friend for dinner. You may forecast there’s a 60% chance he’s going to meet a man, since most of your brothers friends are males. Now, suppose your brother mentioned his dinner companion was a friend from work. You might want to change your forecast, since you know that most of his co-workers are females. Bayes’ rule can calculate the precise odds that your brother’s dinner date is female or male based on just one or two pieces of data and your assumptions. As more information comes in-his companions name is Pat, he or she loves adventure movies and fashion magazines- Bayes’ rule will refine the probability even more.
This is probably the best, the simplest and the most easy-to-understand explanation of Bayesian statistics I’ve ever encountered. Seeing how this concept is applied to Endodontics here.
Making good choices relies on forecasting the future. accurate forecasting required exposing ourselves to as many successes and disappointments as possible. We need to sit in crowded and empty theatre to know how movies will perform; and we need to talk to thriving and failing colleagues to develop good business instincts. Calibrating your base rate required learning from both the accomplished and the humbled. So the next time a friend misses out on a promotion, ask him why. The next time a deal falls through, call up the other side to find out what you did wrong. Don’t simply tell yourself that things will go better next time. Instead, force yourself to really figure out what happened.
How do we learn to make better decisions? In part, by training ourselves to think probabilistically. To do that, we must force ourselves to envision various futures-to hold contradictory scenarios in our minds simultaneously – and then expose ourselves to a wide spectrum of successes and failures to develop an intuition about which forecast more or less likely to come true.
The goals are to see the future as multiple possibilities rather than one predetermined outcome; to identify what you do and don’t know; and to ask yourself, which choice get you the best odds? No one can predict tomorrow with absolute confidence. But the mistake some people make is trying to avoid making any predictions because their thirst for certainty is so strong and their fear of doubt too overwhelming.