What I loved about the book was his approach to decision-making and how it centered on this idea of principles. Here’s a brief explanation of how he thinks about principles:
1. Slow down your thinking so you can note the criteria you are using to make your decision.
2. Write the criteria down as a principle.
3. Think about those criteria when you have an outcome to assess, and refine them before the next “one of those” comes along.
This way, whenever a new decision comes your way, you may be able to recognize its similarity to a decision you’ve made in the past, and then you can apply the same principles you used successfully before.
Voila! A shortcut to better decision-making. :)
The book is a #longread, and there are many, many different categories of principles that Ray talks about. One particular category that has stuck with me is his principles for strategy.
Here they are:
Principles for strategy
Don’t put the expedient ahead of the strategic.
Consider second-and third-order consequences, not just first-order ones.
Beware of paying too much attention to what is coming at you and not enough attention to your machine.
Remember that the WHO is more important than the WHAT
All of your “must-dos” must be above the bar before you do your “like-to-dos.”
These have been useful for me as I’ve weighed strategic decisions at Buffer recently.
For a full list of fallacies and biases, I highly recommend the book The Art of Thinking Clearly. It’s been foundational for the customer research we do at Buffer and for the way we approach problems as clear-minded as possible (we think). Many of the fallacies below were pulled from the book.
Not everyone has time for longform, 2,500-word, SEO-heavy articles. What people do have time for is short stuff. Think: 500-word articles, recaps of stories, daily roundups, etc. Summaries will reign in 2019. When creating content, keep in mind that attention spans are short, and we’re in the golden age of multitasking. Abbreviate, abbreviate, abbreviate.
Optimize for global maximums and not local maximums, says everyone.
Totally. I get it. The idea is incredibly catchy, and the diagrams are no-brainers. If your local maximum (the best you can do in the short-term) is X and your global maximum (the best you can do in the long-term) is X times 100, then duh: optimize for the global maximum.