Believability weight your decision making.
In most organizations, the decisions are made either autocratically—by the leaders—or democratically, where everyone shares their opinions, and the ones with most support are implemented. However, the better system is where decisions are made by the most believable people and worked through disagreements and debates, A system where people seek what’s true and what to do about it.
See below for an explanation of "believability weighting."
If you can’t successfully do something, don’t think you can tell others how it should be done.
If you fail constantly at doing something, ask more questions and seek understanding.
Remember that everyone has opinions, and opinions are often bad.
Most of them are worthless and harmful, including a lot of your own.
Find the most believable people possible who disagree with you and try to understand their reasoning.
This is the quickest way to get an education and to increase your probability of being right.
Think about whether you are playing the role of a teacher, a student, or a peer.
Understand when you should be teaching, learning, or debating.
Understand how people came by their opinions.
If you ask someone a question, they will probably give you an answer, so think about to whom you should address your questions in order to get the best possible answer.
Disagree and make disagreement effective.
Disagreement is encouraged because it helps you find what’s true. However, those who disagree should seek what’s true and seek understanding from believable people first.
What Ray Dalio means by “believability weighting”:
“The best decisions are made by an idea meritocracy with believability-weighted decision making, in which the most capable people work through their disagreements with other capable people who have thought independently about what is true and what to do about it.
It is far better to weight the opinions of more capable decision makers more heavily than those of less capable decision makers. This is what we mean by ‘believability weighting.’”
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