When making decisions in uncertain environments, we can’t look only at the outcome. We have to go back to probabilities, the process, and the event tree! That’s the most valuable thing to take away from *Thinking in Bets*.
Annie Duke repeatedly emphasizes that luck matters because we can’t know every variable or calculate the ultimate impact of all their interactions. Life is full of hidden information; the things we can’t see are where luck comes in. That’s also where the book begins: life is poker, not chess.
For the first ten years of my career, I was a programmer. That stage was a lot like chess: improve your technical skills, and the path to promotion and a higher salary becomes clearer. But after becoming a senior engineer, taking the next step required more than hard work. Luck began to matter.
Starting in my tenth year, I moved into Web3. It’s closer to money, and information gaps are larger. Many decisions initially felt like bets, and even estimating the odds was difficult. Later, I built a personal brand and met more people and encountered more situations. Publishing my thoughts publicly forced me to articulate my judgments, and the boundaries of my circle of competence gradually became clearer. It wasn’t that I could see through every opportunity; rather, I became better at recognizing which foggy areas I shouldn’t venture into yet. My decision-making improved, but it still wasn’t systematic enough.
Reading *Thinking in Bets* was just what I needed to fill in the gaps around decision-making and review. It doesn’t directly add to my trading methods; instead, it strengthens the most important abilities in the middle circle: risk identification, calibrating judgment, and self-correction in competitive environments. I’m still using my “core keywords + two versions” approach, and both the public-facing and archival versions are complete.
The roles of the last three books: *Influence* helps identify influence and risk triggers; *The Great Mental Models* provides general-purpose thinking tools; *Thinking in Bets* adds language for the decision-making process and post-decision review.
The keywords I’m adding to my library from this book are: outcome ≠ decision, decisions are bets, luck and skill, truth-seeking partners, active dissent, and hindsight bias.
Together, they form a workflow: first, treat a decision as a bet and express your judgment in probabilities rather than as right or wrong. Once the outcome is known, separate luck from skill, and beware of hindsight bias collapsing the entire event tree into a single branch. When necessary, bring in truth-seeking partners and active dissent to avoid reinforcing your existing views only through agreement.
I’m still at the stage of building up a library of models, and I haven’t yet encountered enough high-frequency, real-world decision scenarios. So for now, I’m extracting and archiving these ideas, without claiming they’ve become part of my daily operating system. I’ll leave filtering and consolidation to future real-world practice. Until then, I’ll make myself ask two questions in every review: What branches were on the event tree at the time? In this outcome, what came from the decision, and what came from luck?
For me, *Thinking in Bets* has given my circle of competence the language to make judgments with incomplete information—and to avoid learning the wrong lessons once the outcome is known.
This article is part of my work developing the capability of “analyzing mental models.” Its analytical framework comes from the “IP Growth Flywheel Based on the Circle of Competence” system I’m currently building.
This is for informational purposes only and does not constitute investment advice.
BitHappy, continuously evolving
Annie Duke repeatedly emphasizes that luck matters because we can’t know every variable or calculate the ultimate impact of all their interactions. Life is full of hidden information; the things we can’t see are where luck comes in. That’s also where the book begins: life is poker, not chess.
For the first ten years of my career, I was a programmer. That stage was a lot like chess: improve your technical skills, and the path to promotion and a higher salary becomes clearer. But after becoming a senior engineer, taking the next step required more than hard work. Luck began to matter.
Starting in my tenth year, I moved into Web3. It’s closer to money, and information gaps are larger. Many decisions initially felt like bets, and even estimating the odds was difficult. Later, I built a personal brand and met more people and encountered more situations. Publishing my thoughts publicly forced me to articulate my judgments, and the boundaries of my circle of competence gradually became clearer. It wasn’t that I could see through every opportunity; rather, I became better at recognizing which foggy areas I shouldn’t venture into yet. My decision-making improved, but it still wasn’t systematic enough.
Reading *Thinking in Bets* was just what I needed to fill in the gaps around decision-making and review. It doesn’t directly add to my trading methods; instead, it strengthens the most important abilities in the middle circle: risk identification, calibrating judgment, and self-correction in competitive environments. I’m still using my “core keywords + two versions” approach, and both the public-facing and archival versions are complete.
The roles of the last three books: *Influence* helps identify influence and risk triggers; *The Great Mental Models* provides general-purpose thinking tools; *Thinking in Bets* adds language for the decision-making process and post-decision review.
The keywords I’m adding to my library from this book are: outcome ≠ decision, decisions are bets, luck and skill, truth-seeking partners, active dissent, and hindsight bias.
Together, they form a workflow: first, treat a decision as a bet and express your judgment in probabilities rather than as right or wrong. Once the outcome is known, separate luck from skill, and beware of hindsight bias collapsing the entire event tree into a single branch. When necessary, bring in truth-seeking partners and active dissent to avoid reinforcing your existing views only through agreement.
I’m still at the stage of building up a library of models, and I haven’t yet encountered enough high-frequency, real-world decision scenarios. So for now, I’m extracting and archiving these ideas, without claiming they’ve become part of my daily operating system. I’ll leave filtering and consolidation to future real-world practice. Until then, I’ll make myself ask two questions in every review: What branches were on the event tree at the time? In this outcome, what came from the decision, and what came from luck?
For me, *Thinking in Bets* has given my circle of competence the language to make judgments with incomplete information—and to avoid learning the wrong lessons once the outcome is known.
This article is part of my work developing the capability of “analyzing mental models.” Its analytical framework comes from the “IP Growth Flywheel Based on the Circle of Competence” system I’m currently building.
This is for informational purposes only and does not constitute investment advice.
BitHappy, continuously evolving