Thinking in Bets
Authors: Annie Duke
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Overview
My book, Thinking in Bets, explores the art and science of making better decisions in a world of uncertainty. Drawing upon my experience as a professional poker player, I explain how we can use the principles of probabilistic thinking to navigate the complexities of life, business, and even relationships. 핵심은 결과와 결정의 질을 분리하는 것입니다. I teach readers how to reframe decisions as bets, recognizing that good decisions can sometimes lead to bad outcomes, and vice versa. 핵심은 인지 편향과 싸우고 경험으로부터 배우는 것입니다. By acknowledging uncertainty and embracing the possibility of being wrong, we can become more open-minded, objective, and compassionate decision-makers. 핵심은 사고 습관을 바꾸는 것입니다. 핵심은 동료 찾기입니다. I also emphasize the power of truthseeking pods - groups of individuals committed to honest self-reflection and open discussion - to improve our decision-making process. Whether you’re an executive, an investor, a parent, or simply someone striving to make better choices, Thinking in Bets provides practical strategies to help you navigate uncertainty and make smarter bets on your future.
Book Outline
1. Life Is Poker, Not Chess
This isn’t a poker book in the traditional sense. It’s about how the world really works - full of hidden information, chance, and uncertainty. This makes life far more like poker than chess, a game where we can theoretically calculate the optimal strategy given complete information. Unlike chess, we have to embrace not knowing and be comfortable with uncertainty to make good decisions in the real world.
Key concept: Life is more like poker, where all that uncertainty gives us the room to deceive ourselves and misinterpret the data. Poker gives us the leeway to make mistakes that we never spot because we win the hand anyway and so don’t go looking for them, or the leeway to do everything right, still lose, and treat the losing result as proof that we made a mistake.
2. Wanna Bet?
Every decision we make is a bet on an uncertain future. Acknowledging this helps us decouple the quality of a decision from its outcome, reducing the tendency to ‘result’ - to equate a good outcome with a good decision, or a bad outcome with a bad decision. We can then focus on improving our decision process, rather than being overly swayed by whether things turned out well or poorly in any given instance.
Key concept: Treating decisions as bets, I discovered, helped me avoid common decision traps, learn from results in a more rational way, and keep emotions out of the process as much as possible.
3. Bet to Learn: Fielding the Unfolding Future
Learning from experience requires accurately sorting outcomes into the ‘luck’ bucket and the ‘skill’ bucket. This is trickier than it seems because outcomes are rarely all skill or all luck, and because we have an inherent self-serving bias that leads us to attribute good outcomes to skill and bad outcomes to luck. This bias prevents us from identifying areas where we can improve.
Key concept: LEARNING LOOP 2:
BELIEF -> BET -> OUTCOME -> [LUCK or SKILL] -> BELIEF 2
4. The Buddy System
To combat self-serving bias and learn more effectively, we need the help of others. Forming a ‘truthseeking pod’ of people who value accuracy and are committed to open-mindedness can provide an invaluable feedback loop for evaluating our decisions and improving our thinking.
Key concept: A good decision group is a grown-up version of the buddy system.
5. Dissent to Win
To be most effective, truthseeking pods need a clear charter with established norms to guide their interactions. The acronym CUDOS, developed by sociologist Robert Merton, provides an excellent framework. It encourages open sharing of information, applying standards objectively, acknowledging potential biases, and actively seeking out dissenting opinions.
Key concept: CUDOS: Communism (data belong to the group), Universalism (apply uniform standards to claims and evidence, regardless of where they came from), Disinterestedness (vigilance against potential conflicts that can influence the group’s evaluation), and Organized Skepticism (discussion among the group to encourage engagement and dissent).
6. Adventures in Mental Time Travel
Our brains are wired for immediate gratification, making it hard to prioritize long-term goals over short-term desires. ‘Temporal discounting’ describes this tendency to favor our present-self at the expense of our future-self. Mental time-travel techniques, like imagining our future self or pre-experiencing regret, can help us make decisions that are more aligned with our long-term best interest.
Key concept: Saving for retirement is a Night Jerry–versus–Morning Jerry problem.
Essential Questions
1. Why is life more like poker than chess?
Duke argues that life is fundamentally uncertain and unpredictable, making it more like poker than chess. In poker, hidden information and luck play significant roles, just as they do in real life. Embracing uncertainty is essential for making good decisions because it allows us to consider a wider range of possibilities and avoid becoming fixated on a single, potentially flawed, outcome.
2. How are decisions bets on the future?
Every decision we make is a bet on an uncertain future. Acknowledging this helps us decouple the quality of a decision from its outcome, reducing our tendency to ‘result’ - equating a good outcome with a good decision, and vice-versa. This shift allows us to focus on improving our decision process, becoming less emotionally reactive to outcomes, and more open to learning from both our successes and failures.
3. How do we learn from experience, given the influence of luck?
Learning from experience requires accurately sorting outcomes into two buckets: ‘luck’ and ‘skill.’ This is challenging because outcomes are rarely purely one or the other, and we are prone to a self-serving bias that leads us to attribute good outcomes to skill and bad outcomes to luck. Overcoming this bias is crucial for identifying areas where we can improve.
4. Why are truthseeking groups important for better decision-making?
We need the help of others to combat our self-serving bias and learn effectively from experience. Forming a ‘truthseeking pod’ of people who value accuracy and open-mindedness can provide an invaluable feedback loop for evaluating our decisions and improving our thinking.
5. What are the essential elements of a productive truthseeking group?
To maximize their effectiveness, truthseeking pods should establish clear norms to guide interactions. The acronym CUDOS, developed by sociologist Robert Merton, offers a practical framework. It emphasizes ‘Communism’ (open sharing of information), ‘Universalism’ (objective application of standards), ‘Disinterestedness’ (acknowledging biases), and ‘Organized Skepticism’ (active encouragement of dissenting opinions).
Key Takeaways
1. Truthseeking groups help us overcome motivated reasoning.
We are wired to favor confirming information and downplay evidence that contradicts our beliefs. This tendency, known as ‘motivated reasoning,’ is especially strong when our ego and self-narrative are at stake. A truthseeking group, with its focus on accuracy and open-mindedness, provides a powerful antidote to this bias.
Practical Application:
When designing an AI product, incorporate a ‘red team’ into the development process. This team would be tasked with finding flaws in the design and logic, imagining potential misuse cases, and pushing the boundaries of the system’s robustness. By embracing a culture of organized skepticism, we can proactively address potential vulnerabilities and create more robust and reliable AI systems.
2. Premortem thinking helps us anticipate potential risks and failures.
Thinking in bets encourages us to consider multiple possible futures, not just the one we hope for. By imagining negative scenarios, we can identify potential risks and develop strategies to mitigate them, making us better prepared for a wider range of outcomes.
Practical Application:
During brainstorming sessions or product design meetings, ask participants to explicitly consider the downsides and potential risks of each idea. Encourage a culture of ‘premortem’ thinking, where everyone imagines the project has failed and brainstorms reasons why. This approach can uncover potential pitfalls and lead to more robust solutions.
3. Outcome blindness helps us to evaluate decisions more objectively.
Knowing the outcome of a decision can heavily bias how we evaluate the quality of the decision itself. This ‘resulting’ leads us to give too much credit for good outcomes and assign too much blame for bad ones. Outcome blindness helps us to evaluate decisions more objectively, based on the process rather than the result.
Practical Application:
When evaluating the performance of an AI model, blind the evaluators to the model’s identity or development history. This ‘outcome blindness’ can help reduce bias in the assessment, preventing evaluators from unconsciously interpreting results in a way that confirms their expectations or beliefs about the model.
Suggested Deep Dive
Chapter: Chapter 4: The Buddy System
This chapter focuses on the importance of forming ‘truthseeking pods’ and the norms that make them effective. This concept has direct application to AI product design and development, where diverse perspectives and organized skepticism are crucial for mitigating biases and creating more robust and reliable systems.
Memorable Quotes
Introduction. 11
Thinking in bets starts with recognizing that there are exactly two things that determine how our lives turn out: the quality of our decisions and luck. Learning to recognize the difference between the two is what thinking in bets is all about.
Chapter 1: Life Is Poker, Not Chess. 27
“I’m not sure” does not mean that there is no objective truth. Firestein’s point is, in fact, that acknowledging uncertainty is the first step in executing on our goal to get closer to what is objectively true.
Chapter 2: Wanna Bet?. 45
Every decision commits us to some course of action that, by definition, eliminates acting on other alternatives. Not placing a bet on something is, itself, a bet.
Chapter 2: Wanna Bet?. 64
We would be better served as communicators and decision-makers if we thought less about whether we are confident in our beliefs and more about how confident we are.
Chapter 6: Adventures in Mental Time Travel. 177
If a decision is a bet on a particular future based on our beliefs, then before we place a bet we should consider in detail what those possible futures might look like.
Comparative Analysis
Thinking in Bets distinguishes itself by offering a practical, actionable framework for improving decision-making, drawing heavily on the author’s experience in the high-stakes, uncertain world of professional poker. While many books on decision-making, such as Daniel Kahneman’s Thinking, Fast and Slow, explore cognitive biases and heuristics, Duke focuses on concrete strategies to mitigate these biases. This aligns with the pragmatic approach of books like Nudge by Richard Thaler and Cass Sunstein, which emphasizes choice architecture and behavioral interventions to improve decision-making. However, Duke goes beyond individual decision-making and explores the power of group dynamics in promoting truthseeking and calibrating beliefs, a theme also explored in books like Wiser by Sunstein and Reid Hastie. What sets Duke’s work apart is its emphasis on the concept of ‘betting’ as a metaphor for decision-making and its focus on cultivating a mindset of uncertainty and continuous learning.
Reflection
Thinking in Bets offers a compelling argument for incorporating probabilistic thinking into our decision-making. While the author’s background as a professional poker player might initially raise skepticism, the book convincingly demonstrates the relevance of these principles to a wide range of fields, including AI and technology. One potential weakness is the book’s heavy reliance on anecdotes, which, while engaging, might not always be representative of broader trends. However, the author effectively weaves these personal stories with insights from psychology, sociology, and decision science to create a compelling case for adopting a ‘betting’ mindset. The book’s strength lies in its practical, actionable advice, making it a valuable resource for anyone seeking to improve their decision-making in a world of uncertainty. The principles of truthseeking, belief calibration, and scenario planning are particularly relevant to the field of AI, where biases in data and algorithms can have significant consequences.