Taking pleasure in being wrong (with Buck Shlegeris)
1. Exaggerate New Belief Frames
When developing new beliefs or theories, oversimplify and exaggerate them internally, temporarily behaving as if they are 100% accurate. This helps test the theory’s correctness by seeing how often it needs concessions when interpreting new information.
2. Seek Foundational Knowledge Gaps
Actively seek out the simplest-sounding questions in a field that you don’t know the answer to, even if they seem basic. This helps uncover holes in your foundational knowledge and build a more solid understanding of the subject.
3. Identify True vs. Illusory Understanding
Be vigilant about whether you truly understand a concept or are merely ‘cargo culting’ techniques without internalizing the underlying principles. Actively try to explain concepts, draw diagrams, or apply methods to reveal gaps in your comprehension.
4. Seek Expert Feedback as Amateur
To maintain intellectual humility and calibrate your confidence, intentionally study subjects where you are an amateur and engage with experts in those fields. This provides practice in being wrong in situations where your errors are undeniable.
5. Write for Easy Falsification
When writing about complex topics, state your opinions as bluntly and directly as possible, aiming for easy falsification rather than defensibility. This maximizes clarity and makes it easier for others to identify and critique potential mistakes.
6. Separate Others’ Errors from Your Truth
Recognize that noticing widespread false beliefs is easier than forming confident, specific, and correct beliefs of your own. Avoid the jump from ‘mainstream opinion is wrong’ to ‘my specific understanding is accurate,’ and instead commit to deeper investigation.
7. Cultivate Pain Tolerance for Truth
Understand that achieving accurate beliefs often requires enduring the discomfort of being wrong or receiving criticism. Maintain constant vigilance against becoming too afraid of this pain, as it can lead to accumulating unexamined false beliefs.
8. Begin Analysis with Rational Models
When analyzing complex systems, especially in fields like economics, start by modeling everything as if all agents are perfectly rational. This helps identify what truly needs explanation by less predictive theories, preventing premature jumps to behavioral economics.
9. Master Fast, Small-Scale Skills
When learning a new skill, prioritize getting very fast at sub-skills that take less than a minute to perform. This strategy leverages faster feedback loops for more repetitions and better retention, significantly boosting overall productivity.
10. Solve Analogous Future Problems
To address future grave risks (e.g., from AI), identify analogous problems that exist today and solve them using current solutions that resemble future ones. This provides concrete, iterative feedback and builds scalable solutions.
11. Practice Future Critical Capacities
Identify critical capacities needed when deploying powerful future systems (e.g., interpretability, red teaming) and conduct ‘fire drills’ by practicing them on current, less critical problems. This builds necessary skills and processes in advance.
12. Prioritize ML Execution & Tools
Recognize that machine learning’s primary challenge is often difficult engineering and execution rather than just intuition. Dedicate more time to building tools, implementing diagnostics, and ensuring efficient, well-tested code for basic tasks.
13. Post Work for Public Criticism
Share your work publicly (e.g., essays on social media) to receive diverse and challenging criticism. This helps calibrate your confidence, identify flaws in your arguments, and build more robust beliefs over time.