The "Wait, What." Mental Toolkit
Refining the Art of Observation to Engineer Outperformance.
In a financial world obsessed with speed and consensus, true alpha is found in the pause. It is found in the moment you look at a piece of data, a market trend, or a consensus view and ask, "wait, what." That question is the spark of our 4-O Process: it begins with Openness to the unknown, sharpens through rigorous Observation, identifies the Opportunity others miss, and ultimately drives Outperformance.
But to ask the right questions, you need the right frameworks. The markets are complex, adaptive, and often deceptive; relying on a single way of thinking is a liability. Below is our repository of 50 mental models - a multidisciplinary arsenal drawn from physics, biology, economics, and philosophy. We use these tools daily to strip away noise, invert problems, and stress-test our conviction. They are not just academic theories; they are the practical lenses we use to see the world clearly when others are just looking.
Strip problems down to their absolute basics.
Focus on the few drivers that matter.
Profit from the reaction to the reaction.
4. Inversion
5. Circle of Competence
6. Occam’s Razor
7. Game Theory
8. Probabilistic Thinking
9. Catalysts
10. Regret Minimization
11. Satisficing vs. Maximizing
12. Bayesian Updating
13. The Metamorphic Lifecycle
14. Proximate vs. Ultimate Causes
15. Cromwell’s Rule
16. Infinite Monkey Theorem
17. Zero-Sum vs. Non-Zero-Sum Games
18. Falsifiability
19. Intuition Pumps
20. Longtermism
21. Epistemic Humility
22. Nirvana Fallacy
23. Precautionary Principle
24. Focusing Illusion
25. Pragmatic Fallacy in Strategy
26. Bounded Rationality
27. Paradigm Shift
28. Pascal’s Mugging in High-Risk Assets
29. Planck’s Principle
30. Promethean Gap
31. Maslow’s Hammer
32. McNamara Fallacy
33. Middle Ground Fallacy
34. Socratic Method
35. Berkson’s Paradox
Berkson’s Paradox describes a statistical bias in which two independent traits appear negatively correlated due to a selection effect. In fund performance or token selection, focusing only on "surviving" assets distorts reality. For instance, observing that high-performing speculative assets often have poor governance might lead one to believe that bad governance drives returns. In fact, you are just failing to see the thousands of assets with bad governance that went to zero and disappeared from the dataset.
We navigate this via the 4-O Process by acknowledging that the data we see has already been filtered. During Observation, we are wary of concluding "top performer" lists because they exclude the graveyard of failures. This selection bias can lead to spurious correlations that appear to be actionable insights but are actually statistical mirages. We strive to analyze the whole dataset - winners and losers alike - to understand the true drivers of success. By correcting for this filter, we ensure our strategy is based on causal reality, not just the survivorship of the luckiest.
Beware of data that has already been filtered.