Mental Models & Decision Science

Learning objectives

By the end you can:

1. Explain Munger's latticework of mental models and the "man with a hammer" failure: why a small kit of big ideas from many disciplines, used in combination, out-reasons any single framework applied everywhere. Then start your own list of the models you actually use.
2. Define your circle of competence in writing: sort businesses into inside / edge / outside with honest justification, articulate why the perimeter (not the size) is what matters, and state where your own mistakes are most likely to come from.
3. Run inversion and a formal pre-mortem on an investment: attack a thesis by asking "how would I destroy this business / lose money here?", conduct a structured pre-mortem, and convert the failure modes into things to verify or size for.
4. Apply opportunity cost as your true hurdle: evaluate every buy as a rejection of your next-best idea, set a real hurdle rate rather than comparing to zero, and treat "sell A to buy B" as one decision.
5. Think in second order and locate the variant view: trace "and then what?" past the first consequence, and use the consensus-vs-variant grid to see that excess return requires being both non-consensus and right.
6. Separate risk from uncertainty (Knight): distinguish quantifiable odds from true unknowns, explain why markets and models conflate them, and respond correctly (margin of safety and robustness where you cannot compute probabilities).
7. Use base rates and the outside view: anchor a forecast on the reference class before the story, name the planning fallacy, pull Mauboussin's base-rate data, and blend an inside and outside view into a defensible estimate.
8. Compute expected value and separate decision quality from outcome quality: build an EV table for an asymmetric payoff, avoid "resulting," and explain why process is the only thing you can control and therefore the only thing worth scoring.
9. Size a position with the Kelly criterion and fractional Kelly: derive the formula's intuition, compute it for both even-money and capped-downside bets, and explain precisely why full Kelly is too aggressive in practice (the bridge to M9.01).
10. Update beliefs with Bayes' rule: turn a prior, a piece of evidence, and its likelihoods into a numeric posterior; explain base-rate neglect; and run your decision journal as a Bayesian instrument ("strong views, weakly held").
11. Reason about ergodicity and avoid ruin: explain why the time-average of a multiplicative process differs from its ensemble-average, why a 50% loss needs a 100% gain, why volatility drags the compound rate below the arithmetic mean, and why survival is therefore a first-order goal, not caution.

Prerequisites & connections

Builds on. M6.01–M6.02 supply the raw material systematized here. Pabrai's Dhandho asymmetry (M6.02 §4.7) becomes expected value and Kelly here; Marks's second-level thinking and "risk is highest when it feels lowest" (M6.02 §4.6) become second-order thinking and the risk-vs-uncertainty distinction; the concentration-vs-diversification debate (M6.02 §4.9) gets its mathematical spine from Kelly and ergodicity. M3.01–M3.03: time value of money, the cost of capital, and the ~12% INR / ~8–9% USD hurdle (as of mid-2026, verify against your own WACC builds) are the quantitative backbone of the opportunity-cost and hurdle-rate sections. M3.05: the reverse DCF ("what is the price implying?") is the same instrument as the variant-view and base-rate sections, because the market's implied number is the consensus you must beat. M2.07: the Beneish M-Score and the other quant screens are exactly the noisy diagnostic tests that the Bayesian base-rate section teaches you to interpret without being fooled. M4.05: the mean reversion of ROIC and the Competitive Advantage Period are a base-rate phenomenon, and "great returns fade" is the outside view of any high-ROIC business.

Feeds forward. M6.04, behavioral finance: this module is the normative theory (how a rational decider should reason); M6.04 is the descriptive theory (how real human minds actually deviate). The latticework's psychology models (Munger's 25 tendencies, the Lollapalooza effect) are deliberately deferred to M6.04, and every tool here is a pre-commitment device against a bias catalogued there. M6.05, the checklist and temperament capstone: your written circle of competence, your pre-mortem habit, and your base-rate discipline become standing lines in your personal checklist and in the capstone Mental-Model & Moat Checklist. M9.01–M9.02, risk and position sizing: fractional Kelly, ergodicity/ruin-avoidance, and hidden correlation are re-derived and operationalized into a live sizing policy; this module is the theory, Phase 9 is the practice. M9.04 and the decision journal (installed back in M0.04): the journal is a Bayesian instrument and a calibration record, and process-over-outcome is the rule it enforces. Phase 7: the risk-vs-uncertainty distinction is why the macro modules end in humility, because macro is mostly Knightian, so you build robustness, not forecasts. Competency C9 ("investor's temperament and process") is, more than any other module, this one.