Mental models are practical tools for thinking clearly amid uncertainty. Used well, they reduce blind spots, improve decision‑quality, and—crucially—help investors avoid unforced errors. Drawing on Farnam Street’s The Great Mental Models (Vol. 1) and the principles popularised by Charlie Munger, here are the core models, why they matter, and how to apply them in today’s markets.
The habit that compounds: breadth over narrowness. Assemble a latticework across disciplines—psychology, statistics, systems, biology, physics, economics, and basic accounting—then consciously layer the right model for the right problem. Munger’s warning holds: if your only tool is a hammer, everything looks like a nail.
Three common failure modes with reality
- Perspective: your vantage point (e.g., investor vs frontline employee) may omit critical information. Deliberately seek alternative views.
- Ego: we defend beliefs rather than test them. Prefer disconfirming evidence over comfort.
- Distance: delayed feedback (common in investing) entrenches bad models. Build faster feedback loops via pre‑defined KPIs and annual thesis reviews.
Practical routine to build the habit (Parrish’s six steps)
- Choose models deliberately for the problem at hand.
- Apply and observe which helped or hindered.
- Record and reflect (journal decisions, assumptions, outcomes).
- Refine understanding; a model unhelpful in one domain may excel in another.
- Spot models in the wild (earnings calls, policy shifts, cycles).
- Practice—daily small problems, historical cases, and post‑mortems.
1) The map is not the territory
Our representations are simplified; reality is the arbiter. Keep maps (theses) flexible, consider the cartographer (your biases), and avoid letting the map reshape the territory (over‑committing to a narrative). A disciplined response: set “kill criteria” on core KPIs; revisit annually; and explicitly hunt for contrary data. If the crown‑jewel segment deteriorates while adjacencies consume capital without profitability, update the map—and act.
2) Circle of competence
Know where you can move quickly and accurately; know what’s unknowable; and know what you can learn with reasonable effort. Tight circles are fine—John Arrillaga built wealth staying within a mile of Stanford. Expand by deliberate study, live reps (tracking and owning a measured position), and independent feedback. Operating outside your circle? Ask: “Am I capable—and inclined—to understand this?” If yes, proceed with structured research, management dialogue, competitor mapping, then size prudently.
3) First‑principles thinking
Avoid reasoning by analogy for complex decisions. Strip issues to non‑reducible truths via Socratic questioning and “five whys”. In markets, foundational truths include: shares are fractional business ownership; risk is permanent loss of capital; long‑term value tracks cash flows; short‑term prices are driven by human emotion. Build up from these, not from consensus narratives.
4) Thought experiments
Use the scientific method with imagination: define the question, set a hypothesis, mentally test bear/base/bull paths, isolate key drivers, and determine what must be true. Destination analysis (where could this business reasonably be in 10 years?) is a useful filter: if you cannot sketch a plausible destination and path, pass.
5) Second‑order thinking
Always ask, “and then what?” Incentives, cycles and policy deliver unintended consequences (e.g., the Cobra effect). In cyclicals, the “cheapest” (low P/E) at the top is often most expensive once earnings normalise; the inverse can be true at troughs. Superior results tend to come from non‑consensus, second‑order conclusions reached with evidence and patience.
6) Probabilistic thinking
- Bayesian updating: set prior probabilities for bear/base/bull, then update with new information (e.g., tariffs, rates, unit economics).
- Fat tails: extreme events occur more often than bell curves suggest. Layer margin of safety; avoid exposures that can ruin you even with attractive expected value.
- Asymmetry: seek upside that meaningfully exceeds downside, and guard against overconfidence. Track your hit rate and calibrate.
7) Inversion
Start with how to fail—then avoid those conditions. In portfolio practice: list ways investors destroy returns (excess leverage; ignoring history; focusing only on upside; price‑anchoring; momentum‑chasing; ignoring intrinsic value). Company‑level: design the “kill shot” for your holding, then monitor the specific KPIs that would evidence that path. Distinguish noise from signal within your competence.
8) Occam’s razor
Prefer simpler theses with fewer moving parts; they fail less often. Complexity multiplies error probabilities. As a filter, skip ideas requiring a long chain of dependent assumptions; favour businesses where three to five variables explain the outcome (e.g., unit growth, utilisation, pricing power, margin trajectory, capital intensity).
9) Hanlon’s razor
Do not ascribe to malice what is more likely explained by error, bureaucracy, or carelessness. Useful in reading corporate mishaps: differentiate fraud from fixable execution mistakes; mispriced fear can create opportunity—but verify with hard data (cash, controls, governance).
Putting this into practice
- Before buying: Write a one‑page map: thesis, three key drivers, three disconfirming checks, and explicit kill criteria. State your circle boundary and what would expand it.
- Valuation with probabilities: Build bear/base/bull cases with conservative, explicit probabilities. Default to a meaningful bear weight; update Bayesianly each quarter.
- Second‑order scan: For each driver ask “and then what?” Consider incentives, policy spill‑overs, competitive reaction, and financing conditions.
- Inversion audit: List five ways this investment could lose you money. Map each to a monitored KPI (e.g., cohort retention, gross margin trend, leverage ratio, working capital stress).
- Simplicity test: If your thesis needs >8 contingent steps, either simplify to core drivers or pass.
- Process journal: Record assumptions, probabilities, decisions, outcomes. Review misses quarterly; adjust your base rates and circle accordingly.
Useful market context tabs (broad overview)
Checklist: pre‑mortem for a new position
- Is this squarely within my circle? If not, can I get there fast—and do I want to?
- What three facts, if true in 12 months, would invalidate the thesis?
- What are the two most likely second‑order effects if the main driver changes (rates, regulation, input costs)?
- Bear/base/bull with probabilities; expected value positive after conservative haircut?
- Position size consistent with tail risk and my sleep test?
- Can I explain the thesis simply to a sceptical peer in 60 seconds?
Final thought
You do not need genius to compound wealth; you need robust process, breadth of models, and humility. Minimise unforced errors through inversion, keep maps current with reality, reason from first principles, think in probabilities, and prefer simple, high‑signal theses. Over time, that edge is decisive.