Pattern matching bricks find structural similarity between your current situation and known cases, allowing you to import solutions, predictions, and warnings from prior experience.

1.3.1. Analogical reasoning

• Trigger: Novel problems; need for creative solutions 

• Input: A problem or situation 

• Operation: Find a structurally similar solved problem in a different domain; import the solution 

• Output: A candidate solution adapted from the analogous case 

Powerful when the structural analogy is genuine. The history of science and technology is substantially a history of analogical transfer, solutions from one domain illuminating problems in another.

The test for a good analogy: Can you map the components of one situation onto the other in a way that preserves the relationships between them? Surface similarity (both involve water, both happen in offices) is not structural similarity.

Known limits: False analogy (i.e., surface similarity masks structural differences) is one of the most dangerous reasoning errors. Always test the mapping explicitly before trusting it.

1.3.2. Historical precedent

• Trigger: Forecasting; strategy; evaluating a novel situation

• Input: A situation or decision type

• Operation: Find the last several times this pattern appeared; trace outcomes

• Output: A grounded forecast anchored in empirical outcomes

Grounds predictions in empirical outcomes rather than pure reasoning. History doesn’t repeat exactly, but it rhymes structurally and structural patterns repeat with surprising regularity.

Known limits: “This time is different” is sometimes true. The discipline is distinguishing genuine structural novelty from motivated reasoning that wants to dismiss uncomfortable precedents.

1.3.3. Base rates

• Trigger: Any forecast, prediction, or probability estimate 

• Input: A specific situation you are trying to predict

• Operation: Find the reference class; determine the outcome distribution across all cases in that class; anchor your prediction there before adjusting for specifics

• Output: A forecast grounded in the actual distribution of outcomes

Base rates is the single most underused brick in everyday decision-making. The psychological reason is that our situation always feels unique. In its specifics, it often is. But at the level of its category, it almost never is.

New restaurants fail at ~60% in year one. Most start-up ideas that feel promising to their founders don’t succeed. These base rates should dominate the forecast unless you have compelling, specific evidence that your case is a genuine outlier. And, you should be skeptical of that claim, because everyone believes their case is the outlier.

The two-step: (1) Find the right reference class. (2) Anchor heavily on the base rate; adjust only with specific, credible evidence.

Known limits: Wrong reference class produces wrong base rates. Choosing the reference class requires care and intellectual honesty.

1.3.4. Archetype recognition

• Trigger: Any situation that feels familiar but novel

• Input: A situation, conflict, or dynamic

• Operation: Identify which recurring human pattern this is an instance of

• Output: Pattern identification, and the embedded knowledge about how that pattern unfolds

Human situations, despite their surface variety, tend to repeat the same underlying dynamics across history, cultures, and domains. Archetype recognition is the skill of seeing through surface details to identify the recurring pattern operating underneath.

Once you correctly identify the archetype, you import accumulated knowledge about how it tends to unfold, what interventions work, and what makes it worse.

High-value archetypes to know