name: thinking-fast-and-slow
description: "Daniel Kahneman's cognitive psychology framework for understanding decision-making, biases, and judgment."

dimensions:
domain: [decision-making, psychology, risk-assessment]
phase: [diagnosis, evaluation, post-mortem, planning]
problem_type: [cognitive-bias, overconfidence, prediction, judgment-under-uncertainty]

contexts:
- situation: "making a prediction or forecast"
use_when: "need to calibrate confidence, check for overconfidence, consider base rates"
- situation: "evaluating a past decision"
use_when: "identifying which biases may have influenced the outcome"
- situation: "assessing risk"
use_when: "checking if intuition (System 1) is reliable or if deliberate analysis (System 2) is needed"
- situation: "someone is very confident"
use_when: "testing whether confidence is justified by evidence or just coherent storytelling"
- situation: "planning a project"
use_when: "applying reference class forecasting to avoid planning fallacy"

combines_with:
- think-again # for updating beliefs after identifying bias
- black-swan # for tail risk and prediction limits
- antifragile # for building systems that survive prediction failures
- skin-in-the-game # for evaluating whose predictions to trust

contrast_with:
- skill: think-again
distinction: "TF&S diagnoses HOW we think wrong; Think Again focuses on CHANGING beliefs and intellectual humility"
- skill: hidden-potential
distinction: "TF&S is about judgment/decisions; Hidden Potential is about growth and skill development"

Thinking, Fast and Slow Framework

The Two Systems

The mind operates through two distinct modes of thinking:

SYSTEM 1 (Fast)              SYSTEM 2 (Slow)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Automatic                    Effortful
Unconscious                  Conscious
Fast                         Slow
Parallel processing          Serial processing
Associative                  Rule-based
Skilled/habitual             Flexible
Effortless                   Controlled
Pattern recognition          Logical reasoning
Always on                    Lazy, avoids work
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Key insight: System 1 generates impressions, intuitions, and feelings. System 2 endorses, rationalizes, or overrides them. Most of our judgments and choices originate in System 1.

WYSIATI: What You See Is All There Is

System 1 constructs the best possible story from available information. It doesn't flag missing information—it works with what it has.

Consequences:
- Overconfidence: Coherent story = high confidence (regardless of quality of evidence)
- Framing effects: Same information, different presentation = different conclusions
- Base-rate neglect: Vivid specifics override statistical reality
- Jumping to conclusions: Fast, but prone to error

Defense: Ask "What would I need to know to change my mind?" and "What information am I missing?"

Cognitive Biases & Heuristics

Anchoring

The first number you encounter heavily influences subsequent estimates.

Defense: Consider the opposite. Generate your own anchor first. Ask "If this anchor didn't exist, what would I estimate?"

Availability Heuristic

Probability judgments based on how easily examples come to mind.

Leads to:
- Overweighting recent, vivid, or emotional events
- Underweighting statistical base rates
- Risk perception driven by media coverage, not actual frequency

Defense: Ask "Am I judging frequency, or just memorability?" Seek base rate data.

Representativeness Heuristic

Judging probability by similarity to stereotypes, ignoring base rates.

Example: "Steve is meek, tidy, needs order, and has a passion for detail. Is he a librarian or a farmer?"

Most say librarian—but there are 20x more male farmers than librarians. The base rate dominates.

Defense: Always ask "What's the base rate?" before considering the specific case.

Substitution

When facing a hard question, System 1 substitutes an easier one.

Defense: Identify the actual question. Resist the easy substitute.

Prospect Theory

People don't evaluate outcomes absolutely—they evaluate gains and losses relative to a reference point.

                VALUE
                  │
                  │      Gains
                  │    ╱
                  │   ╱
                  │  ╱ (diminishing sensitivity)
                  │ ╱
──────────────────┼──────────────────► OUTCOME
                 ╱│
                ╱ │
               ╱  │
              ╱   │  Losses
             ╱    │  (steeper = loss aversion)
                  │

Key Principles

1. Reference Dependence
Outcomes coded as gains/losses relative to reference point (status quo, expectation, aspiration).

2. Loss Aversion
Losses loom larger than equivalent gains. Ratio ≈ 2:1.
- Losing $100 hurts about 2x as much as gaining $100 feels good
- Explains risk aversion for gains, risk seeking for losses
- Explains endowment effect (owning something increases its value to you)

3. Diminishing Sensitivity
The difference between $100 and $200 feels larger than between $1,100 and $1,200.

Four-Fold Pattern of Risk Attitudes

This explains why people buy both lottery tickets AND insurance.

Overconfidence

The most significant cognitive bias. Manifests in three forms:

1. Overestimation
Thinking you're better than you are.
- 90% of drivers think they're above average
- Entrepreneurs overestimate success probability

2. Overplacement
Thinking you're better than others.
- Easy tasks: overplacement common
- Hard tasks: underplacement common (everyone assumes they're bad)

3. Overprecision
Excessive certainty in accuracy of beliefs.
- 90% confidence intervals contain truth ~50% of the time
- Experts often no better than novices at calibration

Defense: Use reference class forecasting. Track your predictions. Widen confidence intervals.

Planning Fallacy

Systematic underestimation of time, costs, and risks; overestimation of benefits.

Why it persists:
- Focus on the specific case, not the reference class
- Anchoring on best-case scenarios
- WYSIATI: don't consider what could go wrong

Reference Class Forecasting (the cure):
1. Identify appropriate reference class
2. Obtain statistics of that class
3. Use as baseline
4. Adjust for specifics of current case (minimally!)

Example: Don't estimate "how long will MY kitchen renovation take?" Ask "How long do kitchen renovations typically take?" (Answer: almost always longer than estimated.)

The Halo Effect

Global evaluation (like/dislike) influences perception of specific traits.

"I like the CEO" ──► "Their strategy must be good"
"The product succeeded" ──► "The decision was wise" (even if it was lucky)
"They're attractive" ──► "They must be competent"

Defense: Evaluate traits independently. Collect judgments from multiple people before discussion.

Hindsight Bias

"I knew it all along" after learning the outcome.

Problems:
- Makes us overestimate predictability of events
- Prevents learning from surprises
- Unfair to decision-makers ("obvious" in hindsight)

Defense: Record predictions BEFORE outcomes. Conduct pre-mortems, not just post-mortems.

Regression to the Mean

Extreme performance is followed by less extreme performance—not because of causation, but statistical inevitability.

Misinterpretations:
- "Punishment works better than praise" (performance naturally regresses after extremes)
- "The Sports Illustrated cover jinx"
- "Treatment worked!" (patient was measured at their worst)

Defense: Expect regression. Don't over-explain it with causal stories.

Pre-Mortem Technique

Before starting a project, imagine it has failed. Ask: "What went wrong?"

Benefits:
- Overcomes groupthink
- Legitimizes doubt
- Surfaces risks that optimism suppresses
- Uses prospective hindsight

Application Framework

When making decisions:

1. Identify System 1 Signals

• Strong intuition or gut feeling?
• Immediate answer that "feels right"?
• Emotional response?

2. Engage System 2 Checks

• [ ] What's the base rate for this type of outcome?
• [ ] What information am I missing? (WYSIATI)
• [ ] Am I anchored on an irrelevant number?
• [ ] Am I substituting an easier question?
• [ ] What's the reference class forecast?
• [ ] What would a pre-mortem reveal?

3. Calibrate Confidence

• [ ] Would I bet money at these odds?
• [ ] Widen your confidence interval
• [ ] Remember: coherence ≠ truth

4. Protect Against Loss Aversion

• [ ] Am I avoiding a wise risk because losses loom large?
• [ ] Would I take this deal if framed differently?
• [ ] Evaluate gains and losses by their actual size, not their feeling

Quick Reference: Common Biases