Definition
Likelihoods are inferred from superficial resemblance to familiar stories instead of base rates.
Example
A trader assumes a new fintech start-up will outperform simply because it resembles a previous high-profile winner. The surface similarity substitutes for genuine analysis. Base-rate failure rates, sector dispersion, and macro conditions are ignored because the narrative "feels familiar."
Cognitive Driver
The mind relies on pattern recognition as a shortcut in complex environments. When two situations appear similar, the brain automatically assumes the underlying dynamics match as well. This creates overconfidence in analogies and underweighting of distributional data.
Market Expression
Analyst notes lean heavily on historical parallels ("this looks like 2013 taper", "this resembles the 2000 tech cycle"). Trades are entered based on superficial similarities rather than current fundamentals. Probability assessments become driven by narrative fit instead of actual frequency.
Trigger Conditions
- Environments with strong historical analogues
- Thematic trades where past winners are highly salient
- Periods where investors search for simple explanations
- High uncertainty that increases reliance on heuristics
- Markets with frequent storytelling and visual pattern-matching
Diagnostic Markers
- Heavy use of analogies ("this chart mirrors...", "this cycle equals...")
- Limited reference to base rates or statistical dispersion
- Confidence driven by resemblance, not probability
- Overuse of historical templates even when macro structure differs
- Rationale built around pattern-fit rather than mechanics
Cost Profile
- Mispricing of probabilities due to narrative oversimplification
- Vulnerability to false analogies and superficial pattern matches
- Poor performance when regimes differ from assumed templates
- Underestimation of structural change
- Drift toward story-driven rather than data-driven trading
Differentiation From Adjacent Biases
- Not availability bias: availability is about salience; representativeness is about similarity.
- Not recency bias: representativeness uses past prototypes, not recent events.
- Not confirmation bias: confirmation searches for supportive evidence; representativeness substitutes stereotypes for analysis.
Corrective Lens
Anchor decisions in base-rate data and objective distributions. Validate analogies by testing whether underlying causal drivers actually match, not just surface patterns. Use counterexamples--situations that looked similar but behaved differently--to break narrative shortcuts and force deeper analysis.