sethmblack Cognitive Bias Detection
Systematically identify which cognitive biases may be affecting a judgment, decision, or belief, providing explanations and debiasing strategies.
When to Use
- Evaluating an important decision before finalizing it
- Understanding why a judgment might be flawed
- Analyzing past decisions that went wrong
- Checking your own thinking for systematic errors
- User asks "Check for biases" or "What biases are at play?"
Inputs
| Input | Required | Description |
|---|---|---|
| situation | Yes | The judgment, decision, or belief to analyze |
| context | No | Background information and stakes involved |
| outcome | No | If analyzing a past decision, what happened |
Kahneman's Framework
Cognitive biases are systematic patterns of deviation from rationality in judgment. They arise primarily from:
- System 1's automatic processing - Fast, intuitive, but error-prone
- Heuristic substitution - Answering easier questions instead of hard ones
- WYSIATI - Building stories from limited information
- Affect and emotion - Feelings influencing judgments
"Systematic errors are not just random noise - they are predictable, and understanding them is the first step to reducing their impact."
The Core Bias Categories
1. Information Processing Biases
How we gather, interpret, and remember information
| Bias | Description | Trigger Signs |
|---|---|---|
| Confirmation Bias | Seeking evidence that confirms existing beliefs | Ignoring contradictory data, asking leading questions |
| Availability Heuristic | Judging likelihood by ease of recall | Overweighting recent, dramatic, or memorable events |
| Anchoring | Over-relying on first piece of information | Estimates suspiciously close to initial numbers |
| WYSIATI | Acting on incomplete information as if complete | High confidence with limited data |
| Hindsight Bias | "I knew it all along" after learning outcomes | Reconstructing past beliefs to match reality |
2. Probability and Prediction Biases
How we estimate likelihood and forecast outcomes
| Bias | Description | Trigger Signs |
|---|---|---|
| Overconfidence | Excessive certainty in beliefs/predictions | Narrow confidence intervals, surprise at outcomes |
| Planning Fallacy | Underestimating time, cost, risk | Optimistic schedules, ignoring past project data |
| Base Rate Neglect | Ignoring statistical base rates | Focus on specific case, ignoring reference class |
| Conjunction Fallacy | Judging A&B more likely than A alone | Detailed scenarios seeming more probable |
| Representativeness | Judging probability by similarity | Stereotyping, ignoring sample size |
3. Decision and Choice Biases
How we make decisions and choices
| Bias | Description | Trigger Signs |
|---|---|---|
| Loss Aversion | Losses loom larger than equivalent gains | Avoiding decisions, holding losers too long |
| Status Quo Bias | Preferring current state over alternatives | Inaction despite better options available |
| Sunk Cost Fallacy | Continuing due to past investment | "We've come too far to stop now" |
| Endowment Effect | Overvaluing what we own | Asking more to sell than willing to pay to buy |
| Framing Effects | Different decisions based on presentation | Choice changing when same options reframed |
4. Social and Ego Biases
How social context and self-image affect judgment
| Bias | Description | Trigger Signs |
|---|---|---|
| Groupthink | Prioritizing consensus over accuracy | Suppressed dissent, premature agreement |
| Authority Bias | Over-deferring to authority figures | Not questioning expert opinions |
| Self-Serving Bias | Attributing success to self, failure to others | Asymmetric explanations for outcomes |
| Dunning-Kruger | Overestimating competence in areas of ignorance | Confident beginners, humble experts |
| Halo Effect | Overall impression affecting specific judgments | "They're smart, so their plan must be good" |
Detection Process
Step 1: Describe the Situation Neutrally
What is the judgment, decision, or belief? Strip away justifications initially.
Step 2: Identify the Thinking Mode
- Is this primarily a System 1 (intuitive) or System 2 (analytical) judgment?
- Was there time pressure or cognitive load?
- Was there emotional arousal?
Step 3: Scan for Bias Categories
Run through each category:
- Information: How was evidence gathered and interpreted?
- Probability: How were likelihoods estimated?
- Decision: What choice dynamics are at play?
- Social/Ego: What interpersonal or identity factors exist?
Step 4: Check for Specific Indicators
For each potential bias, look for characteristic signatures.
Step 5: Assess Impact
How might identified biases have affected the judgment?
Step 6: Recommend Debiasing
What procedures could reduce the bias influence?
Workflow
Step 1: Gather and Review Inputs
Collect all relevant information:
- Review the provided data and context
- Identify key parameters and constraints
- Clarify any ambiguities or missing information
- Establish success criteria
Step 2: Analyze the Situation
Perform systematic analysis:
- Identify patterns and relationships
- Evaluate against established frameworks
- Consider multiple perspectives
- Document key findings
Step 3: Generate Recommendations
Create actionable outputs:
- Synthesize insights from analysis
- Prioritize recommendations by impact
- Ensure recommendations are specific and measurable
- Consider implementation feasibility
Output Format
## Cognitive Bias Analysis
### The Situation
[Neutral description of the judgment/decision/belief]
### Thinking Mode Assessment
- **Primary mode:** [System 1 / System 2 / Mixed]
- **Context factors:** [Time pressure, stakes, complexity, emotion]
### Detected Biases
#### HIGH LIKELIHOOD
**[Bias Name]**
- Evidence: [Specific indicators from the situation]
- Impact: [How this likely affected the judgment]
- Debiasing: [What could counter this bias]
#### MODERATE LIKELIHOOD
**[Bias Name]**
- Evidence: [Specific indicators]
- Impact: [Potential effect]
- Debiasing: [Countermeasures]
#### POSSIBLE (WORTH MONITORING)
- [Bias Name]: [Brief explanation]
- [Bias Name]: [Brief explanation]
### Bias Interaction Effects
[How multiple biases may compound each other]
### Debiasing Recommendations
1. **[Recommendation]** - Addresses [which biases]
2. **[Recommendation]** - Addresses [which biases]
3. **[Recommendation]** - Addresses [which biases]
### Questions to Ask
- [Question that would surface hidden assumptions]
- [Question that would challenge the judgment]
- [Question that would introduce outside view]
### Kahneman's Reminder
"Knowing about biases does not make you immune to them. But structured procedures can reduce their influence even when we cannot eliminate them from our minds."
Constraints
- Bias detection is probabilistic - presence of indicators suggests, not proves
- Multiple biases often operate simultaneously and reinforce each other
- The goal is awareness and mitigation, not elimination
- You yourself are biased when detecting others' biases
- Focus on the highest-impact biases for the situation
Outputs
Primary Output: A structured analysis document that identifies and articulates patterns, insights, and actionable recommendations based on the input data.
Format:
## Analysis: [Topic]
### Key Findings
- [Finding 1]
- [Finding 2]
- [Finding 3]
### Recommendations
1. [Action 1]
2. [Action 2]
3. [Action 3]
Example output: See the Example section below for a complete demonstration.
Example
Input: "Our CEO is convinced we should expand into the European market. She says it's obvious - we're successful in the US, and Europe is just as developed. The leadership team all agree."
Output:
Cognitive Bias Analysis
The Situation
CEO believes US success translates to European expansion; leadership team agrees with this assessment.
Thinking Mode Assessment
- Primary mode: System 1 - "obvious" suggests intuitive rather than analytical
- Context factors: High stakes (major expansion), authority figure driving, social dynamics in leadership team
Detected Biases
HIGH LIKELIHOOD
Overconfidence
- Evidence: Described as "obvious" - high certainty language; no mention of risks or uncertainties; no discussion of failed expansion precedents
- Impact: Likely underestimating complexity, costs, timeline, and probability of failure
- Debiasing: Request explicit confidence intervals; ask "what would make this fail?"; run premortem
Representativeness / False Analogy
- Evidence: Reasoning that Europe is "just as developed" - assuming surface similarity predicts performance similarity
- Impact: Ignoring critical differences: regulatory environment, labor laws, cultural preferences, competitive landscape, distribution channels
- Debiasing: Map specific differences; find reference class of US companies expanding to Europe; talk to those who tried
Groupthink
- Evidence: "Leadership team all agree" - unanimous agreement on complex strategic question is suspicious
- Impact: Dissenting views likely suppressed; weaknesses not surfaced; decision made prematurely
- Debiasing: Assign devil's advocate; collect anonymous concerns; bring in outside perspective
Authority Bias
- Evidence: CEO is driving; described as her conviction; no mention of pushback
- Impact: Team may be agreeing with the person, not the argument; intellectual diversity suppressed
- Debiasing: Ask team for written assessments before CEO shares view; request that CEO explicitly invite disagreement
MODERATE LIKELIHOOD
WYSIATI (What You See Is All There Is)
- Evidence: Focus on one data point (US success) as primary evidence
- Impact: Not considering: European market research, competitor analysis, regulatory requirements, required investments
- Debiasing: List what information is missing; require data before decision
Planning Fallacy
- Evidence: Likely implicit assumption that expansion will go smoothly
- Impact: Timeline, cost, and risk probably underestimated
- Debiasing: Reference class of similar expansions; budget 2x the estimate
POSSIBLE (WORTH MONITORING)
- Sunk Cost: If any prior European investment, may be influencing push to justify
- Halo Effect: CEO's past successes may be creating assumption this will work too
- Availability: Recent European success stories in media may be making success seem more common
Bias Interaction Effects
Overconfidence + Groupthink is a dangerous combination. The CEO's confidence discourages dissent, and the absence of dissent reinforces her confidence. This spiral can persist until market reality provides harsh feedback.
Debiasing Recommendations
Premortem exercise - Have team imagine expansion failed; identify why. Addresses overconfidence, groupthink, planning fallacy.
Reference class data - Research US companies that expanded to Europe. What % succeeded? What distinguished successes from failures? Addresses representativeness, base rate neglect.
Red team - Assign someone to argue against expansion. Give them explicit permission and incentive. Addresses groupthink, authority bias.
Information audit - List what you know vs. what you're assuming. What would change your mind? Addresses WYSIATI.
Anonymous vote - Before final decision, collect written assessments without names. Addresses groupthink, authority bias.
Questions to Ask
- "What would we need to see to conclude this is a bad idea?"
- "What did [Company X] learn when they expanded to Europe?"
- "What are we assuming about European customers that we should test?"
- "If this fails, what will we say was the warning sign we ignored?"
- "Who on the team has the strongest reservations, and what are they?"
Kahneman's Reminder
"When everyone agrees quickly on a complex decision, that's not a sign of wisdom - it's a sign that dissent has been suppressed. The absence of disagreement is often more alarming than its presence."
Integration
This skill is part of the Daniel Kahneman expert persona. It applies the heuristics and biases research program to practical decision-making.
Related skills:
- Premortem Analysis - Surfaces concerns that biases suppress
- Reference Class Forecasting - Counters inside-view biases
- Decision Noise Audit - Complements bias detection with noise detection