# Cognitive Bias Catalog ## Quick Reference Table | Bias | Category | Impact | Detection | Remediation | |------|----------|--------|-----------|-------------| | Confirmation | Confirmation | Seek supporting evidence only | Search for disconfirming evidence? | Red team your forecast | | Desirability | Confirmation | Want outcome → believe it's likely | Do I want this outcome? | Outsource to neutral party | | Availability | Availability | Recent/vivid events dominate | What recent news influenced me? | Look up actual statistics | | Recency | Availability | Overweight recent data | Considering full history? | Expand time window | | Anchoring | Anchoring | First number sticks | Too close to initial number? | Generate estimate first | | Affect | Affect | Feelings override data | How do I feel about this? | Acknowledge, then set aside | | Loss Aversion | Affect | Overweight downside | Weighting losses more? | Evaluate symmetrically | | Overconfidence | Overconfidence | Intervals too narrow | Track calibration | Widen intervals to 20-80% | | Dunning-Kruger | Overconfidence | Novices overestimate | How experienced am I? | Seek expert feedback | | Optimism | Overconfidence | "Won't happen to me" | What's the base rate? | Apply base rate to self | | Pessimism | Overconfidence | Overweight negatives | Only considering downsides? | List positive scenarios | | Attribution Error | Attribution | Blame person, not situation | What situational factors? | Consider constraints first | | Self-Serving | Attribution | Success=skill, failure=luck | Consistent attribution? | Same standard for both | | Framing | Framing | Presentation changes answer | How is this framed? | Rephrase multiple ways | | Narrative Fallacy | Framing | Simple stories mislead | Story too clean? | Prefer stats over stories | | Sunk Cost | Temporal | Can't abandon past investment | Only future costs/benefits? | Decide as if starting fresh | | Hindsight | Temporal | "Knew it all along" | Written record of prediction? | Record forecasts beforehand | | Planning Fallacy | Temporal | Underestimate time/cost | Reference class timeline? | Add 2-3x buffer | | Outcome Bias | Temporal | Judge by result not process | Evaluating process or outcome? | Judge by info available then | | Clustering Illusion | Pattern | See patterns in randomness | Statistically significant? | Test significance | | Gambler's Fallacy | Pattern | Expect short-term balancing | Are events independent? | Use actual probability | | Base Rate Neglect | Bayesian | Ignore prior probabilities | Did I start with base rate? | Always start with base rate | | Conjunction Fallacy | Bayesian | Specific > general | Is A&B > A alone? | P(A&B) ≤ P(A) always | | Halo Effect | Social | One trait colors everything | Generalizing from one trait? | Assess dimensions separately | | Authority Bias | Social | Overweight expert opinions | Expert's track record? | Evaluate evidence not credentials | | Peak-End | Memory | Remember peaks/endings only | Remembering whole sequence? | Review full historical record | --- ## Confirmation Cluster ### Confirmation Bias **Definition:** Search for, interpret, and recall information that confirms pre-existing beliefs. **Affects forecasting:** Only look for supporting evidence, discount contradictions, selective memory. **Detect:** Did I search for disconfirming evidence? Can I steelman the opposite view? **Remediate:** Red team your forecast, list disconfirming evidence first, ask "How could I be wrong?" ### Desirability Bias **Definition:** Believing outcomes you want are more likely than they are. **Affects forecasting:** Bullish on own startup, wishful thinking masquerading as analysis. **Detect:** Do I want this outcome? Am I emotionally invested? **Remediate:** Outsource to neutral party, imagine opposite outcome, forecast before declaring preference. --- ## Availability Cluster ### Availability Heuristic **Definition:** Judging probability by how easily examples come to mind. **Affects forecasting:** Overestimate vivid risks (terrorism), underestimate mundane (heart disease), media coverage distorts frequency perception. **Detect:** What recent news am I thinking of? Is this vivid/emotional/recent? **Remediate:** Look up actual statistics, use reference class not memorable examples. ### Recency Bias **Definition:** Overweighting recent events relative to historical patterns. **Affects forecasting:** Extrapolate recent trends linearly, forget cycles and mean reversion. **Detect:** How much history am I considering? Is forecast just recent trend? **Remediate:** Expand time window (decades not months), check for cyclicality. --- ## Anchoring Cluster ### Anchoring Bias **Definition:** Over-relying on first piece of information encountered. **Affects forecasting:** First number becomes estimate, can't adjust sufficiently from anchor. **Detect:** What was first number I heard? Am I too close to it? **Remediate:** Generate own estimate first, use multiple independent sources. ### Priming **Definition:** Prior stimulus influences subsequent response. **Affects forecasting:** Reading disaster primes pessimism, context shapes judgment unconsciously. **Detect:** What did I just read/see/hear? Is mood affecting forecast? **Remediate:** Clear mind before forecasting, wait between exposure and estimation. --- ## Affect Cluster ### Affect Heuristic **Definition:** Letting feelings about something determine beliefs about it. **Affects forecasting:** Like it → think it's safe, dislike it → think it's dangerous. **Detect:** How do I feel about this? Would I forecast differently if neutral? **Remediate:** Acknowledge emotion then set aside, focus on base rates and evidence. ### Loss Aversion **Definition:** Losses hurt more than equivalent gains feel good (2:1 ratio). **Affects forecasting:** Overweight downside scenarios, status quo bias, asymmetric risk evaluation. **Detect:** Am I weighting losses more? Would I accept bet if gains/losses swapped? **Remediate:** Evaluate gains and losses symmetrically, use expected value calculation. --- ## Overconfidence Cluster ### Overconfidence Bias **Definition:** Confidence exceeds actual accuracy. **Affects forecasting:** 90% intervals capture truth 50% of time, narrow ranges, extreme probabilities. **Detect:** Track calibration, are intervals too narrow? Can I be surprised? **Remediate:** Widen confidence intervals, track calibration, use 20-80% as default. ### Dunning-Kruger Effect **Definition:** Unskilled overestimate competence; experts underestimate. **Affects forecasting:** Novices predict with false precision, don't know what they don't know. **Detect:** How experienced am I in this domain? Do experts agree? **Remediate:** If novice widen intervals, seek expert feedback, learn domain deeply first. ### Optimism Bias **Definition:** Believing you're less likely than others to experience negatives. **Affects forecasting:** "My startup is different" (90% fail), "This time is different" (rarely is). **Detect:** What's base rate for people like me? Am I assuming I'm special? **Remediate:** Use reference class for yourself, apply base rates, assume average then adjust slightly. ### Pessimism Bias **Definition:** Overweighting negative outcomes, underweighting positive. **Affects forecasting:** Disaster predictions rarely materialize, underestimate human adaptability. **Detect:** Only considering downsides? What positive scenarios missing? **Remediate:** Explicitly list positive scenarios, consider adaptive responses. --- ## Attribution Cluster ### Fundamental Attribution Error **Definition:** Overattribute behavior to personality, underattribute to situation. **Affects forecasting:** "CEO is brilliant" ignores market conditions, predict based on person not circumstances. **Detect:** What situational factors am I ignoring? How much is luck vs. skill? **Remediate:** Consider situational constraints first, estimate luck vs. skill proportion. ### Self-Serving Bias **Definition:** Attribute success to skill, failure to bad luck. **Affects forecasting:** Can't learn from mistakes (was luck!), overconfident after wins (was skill!). **Detect:** Would I explain someone else's outcome this way? Do I attribute consistently? **Remediate:** Apply same standard to wins and losses, assume 50% luck/50% skill, focus on process. --- ## Framing Cluster ### Framing Effect **Definition:** Same information, different presentation, different decision. **Affects forecasting:** "90% survival" vs "10% death" changes estimate, format matters. **Detect:** How is question framed? Do I get same answer both ways? **Remediate:** Rephrase multiple ways, convert to neutral format, use frequency (100 out of 1000). ### Narrative Fallacy **Definition:** Constructing simple stories to explain complex reality. **Affects forecasting:** Post-hoc explanations feel compelling, smooth narratives overpower messy data. **Detect:** Is story too clean? Can I fit multiple narratives to same data? **Remediate:** Prefer statistics over stories, generate alternative narratives, use base rates. --- ## Temporal Biases ### Sunk Cost Fallacy **Definition:** Continuing endeavor because of past investment, not future value. **Affects forecasting:** "Invested $1M, can't stop now", hold losing positions too long. **Detect:** If I started today, would I choose this? Considering only future costs/benefits? **Remediate:** Consider only forward-looking value, treat sunk costs as irrelevant. ### Hindsight Bias **Definition:** After outcome known, "I knew it all along." **Affects forecasting:** Can't recall prior uncertainty, overestimate predictability, can't learn from surprises. **Detect:** What did I actually predict beforehand? Written record exists? **Remediate:** Write forecasts before outcome, record confidence levels, review predictions regularly. ### Planning Fallacy **Definition:** Underestimate time, costs, risks; overestimate benefits. **Affects forecasting:** Projects take 2-3x longer than planned, inside view ignores reference class. **Detect:** How long did similar projects take? Using inside view only? **Remediate:** Use reference class forecasting, add 2-3x buffer, consider outside view first. ### Outcome Bias **Definition:** Judging decision quality by result, not by information available at time. **Affects forecasting:** Good outcome ≠ good decision, can't separate luck from skill. **Detect:** What did I know when I decided? Evaluating process or outcome? **Remediate:** Judge decisions by process not results, evaluate with info available then. --- ## Pattern Recognition Biases ### Clustering Illusion **Definition:** Seeing patterns in random data. **Affects forecasting:** "Winning streak" in random sequence, stock "trends" that are noise, "hot hand" fallacy. **Detect:** Is this statistically significant? Could this be random chance? **Remediate:** Test statistical significance, use appropriate sample size, consider null hypothesis. ### Gambler's Fallacy **Definition:** Believing random events "balance out" in short run. **Affects forecasting:** "Due for a win" after losses, expecting mean reversion too quickly. **Detect:** Are these events independent? Does past affect future probability? **Remediate:** Recognize independent events, don't expect short-term balancing. --- ## Bayesian Reasoning Failures ### Base Rate Neglect **Definition:** Ignoring prior probabilities, focusing only on new evidence. **Affects forecasting:** "Test is 90% accurate" ignores base rate, vivid case study overrides statistics. **Detect:** What's the base rate? Did I start with prior probability? **Remediate:** Always start with base rate, update incrementally with evidence. ### Conjunction Fallacy **Definition:** Believing specific scenario is more probable than general one. **Affects forecasting:** "Librarian who likes poetry" > "Librarian", detailed scenarios feel more likely. **Detect:** Is A&B more likely than A alone? Confusing plausibility with probability? **Remediate:** Remember P(A&B) ≤ P(A), strip away narrative details. --- ## Social Biases ### Halo Effect **Definition:** One positive trait colors perception of everything else. **Affects forecasting:** Successful CEO → good at everything, one win → forecaster must be skilled. **Detect:** Am I generalizing from one trait? Are dimensions actually correlated? **Remediate:** Assess dimensions separately, don't assume correlation, judge each forecast independently. ### Authority Bias **Definition:** Overweight opinions of authorities, underweight evidence. **Affects forecasting:** "Expert said so" → must be true, defer to credentials over data. **Detect:** What's expert's track record? Does evidence support claim? **Remediate:** Evaluate expert track record, consider evidence not just credentials. --- ## Memory Biases ### Peak-End Rule **Definition:** Judging experience by peak and end, ignoring duration and average. **Affects forecasting:** Remember market peak, ignore average returns, distorted recall of sequences. **Detect:** Am I remembering whole sequence? What was average not just peak/end? **Remediate:** Review full historical record, calculate averages not memorable moments. ### Rosy Retrospection **Definition:** Remembering past as better than it was. **Affects forecasting:** "Things were better in old days", underestimate historical problems. **Detect:** What do contemporary records show? Am I romanticizing the past? **Remediate:** Consult historical data not memory, read contemporary accounts. --- ## Application to Forecasting ### Pre-Forecast Checklist 1. What's the base rate? (Base rate neglect) 2. Am I anchored on a number? (Anchoring) 3. Do I want this outcome? (Desirability bias) 4. What recent events am I recalling? (Availability) 5. Am I overconfident? (Overconfidence) ### During Forecast 1. Did I search for disconfirming evidence? (Confirmation) 2. Am I using inside or outside view? (Planning fallacy) 3. Is this pattern real or random? (Clustering illusion) 4. Am I framing this question neutrally? (Framing) 5. What would change my mind? (Motivated reasoning) ### Post-Forecast Review 1. Record what I predicted before (Hindsight bias) 2. Judge decision by process, not outcome (Outcome bias) 3. Attribute success/failure consistently (Self-serving bias) 4. Update calibration tracking (Overconfidence) 5. What did I learn? (Growth mindset) --- ## Bias Remediation Framework **Five principles:** 1. **Awareness:** Know which biases affect you most 2. **Process:** Use checklists and frameworks 3. **Calibration:** Track accuracy over time 4. **Humility:** Assume you're biased, not immune 5. **Updating:** Learn from mistakes, adjust process **Key insight:** You can't eliminate biases, but you can design systems that compensate for them. --- **Return to:** [Main Skill](../SKILL.md#interactive-menu)