Decision‐Making Biases & Remedies Study Notes

Human cognition is as vulnerable to mental illusions as human vision is to optical illusions.
No person – regardless of intelligence, education, or experience – is naturally immune.
Cluster of remedies:
- AWARENESS → know the biases exist.
- TRAINING → practice identifying them in real and simulated decisions.
- MINDFULNESS → pause, reflect, apply critical thought.
Biases rarely work in isolation; e.g., overconfidence often pairs with hindsight bias (“I knew it all along”).
Meta-rule: ASSUME NOTHING…QUESTION EVERYTHING!

Definition: Systematic tendency to overestimate the accuracy of one’s knowledge, judgments, and forecasts.
Heightened when we leave our comfort zone or domain of expertise.
Danger sign: phrases like “I’m $99\%$ sure.”
WHY it happens
- Illusion of superiority (better-than-average effect).
- Illusion of control over random events.
- Ignorance of the full set of possible outcomes.
- Selective memory: successes easily recalled, failures fade.
- Confirmation-seeking search (feeds confirmation bias).
Practical consequence: overly narrow confidence intervals, underestimated risk buffers, and surprise when outcomes deviate.

Disposition to postpone or avoid decisions/action.
Mechanisms
- Conflict-avoidance → “analysis paralysis” (continuously gathering data because choice feels painful).
- Unpleasantness avoidance → delay when short-term costs loom larger than long-term gains.
Antidote: make the solution clear-cut and reduce psychological cost of initiating it.

Mirror image of inertia but rooted in the same self-control weakness.
Short-term rewards overweighted; future outcomes discounted to near zero.
Manifestations: impulsive purchases, under-saving, unhealthy consumption.
Economic parallel: hyperbolic discounting $V=\frac{A}{1+k\,t}$ where a high $k$ signals steep present bias.

People fixate on an initial value (anchor) and insufficiently adjust for subsequent data.
Strongest when no objective benchmarks exist (e.g., pricing exotic derivatives like credit-default swaps pre-2008).
Trivial or irrelevant anchors still pull estimates (classic "last 2 digits of SSN" experiment).
Guardrail: generate anchors from data, not from arbitrary numbers, and force multiple adjustment rounds.

Under ambiguity, interpretation reflects attitudes, interests, experience, and cultural background more than the stimulus itself.
Consequence: two observers can see the same event and walk away with incompatible narratives.
Skill: intentionally adopt another perspective (“see through their eyes”).

Data collection is subjective—we seek, notice, and recall evidence that supports existing beliefs.
Psychological roots
- Cognitive consistency is pleasurable.
- Conflicting information creates dissonance (mental discomfort).
- Reduces complexity: easier to work with confirming evidence than conflicting data.
Creates echo chambers and amplifies polarisation.

“Frames” are mental structures that shape meaning; wording or context steers decisions.
Losses vs. gains
- People are risk-averse in the domain of gains, risk-seeking in the domain of losses (Prospect Theory).
Three classic types
1. Risky-choice framing (Asian disease problem)
- Program A: “Save $200$ people.”
- Program B: $\tfrac{1}{3}$ chance save $600$ / $\tfrac{2}{3}$ chance save $0$ .
- Equivalent negative frame: C “ $400$ will die” vs. D $\tfrac{1}{3}$ chance none die / $\tfrac{2}{3}$ all $600$ die.
- Majority choose A over B (gain frame) but choose D over C (loss frame)—pure wording shift.
1. Attribute framing: “ $75\%$ lean” vs. “ $25\%$ fat.” Same product, different perception.
2. Goal framing: Breast-exam messages
- Positive: “Women who DO exams have an increased chance of detecting tumors early.”
- Negative: “Women who do NOT do exams have a decreased chance of detecting tumors early.”
Managerial implication: craft frames ethically; test multiple frames before deciding.

Decisions lean on information that is vivid, recent, or emotionally charged rather than statistically sound.
Example: Fear of flying spikes after a plane crash, despite P(\text{car death}) > P(\text{plane death}).
Experiences (news, anecdotes) dominate abstract statistics.
Leads to distorted risk assessment and resource misallocation (e.g., over-investing in low-frequency hazards).

Probability judged by similarity to a prototype rather than by base rates or laws of randomness.
Patterns inferred where none exist: “I’m on a lucky streak.”
Regression to the mean
- Extreme outcomes tend to move toward average next period.
- Formal: E[X{t+1}\,|\,Xt]=\mu+\rho\,(X_t-\mu),\;|\rho|<1.
Sample-size neglect: treating “ $3/5$ successes” as equally credible as “ $1200/2000$ successes.”

Humans over-interpret chance events; we crave causal stories.
Practical guidelines
- Accept that coincidences happen without deeper meaning.
- Resist attributing random events to “fate.”
- Avoid forming superstitions from illusory patterns.

Treating past, irrevocable costs as reasons to continue an endeavour.
Rational rule: only future costs and benefits matter—today’s decisions can’t rewrite the past.
Psychological drivers
- Desire to appear consistent (“saving face”).
- Aversion to waste.
Tip: reframe decision as “fresh project starting today.”

Cognitive limits → we simplify by searching only a slice of the alternative space.
May satisfice: accept first option that meets a minimally acceptable threshold.
Simon’s Bounded Rationality: optimisation is impossible; aim for “good enough.”
Check: Does the option align with values, goals, and plans?
Einstein reminder: “Everything should be made as simple as possible, but not simpler.”

High arousal (stress or excitement) narrows attention and speeds up choices, often impulsively.
Negative emotions
- Constrict cognition, encourage rapid, oversimplified decisions.
Positive over-excitement
- Can lead to overcommitment, ignoring downside risk.
Strategy: acknowledge emotion, delay key decisions until emotional intensity subsides or use pre-commitment devices.

Personal outcomes
- Success → internal attribution (“I’m skilled”).
- Failure → external attribution (“Bad luck”).
Social reversal
- When judging others’ failures, we downplay situational factors and blame internal flaws.
Organizational impact: fosters blame culture and hampers honest post-mortems.

After the fact, outcomes appear obvious (“I knew it all along”), masking past uncertainty.
Mechanisms
- Faulty memory reconstruction: we misremember prior probability estimates as more extreme.
Consequence: reduces learning—if outcome seemed inevitable, we never probe why it actually happened.

Deliberately search for disconfirming evidence.
Perform a “bias audit” on yourself: impatience? overconfidence? risk appetite?
Prioritise long-term over myopic goals.
Experiment with alternative frames—rewrite problem statements.
Conduct an empathy exercise: “Walk in someone else’s shoes.”
Diversify experiences to expand mental models.
Treat extreme performances as temporary; remember regression to the mean.
Accept randomness: not every pattern is causal.
Push for “outside-the-box” options; widen the choice set beyond the obvious.
Manage emotion: build pauses, ask neutral parties, or use decision pre-mortems.