Unit 2: Heuristics and Biases

Heuristics & Biases

This is a focused, MBA-friendly walk-through of key heuristics and biases: Two Systems, Familiarity & related heuristics, Anchoring, Irrationality & adaptation, and Hyperbolic discounting. For each concept you’ll get: what it is, a classic finding or example, why it matters in business, and how managers can act on or guard against it.

1) The Two Systems (Thinking fast and slow)

What it is: People use two kinds of mental processes:

• System 1 — Fast, automatic, intuitive, effortless (pattern recognition, gut feelings). Examples: reading emotions on faces, detecting anger in a voice, instantly answering 2+2.
• System 2 — Slow, deliberative, analytical, effortful (reasoning, calculations, conscious decisions). Examples: solving a complex spreadsheet formula, planning a strategy, checking assumptions.

Classic insight: System 1 generates quick impressions and suggestions; System 2 can endorse, override, or rationalize them — but it is lazy and often accepts System 1 outputs.

MBA implications:

• Consumers often use System 1 for everyday purchases (brand, packaging, price landmarks).
• Strategic decisions require deliberate System 2 processes (scenario planning, forecast validation).
• Managers who assume purely rational (System 2) behavior will often be surprised.

Practical actions:

• Use System 1 cues in marketing: clear visuals, simple messages, branded cues.
• Force System 2 where needed: structured decision protocols, pre-mortems, checklists, red-team reviews.
• Train employees to spot “intuition traps” and pause for a 10–15 min check before big commitments.

2) Familiarity and Related Heuristics

What it is: Familiarity strongly influences judgment: people prefer what is familiar or easily recalled. This shows up in several heuristics:

• Availability heuristic: We judge probability by how easily examples come to mind. (E.g., after news of airline accidents, people overestimate crash risk.)
• Recognition heuristic: If you recognize one option but not the other, you tend to pick the recognized one.
• Fluency (processing ease): Information that’s easier to process (clear font, simple words) seems truer or more attractive.

Classic examples:

• People overestimate rare but dramatic risks if media coverage is high.
• Consumers choose known brands over unknown ones even if objective quality is similar.

MBA implications:

• Brand familiarity is huge — repeated exposure increases choice share.
• Risk communication must account for availability: dramatic incidents skew perception of likelihood.
• User experience (UX) that increases fluency increases trust and conversions.

Practical actions:

• Build familiarity via consistent branding, repeated gentle exposures (ads, emails).
• Use vivid, memorable examples carefully — they shape perceived risk.
• Improve website copy and UX to increase processing fluency (short sentences, clear headings, fast load).

3) Anchoring

What it is: People rely heavily on the first number or piece of information they see (the anchor), and they insufficiently adjust from it when making estimates or negotiating.

Classic finding: If you ask people whether the population of Turkey is more or less than 35 million, then ask for a best estimate, answers cluster around the anchor 35M — even though that number is arbitrary.

Managerial and marketing examples:

• Pricing: Showing an “original price” (anchor) makes a discounted price feel like a bargain.
• Negotiation: First offer sets the anchor and strongly influences the final settlement.
• Forecasting: An initial sales forecast can anchor subsequent planning and budgets.

How anchoring works: partly via focus/priming, partly because adjustments are effortful and often insufficient.

Mitigation & use:

• To use anchoring: set favorable initial offers (in bids, pricing, proposals).
• To mitigate anchors: deliberately generate multiple independent estimates, use blind forecasting, and force consideration of counter-anchors (reverse scenarios). Use data and benchmarks as anchors rather than arbitrary numbers.

4) Irrationality & Adaptation (bounded rationality, prospect theory, endowment)

What it is: Humans are predictably irrational — they systematically deviate from the utility-maximizing, perfectly rational agent assumed in classic economics.

Key phenomena:

• Bounded rationality: People satisfice with “good enough” decisions due to limited attention and computation.
• Prospect theory (Kahneman & Tversky): People value gains and losses differently — losses loom larger than equivalent gains (loss aversion).
• Endowment effect: Owning something increases its perceived value; owners demand more to give it up than buyers are willing to pay.
• Reference dependence: Utility depends on changes relative to a reference point, not absolute levels.
• Adaptation (hedonic treadmill): Emotional reactions to gains or losses fade over time — people adjust to new incomes, status, or experiences and return toward baseline happiness.

Business implications:

• Pricing, promotions, and product returns must account for loss aversion and endowment (free trials increase ownership feeling and reduce churn).
• Compensation design should manage reference points (e.g., a loss framed as a “bonus clawback” will feel worse than a lower base pay).
• Customer satisfaction spikes after improvements but often adapts — continuous innovation or new value additions needed.

Practical actions:

• Frame changes carefully (loss vs gain frames). Example: “You’ll lose your upgraded plan after X” vs “You’ll gain...” — the former can be more motivating.
• Use free trials and “try-before-you-buy” to create endowment.
• Build recurring small delights to combat adaptation; don’t rely on a one-time launch excitement.

5) Hyperbolic Discounting (present bias)

What it is: People disproportionately prefer smaller-sooner rewards over larger-later rewards — more so than exponential discounting predicts. This creates time-inconsistent preferences: today’s preference may differ from tomorrow’s.

Difference vs. exponential discounting:

• Exponential (rational) discounting: consistent rate over time (preferences stable).
• Hyperbolic discounting: steep near-term discounting that flattens for long delays — large present bias.

Classic examples:

• People choose ₹100 today over ₹120 in a week, but will prefer ₹120 in 13 months over ₹100 in 12 months — inconsistency across short vs long horizons.

Implications for business:

• Consumers procrastinate on beneficial actions (savings, training, health sign-ups).
• Subscription businesses exploit present bias (low immediate cost, future inertia).
• Commitment devices (pre-commitments) can help users achieve long-term goals.

Practical actions:

• Offer small immediate rewards to encourage beneficial behaviors (signup discounts, instant cashback).
• Use commitment devices: prepayments, automatic enrollments, deadlines.
• Structure prices to reduce friction for initial adoption (free trial, low upfront cost) then retain via habit and switching costs.

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6) Putting it together — Practical applications for managers

1. Pricing & Promotions

• Use anchors (RRP), loss-frame limited-time offers, and free trials to increase conversions.
• Beware of habituation — rotate offers and add incremental value.

2. Negotiations

• Make the first reasonable offer to set a favorable anchor; prepare counter-anchors and walk-away points.

3. Product Design & UX

• Increase familiarity and fluency (simpler language, recognizable icons) to instill trust.
• Use progressive disclosure: show small, immediate wins to overcome hyperbolic discounting.

4. Marketing & Branding

• Invest consistently in brand exposure (to build familiarity/availability).
• Use vivid stories carefully — they shift perceived probabilities.

5. Behavioral HR / Policy

• Use automatic enrollments (e.g., auto-enrol into savings/pension) to leverage inertia beneficially.
• Frame feedback and bonuses to account for reference points and loss aversion.

6. Strategy & Forecasting

• Counter groupthink and System 1 rushes with red teams, pre-mortems, and structured decision tools that force System 2 thinking.

7) How to teach or discuss this in an MBA class (activities)

• Quick experiment (availability): Ask students to list causes of death they can recall. Discuss frequency vs vividness.
• Anchoring demo: Give two groups different anchors before they estimate a number (e.g., % of world water that’s freshwater). Compare.
• Choice architecture design exercise: Have teams redesign a signup flow to increase savings signups using commitment devices and present-bias mitigation.
• Negotiation role-play: First offer vs counter-offer — observe how anchors influence outcomes.

8) Short cheat-sheet (one-liners)

• System 1: Fast, intuitive; System 2: slow, effortful.
• Availability: What's easy to recall seems more likely.
• Anchoring: First number sticks.
• Loss aversion: Losses > gains in psychological weight.
• Endowment: Ownership increases perceived value.
• Hyperbolic discounting: People overweight the present — prefer now over later, inconsistently.

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9) Quick managerial checklist to reduce costly biases

1. For big decisions, require at least two independent forecasts.
2. Pre-mortem: imagine the plan failed, generate causes.
3. Use default choices that help users (auto-enroll) where beneficial.
4. Test multiple price anchors in pilot markets.
5. Break big investments into staged approvals to combat present bias and sunk-cost escalation.
6. Use data dashboards that show long-term trends (counter availability bias from vivid short-term events).

SELF-DECEPTION & OVERCONFIDENCE

1. Introduction to Self-Deception

Self-deception is the human tendency to believe something that is false, exaggerated, or selectively true because it feels comfortable, rewarding, or ego-protective.

It is not deliberate lying — it is unconscious distortion of reality.

In behavioural finance, self-deception explains why investors make errors even when they have information, tools, and experience.

Key ideas:

• People overestimate their abilities and knowledge.
• People ignore contradictory facts to preserve their ego.
• Confidence grows faster than competence.
• Reality is filtered through personal biases.

Example: A trader believes he “understands the market better than most” even when his long-term performance is average.

2. Miscalibration

Miscalibration refers to the gap between subjective confidence and objective accuracy.

People think their predictions, estimates, or judgments are more accurate than they actually are.

Types of Miscalibration

1. Overprecision – excessive certainty about beliefs. Example: “I’m 95% sure the stock will stay between ₹1,100 and ₹1,200,” but actual prices move far outside that range.
2. Overestimation – overestimating one’s actual ability or skill. Example: Most people think they are above-average drivers or above-average investors.
3. Overplacement – believing one is better than others. Example: 80% of fund managers believe they are in the top 20%.

Why it matters: Miscalibration leads to wrong forecasts, narrow confidence intervals, poor risk management, and aggressive investment decisions.

3. Forms of Overconfidence

Overconfidence is the most prominent form of self-deception in behavioural finance.

A) Overestimation

Believing you are more skilled, knowledgeable, or capable than you actually are. Example: A retail investor thinks he can beat expert fund managers by “picking hot stocks.”

B) Overplacement (Better-than-average effect)

Believing you are better than others. Example: New traders assume they will perform better than the market average.

C) Overprecision

Being overly certain about your predictions. Example: Giving extremely narrow predictions in stock price forecasts.

4. Causes of Overconfidence

Several psychological and environmental factors contribute to overconfidence:

1. Cognitive Reasons

• Self-attribution bias: Success is credited to skill, failures blamed on external factors.
• Selective memory: People remember successes more than failures.
• Confirmation bias: Seeking information that supports one’s views and ignoring contradictions.
• Illusion of control: Belief that one can influence outcomes that are mostly random.

2. Emotional Reasons

• Need to protect self-esteem
• Desire to feel skilled and knowledgeable
• Emotions like excitement, greed, or ego

3. Social & Environmental Reasons

• Media hype about “star investors”
• Social pressure to appear confident
• Overexposure to noisy financial information
• Easy access to trading platforms promoting illusion of expertise

4. Experience Without Feedback

When people act without clear feedback or accountability, confidence often grows unchecked.

5. Other Forms of Self-Deception

Self-deception does not end with overconfidence. Other related biases include:

A) Optimism Bias

Belief that the future will be more positive than justified by data.

B) Self-Attribution Bias

Taking full credit for success and blaming others or luck for failures.

C) Illusion of Knowledge

Belief that more information equals better decisions — even if the information is irrelevant.

D) Illusion of Control

Thinking one can control unpredictable financial outcomes.

E) Hindsight Bias

After an event, believing “I knew it all along,” leading to false belief in one’s predictive ability.

F) Escalation of Commitment

Continuing a losing strategy because admitting failure hurts the ego.

6. Implications of Overconfidence for Financial Decision-Making

Overconfidence has serious consequences for investors, managers, and markets.

1. Excessive Trading

Overconfident investors trade too frequently, believing they can outperform others.

Result: Lower net returns due to transaction costs and timing errors.

2. Underestimation of Risk

• Narrow confidence intervals
• Ignoring tail events
• Taking bigger positions than justified

This leads to losses in volatile markets.

3. Poor Portfolio Diversification

Overconfident investors:

• Put too much money in a few stocks
• Prefer familiar companies (home bias)
• Avoid diversification thinking they “know better”

4. Overreaction to Private Information

Investors give too much importance to their own research or tips.

5. Corporate Decision Errors

Overconfident CEOs & managers may:

• Overinvest in risky projects
• Take excessive debt
• Overpay in mergers and acquisitions
• Ignore expert advice and risk reports

6. Market-Level Effects

Collective overconfidence leads to:

• Asset bubbles
• Sudden crashes
• Mispricing
• Irrational trading volume

7. Lower Investment Performance

Many studies show that overconfident investors systematically underperform the market.

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Summary Table 

Concept	Meaning	Effect
Self-Deception	Believing something false for comfort	Distorts financial judgement
Miscalibration	Gap between confidence & accuracy	Wrong forecasts, poor risk control
Overestimation	Thinking you are better than you are	Excessive risk-taking
Overplacement	Thinking you are better than others	Competitive overtrading
Overprecision	Too much certainty in predictions	Narrow forecasts, big losses
Illusion of Control	Belief in controlling randomness	Overactive trading
Optimism Bias	Unrealistic future expectations	Underestimating risk
Hindsight Bias	“I knew it” after events	Overconfidence in future predictions

Factors Impeding Correction

These are the forces that prevent people from correcting their wrong beliefs, forecasting errors, or overconfidence—even when evidence is clearly against them.

A) Confirmation Bias

People look for information that supports their existing beliefs and avoid contradictory evidence.
→ Blocking correction of false beliefs.

B) Commitment & Consistency Pressure

Once a decision is taken, people want to appear consistent, so they resist admitting mistakes.
→ “I can't change now; it will look like I was wrong.”

C) Ego Defence & Self-enhancement

Accepting mistakes hurts self-esteem.
→ People ignore evidence that contradicts their self-image of being smart.

D) Illusion of Control

Believing one can control outcomes prevents accepting randomness or luck.
→ People hold on to strategies even when results are random.

E) Selective Memory

We remember successes more than failures.
→ Past errors are forgotten, so learning doesn’t occur.

F) Social Influences

Pressure to fit in with a group or maintain reputation.
→ In financial markets, analysts avoid deviating from consensus forecasts.

G) Short-Term Feedback Noise

Markets give noisy, confusing signals.
→ Wrong decisions sometimes still produce profits (because of luck), so people don’t correct themselves.

H) Structural Incentives

Managers may get rewarded even for lucky outcomes.
→ No incentive to examine or correct decision errors.

2. How Much Do the Experts Know?

This concept questions the actual predictive power of experts—financial analysts, fund managers, economists, CEOs, consultants, etc.

Research shows:

A) Experts Often Overestimate Their Knowledge

• Forecasts of GDP, inflation, stock prices, or interest rates regularly deviate far from reality.
• Experts give very narrow forecasting ranges—classic overprecision.

B) Many Domains Are Too Noisy or Complex

In environments driven largely by randomness (stock markets, macroeconomics), even experts cannot beat chance consistently.

Key findings:

• Most mutual fund managers underperform the market consistently.
• Analyst forecasts of earnings have large and systematic errors.
• Long-term economic forecasts are barely better than simple statistical models.

C) Experts Perform Best in Stable, Rule-Based Environments

When:

• Rules don’t change frequently
• Patterns repeat
• Feedback is quick and clear

Examples: Medicine dosage, chess, pilot training.

Experts perform poorly in:

• Stock markets
• Political forecasting
• Venture capital
• Start-up success prediction

Because these fields involve high uncertainty and randomness.

D) The “Illusion of Expertise”

Experts often sound more confident than justified.
People mistake confidence for competence.

E) When Experts DO Add Value

• When analyzing complex accounting frauds
• When identifying qualitative strategic risks
• When evaluating management quality
• When synthesizing non-public, domain-specific insights

But prediction accuracy in highly random domains remains weak.

3. The Success Equation: Untangling Skill and Luck in Business

Popularized by Michael Mauboussin, this concept helps us distinguish how much of success is due to skill and how much is due to luck.

A) Success = Skill + Luck

Skill

• Ability, processes, strategies, discipline
• Repeatable, controllable, learnable
• Example: Operational efficiency, leadership, technical competence.

Luck

• Randomness, market timing, external environment
• Not repeatable or controllable
• Example: Being early in a rising industry, regulatory favor, unexpected market shifts.

B) Continuum of Activities

Every activity lies on a spectrum:
Pure Skill ←————————→ Pure Luck

More Skill-driven	More Luck-driven
Chess	Gambling
Surgery	Stock market returns
Accounting	Entrepreneurship outcomes
Manufacturing	Venture capital success

Most business performance lies in the middle.

C) How to Separate Skill from Luck

1. Look at Long-term Consistency

Skill shows repeatability across time.
Luck produces short-lived spikes.

2. Compare Performance with Peers

If everyone is doing well in a booming economy, individual success may be due to luck.

3. Analyze Process, Not Just Outcomes

Good decisions can lead to bad outcomes (because of luck) — and vice versa.
Evaluate:

• Decision quality
• Data use
• Processes followed

4. Use Base Rates

Use historical data of similar companies to understand realistic performance expectations.

5. Recognize Regression to the Mean

Extreme success often returns to normal levels as luck’s effect fades.

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D) Why People Fail to See Luck

• Success stories (hero narratives) ignore randomness.
• Survivorship bias — focusing on winners, ignoring failures.
• Ego needs: managers want credit for success.
• Media celebrates individuals, not circumstances.

Examples:

• Many CEOs get credit for bull-market success.
• Start-up founders are labeled geniuses even when timing was the main factor.

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E) Business Implications

1. Avoid Over-rewarding or Over-punishing

A CEO may perform well because of luck; a good manager may face bad luck.
Boards should evaluate process, not just outcomes.

2. Better Hiring Decisions

Use structured assessments; avoid hiring based purely on past success stories.

3. Improve Strategic Planning

Recognize randomness and plan for multiple scenarios.

4. Develop Robust, Repeatable Systems

Systems reduce dependency on luck and increase the role of skill.

5. Avoid Overconfidence

Understanding luck prevents excessive risk-taking.

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Quick Summary Table

Concept	Meaning	Business Impact
Factors impeding correction	Psychological & environmental forces blocking error-correction	Persistent mistakes, poor decisions
Experts’ knowledge limits	Experts often overrate their prediction ability; many fields are too random	Overreliance on forecasts; misallocated capital
Success Equation	Success = Skill + Luck; important to separate the two	Better hiring, performance evaluation, risk management