Decision-Making/ heuristics

Mental activities that take place when choosing among alternatives

Usually done under uncertainty

Sometimes have few choices, sometimes have many

AI :Mental processes used to choose among alternatives.

Decision-Making

• Decisions are often made when outcomes are unknown or unclear.

• We may not have all the information or know the consequences.

• People often do not optimize perfectly under uncertainty → instead, use shortcuts or heuristics.

Role of uncertainty

Methods that are quick and east, intuitions

Mental shortcuts

• quick and easy methods or intuitive rules of thumb for making decisions

• Help simplify complex judgments and predictions

Heuristics reduce mental effort but can cause mistakes → useful for efficiency, risky for accuracy.

Heuristics

Similarity judgments (representativeness) can override base-rate data, leading to biased or inaccurate decisions.

Similarity to make judgments

Judging the probability of an event based on how similar it is to a prototype or typical case

How it works:

• If something looks or seems like a category member, people think it’s likely to be one.

• Example (slides): Steve → "shy, orderly" → judged as librarian, ignoring base rates.

✅ Problems (slides):

• Ignores important statistical info:

  • Base-rate frequency

  • Chance/randomness

  • Sample size

  • Regression to the mean

  • Predictability

Representativeness = similarity-based shortcut, but it often causes errors by ignoring base rates and probability laws.

Representativeness Heuristic

Representativeness focuses on similarity, causing people to ignore critical statistical factors that influence actual probability.

• Base-rate frequency → how common something is in reality

• Chance/randomness → randomness is often misunderstood (e.g. Gambler’s Fallacy)

• Sample size → smaller samples are less reliable, but people ignore this

• Regression toward the mean → extreme cases tend to become more average

• Predictability → limits to how well we can predict future events

Factors that are ignored because they don't affect similarity even though they affect probability

The actual proportion or likelihood of different categories in the population

→ People tend to ignore base rates when they have stereotype-based (similarity) information, leading to biased judgments.

Base rate frequency

• Participants read descriptions and knew the base-rates (e.g. 70 engineers / 30 lawyers OR vice versa).

• When given personality descriptions that fit a stereotype (e.g. "seems like an engineer"), they ignored the base-rate and went with similarity.

• When descriptions were absent or uninformative, people did use the base-rate.

Kahneman & Tversky (1973) results (slides):

• The mistaken belief that small samples or short sequences should reflect the general properties of randomness.

• People expect random sequences to "look random" even in the short term.

Misconception of Chance

• The belief that after a run of one outcome, the opposite outcome is "due."

• Example: After flipping several heads, people think tails must come next (even though each flip is independent).

Gamblers Fallacy

Larger samples more likely to approximate the characteristics of the population they came from

Sample Size

• Allowed juries to be reduced from 12 to 6 people.

• Ignored that smaller juries are less likely to represent minority groups.

Supreme Court Decision (williams v. Florida, 1970)

In a community with 90% majority and 10% minority →

• 12-person jury → 72% chance of including a minority member

• 6-person jury → only 47% chance

What the supreme court ignored

Judging the probability or frequency of an event based on how easily examples come to mind

How it works

• If something is easier to imagine, remember, or mentally generate, people assume it is more likely or common.

• Example (slides): People misjudge how thick folded paper becomes → hard to imagine, so underestimated.

Factors that affect availability

• Saliency → stands out

• Familiarity → known experiences

• Recency → happened recently

Ross and Sicoly (1979)

• Couples overestimate their own household contributions → easier to recall own actions → judged as doing more.

Availability = ease of recall → affects judgment of likelihood, but is often influenced by salience, familiarity, and recency rather than true probability.

Availability Heuristic

• How much something stands out or grabs attention.

• More vivid or dramatic events are easier to remember.

Factors that affect Availability Saliency

• How well-known or common something is to you personally.

• Familiar events come to mind more easily.

Factors that affect Familiarity

• How recently something occurred.

• Recent events are more accessible in memory.

Factors that affect Recency

→ Saliency, familiarity, and recency make events easier to recall, but they do NOT make them more likely to actually occur.

What 3 factors affect Availability but do NOT affect actual probability?

✅ Study:

• Asked couples to rate who did more household chores.

• For 16 out of 20 chores, both partners said they did more.

✅ Explanation (slides):

• People could easily recall their own contributions → more salient, familiar, and recent.

• Partners had less access to the other person’s efforts.

✅ Conclusion:

• People judge based on what is easier to recall (availability), leading to biased self-perceptions.

→ Ross & Sicoly → easier recall of own actions → overestimation of personal contributions → classic Availability bias.

Ross & Sicoly (1979)

• People start with an initial value (anchor) and make adjustments from it, but the adjustments are often insufficient.

• The anchor biases the final judgment.

→ Anchoring → initial number affects judgment → even irrelevant anchors (like math order or requested damages) can bias decisions.

Anchoring & Adjustment heuristic

Math problem → Group 1 vs. Group 2:

• 1 × 2 × 3 × ... × 8 vs. 8 × 7 × 6 × ... × 1 → higher estimated total when starting with larger number.

Examples of Anchoring & Adjustment heuristic

• Personal injury case → different requested damages ($10K, $75K, $150K).

• Higher requests led to higher awarded amounts, even though damages should be based on facts.

Mock Jury Study

✅ Definition:

• Judging quality or value based on quantity or size → "more is better" thinking.

• People may confuse amount with quality.

✅ Examples (slides):

📌 Josephs, Giesler, & Silvera (1994) → Experiment 1:

• Proofreading task →

  • Single page → rated as less productive.

  • Same pages bound in a journal (looked like more) → rated as more productive.

📌 Experiment 3:

• Essay writing task →

  • 12-point font → wrote longer essays.

  • 24-point font → wrote shorter essays.

  • Participants used essay length as cue for quality → longer seemed better.

✅ General example (slides):

• “The food may not be good, but at least there is plenty of it.”

→ Quantity Heuristic → people sometimes use "amount" as a shortcut for "quality," even when it’s not a valid indicator.

Possible Quantity heuristic

✅ Experiment 1 (slides):

• Participants proofread pages.

  • Single page → rated as less productive.

  • Same pages bound in a journal → rated as more productive.

  • → Larger-looking amount → judged as higher productivity.

✅ Experiment 3 (slides):

• Participants wrote essays aiming for an "A" level.

  • 12-point font → wrote longer essays (~377 words).

  • 24-point font → wrote shorter essays (~321 words).

  • → Longer essays → judged as higher quality.

✅ Overall conclusion:

• People confused quantity with quality, using the amount of visible or produced material as a shortcut for judgment.

Joseph, Giesler, & Silvera (1994)