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Biases in Human

Decision Making

Yuval Shahar M.D., Ph.D.

Medical Decision Support Systems

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The Need to Assess Probabilities

People need to make decisions constantly, such asduring diagnosis and therapy

Thus, people need to assess probabilities to classifyobjects or predict various values, such as theprobability of a disease given a set of symptoms

People employ several types ofheuristics to assess

probabilities However, these heuristics often lead to significant

biases in a consistent fashion

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Three Major Human Probability-

Assessment Heuristics/Biases(Tversky and Kahneman, 1974)

Representativeness

The more object X is similar to class Y, the

more likely we think X belongs to Y

Availability

The easier it is to consider instances of class Y,

the more frequent we think it is Anchoring

Initial estimated values affect the finalestimates, even after considerable adjustments

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A Representativeness Example

Consider the following description:

Steve is very shy and withdrawn, invariably

helpful, but with little interest in people, orin the world of reality. A meek and tidysoul, he has a need for order and structure,and a passion for detail.

Is Steve a farmer, a librarian, a physician,an airline pilot, or a salesman?

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The Representativeness Heuristic

We often judge whether object X belongs to

class Y by how representative X is of class Y

For example, People order the potential

occupations by probability and by similarity in

exactly the same way

The problem is that similarity ignores multiplebiases

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Representative Bias (1):

Insensitivity to Prior Probabilities The base rate of outcomes should be a major

factor in estimating their frequency

However, people often ignore it (e.g., there aremore farmers than librarians)

E.g., the lawyers vs. engineers experiment:

Reversing the proportions in the group had no effect onestimating the profession, given a description

Giving worthless evidence caused the subjects to ignore theodds and estimate the probability as 0.5

Thus, prior probabilities of diseases are often ignoredwhen the patient seems to fit a rare-disease description

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Representative Bias (2):

Insensitivity to Sample Size The size of a sample withdrawn from a

population should greatly affect the

likelihood of obtaining certain results in it People, however, ignore sample size and

only use the superficial similarity measures

For example, people ignore the fact thatlarger samples are less likely to deviatefrom the mean than smaller samples

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Representative Bias (3):

Misconception of Chance People expect random sequences to be representatively

random even locally

E.g., they consider a coin-toss run of HTHTTH to be morelikely than HHHTTT or HHHHTH

The Gamblers Fallacy

After a run of reds in a roulette, black will make the overall runmore representative (chance as a self-correcting process??)

Even experienced research psychologists believe in a lawof small numbers (small samples are representative ofthe population they are drawn from)

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Representative Bias (4):

Insensitivity to Predictability People predict future performance mainly by

similarity of description to future results

For example, predicting future performance

as a teacher based on a single practice lesson

Evaluation percentiles (of the quality of the

lesson) were identical topredictedpercentiles of5-year future standings as teachers

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Representative Bias (5):

The Illusion of Validity A good match between input information and output

classification or outcome often leads to unwarranted

confidence in the prediction Example: Use ofclinical interviews for selection

Internal consistency of input pattern increases

confidence

a series of Bs seems more predictive of a final grade-point

average than a set of As and Cs

Redundant, correlated data increases confidence

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Representative Bias (6):

Misconceptions of Regression People tend to ignore the phenomenon of

regression towards the mean

E.g., correlation between parents and childrens heightsor IQ; performance on successive tests

People expect predicted outcomes to be asrepresentative of the input as possible

Failure to understand regression may lead tooverestimate the effects of punishments andunderestimate the effects of reward on futureperformance (since a good performance is likely tobe followed by a worse one and vice versa)

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The Availability Heuristic

The frequency of a class or event is often

assessed by the ease with which instances of

it can be brought to mind

The problem is that this mental availability

might be affected by factors other than the

frequency of the class

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Availability Biases (1):

Ease of Retrievability Classes whose instances are more easily

retrievable will seem larger

For example, judging if a list of names hadmore men or women depends on the relative

frequency of famous names

Salience affects retrievabilityE.g., watching a car accident increases

subjective assessment of traffic accidents

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Availability Biases (2):

Effectiveness of a Search Set We often form mental search sets to

estimate how frequent are members of some

class; the effectiveness of the search mightnot relate directly to the class frequency

Who is more prevalent: Words that start with r

or words where ris the 3rd

letter?Are abstract words such as love more frequent

than concrete words such as door?

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Availability Biases (3):

Ease of Imaginability Instances often need to be constructed on the

fly using some rule; the difficulty of

imagining instances is used as an estimate oftheir frequency

E.g. number of combinations of 8 out of 10

people, versus 2 out of 10 peopleImaginability might cause overestimation of

likelihood of vivid scenarios, and underestimation

of the likelihood of difficult-to-imagine ones

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Availability Biases (4):

Illusory Correlation People tended to overestimate co-occurrence of

diagnoses such as paranoia or suspiciousness

withfeatures in persons drawn by hypotheticalmental patients, such as peculiar eyes

Subjects might overestimate the correlation due

to easier association of suspicion with the eyesthan other body parts

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The Anchoring and Adjustment

Heuristic People often estimate by adjusting an initial

value until a final value is reached

Initial values might be due to the problempresentation or due to partial computations

Adjustments are typically insufficient and

are biased towards initial values, the anchor

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Anchoring and Adjustment Biases (1):

Insufficient Adjustment Anchoring occurs even when initial estimates (e.g.,

percentage of African nations in the UN) were explicitlymade at random by spinning a wheel!

Anchoring may occur due to incomplete calculation, suchas estimating by two high-school student groups

the expression 8x7x6x5x4x3x2x1 (median answer: 512)

with the expression 1x2x3x4x5x6x7x8 (median answer: 2250)

Anchoring occurs even with outrageously extreme anchors(Quattrone et al., 1984)

Anchoring occurs even when experts (real-estate agents)estimate real-estate prices (Northcraft and Neale, 1987)

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Anchoring and Adjustment Biases (2):Evaluation of Conjunctive and Disjunctive Events

People tend to overestimate the probability of

conjunctive events (e.g., success of a plan that

requires success of multiple steps)

People underestimate the probability of

disjunctive events (e.g. the Birthday Paradox)

In both cases there is insufficient adjustment

from the probability of an individual event

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Anchoring and Adjustment Biases (3):

Assessing Subjective Probability Distributions

Estimating the 1st and 99th percentiles often leads to

too-narrow confidence intervals

Estimates often start from median (50th

percentile) values,and adjustment is insufficient

The degree of calibration depends on the elicitation

procedure

state values given percentile: leads to extreme estimates

state percentile given a value: leads to conservativeness

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A Special Type of Bias: Framing

Risky prospects can be framed in differentways- as gains or as losses

Changing the description of a prospectshould notchange decisions, but it does

Prospect Theory (Kahneman and Tversky,1979) predicts such anomalies due to thefact that the negative effect of a loss islarger than the positive effect of a gain

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Framing Experiment (I)

Imagine the US is preparing for theoutbreak of an Asian disease, expected to

kill 600 people (N = 152 subjects):If program A is adopted, 200 people will be

saved (72% preference)

If program B is adopted, there is one third

probability that 600 people will be saved andtwo thirds probability that no people will besaved (28% preference)

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Framing Experiment (II)

Imagine the US is preparing for theoutbreak of an Asian disease, expected to

kill 600 people (N = 155 subjects):If program C is adopted, 400 people will die

(22% preference)

If program D is adopted, there is one third

probability that nobody will be die and twothirds probability that 600 people will die (78%

preference)

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Summary: Heuristics and Biases

There are several common heuristics people employ

to estimate probabilities

Representativeness of a class by an object Availability of instances as a frequency measure

Adjustment from an initial anchoring value

All heuristics are quite effective, usually, but lead to

predictable, systematicerrors and biases

Understanding biases might decrease their effect