MARK 5342 Advanced Topics

Day 2Evolution of Decision-Making

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Heuristics and Biases

Tversky and Kahneman, 1974 Heuristics – general rules of thumb,

or habits Generally result in decent estimates Can be fooled with systematic biases

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Representativeness

Judging probabilities by the degree to which A is representative of B

Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in antinuclear demonstrations. Please check the most likely alternative: Linda is a bank teller Linda is a bank teller and is active in the feminist

movement

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Representativeness

Most people (9 out of 10) answer B However, B is more specific than A

and is a smaller subset of the population, therefore is not more likely

Bank Tellers Feminists

Bank Tellers who are also Feminists

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Law of Small Numbers

Belief that random samples of a population will resemble each other

The mean IQ of a population of eighth graders in a city is known to be 100. You have selected a random sample of 50 children for a study of educational achievements. The first child tested has an IQ of 150. What do you expect the mean IQ to be for the whole sample?

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Law of Small Numbers

First child has IQ of 150 Remaining 40 have mean IQ of 100 Total is 5050, average is 101, not

100 We expect remaining sample to

somehow “balance out” Small samples don’t randomly cancel

out outliers with other outlier values Fallacy of the Hot Hand

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Availability

Heuristic in which decision-makers “assess the frequency of a class or the probability of an event by the ease with which instances or occurrences can be brought to mind

What is a more likely cause of death in the US – being killed by falling airplane parts or by a shark?

Death by falling airplane parts is 30 times more likely, but shark death is more easily imagined

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When Availability Fails

When imagination is limited When imagining an event is so

upsetting that it leads to denial

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Context and Framing

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Framing

Tversky and Kahneman 1981 define framing as “the decision maker’s conception of the acts, outcomes, and contingencies associated with a particular choice”

Frames are partly controlled by formulation of the problem, and partly controlled by norms, habits, and characteristics of the decision maker

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Impact of gains versus losses Decision 1 (risk aversion with gains at stake)

Alternative A – a sure gain of $240 Preferred Alternative B – a 25% chance to gain $1000, and

a 75% chance to gain nothing

Decision 2 (risk seeking with losses at stake) A sure loss of $750 A 75% chance to lose $1000, and a 25% chance

to lose nothing Preferred

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Asian Disease Problem

Classic exercise in loss aversion Two choices presented to

respondents They had to choose option A or

option B

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First Scenario

The United States is preparing for the outbreak of an unusual Asian disease, which is expected to kill six hundred people. If program A is adopted, two hundred people will be saved; if program B is adopted, there is a one-third probability that six hundred people will be saved and a two-thirds probability that no people will be saved. Which program do you favor?

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When asked of physicians, 72% chose option A, the safe-and-sure strategy

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Second Scenario

The United States is preparing for the outbreak of an unusual Asian disease, which is expected to kill six hundred people. If program C is adopted, four hundred people will die. If program D is adopted, there is a one-third probability that nobody will die and a two-thirds probability that six hundred will die. Which of the two programs do you favor?

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When described in terms of deaths rather than lives saved, physicians reversed their choices, with 78% selecting option D, the risky strategy

Both scenarios are identical in lives lost or saved

Loss aversion is a way of skipping the math and using emotion to make the decision

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Three Major Decision-Making Theories

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Expected Utility Theory

Bernoulli 1713 Linear model of utilities Six axioms of expected utility theory Plous

p. 81 Ordering of alternatives – comparing choices Dominance – some better than others Cancellation – common factors cancel out Transitivity – if a>b and b>c, then a>c Continuity – gamble preferred over

intermediate Invariance – not affected by presentation style

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Satisficing Theory

Herbert Simon, 1955 First major advance in decision

theory since Bernoulli’s in 1700’s Better match to real world decision-

making Satisfice rather than optimize Satisficing finds alternative that

meets most of the major criteria, then stops

Example – apartment search in Arlington

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Example of an Apartment Search

Feature 1

Feature 2

Feature 3

Feature 4…

Feature n

Price Nearness

Size Security Neighbors

Complex 1

$650 2 miles 800 sq ft Limited Flight att.

Complex 2

$600 3 miles 810 sq ft None Cars on blocks

Complex 3

$350 4 miles 600 sq ft None Lots of kids

Complex 4

$850 1 mile 900 sq ft Gated Nearby sorority

Complex 5…

$1200

Complex n

$900

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Unexplained Consumer Behaviors

Lichtenstein (1971) Utility theory doesn’t quite all

aspects of consumer behavior Rating of attractiveness (a weight

applied to a probably outcome) A gamble is seen by the authors as a

multi-dimensional stimulus Some dimensions of affect are

playing a role in what should be a cognitive decision

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Prospect Theory

Kahneman and Tversky, 1979 Replaces “utility” concept with

“value” Utility is defined in terms of net worth Value is defined in terms of gains and

losses Losses loom larger than gains Endowment effect

What one owns is more valuable than what someone else owns

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Weighting Function

The value of each outcome is multiplied by a decision weight

Decision weights are very subject to biases

This leads to a decidedly n0n-symmetrical value function, where the value function for losses is decidedly steeper than that for gains

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Weighting Biases

Outcomes with small probabilities are overweighted relative to outcomes with higher certainty

This tendency leads to the concepts of insurance and gambling as industries

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Losses and Gains versus Value

High Value

Low Value

GainsLosses

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Impact of Losses on Risk

Example of $1 found by a millionaire or a homeless person – who values the incremental $1 more?

Who would be more concerned with the loss of that incremental $1?

For gains, this produces risk aversion For losses, this produces gambling

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Importance of losses

Damasio and Loewenstein investing game In each round, subject decides to invest

$1 or invest nothing No invest, subject keeps dollar Invest, researcher flips coin for $1 loss or

$2.50 gain Rational investors should always choose

to invest

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Regret

Decisions made relative to a reference point

Comparison of imaginary outcomes referred to as “counterfactutal reasoning”

Regret is based on two assumptions: People experience sensations of regret and

rejoicing When making decision under uncertainty,

people try to anticipate and take into account these sensations

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Risk

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Estimation of Risk

People tend to evaluate personal risk outcomes based on the valence Positive outcomes – more probable Negative outcomes – less probable Rose colored glasses as a lens to our

lives

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Compound Events

Conjunctive compound events are A+B, where A and B are simple events

Compound events are preferred when conjunctive

Simple events are preferred when compound events are disjunctive (A or B)

People anchor on the probabilities of the simple events that make up the compound event and fail to adjust probabilities

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Conservatism

Once we estimate probabilities, we are slow to modify those estimates

When modified, the estimates are changed more slowly than the data would dictate

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Perceptions of Risk

Three dimensions for public perception of risk (Slovic, 1987) Dread risk – lack of control, catastrophic

potential Unknown risk – risks that are

unobservable Magnitude of risk – number of people

exposed to it

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Reducing Biases in Risk Estimation

Maintain accurate records – minimize primacy and recency effects

Beware of wishful thinking – wishing for positive outcomes

Disaggregate compound events into simple events

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Correlation and Causation

What is correlation?

What is causation?

Are they the same?

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Correlation and Causation

What is correlation? Degree of covariation between two variables

What is causation? The outcome of one event resulting in the outcome of another event

Are they the same? No – a common mistake made by many market researchers

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Illusory and Invisible Correlation

Illusory - People can “see” a correlation between objects based on the objects semantic similarities, even when no correlation exists

Invisible – People fail to see a correlation even when it exists – our expectations of visible patterns causes us to miss some strong but unexpected patterns

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Causalation

Einhorn and Hogarth, 1986 Correlation need not imply causal

connection Causation need not imply a strong

correlation Some people believe that causation

implies correlation – they called it “causalation”

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Attribution Theory

How people make causal attributions (Kelley, 1967)

Three main variables to explain behavior The person The entity – feature of the situation The time – feature of the occasion

Based on three sources of information Consensus, distinctiveness, and

consistency

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Consensus

Sometimes people ignore base rate information

Sometimes people focus on salient, available, or vivid information

Fundamental attribution error (Ross, 1958) is that people’s behaviors tend to swamp all other situational variables

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Self-Serving Bias

When faced with a successful outcome, people are more likely to accept responsibility and take more credit for the outcome

When faced with an unsuccessful outcome, people are more likely to attribute blame to others

Ego-centric biases – married couple example Positivity effect – attribute positive

behaviors to dispositional factors and negative behaviors to situational factors