Framing, biases, heuristics, the normative ... Megan's law

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Framing, biases, heuristics, the
normative/descriptive gap, and ecologically
rational choice

• W. Troy Tucker
• Applied Biomathematics, Inc.
• Troy_at_ramas.com

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A cognitive illusion
From Pinker 2002
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A cognitive illusion
From Pinker 2002
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A cognitive illusion
From Pinker 2002
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A cognitive illusion
From Pinker 2002
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A cognitive illusion
From Pinker 2002
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A cognitive illusion
From Pinker 2002
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A cognitive illusion
From Pinker 2002
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A cognitive illusion
From Pinker 2002
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Hunting-gathering lifestyle

• Deep time
• 1st tools 2.5 mya
• Paleolithic 1.5 mya
• Anatomically modern 500 kya
• Fully modern 40 kya

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Axioms of rational choice

• Transitivity, ordering AgtB, BgtC, AgtC
• Monotonicity more is better.
• Invariance, stochastic dominance expected value
  determines preference.
• Reduction, substitutability probability
  calculus doesnt affect decisions.
• Continuity p can be anything between 0 and 1.
• Finiteness no infinite values.

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Biases and heuristics

• Anchoring and adjustment heuristic
• Availability heuristic
• Representativeness heuristic
• Simulation heuristic
• Base rate fallacy
• Conjunction fallacy
• Framing
• Status quo bias

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Availability heuristic

• Ease of recall (bias), vividness (bias)
• Megans law
• Violent crime
• declined 10 straight years, as Fox news style and
  capability spread, 3-strikes laws, mandatory
  sentences, etc.

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Representativeness heuristic

• Regression to the mean (bias)
• Good sticks and bad carrots?
• Peak-end rule (bias)
• Recall average not sum
• Perceived happiness
• Conjunction fallacy (bias)
• Base rate fallacy (bias)

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Conjunction fallacy

• Linda problem

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 anti-nuclear demonstrations. Which
is more likely? A. Linda is a bank teller. B.
Linda is a bank teller and is active in the
feminist movement.
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Base rate fallacy

• Another Linda problem

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 anti-nuclear demonstrations. Which
is more likely? A. Linda is a bank teller. B.
Linda is active in the feminist movement.
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Base rate fallacy

• Green cabs

A cab was involved in a hit and run accident.
85 of cabs are green. 15 of cabs are blue. A
witness says the cab was blue. In a reliability
test, the witness correctly identifies the cab
color 80 of the time. The witness is wrong 20
of the time. What is the probability the cab
involved in the accident was blue, as the witness
stated?
In 100 accidents 85 are green, 15 are
blue. Witness sees 17 of 85 green cabs as blue
(20 error). Witness sees 12 of 15 blue cabs as
blue (80 correct). 17/29 blue cabs seen in 100
accidents are really green. 60 chance witness
was wrong. Just because there are so many green
cabs.
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Base rate fallacy

• HIV

About 0.01 percent of men with no risk behavior
are infected with HIV (base rate). If such a man
has the virus, there is a 99.9 percent chance
that the test result will be positive
(sensitivity). If a man is not infected, there
is a 99.99 percent chance that the test result
will be negative (false positive rate). What is
the chance that a man who tests positive has the
disease?
Imagine 10,000 men who are not in any known risk
category (blood donors). One is infected (base
rate) and will test positive with practical
certainty (sensitivity). Of the 9,999 donors not
infected, another one will also test positive
(false positive rate). Thus, two will test
positive, one of whom actually has the disease.
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Anchoring/Adjustment heuristic(Tversky and
Kahneman)

• Anchoring effect (bias)
• Excimer laser engineer salary?
• 99,790 vs. 68,860
• Effect size of anchor is 45
• 13 trials over 10 yrs.
• Last 4 digits of SSN and number of docs in NY
• r0.4

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Framing (1)

• Asian disease problem

Imagine that the U.S. is preparing for the
outbreak of an unusual Asian disease, which is
expected to kill 600 people. Two alternative
programs to combat the disease have been
proposed. Assume the exact scientific estimate of
the consequences of the programs are as
follows Program A "200 people will be
saved" Program B "there is a one-third
probability that 600 people will be saved, and a
two-thirds probability that no people will be
saved" From Tversky and Kahneman 1981
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Framing (2)

• Asian disease problem

Imagine that the U.S. is preparing for the
outbreak of an unusual Asian disease, which is
expected to kill 600 people. Two alternative
programs to combat the disease have been
proposed. Assume the exact scientific estimate of
the consequences of the programs are as
follows Program C 400 people will die" Program
D "there is a one-third probability that nobody
will die, and a two-thirds probability that 600
people will die" From Tversky and Kahneman 1981
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Framing (3)

• Asian disease problem

Program A "200 people will be saved" Program B
"there is a one-third probability that 600 people
will be saved, and a two-thirds probability that
no people will be saved" Program C 400 people
will die" Program D "there is a one-third
probability that nobody will die, and a
two-thirds probability that 600 people will
die" From Tversky and Kahneman 1981
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Framing (4)

• Framing effects are widely observed
• Wason selection task details of the story
• Bayesian reasoning existence of or decimals
• Positive and negative affect prospect theory
• Endowment discount vs. surcharge prospect
  theory
• Some theories I like
• Cosmides - social contract theory
• Gigerenzer - natural frequencies
• Wang - group size signals kith and kin

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Framing (4)

• Framing effects are widely observed
• Wason selection task
• Bayesian reasoning
• Positive and negative affect
• Endowment discount vs. surcharge
• Some theories I like
• Cosmides - social contract theory
• Gigerenzer - natural frequencies
• Wang - group size signals kith and kin

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Bayesian reasoning (1)

If a test to detect a disease whose prevalence is
1/1000 has a false positive rate of 5, what is
the chance that a person found to have a positive
result actually has the disease, assuming that
you know nothing about the persons symptoms or
signs? ___ Casscells et al. 1978 replicated in
Cosmides and Tooby 1996

• 12-18 correct Bayesian reasoning

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Bayesian reasoning (2)

If a test to detect a disease whose prevalence is
1/1000 has a false positive rate of 50/1000, what
is the chance that a person found to have a
positive result actually has the disease,
assuming that you know nothing about the persons
symptoms or signs? ___ out of ___. Casscells et
al. 1978 replicated in Cosmides and Tooby 1996
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76-92 correct Bayesian reasoning
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Framing (4)

• Framing effects are widely observed
• Wason selection task
• Bayesian reasoning
• Positive and negative affect
• Endowment discount vs. surcharge
• Some theories I like
• Cosmides - social contract theory
• Gigerenzer - natural frequencies
• Wang - group size signals kith and kin

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• Asian disease problem

Program A "200 people will be saved" Program B
"there is a one-third probability that 600 people
will be saved, and a two-thirds probability that
no people will be saved" Program C 400 people
will die" Program D "there is a one-third
probability that nobody will die, and a
two-thirds probability that 600 people will
die" From Tversky and Kahneman 1981
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Kith and Kin (Wang 2007)
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Framing (4)

• Framing effects are widely observed
• Wason selection task details of the story
• Bayesian reasoning existence of or decimals
• Positive and negative affect prospect theory
• Endowment discount vs. surcharge prospect
  theory
• Some theories I like
• Cosmides - social contract theory
• Gigerenzer - natural frequencies
• Wang - group size signals kith and kin

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Prospect theory
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Prospect theory
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Prospect theory
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Prospect theory
losses
gains
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Emotion and Reason

• Risk premium
• Ambiguity premium
• Brain processes them differently
• When ambiguous, assume worst-case
• Emotion and reason

A. Rustichini, Science 310, 1624 (2005)
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Ellsberg paradox
Glimcher and Rustichini, Science 306, 447 (2004)
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Ellsberg paradox
Glimcher and Rustichini, Science 306, 447 (2004)
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Anticipatory skin conductance
Bechara et al., Science 275, 1293 (1997)
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Ultimatum game

• Selfishness axiom
• utility maximization
• Homo economicus
• Definition of rationality
• What are the rational optimum strategies?
• Proposer always offer the smallest possible
  amount
• Responder always accept

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Ultimatum game

• 15 years of research in 25 western societies
• More than 100 studies published
• Homo reciprocans
• Cares about fairness and reciprocity
• is willing to change the distribution of material
  outcomes among others at a personal cost to
  themselves
• Rewards those who act in a prosocial manner
• Punishes those who act selfishly, even when
  punishment costs
• Is this pattern a human universal?
• Designed by natural selection to engage in
  reciprocal altruism

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Ultimatum game

• Evolutionary anthropology
• 11 anthropologists and one economist
• Twelve countries on four continents and New
  Guinea
• Fifteen small-scale societies
• Three foraging societies
• Six slash-and-burn horticulturalists
• Four nomadic herding groups
• Two sedentary, small-scale agricultural societies
• All games played for real stakes
• Equal to one-days local wages

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Ultimatum game
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Ultimatum game

• Results
• The selfishness-based model always wrong
• Cross-cultural variability
• Economic organization explains much of this
• Market integration
• Payoff to cooperation
• Individual-level variables do not explain game
  behavior
• Experimental play reflects common interactional
  patterns

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Ultimatum game
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Neuroscience of risk perception

• The brain has many domain-specific calculators
• (Marr 1982 Barkow et al. 1992 Pinker 1997,
  2002)
• Information format triggers specific calculators
• (e.g. Cosmides Tooby 1996 Gigerenzer 1991)
• Different calculators give contrasting solutions
• Or calculate different components of total risk
• (e.g. Glimcher Rustichini 2004 and references
  therein)

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List of mental calculators(after Pinker 2002)

• Language (grammar and memorized dictionary)
• Practical physics (pre-Newtonian)
• Intuitive biology (animate differs from
  inanimate)
• Intuitive engineering (tools designed for a
  purpose)
• Intuitive psychology (theory of mind, autism,
  deception)
• Spatial sense (dead reckoner and mental maps)
• Number sense (1, 2, 3, many)
• Probability sense (frequentist Bayes)
• Intuitive economics (reciprocity, trust, equity,
  fairness)
• Mental database and logic (assertions linked with
  logical and causal operators)

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People are bad risk calculators

• or often said to be bad when
• Presented with percentages, large numbers, or
  single-event probabilities
• Experts tell them the risk
• Presented with incertitude (versus variability)
• Risk is seen to be imposed
• Risk is out of personal control
• Rare events are observed - representativeness
• When children are at risk
• etc.

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People are bad risk calculators

• or often said to be bad when
• Presented with percentages, large numbers, or
  single-event probabilities
• Experts tell them the risk
• Presented with incertitude (versus variability)
• Risk is seen to be imposed
• Risk is out of personal control
• Rare events are observed - representativeness
• When children are at risk
• etc.

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When risk is imposed

• People perceive more risk
• Even when the risk is smaller than voluntary
  risks
• Multiple mental risk calculators perceive risk
• Some perceive risk of disease, death, economic
  cost
• Some perceive risk of social contract violation
  (e.g. Cosmides 1989, Guth 1995, Henrich et
  al. 2005)
• Bilateral anterior insula disgust (e.g. Sanfey
  et al. 2003)

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The problem of altruism

• Kin-directed altruism
• (Hamilton 1964, Haldane)
• Explains social insects, parenting, nepotism
• How can non-relatives cooperate?

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Cooperation with non-relatives

• Reciprocal altruism
• (Trivers 1971, Axelrod Hamilton 1981)
• 2 problems how to evolve, how to maintain
• Evolve is still not well understood
• Prisoners dilemma Tragedy of the commons
• Maintaining cooperation requires 5 emotions (at
  least)
• Friendship, moralistic aggression, forgiveness,
  guilt/shame, sympathy/gratitude

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Risk of being cheated

• Wason selection task and logic
• Evidence of cheater detection module patterned
  violation of logical deduction
• (Cosmides Tooby, Gigerenzer Hug)
• Cheaters looked at longer, remembered better
• (Chiappe, Brown, Dow, Koontz, Rodriguez,
  McCulloch 2004 Mealey, Daood, Krage, 1996
  Oda, 1997)
• Neuropsychology - Bilateral limbic system damage
  to temporal pole and amygdala impairs detection
• (Stone, Cosmides, Tooby, Kroll, Knight 2002)

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Strong Reciprocity

• Ernst Fehr and Simon Gächter
• team earns money when all cooperate
• Punishers (moralistic aggression)
• Spend money to ensure freeloaders dont prosper
• Note this is irrational.
• People do pursue own self interest
• But, definition of self interest includes
  fairness, equity, justice, prudence, generosity,
  etc.

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Strong reciprocity (2)

• Human emotional constitution embraces prosocial
  and altruistic notions of in-group and out-group
  identification, and reciprocity
• A direct result of evolutionary history
• (Gintis 2005, Bowles and Gintis 2003)
• Moral principles are evolved facts in the world
• Evolved and transformed according to natural laws

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Ecologically rational choice

• The normative/descriptive gap
• Panglossians vs. meliorists
• Stanovich and West 2000
• Evolutionary social sciences are panglossian
• Expect people are generally well designed.
• Deviation from a normative model signals
  deficiency of the model or an ecological
  mismatch.
• Two categories of error, pardonable errors by
  subjects and unpardonable ones by psychologists
  Kahneman 1981.
• The real trick is to disentangle the two.
• Novel environmental factors

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Anthropology of risk

• 6 Risks
• Accidents
• Subsistence failure
• Disease
• Inter-group competition (war)
• Cooperation failure (free riders)
• Paternity

Pueblo Bonito in Chaco Canyon, NM
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Economic Decisions and Foraging Theory

• Harvesting resources from the environment is a
  problems all organisms must solve
• Natural selection should lead to efficient
  harvest and use of scarce resources
• Two early models
• Patch Choice
• Prey Choice

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Prey choice

• Fine-grained environments
• Composed of randomly distributed prey
• Need to know which prey types to pursue
• Decision pursue or not upon encounter
• Currency long term net rate of energy capture
• Constraints
• Search and handle mutually exclusive
• encounters sequential, random, and in proportion
  to abundance
• No short-term effect of foraging on prey
  abundance
• Forager has complete information

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Construction of foraging models

• Decisions - what we want to explain
• How long to stay in a patch, what subset of foods
  to pursue
• Currencies - what we assume to correlate with
  fitness
• This is what is maximized or minimized
• Time, net calories, calories per hour, protein
  per hour
• Constraints - what limits the forager
• Cognitive capability (rules of thumb)
• Physiological limitations - pigs can't fly

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The Prey-choice model

• ei average net energy from prey type i upon
  encounter
• hi averge handling time per encounter for prey
  type i
• li abundance of prey type i
• pi probability of pursuing prey type i upon
  encounter

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Prey choice model test

• The Ache of Paraguay
• First contacted around 1978
• About 200 individuals
• 15-50 people in a foraging group

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Data

• data gathered about Ache foraging

From Hawkes et al. 1982
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Result
From Hawkes et al. 1982
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Time discounting

• Equity premium puzzle
• 5050 50k or 100k 51,209?!?
• Glimcher and Kable - hyperbolic function

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