Behavioral Economics, Frameworks, Design Patterns

Behavioral Economics
Biases, Design Patterns, Solutions

1
Thursday, August 26, 2010

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Biases, Design Patterns, Solutions 2

Outline

What it is not! Talk

Exhaustive survey of biases and heuristics How Behavioral Economics came to be?

Quick survey of biases and cognitive
Pedantic one-way ramblings
heuristics

Theoretically/Academically rigorous Stimulate ideas, thinking and discussions

Develop Design Patterns & Solutions
Framework based on BE

Learn & Explore applications of these ideas

The Good, the Bad and the Ugly


This is not the end!

Design Patterns
Survey of Behavioral
Solutions
Economics
Frameworks

Past, Present, Future
Psych & Econ
Thinking Models Choice Architecture
Rationality Frameworks: Applications for non-gaming
Heuristics Thaler: NUDGE online environments
Biases Cialdini: RSS-CAL New Product Features
Prospect Theory Loss/Gain Framework Drive User Engagement
Intertemporal Choice Drive User Satisfaction
Hyberbolic discounting

Survey of Game User Behavior
Mechanics Patterns


Chocolate cake or fruit salad

Cognitive Load


Organ Donation - Saving Lives


Evolution of BE

Classical Economics
Adam Smith
1776: The Wealth of Nations

Economics
closely linked
to psychology
Ricardo
John Mill


Evolution of BE

Prospect Theory
1870s Bounded Rationality
Modern Decision Theory Framing Effects
Neo-classical Economics 1960s: Herb Simon
1940s: Von Neumann 1980: Kahneman & Tversky
Keynesian Economics

1975: Hyperbolic
Supply & Demand
discounting
Rational economic
agents

1955: Kahneman &
Tversky: cognitive
psychology experiments


Evolution of BE

1980s: Richard Thaler
Integration of Econ & Psych 2002: Kahneman gets
begins: Humans v/s Econs Nobel price in Economics

2000 2008

1997: Special issue of
Journal of Economics
devoted to BE


Quiz Time!

10

Linda

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.
Representativeness
(Similarity)
Rank the following in order of likelihood? Heuristic/Shortcut

C: Linda is active in the feminist movement

F: Linda is a bank teller
H>F Conjunction
G: Linda is an insurance salesperson
Fallacy
H: Linda is a bank teller and is active in the feminist
movement


Thinking

Emotional Effortless Slower Explicit
Automatic Implicit Conscious Logical
Fast Shortcuts Effortful Deliberate

System I System II


Dating & happiness

0.11

Anchoring Heuristic

0.62


Coin toss

Which of the following series of coin tosses are
more likely? Representativeness
(Similarity)
A: H-T-H-T-T-H Heuristic

B: H-H-H-T-T-T

C: H-H-H-H-H-T A>B>C Gambler’s
Fallacy


Flight tomorrow

Monday evening 10:00pm: Your boss calls to tell
you that you must be at the South Beach office
by 10:00 am tomorrow
Representativeness
Only 5 flights in the morning can get you their by
(Similarity)
9:30 am. All are booked solid. Heuristic

The (unbiased and independent) probability of
getting on each of the flights are 30%, 25%,
15%, 20% and 25% Disjunctive
Bias
Do you expect to get on one of the flights?

Causes of Death

Rank order of deaths in the US (1990 - 2000)

Smoking 435K
Availability
Heuristic
Poor diet and physical activity 400K

Auto accidents 43K

Gun violence Ease of Recall 29K
Fallacy
(Vividness, Recency)
Drugs 17K


Herding


Rationality

Everyday use Economists, Decision Theorists

Reasonable - views are realistic Logic
Actions guided by self-interest & values Internally consistent beliefs
No requirement on reasonability

Entitled to belief/want anything
“80% chance that it will rain today” implies
“20% chance it will not”

Methodological function
Simpliﬁes theory construction
Predict the behavior of markets


Rationality

Dominance: If prospect A > prospect B (in at
least one respect), then A should be preferred
to B

Invariance: Preference order is independent
on the manner in which the prospects are
described


Framing

Participants were asked to 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:


Framing

The first group of participants were presented
with a choice between two programs:

72% Program A: 200 people will be saved

28% 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


Framing

The second group of participants were
presented with the choice between:

22% Program C: 400 people will die

78% Program D: there is a one-third probability
that nobody will die, and a two-third probability
that 600 people will die


Choice

Choose between:

E: 25% chance to win $240 and 75%
chance to lose $760

F: 25% chance to win $250 and 75% chance
to lose $750 100%


Concurrent Choices

Imagine that you face the following pair of concurrent
decisions

Decision I: Choose between:

A: A sure gain of $240 84%
B: 25% chance to gain $1000 and 75% chance to gain
nothing 16%
Decision II: Choose between:

C: A sure loss of $750 13%
D: 75% chance to lose $1000 and 25% chance to lose
nothing 87%

Prospect Theory

Framing: Value
defined on gains and
losses (relative to a
reference point)
rather than on total
wealth

Diminishing
sensitivities in the
domain of gains and
losses

Loss Aversion:
considerably steeper
for losses than for
gains


Summary of Biases


Convergence of BE & Solutions

How social, cognitive and
emotional factors inﬂuence decision
making, trade-offs, options

Establish relationships between
what is observed and the underlying
emotions & biases

Reconsider Anticipate
Develop
the solution problems
informed
space with with new
hypotheses
a new lens solutions


Gas Mileage & Car Shopping


MPG Illusion

Focus on Car Overvalue large jumps
efficiency between efficient cars

MPG MPG Illusion

Undervalue small mpg
But obscures the
improvements on
value of improvements
inefficient cars

1200

900
Gallons Per 10,000 Miles

600

300

0
5 10 15 20 25 30 35 40 45 50 55
Miles Per Gallon


Next Steps

Increase Salience: Cost Meter

Give Feedback: Prius battery/fuel consumption
display

Mappings: Virtual plant


Design Patterns

31

Gains NOW, Losses later

Intertemporal Hyberbolic
Loss Aversion
Choice Discounting

Optimism Bias
Gains NOW, Losses Later
Increase present gains
Delay losses

Shift gains from the future Delay losses/cost or Frequent ﬂyer
into present payment to sometime in cards
the future

Break up future gains, so Credit cards
Break up present losses,
that pieces can be shifted min payments
so that pieces can be
to present
shifted to the future


Break up Gains, Lump Losses
Prospect
Loss Aversion
Theory

Geico advertises
discounts separately
Break up Gains
Lump Losses together

Travel insurance on
Which smaller losses can travelocity.com
be combined into one? Separate large gains and
incremental achievements

Talk about larger gains in
terms of smaller gains
Phrase abstract gains into
concrete gains


Smarter Defaults
Status Quo
Bias

Opt-out for organ
donation
Smarter/Desirable Defaults

Opt-out for 401K
How can we capture the
the desired behavior as a enrollment
default option?

Reduce effort involved for Eliminate unnecessary
the desired behavior information
Reveal bits of relevant
information over time


Ownership
Endowment Status Quo
Loss Aversion
Effect Bias

Ownership

Premium (cables)
Emphasize the risk of channels
losing an owned item?

Can we create a
perception of user
ownership?


Shift Reference Point

Loss Aversion Framing

Shift the current reference point

How are customers “Biggest loser” TV
comparing losses & show
gains?

How can the reference
point be shifted?


Reframe losses & gains


Opt-out for organ
donation
Reframe

Premium (cables)
Reframe to emphasize the channels
relevant gains and losses?

Use “loss” language: “If
you do not do X, you will
lose Y”


Loss with the undesirable option


Associate a loss with the
undesirable option

stickk.com
How can the undesirable anti-charity
behavior result in a loss?

Use “loss” language: “If
you do not do X, you will
lose Y”


Vividness, Salience

Anchoring Availability

Vividness, Salience

Narrate details as stories Use similar, vivid and Various charity
Make stories more popular stories as advertisements
emotional, personal examples

Make the user recall
pleasurable memories


Scarcity

foodspotting.com
How can we give users
Battery meter to solicit nominations
information about
relative/comparative input
depleting inventory?

Limited character count TW character limits
for feedback
Throttle items that can be
Seriosity uploaded Dribbble.com
email currency image throttling


Next Steps

Design Patterns
Survey of Behavioral
Solutions
Economics
Frameworks

Past, Present, Future
Psych & Econ
Thinking Models Choice Architecture
Rationality Frameworks: Applications for non-gaming
Heuristics Thaler: NUDGE online environments
Biases Cialdini: RSS-CAL New Product Features
Prospect Theory Loss/Gain Framework Drive User Engagement
Intertemporal Choice Drive User Satisfaction
Hyberbolic discounting

Survey of Game User Behavior
Mechanics Patterns


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