At the 2012 Face of Finance Conference, at Bentley University, in Waltham, MA, Eric Gold (Fidelity Investments) presented "How Behavioral Economics Informs Website Design".

1. How Behavioral
Economics Informs
Website Design
Eric Gold, PhD
Fidelity Investments
October 24, 2012

2. How Behavioral Economics Informs
Website Design
►Getting in the way of good decision making
►Seemingly inconsequential design
decisions
►Taking advantage of the web
►Advice

3. Getting in the Way
of Good Decision
Making

4. Anchoring
Getting in the way of good
decision making

5. Anchoring
Question: What percent of UN members are
African countries?
But, before answering, spin the wheel of fortune
Mean Mean
answer: answer:
25% 45%
Tversky & Kahneman (1974)

6. Choice Paralysis
Getting in the way of good
decision making

7. Choice Paralysis
6 24
kinds of jams kinds of jams
Shoppers
who 40% 60%
stopped
Iyengar & Lepper (2000)

8. Choice Paralysis
6 24
kinds of jams kinds of jams
Shoppers
who 40% 60%
stopped
Shoppers
who bought 30% 3%
jam
Iyengar & Lepper (2000)

9. Choice Paralysis
7000
Mutual
Funds
5000
3000
1000 NYSE
1940 1960 1980 2000

10. Choice Paralysis
80%
PARTICIPATION
70%
60%
50%
0 20 40 60
FUNDS OFFERED
Iyengar et al. (2003)

11. Confirmation Bias
Getting in the way of good
decision making

12. Confirmation Bias
“If there is a vowel on one side,
then there is an even number on the other.”
Which card(s) would you need to turn over to
decide whether the statement is true or false?
E K 4 7
Johnson-Laird (1983)

13. Irrelevant Information
Getting in the way of good
decision making

14. Irrelevant Information
►Use of irrelevant information increases with
time, effort and cost of obtaining the information
►People are more likely to pursue irrelevant
information for complex tasks
►People consider information relevant if they
must wait for it
►Even mentioning information makes it seem
more relevant
►People overweight recent information
►The internet is full of irrelevant advice
Bastardi & Shafir (1998), Baron, et al. (1988), DeBondt & Thaler (1985)

15. Irrelevant Information
You are considering registering for a course in your major that has very
interesting subject matter and will not be offered again before you graduate.
While the course is reputed to be taught by an excellent professor, you have
discovered that he will be on leave, and that a less popular professor will be
teaching the course.
Immedia After Total
Simple version te waiting
(a) Decide to register for the course (82%) Simple Register 82 - 82
(b) Decide not to register for the course (18%) Not 18 - 18
register
Uncertain version Uncertain Register 42 29 71
(a) Decide to register for the course (42%) Not 2 27 29
(b) Decide not to register for the course (2%) register
(c) Wait until tomorrow (after finding out if the regular professor will be teaching) to decide
about registering for the course (56%)
If you chose c in the question above, please answer the following:
It is the next day, and you find out that the less popular professor will be teaching the course.
(a) Decide to register for the course (29%)
(b) Decide not to register for the course (27%)
Bastardi & Shafir (1998)

16. Recommendations
►Don’t use default numbers unless meaningful.
►Make sure anchors represent a sensible
estimation.
►Keep users away from irrelevant information
►Afford disconfirming evidence

17. Recommendations
►Organize choices
►Give strategies for making choices
►Avoid choices that are essentially the same
►Limit choices
►Use defaults

18. Seemingly Inconsequential
Design Decisions

19. Framing
Seeming inconsequential design
decisions

20. Framing
Imagine that the U.S. is preparing for the outbreak of an unusual Asian disease, which is
expected to kill 600 people.
Framing I: Which would you choose?
A B
200 people will be saved 1/3 probability 600 people will be saved
2/3 probability no people will be saved
Tversky and Kahneman (1981)

21. Framing
Imagine that the U.S. is preparing for the outbreak of an unusual Asian disease, which is
expected to kill 600 people.
Framing I: Which would you choose?
A B
200 people will be saved 1/3 probability 600 people will be saved
2/3 probability no people will be saved
Framing II: Which would you choose?
A B
400 people will die 1/3 probability that nobody will die
2/3 probability that 600 people will die
Tversky and Kahneman (1981)

22. Framing
Imagine that the U.S. is preparing for the outbreak of an unusual Asian disease, which is
expected to kill 600 people.
Framing I: Which would you choose?
A B
200 people will be saved 1/3 probability 600 people will be saved
2/3 probability no people will be saved
72% 28%
Framing II: Which would you choose?
A B
400 people will die 1/3 probability that nobody will die
2/3 probability that 600 people will die
22% 78%
Tversky and Kahneman (1981)

23. Framing
Surcharge for Credit
vs.
Discount for Cash

26. Attraction Effect
Seeming inconsequential design
decisions

27. Attraction Effect
Patient suffers from migraine headaches that:
Last about 3 hours.
Involve intense pain, nausea, dizziness, and hypersensitivity.
Occur about 100 of those days/year (8.3 per month).
You are considering 3 medications.
All have same minor side effects, and are pills taken 1/day.
Each differs in effectiveness and cost.
The patient pays cost.
Chapman & Malik (1995)

28. Attraction Effect
Which would you choose?
Drug A Reduces the number of headaches
from 100 days with a headache per year
to 30 days with a headache per year.
It costs $350 per year.
Drug B Reduces the number of headaches
from 100 days with a headache per year
to 50 days with a headache per year.
It costs $100 per year.
Chapman & Malik (1995)

29. Attraction Effect
Drug A Reduces the number of headaches 36%
from 100 days with a headache per year
to 30 days with a headache per year.
It costs $350 per year.
Drug B Reduces the number of headaches 64%
from 100 days with a headache per year
to 50 days with a headache per year.
It costs $100 per year.
Chapman & Malik (1995)

30. Attraction Effect
Drug A Reduces the number of headaches
from 100 days with a headache per year
to 30 days with a headache per year.
It costs $350 per year.
Drug B Reduces the number of headaches
from 100 days with a headache per year
to 50 days with a headache per year.
It costs $100 per year.
Drug C Reduces the number of headaches
from 100 days with a headache per year
to 60 days with a headache per year.
It costs $100 per year.
Chapman & Malik (1995)

31. Attraction Effect
2 choices 3 choices
Drug A Reduces the number of headaches 36% 9%
from 100 days with a headache per year
to 30 days with a headache per year.
It costs $350 per year.
Drug B Reduces the number of headaches 64% 81%
from 100 days with a headache per year
to 50 days with a headache per year.
It costs $100 per year.
Drug C Reduces the number of headaches 10%
from 100 days with a headache per year
to 60 days with a headache per year.
It costs $100 per year.
Chapman & Malik (1995)

32. Joint vs. Separate
Presentation
Seeming inconsequential design
decisions

33. Joint vs. Separate Presentation
Dictionary A: 10,000 entries, like new
Dictionary B: 20,000 entries, the cover is torn
Jointly
► Dictionary A: 10,000 entries, like new - $19
► Dictionary B: 20,000 entries, the cover is torn - $27
Separately
► Dictionary A: 10,000 entries, like new - $24
► Dictionary B: 20,000 entries, the cover is torn - $20
Hsee(1996)

34. Diversification Bias
Seeming inconsequential design
decisions

35. Diversification Bias
Read & Loewenstein (1995)

36. Diversification Bias
Combined choice: 100%
chose different candy bars
Read & Loewenstein (1995)

37. Diversification Bias
Combined choice: 100%
chose different candy bars
Separate choice: 48%
chose different candy bars
Read & Loewenstein (1995)

38. Diversification Bias
1/n strategy – participants in 401(k) plans distribute their contributions
more or less evenly over the funds offered.
Implied
Allocation Allocation
Fund A Fund B for Fund A for Fund A
Stocks Bonds 54%
Stocks ½ Stocks, ½ Bonds
½ Stocks, ½ Bonds Bonds
Benartzi & Thaler (1999)

39. Diversification Bias
1/n strategy – participants in 401(k) plans distribute their contributions
more or less evenly over the funds offered.
Implied
Allocation Allocation
Fund A Fund B for Fund A for Fund A
Stocks Bonds 54%
Stocks ½ Stocks, ½ Bonds 46% 21%
½ Stocks, ½ Bonds Bonds
Benartzi & Thaler (1999)

40. Diversification Bias
1/n strategy – participants in 401(k) plans distribute their contributions
more or less evenly over the funds offered.
Implied
Allocation Allocation
Fund A Fund B for Fund A for Fund A
Stocks Bonds 54%
Stocks ½ Stocks, ½ Bonds 46% 21%
½ Stocks, ½ Bonds Bonds 69% 87%
Benartzi & Thaler (1999)

41. Recommendations
►Run usability tests to determine best frame
►When appropriate, avoid negative frames that
may paralyze the customer
►Encourage making decisions all at once, not
piecemeal

42. Taking Advantage
of the Web

43. Disambiguation
Taking advantage of the web

44. Ambiguity Aversion
An urn contains 30 red balls and 60 additional balls that
are either yellow or black
Gamble I:
A: $100 if red is drawn
B: $100 if black is drawn
Gamble II:
A: $100 if red or yellow is drawn
B: $100 if black or yellow is drawn
Ellsberg (1961)

45. Disambiguation
Some subjects were tested with a variation
of the Ellsberg Paradox, measuring
ambiguity aversion.
Other subjects were tested for risk aversion.
Response to ambiguity Response to risk
Hsu et al. (2006)

46. Discounting of Ambiguous Information
Earlier this year you decided to exercise and opted for tennis. You purchased a one-year
season ticket at a luxurious tennis club in your neighborhood. This means that during the
year you can play on each Wednesday afternoon. The membership fee is 600 Euros for
the entire year, and the costs have to be paid monthly (50 Euros per month). After a few
weeks you injure your elbow, and the pain progresses. After two months it appears that
you have developed tennis elbow. There are two options: either you continue to play and
retain your season ticket for the year; or you quit, and return the season ticket to the club.
The club has a standard arrangement: if you decide to return the season ticket, you are
granted a refund so that you not have to play for the months to come. What would you
do?
Control Low Cost High Cost Ambiguous
Continue 16% 38% 52% 22%
Stop 84% 62% 48% 78%
Van Dijk & Zeelenberg (2003)

47. Disjunction Effect
Shafir & Tversky (1992)

48. Trade-offs
Taking advantage of the web

50. Trade-offs
► Satisficing
► Elimination by aspects
► Most important attribute
► Majority of confirming dimensions
► Avoiding the decision
Simon (1969), Fishburn (1974), Russo & Dosher (1983), Beattie & Barlas (2001)

51. Recommendations
►Run studies to determine what trade-off
strategies customers use.
►Make sure that important possibilities are
presented as trade-offs and understood that way.
►Make clear what attributes are important.
►Consider asking users to rate utilities and weights
explicitly.
►When a trade-off is required, use similar
attributes.
►Inform users that they can change their mind
(when this is so).

52. Recommendations
►Identify and resolve ambiguities
►Build tools that don’t needlessly resolve
ambiguity

53. Advice

54. Hypotheses
Advice

55. Advisors: This is not how it works
We often expect that people will just do what we tell them…
Problem to solve
Advisor
Request for help The Judge
(“me”)
Advice
“Just do it!”
Swoll & Sniezek (2005), Gino & Moore (2006), Rantilla & Budescu (1999)

56. Advisors: This is not how it works
We often expect that people will just do what we tell them…
…sometimes without their even asking!
Problem to solve
?!
Advisor
The Judge
(“me”)
Advice
“Just do it!”
Swoll & Sniezek (2005), Gino & Moore (2006), Rantilla & Budescu (1999)

57. Advisors: The Judge-Advisor System
Judges (i.e., people) make decisions with or
without one or more advisors.
Advisors Problem to solve
Advice The Judge
(“me”)
What gives the Judge
confidence in the
advice?
Decision
Swoll & Sniezek (2005), Gino & Moore (2006), Rantilla & Budescu (1999)

58. Changing Minds – Information
Acquisition
►Subjects were asked to choose a mountain
bike
►Subjects were presented with either 5 (low
complexity), 9 (medium complexity), or 13 (high
complexity) possible bikes
►Each bike was rated on 6 attributes
Schrah, Dalal & Sniezek (2006)

59. Changing Minds – Information Acquisition
Low Medium High
complexity complexity complexity
Pre-Advice Post- Pre- Post- Pre- Post-
Advice Advice Advice Advice Advice
Depth of search 2.41 1.28 2.16 0.69 1.45 0.49
Variability of search - 3.96 2.40 7.38 2.46 8.15 2.42
attributes
Variability of search - 5.46 6.27 6.10 5.15 5.22 4.15
alternatives
Search pattern -0.05 0.16 -0.17 0.43 -0.02 0.25
Latency of search 1.18 1.56 0.92 1.71 1.20 1.65
Proportion of 0.96 0.74 1.00 0.43 1.00 0.40
alternatives searched
Proportion of 0.31 0.64 0.20 0.62 0.18 0.54
information search
dedicated to
selected/recommended
alternative
Schrah, Dalal & Sniezek (2006)

60. Weighting Evidence
Advice

61. Egocentric Discounting
Phase 1
In what year was the Suez Canal first opened for use?
Your best estimate____
Phase 2
In what year was the Suez Canal first opened for use?
Your previous estimate was 1905
The best estimate of advisor K was 1830
Your final best estimate ____
Judges Weight of
knowledge advice
High 0.20
Low 0.33
Yaniv (2004)

62. Other’s Advice
Phase 1
In what year was the Suez Canal first opened for use?
Your best estimate____
Phase 2
In what year was the Suez Canal first opened for use?
Your previous estimate was 1905
The best estimate of advisor K was 1830
Your final best estimate ____
Distance
Near Intermediate Far
Decision
Makers
Knowledge
High 0.31 0.28 0.23
Low 0.38 0.34 0.30
Yaniv (2004)

63. Recommendations
►Let the customers develop their own
hypotheses
►Keep a clear and consistent position
►Base advice on multiple sources
►Keep advice relevant
►Encourage people to consider all available
options

64. Conclusion

65. Topics
►Emotions
► Regret
► Anxiety
► Fear
►Overconfidence
►Financial literacy
►Numeracy
►Inertia
►Mental accounting
►Risk communication