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PERSONAL
FINANCE FOR
ENGINEERS
CS 007: SESSION 2
PREDICTABLY
IRRATIONAL
CS 007
* DesignHacks.co: 188 Known Cognitive Biases
*”Predictably Irrational” is the title of a book by Dan Ariely
BEHAVIORAL FINANCE
How many of you think you are
rational with your money?
(show of hands)
YOU ARE NOT
RATIONAL
ANCHORING MENTAL ACCOUNTING CONFIRMATION &


HINDSIGHT BIAS
GAMBLER’S FALLACY
HERD BEHAVIOR OVERCONFIDENCE OVERREACTION &


AVAILABILITY BIAS
LOSS AVERSION
WHY BEHAVIORAL ECONOMICS?
• A number of economic frameworks
assume that humans evaluate
financial decisions consistently &
rationally


• Daniel Kahneman & Amos Tversky
(1960s)


• 2002 Nobel Memorial Prize in
Economic Sciences


• Prospect Theory
THREE THEMES IN BEHAVIORAL ECONOMICS
• Heuristics


Humans make a vast majority of
their decisions using mental
shortcuts or rules of thumb.


• Framing


Humans use anecdotes &
stereotypes to understand &
respond to events


• Market Inefficiencies


Mis-pricing or non-rational decision
making
ANCHORING
• People estimate answers to new &
novel problems with a bias towards
reference points


• Tversky & Kahneman (1974)


(quick multiplication)


• Dan Ariely


(social security numbers & prices)


• Common examples:


• Price you bought a stock at


• High point for a stock
MENTAL ACCOUNTING
• Money is fungible, but people put it
into separate “mental accounts”


• Also known as “bucketing”


• Example: Lost Movie Tickets


• Example: “Found Money”


• Real world problems:


Vacation Fund & Credit Card Debt
CONFIRMATION & HINDSIGHT BIAS
• Very different biases, but often conflated
with each other.


• Confirmation Bias


We selectively seek information that
supports pre-existing theories, and we
ignore / dispute information that
challenges or disproves them.


• Hindsight Bias


We overestimate our ability to predict the
future based on the “obviousness” of the
past.


• Combination of the two is particularly bad.
GAMBLER’S FALLACY
• We see patterns in independent,
random chains of events.


• We believe that, based on a series of
previous events, an outcome is more
likely than odds actually suggest.


• Example: Dinner Party & Coin Flips


• Real odds might be 51/49, but we tend
to jump to 80/20.


• Likely cause: the rarity of “independent
events” in day-to-day experience.
HERD BEHAVIOR
• We have a tendency to mimic the actions of
the larger group


• Example: Building Psych Experiment


• Example: Empty Supermarket


• Crowd psychology may be a contributor to
bubbles.


• Bucking the crowd creates stress & fatigue.
It gets harder, not easier.


• Easier to be “wrong with everyone” than
“right and alone”


• No gets fired for buying IBM?
OVERCONFIDENCE
• In “Behaving Badly” (2016), 74% of
investment managers believed that
they deliver above-average
performance.


• Dunning-Kruger Effect. The more
poorly you perform, the more you
over-estimate your performance.


• Capability in one domain can lead to
overconfidence in others.


• Humility is a virtue.
RECENCY & AVAILABILITY BIAS
• Recency Bias


We overreact to recent events


• Example: Celebrity Illness


• Availability Bias


We assume that the data we have been
provided is representative of the entire data
set.


• The combination is particularly bad.


• Studies show checking stock prices daily leads
to more trading & worse results on average.


• Worse for engineers, because we are immersed
in “game changers” & “it is different this time”
YOU HAVE $1,000 AND YOU MUST PICK
ONE OF THE FOLLOWING GAMES
You have a 100% chance of gaining $500.
B
You have a 50% chance of gaining $1,000, and
a 50% chance of gaining $0.
A
OR
NOW, YOU HAVE $2,000 AND YOU MUST
PICK ONE OF THE FOLLOWING GAMES
B
A
OR
You have a 50% chance of losing $1,000, and
a 50% chance of losing $0.
You have a 100% chance of losing $500.
LOSS AVERSION (PROSPECT THEORY)
• We hate losses more than we love winning


• Average loss aversion across multiple
studies is between 2:1 and 3:1


• Affects our views on a wide range of
situations, including career decisions,
dating, purchasing, investing and taxes.


• We even hate being responsible for
decisions that could result in a loss


• Example: “Sunk Cost” mistakes with
investments


• Currently under debate!*
* https://blogs.scientificamerican.com/observations/why-the-most-important-idea-in-behavioral-decision-making-is-a-fallacy/?amp
IT’S OK TO NOT BE RATIONAL
IT’S OK TO NOT BE RATIONAL
• As Dan Ariely beautifully put it, the
key is that humans are predictably
irrational


• Know your own flaws, and you can set
up systems to help account for them


• Self awareness is key


(yes, my Mom is a psychologist)
ADDITIONAL COURSES & MATERIAL
• Econ 178: Behavioral Economics


• Econ 278: Behavioral & Experimental
Economics


• ACCT 618 (GSB): Market Efficiency &
Informational Arbitrage


• Coursera / Duke: Behavioral Finance


https://www.coursera.org/learn/duke-behavioral-finance
QUESTIONS
CS 007
NEXT WEEK: GETTING PAID
• Compensation


• Different models & methods


• Public vs. Private Companies


• Stock Options vs. Restricted Stock


• Understanding Equity Compensation


• Taxes

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Stanford CS 007-2 (2021): Personal Finance for Engineers / Behavioral Finance

  • 2. PREDICTABLY IRRATIONAL CS 007 * DesignHacks.co: 188 Known Cognitive Biases *”Predictably Irrational” is the title of a book by Dan Ariely
  • 3. BEHAVIORAL FINANCE How many of you think you are rational with your money? (show of hands)
  • 4. YOU ARE NOT RATIONAL ANCHORING MENTAL ACCOUNTING CONFIRMATION & 
 HINDSIGHT BIAS GAMBLER’S FALLACY HERD BEHAVIOR OVERCONFIDENCE OVERREACTION & 
 AVAILABILITY BIAS LOSS AVERSION
  • 5. WHY BEHAVIORAL ECONOMICS? • A number of economic frameworks assume that humans evaluate financial decisions consistently & rationally • Daniel Kahneman & Amos Tversky (1960s) • 2002 Nobel Memorial Prize in Economic Sciences • Prospect Theory
  • 6. THREE THEMES IN BEHAVIORAL ECONOMICS • Heuristics 
 Humans make a vast majority of their decisions using mental shortcuts or rules of thumb. • Framing 
 Humans use anecdotes & stereotypes to understand & respond to events • Market Inefficiencies 
 Mis-pricing or non-rational decision making
  • 7. ANCHORING • People estimate answers to new & novel problems with a bias towards reference points • Tversky & Kahneman (1974) 
 (quick multiplication) • Dan Ariely 
 (social security numbers & prices) • Common examples: • Price you bought a stock at • High point for a stock
  • 8. MENTAL ACCOUNTING • Money is fungible, but people put it into separate “mental accounts” • Also known as “bucketing” • Example: Lost Movie Tickets • Example: “Found Money” • Real world problems: 
 Vacation Fund & Credit Card Debt
  • 9. CONFIRMATION & HINDSIGHT BIAS • Very different biases, but often conflated with each other. • Confirmation Bias 
 We selectively seek information that supports pre-existing theories, and we ignore / dispute information that challenges or disproves them. • Hindsight Bias 
 We overestimate our ability to predict the future based on the “obviousness” of the past. • Combination of the two is particularly bad.
  • 10. GAMBLER’S FALLACY • We see patterns in independent, random chains of events. • We believe that, based on a series of previous events, an outcome is more likely than odds actually suggest. • Example: Dinner Party & Coin Flips • Real odds might be 51/49, but we tend to jump to 80/20. • Likely cause: the rarity of “independent events” in day-to-day experience.
  • 11. HERD BEHAVIOR • We have a tendency to mimic the actions of the larger group • Example: Building Psych Experiment • Example: Empty Supermarket • Crowd psychology may be a contributor to bubbles. • Bucking the crowd creates stress & fatigue. It gets harder, not easier. • Easier to be “wrong with everyone” than “right and alone” • No gets fired for buying IBM?
  • 12. OVERCONFIDENCE • In “Behaving Badly” (2016), 74% of investment managers believed that they deliver above-average performance. • Dunning-Kruger Effect. The more poorly you perform, the more you over-estimate your performance. • Capability in one domain can lead to overconfidence in others. • Humility is a virtue.
  • 13. RECENCY & AVAILABILITY BIAS • Recency Bias 
 We overreact to recent events • Example: Celebrity Illness • Availability Bias 
 We assume that the data we have been provided is representative of the entire data set. • The combination is particularly bad. • Studies show checking stock prices daily leads to more trading & worse results on average. • Worse for engineers, because we are immersed in “game changers” & “it is different this time”
  • 14. YOU HAVE $1,000 AND YOU MUST PICK ONE OF THE FOLLOWING GAMES You have a 100% chance of gaining $500. B You have a 50% chance of gaining $1,000, and a 50% chance of gaining $0. A OR
  • 15. NOW, YOU HAVE $2,000 AND YOU MUST PICK ONE OF THE FOLLOWING GAMES B A OR You have a 50% chance of losing $1,000, and a 50% chance of losing $0. You have a 100% chance of losing $500.
  • 16. LOSS AVERSION (PROSPECT THEORY) • We hate losses more than we love winning • Average loss aversion across multiple studies is between 2:1 and 3:1 • Affects our views on a wide range of situations, including career decisions, dating, purchasing, investing and taxes. • We even hate being responsible for decisions that could result in a loss • Example: “Sunk Cost” mistakes with investments • Currently under debate!* * https://blogs.scientificamerican.com/observations/why-the-most-important-idea-in-behavioral-decision-making-is-a-fallacy/?amp
  • 17. IT’S OK TO NOT BE RATIONAL
  • 18. IT’S OK TO NOT BE RATIONAL • As Dan Ariely beautifully put it, the key is that humans are predictably irrational • Know your own flaws, and you can set up systems to help account for them • Self awareness is key 
 (yes, my Mom is a psychologist)
  • 19. ADDITIONAL COURSES & MATERIAL • Econ 178: Behavioral Economics • Econ 278: Behavioral & Experimental Economics • ACCT 618 (GSB): Market Efficiency & Informational Arbitrage • Coursera / Duke: Behavioral Finance 
 https://www.coursera.org/learn/duke-behavioral-finance
  • 21. NEXT WEEK: GETTING PAID • Compensation • Different models & methods • Public vs. Private Companies • Stock Options vs. Restricted Stock • Understanding Equity Compensation • Taxes