Lecture 15: How We Decide Flashcards
what types of decisions do we make?
lower stakes: ice cream flavor
higher stakes: stock market, investments
decisions about other people: tinder, relationships
how SHOULD we decide? (ideally)
use the expected value formula
expected value formula
expected value: odds of gain * value of gain
what were the two game examples given for expected value and should we play them or not and why?
- coin flipping: Would you pay $4 to play a coin flipping game where you win $18 if you guess right about the coin flip (H or T)
–> YES play: expected value = (1/2) * 18 = 9 > 4 - dice rolling: Would you pay $4 to play a dice rolling game where you win $18 if you guess the dice side right?
–> NO play: expected value = (1/6) * 18 = 3 < 4
ideally the expected value should be ___ than the amount you pay for you to play the game
expected value should be greater than the amount you pay to have a higher chance of gaining a positive profit
why do we not follow the expected value formula?
we make 2 types of errors in the expected value formula:
1. errors of odds
2. errors of value
explain the example given by the lottery and why people play it despite it not being supported by the expected value formula
lottery: Ie. ticket for a lottery: a game where you pay $1 and you pick 6 numbers between 1-75. If you get 5 right, you win $1,000,000
–> expected value = $1 million / 18 million = ~$0.05 < 1
people actually BELIEVE they have a chance of winning
a lot of the work and study about decision making was done by which two people?
Daniel Kahneman and Amos Tversky
what are heuristics?
cognitive shortcuts that (usually) work
Shortcuts that we take for problems that are too complicated/messy for us to solve exactly, but often times an approximation is good enough
however for the questions proposed in class, often times simply relying on our heuristics gives us the WRONG answer
examples of sample size neglect
- babies in hospitals
- kidney cancer
what principle explains the sample size neglect?
the law of small numbers
what is the law of small numbers?
when you have a small number of observations, there is a greater chance of weird and extreme results
explain the babies in hospital sample size neglect example
Question 1: “400 babies are born each day in a large hospital, and 40 babies are born each day in a small hospital. For any day in which more than 60% of the births at a hospital are girls, that hospital hands out free girl scout cookies.” Which hospital will have more free-cookie days?
400
40
same
→ most people in the world are probably thinking the answer is the “same” (C)
The prob of having a boy and a girl is the same and that is true for a small or large hospital
However answer (C) is WRONG; answer (B) is right (the smaller hospital)
Explanation: smaller samples are more prone to extreme things happening to them
As the hospital size gets bigger and bigger, it will converge closer and closer to the expected 50-50 split between boys and girls
However a smaller hospital is more likely to have extreme days (aka 70-30 split or 20-80 split another day)
base rate neglect
Failing to take into account the underlying odds of something being the case
What IS the same for the babies in hospital example?
the thing that IS the same between the two hospitals is that they are equally likely to have more boy days or more girl days (but the smaller hospital is more likely to have more extreme days)
explain the kidney cancer sample size neglect example
The counties in the US with the lowest incidence of kidney cancer had the following properties
Rural
Sparsely populated
Religious
→ HOWEVER… it was also true that the counties with the highest incidence of kidney cancer is ALSO
Rural
Sparsely populated
Religious
→ the reason for this is the key word “sparsely populated” since in smaller sample sizes, there are more extreme cases on both ends of the spectrum
give 2 examples of base rate neglect
- Ron trumpeter or farmer
- Ron 6’7’’ tall NBA player or teacher
explain the Ron trumpeter or farmer base rate neglect example
Ron is an opera buff who enjoys touring art museums when on vacation. Growing up, he loved listening to classical music and playing chess with friends and family. Which is more likely?
Ron plays trumpet in a major symphony orchestra
Ron is a farmer
Answer: It is more probable that Ron is a farmer since there are way more farmers than people who plays trumpets in a major symphony orchestra (aka there are very few people who have this job)
Trumpeters in major symphony orchestras: ~300
Farmers: ~2,000,000
explain the Ron NBA starter or teacher base rate neglect example
Ron is 6ft 7in tall. Which is more likely?
Ron starts for an NBA team
Ron is a teacher
Answer: it is much more likely that he is a teacher than an NBA starter since there are a lot less NBA starters than there are teachers
NBA starters: 150
Teachers: ~3,200,000
what are we tempted to make the base rate neglect error?
representativeness heuristic
give 2 examples of the availability bias and one of the availability heuristic
availability bias:
1. Are there more English words with “r” as the first letter, or with “r” as the third letter?
2. risk of airplane, car, and bathtub
availability heuristic: Which is more likely? A dog on a lease or a pig on a lease?
representativeness heuristic
representativeness heuristic: which of these given options is more representative of this information?
- if it seems like “X”, then it must be “X” (if it looks like a duck and quacks like a duck, then it’s probably a duck” type of thinking)
Ie. the description seems more representative of a classical musician than a farmer
Ie. the description of 6’7’’ seems more representative of an NBA starter than a teacher
availability heuristic
what is more cognitively accessible (easier for me to think of) to me?
difference between availability heuristic vs availability bias
When the availability heuristic is helpful and accurate → call it availability heuristic
When it is NOT helpful and instead misleading → call it availability bias
explain the Are there more English words with “r” as the first letter, or with “r” as the third letter? availability bias example
Question 3: Which has more? Are there more English words with “r” as the first letter, or with “r” as the third letter?
r _ _ _ …
_ _ r _ …
Answer: (b) there are ~twice as many words with “r” as their 3rd letter vs those with “r” as their 1st letter; but you may have been tempted to pick (a) since it is easier for you to think of words that that with r vs words that have r as their 3rd letter → falling prey to the availability heuristic
explain the Which is more likely? A dog on a lease or a pig on a lease? example of availability heuristic
Question 4: Example of a similar question but very straightforward…Which is more likely? A dog on a lease or a pig on a lease?
Answer: dog on a lease (obviously): but the way you probably thought about it was NOT doing the actual calculations and instead what comes to mind more easily?
explain the Which picture seems the most dangerous? → Are you more scared of a plane, car, or bathtub? example of availability bias
Ie. Which picture seems the most dangerous? → Are you more scared of a plane, car, or bathtub?
Most people will rank the plane being most scary, car, and then bathtub
HOWEVER, in reality and statistically…planes are much much safer than cars and even safer than bathtubs but people think the opposite
When you think of a bathtub, you think of peaceful experience and NOT bathtub accidents/deaths
HOWEVER, when you think of planes, you think of plane crashes/accidents since that is what you usually see in the news about planes
→ may also be why people play lotteries: we see and hear hundreds of stories of people winning the lotteries so when we think of lotteries, we think of WINNING the lottery and not losing
give 2 examples of conjunction fallacy
- Pres Daniel vs Lebron James
- rain
conjunction fallacy
the false thinking that the conjunction of 2 events is more probable than 1 of the conjuncts
- the probability of A AND B must be less than or equal to the probability of A
P(A and B) < or = P(A)
^^ think in terms of probability
explain the Pres Daniel vs Lebron James example of conjunction fallacy
Pres. Daniels plays LeBron James in a one-on-one game of basketball. What are the odds that…
The first scoring play of the game is a layup by President Daniels
The first scoring play of the game is a layup by President Daniels, but then King James takes over and crushes President Daniels
The intuitive answer is that the second scenario is more likely but it is NOT true since the 1st scenario is basically a SUBSET of the 2nd scenario (scenario 2 includes scenario 1) so it CANNOT be more likely than scenario 1 since it requires scenario 1 to happen in order for scenario 2 to happen
explain the rain example of conjunction fallacy
Ie. What’s the probability that it will rain tomorrow? Vs What’s the probability that it will rain tomorrow AND I’ll wear my rain jacket?
why do we fall victim to the conjunction fallacy?
representativeness could be the reason
Ie. Scenario A does not feel very representative of how the game will go between President Daniels and Lebron James
give 2 examples of the planning fallacy
- JHU Student center’s construction
- college students honor thesis
explain the JHU student center’s construction planning fallacy example
Supposed to open in Fall 2024, but it is still not open → a lot of construction around campus
Quite common for construction projects to be delayed → so common that there is a name for this type of delay: aka the planning fallacy
explain the college students honor thesis planning fallacy example
The main study of the planning fallacy is focused on college students: researchers recruited college seniors who were working on their college thesis and asked them how long it would take them to complete/turn in their college thesis?
What is your prediction of your “best-case” scenario (earliest time you will turn it in)
What is your prediction of your “worst-case” scenario (latest time you will turn it in)
Results: in fact it took longer than the “worst-case” scenario → everything takes longer than you think
planning fallacy
the planning fallacy: our failure to anticipate how the future is going to go especially when it involves our own actions
why do we fall victim to the planning fallacy?
availability bias
it is easy to imagine what it will be like to do the work (yourself working and completing the honors thesis) but it is hard to imagine all of the distractions (there are a lot of things that can distract you from it) like concerts, personal life stuff…
college students honor thesis bar graph results
days until submission
best-case: <15
prediction: 30
worst-case: >45
actual: <60
what are the 5 errors of odds
Sample size neglect
Base rate neglect
Availability bias
Conjunction fallacy
Planning fallacy
what are the 2 errors of value
- endowment effect
- temporal discounting
endowment effect
a cognitive bias that causes people to value items they own more than they would if they didn’t own them
give the 3 questions/examples of endowment effect
Question 1: You want to buy a car stereo. You can buy one near your house for $248, or you can go to DC to get the same exact stereo for just $48. Would you spend 2 hours to save $200?
Good idea to go to DC (yes)
Question 2: You want to buy a car. You can buy one near your house for $30,348, or you can go to DC to get the same exact car for just $30,148. Would you spend two hours to save $200?
Feels like the answer is NO, BUT this is actually the same question as question 1 (aka still saving 200$ to go to DC)
Question 3: This mug is for sale at the JHU Barnes & Noble. What is it worth? What would you pay for it? What would you sell it for?
Willingness to pay: a measure of how much you value something → ask someone “how much would you pay for it?”
BUT there is a different way to ask this question → “What would you SELL it for?”
People give a higher number for selling (vs buying) when they have to give up a thing that is THEIRS vs when they have to pay for something that is NOT THEIRS → aka the endowment effect
Weird that people give different answers for these two questions
what finding is Richard Thaler known for and what is the significance of it?
Richard Thaler: won a nobel prize and known for the finding of literally that you want to sell your mug for more than you want to pay for it
This finding is part of what founded the field of behavioral economics
behavioral economics
asks the question how do people behave in economic situations?
You first model how people SHOULD behave and then learn that people don’t actually model the way that behavioral economics say they should
describe the graph of behavioral economics
X-axis: the objective gains and losses that you could incur
Y-axis: How you experience these gains and losses (how do you feel about the gains and losses)
If we were perfectly rational, the graph should show a linear relationship/line and should be symmetry about the y-axis
IN REALITY, people feel the way on the right and notice the 2 features…
1. The slope of the line is steeper on the loss side than the gain side → losses are felt more than gains (it feels worse to lose the same amount of money than it feels good to gain the same amount of money)
2. Both sides of the line asymptote → the more you gain/loss, the less you feel about them
How do you not make these errors of odds/value mistakes in the first place? How do we know this is the solution?
LITERALLY JUST THINK SLOWER
When you use your fast gut reactions, you often make these mistakes → LITERALLY JUST THINK SLOWER and don’t always automatically rely on your heuristics and instead take more time to think it out
The way we know this is true is to take the Cognitive Reflection Test (CRT) ← literally a test of slowness
temporal discounting
the finding that we seem not to value something the same way if it is a long way off into the future even if it is worth the same
what are the 2 ways to help us make better decisions?
- Try not to make these mistakes to begin with
- Exploit these biases to improve society!
give an example of temporal discounting
Ask people would they rather get $10 now or $12 in 2 weeks?
A lot of people will say $10 now
HOWEVER, when asked people a second question… would they rather get $10 in 50 weeks or $12 in 52 weeks?
A lot of people change their answer and choose $12 in 52 weeks instead despite there being the same $2 in 2 weeks difference
what is the cognitive reflection test (CRT)
a test of slowness
- For each of these questions, there is an answer that seems correct at first but if you think slower you will realize that it is wrong
- If you did well on this test, it means you think slower and take time to double check your answers
a higher CRT score correlates to what 3 things?
To being patient in general
Doing less temporal discounting
Better alignment with expected value in general
A bat and a ball cost $1.10. The bat costs a dollar more than the ball. How much does the ball cost?
Answer: 5 cents! (but 10 cents “feels” like the right answer and 21% of you said it!)
Explanation: x + y = 1.10 → x + (x+1.00) = 2x + 1.00 = 1.10 → 2x = 0.10 → x = 0.05
It takes 5 machines 5 minutes to make 5 widgets; how long will it take 100 machines to make 100 widgets?
Answer: 5 minutes! (but 100 minutes “feels” like the right answer and 18% of you said it!)
Explanation: Since 1 machine takes 5 minutes to make 1 widget, 100 machines can simultaneously make 100 widgets in the same 5 minutes since the machines are working in parallel
In a lake, there is a patch of lily pads. Every day, the patch doubles in size. If it takes 48 days for the patch to cover the entire lake, how long would it take for the patch to cover half the lake?
Answer: 47 days! (but 24 days “feels” like the right answer and 12% of you said it!)
Explanation: it is exponential growth, NOT linear growth
give 2 examples of a nudge
- housefly in men’s urinals
- the default organ donation policy
nudge
subtle changes to our environment that cause us to behave in a way that is better for ourselves and others
aka some small feature of the environment that attracts our attention and alters our behavior
what book did Richard Thaler write and what was it about?
Richard Thaler (endowment effect) came up with some ideas of how to use these biases for good → wrote a book called Nudge
explain the housefly in urinal nudge example
placing an image of a housefly into the men’s urinals
When men are peeing, they are not fully attending to the task at hand and if you give them a target, they will aim at it (in this case the housefly) → as a result, spillage has been reduced by 80%
That housefly has become the most well-known example of a nudge
status-quo bias
a bias against change and thus a bias for the status-quo (how things are right now)
how can you exploit this status-quo bias to get more organ donations/donors?
Organ donations are very low in some countries and very high in other countries → what is the difference between these countries??
Organ donations are very low in some countries and very high in other countries → what is the difference between these countries?? (They are culturally similar countries and neighboring countries)
Answer: countries with very low organ donations have their organ donation policy as an opt-in policy whereas the countries with very high organ donations have their organ donation policy as an opt-out policy
Just making something the default makes it sticky
An example of a nudge and causes millions more of people to be organ donors
the thought is to take advantage of this status-quo bias for the better good
“Cognitive dimension” of clinical decision making
the process by which doctors interpret their patients’ symptoms and weigh test results in order to arrive at a diagnosis and a treatment plan
opt-in policy vs opt-out policy
opt-in policy: you have to tell the government that you want to be a organ donor
opt-out policy: you have to tell the government that you do NOT want to be a organ donor
what are the pros and cons of heuristics for doctors/medical field?
Pro: necessary in medicine, especially in ERs when physicians most make quick judgements about how to treat a patient on the basis or a few, potentially serious, symptoms
Con: however, heuristics can also lead doctors to make grave errors (ie. representativeness errors)
when do doctors make representativeness errors?
Doctors make such errors when their thinking is overly influenced by what is TYPICALLY true and fail to consider possibilities that contradict their mental templates of a disease → thus attribute symptoms to the wrong cause
availability heuristic in context of doctors
the tendency to judge the likelihood of an event by the ease with which relevant examples come to mind
confirmation bias
confirming what you expect to find by selectively accepting or ignoring information
affective error
the tendency to make decisions based on what we wish were true
defaults can influence choices in which 3 ways?
- Decision-makers might believe that defaults are suggestions by the policy-maker, which imply a recommended action
- Making a decision often involves effort, whereas accepting the default is effortless
–> Many people would rather avoid making an active decision about donation, because it can be unpleasant and stressful
–> Physical effort such as filling out a form may also increase acceptance of the default - Defaults often represent the existing state or status quo, and change usually involves a trade-off
loss aversion
the tendency for people to strongly prefer avoiding losses over acquiring gains despite the losses and gains being of the same magnitude
–> Aka the pain of losing something is more impactful than the pleasure from gaining the same thing
what were the 3 experiments done in the organ donor HW reading?
- online experiment with 3 different forms/default choices
- European real world consent comparison between opt-in and opt-out default policies
- Analysis of relationship between agreeing to be an organ donor vs actual donations
describe the 1. online experiment with 3 different forms/default choices
Online Experiment: In a controlled online setting, 161 participants were asked about their organ donation preferences under different default conditions: 1. opt-in 2. opt-out 3. no default
In the opt-in scenario (where participants were not donors unless they chose to be), consent rates were significantly lower
The opt-out condition (where participants were donors unless they opted out) nearly doubled the consent rate
The neutral condition (no default) showed consent rates similar to the opt-out
→ This experiment highlighted the strong influence of defaults on decision-making
describe the 2. European real world consent comparison between opt-in and opt-out default policies
This study analyzed real-world consent rates across European countries with explicit-consent (opt-in) and presumed-consent (opt-out) policies
findings: Countries with opt-out policies showed significantly higher effective consent rates, with about a 60-percentage-point gap between opt-in and opt-out countries
describe the 3. Analysis of relationship between agreeing to be an organ donor vs actual donations
answers the question: Do increases in agreement rates (from presumed-consent policies) result in increased rates of donation?
Using a multiple regression model, the analysis found that presumed-consent (opt-out default) policies correlated with a 16.3% increase in donation rates
→ This suggests that opt-out policies not only raise consent rates but may also lead to a modest increase in actual donations
defaults can lead to which 2 kinds of misclassifications?
- Willing donors who are not identified
- People who become donors against their wishes
Balancing these errors with the good done by the lives saved through organ transplantation leads to delicate ethical and psychological questions
