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CMU / Social Science / Soc 120 / Descriptive theories are the theory of what?

Descriptive theories are the theory of what?

Descriptive theories are the theory of what?

Description

School: Carnegie Mellon University
Department: Social Science
Course: Reason, Passion, and Cognition
Professor: Julie downs
Term: Fall 2016
Tags:
Cost: 25
Name: Reason, Passion, & Cognition, Week 3 Notes
Description: - Lecture 5 - Lecture 6 - notes on chapter 3 of textbook
Uploaded: 09/17/2016
8 Pages 48 Views 2 Unlocks
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Week 3


Descriptive theories are the theory of what?



LECTURE 5: PROSPECT THEORY

(for practice questions on lecture 4, take a look at the first part of  lecture 5)

Prescriptive vs. Descriptive: 

- Prescriptive/normative theories: what we SHOULD do  o Expected value

 Used to maximize $$

 Failed to account for diminishing marginal utility

o Expected utility (Bernoulli)

 Used to maximize utility  

 we do not think of outcomes as states of wealth.  Instead, we use relativity which is based on  

changes as opposed to absolutes

- Descriptive theories: what we ACTUALLY do

o Example:


What is the meaning of the isolation effect?



 Case 1: If someone gives you $1000 and tells you  If you want to learn more check out What is the meaning of household in family life?

that you can either get $500 automatically or take  

a 50/50 chance to win either $1000 or $0 more,  

what would you choose? Most people would choose

to take the $500.  

 Case 2: If someone gives you $2000 and tells you  

that you either have to lose $500 or take a 50/50  

chance to lose either $1000 or $0, what would you  

choose? Most people would choose to take the risk  

this time.  

 The two cases above have the same wealth  

change, but there is a natural difference in  

response: Case 1: risk aversion preference. Case 2:  We also discuss several other topics like What is entropy in systems theory?

risk seeking preference.  


Prospect theory is the theory of what?



 The Isolation Effect: when the current state is  

ignored. What’s important is relative gains and  

losses.

 o Prospect Theory: 

Prospect theory is a theory that describes how people  decide between two alternatives under risk and it  

corrects two of Bernoulli’s assumptions:

1. Carriers of utility are relative gains and losses as  

opposed to states of wealth

2. Outcomes have weighted averages, but not exactly probabilities

Prospect Theory: V (G )=π( X)∗v (X) +…

o Where V is the value function (the s-curve)

 Reference Point: Utility is not based on  

not states of wealth, but based on relative  

gains and losses, relative to a reference  

point of a decision (usually status quo)

 Loss Aversion (greater slope for losses):  

Gains sand losses aren’t symmetric. Losses  We also discuss several other topics like What is needed to gain a competitive advantage over competitors?

have greater utility than gains of the same  

magnitude  

 Diminishing marginal utility (makes an s

shape curve):

∙ Gains - has concave shape, predicts  

risk aversion

∙ Losses – has convex shape, predicts  

risk seeking

o Where π is the decision weight function:

 Increasing chances from 99% to 100% or  

increasing chances from 0% to 1% make a  

bigger subjective difference than going from

47% to 48% because decision weights are  

steep near certainty. Don't forget about the age old question of Do gases have weak intermolecular forces?

 Low probabilities overweighed, high  

probabilities underweighted

 Example: Lotteries: We also discuss several other topics like How can you determine the effects of intermolecular forces on the properties of matter?

∙ If the value function predicts risk  

aversion for gains, why do we buy  

lotteries? Because small probabilities  

are overweighted (decision weight  

function)

Prospect Theory shows why we make irrational decisions: - Framing:

o The framing of a question can reverse what people’s  responses depending on perceptions of gains and losses o Example: Asian Disease Problem – There are 60,000  people with a disease.  

 Case 1:

∙ Program A: 20,000 people saved Don't forget about the age old question of What is john maynard keynes best known for?

∙ Program B: 1/3 chance 60,000 are saved, and

2/3 chance no one is saved

 Case 2:

∙ Program C: 40,000 people die

∙ Program D: 1/3 chance no one dies, and 2/3  

chance 60,000 die

 People will choose Program A over B and program  

D over C because of risk averse for gains and risk  

seeking for losses

- The Endowment Effect:

o Example:

 One person owns a mug and is going to sell it.  

Another person is going to buy this mug. The seller  

would give a much higher price for selling the mug  

than the buyer would give for buying the mug

 Consequence of loss aversion

- Status Quo Bias:

o A biased preference for the current state of things

o It’s a consequence of loss aversion. Since losses have  more powerful utility than gains, change can be more  difficult when the options have equal value

o Example:  

 When it comes to cars, you care about GPS  

presence and safety equally. You currently have a  

car with a GPS but is not very safe. You have 2  

options for a new car:

∙ Car 1: has a GPS, not very safe

∙ Car 2: does not have a GPS, very safe

 You would choose car 2 because of loss aversion:  

you are more scared of loss than you are in seek of  

gain.

- Diminishing Marginal Utility:

o Example:

 Scenario 1: If you drive to another town, you can  

get a refrigerator for $50 instead of $100, would  

you drive?

 Scenario 2: If you drive to another town, you can  

get a car for $49,900 instead of $50,000, would  

you drive?

 You are more likely to drive in scenario 1, since  

there is a larger subjective difference.  

Key ideas: 

- Prospect Theory: V (G )=π( X)∗v (X) +…

o Value function:

 Reference Point: Utility based on relative gains and

losses, relative to a reference point of a decision

 Loss Aversion (greater slope for losses): Losses  

have greater utility than gains of same degree

 Diminishing marginal utility (makes an s-shape  

curve, gains predict risk aversion (concave) losses  

predict risk seeking (convex)

o Decision weight function:

 Probabilities aren’t weighed linearly

 Decision weights are steep near certainty

 Low probabilities overweighed, medium-high  

probabilities underweighted

LECTURE 6: Attribution Substitution Heuristics

How to study decision-making: 

- Bounded rationality – satisfice instead of maximize, we find  good enough solutions by “filling in”/using heuristics

- Heuristics: a shortcut to getting satisfactory solutions quickly  (rules of thumb)

- Since heuristics don’t follow normative rules they lead to  systematic and predictable errors/biases, which reveal the  heuristics we use and tell us how the brain works

- i.e. discover bias-  identify heuristic

- Psychologists have been using this approach to study judgment and decision-making for 30 years.  

Heuristics and Biases: 

Attribute Substitution Heuristic:

- When asked question we’re not evolved to answer optimally - We don’t answer the exact question, but a slightly different  question we know the answer to

- This will occur when 3 conditions satisfied

o target attribute is relatively inaccessible

o related attribute/value is more accessible

o we do not reject the substitution of heuristic attribute  even though we are conscious of it

- Bias: Base rate neglect and the Conjunction Fallacy o How to identify the bias: we judge the probability of the  conjunction of two events to be more than that of each of the elements alone, which is not logically possible

Heuristic: Representativeness:

o Judging probability by similarity (stereotypes), ignoring

base rates

o Example: The Linda Problem

o We should combine diagnostics and base rates when  making a conclusion, but we overweight diagnostics and  this leads to bias

- Bias: Probability Overestimation  

o How to identify this bias: Estimated probability compared to objective probabilities  

Heuristic: Availability:

o What is easier to call to mind is what we use to inform a  decision, ease of retrieval as opposed to number of  instances

o Example: First, estimate how many words in 2 pages of a  book would have this pattern _ _ _ _ _ _ n _. Now estimate how many words would have this pattern _ _ _ _ _ i n g.  People tend to estimate much less words for the first  pattern even though it is broader than the second  pattern.

o Example2: One group of people were asked to recall 6  examples of when they were assertive and rank their  assertiveness. Another group of people were asked to  recall 12 examples of when they were assertive and rank  their assertiveness. The group who had to come up with  12 examples tended to rank themselves as less assertive b/c it became harder for them to recall examples. The  greater psychological struggle in the act of recalling  made them think they were less assertive in reality.  

- Bias: the duration of an event is not important when it comes  to judging goodness/badness

Heuristic: Peak-End Heuristic:

o We confuse the experiencing self with the remembering  self

o We perceive happiness by averaging the peak and end  feelings

o Example: Daniel Kahneman’s colonoscopy experiment -  people who suffered less thought they suffered more  because their treatment stopped at high pain while the  others had the pain decrease before stopping treatment,  even though they suffered the same peak at one point

- Bias: Scope neglect

o We ignore numerical values in problem descriptions Heuristic: Affect Heuristic:

o Assign values by feeling, as opposed to number

o Example: How much would you pay to save 2,000 birds?  How much would you pay to save 20,000 birds? How  

much would you pay to save 200,000 birds? Not only  

were answers not proportional, but the average price for  2,000 birds was greater than that for 20,000 birds.  

o Example2: Jury, compensating for loss: it’s very  

subjective, we don’t know how to compensate for  

physical and mental pain

- Bias: Effort Bias

o Effort/time spent translates to how valuable something is - Heuristic: Effort Heuristic:

o When something is easy to judge, effort is not a factor  considered

o When something is hard to judge, effort becomes an  important factor in judgment because of attribute  

substitution (we don’t know how to evaluate the actual  thing, so we find a related attribute to assess instead) o Example: We want to buy a painting, but we don’t know  how much we’d pay for it. We would pay more money for  it if there was relatively more time spent on it.

Overview of Attribute Substitution: 

We assess attributes that are easier to evaluate:  

- Instead of probability, evaluate:

o Representativeness (similarity)

o Availability (ease of retrieval)

- Instead of feelings over time, evaluate:

o Peak-end heuristic (crucial moments)

- Instead of numerical value, evaluate:

o Affect heuristic (feeling)

- Instead of quality, evaluate:  

o Effort heuristic (effort)

Attribute substitution heuristics help relieve the effort required in  decision-making:

- Examine less cues

- Facilitate retrieving/storing cue values

- Simplify assessing weights for cues

- Integrate less info

- Assess fewer alternatives

Key ideas: 

- We do not seek optimality in our reasoning and rationality  (bounded rationality)

- Reasoning is driven by heuristics

o Heuristics allow us to live efficiently, our intuitions are  often right  

o But, heuristics also lead to biases

NOTES on reading Chapter 3: Judging Probability and  Frequency

Support Theory:

- Extension of the availability heuristic

- Support theory says that people judge hypotheses as  opposed to events by considering the either implicit or explicit  support for the hypotheses

- Example: When students were asked to judge the probability of death due to natural causes (which is an “implicit  

disjunction” in this case because it can be split into more  specific categories), they said 58%, but when students were  asked to give probabilities for death due to heart disease,  cancer, and other natural causes, the probability was 73% for  other natural causes.  

o Therefore, for an implicit disjunction, support is usually  subadditive b/c support is relatively less compared  

explicit disjunctions (if the implicit disjunction was split into more specific components)

Minerva-DM: A memory model of judgment: 

- The heuristics approach to describing processes of judgment is  lacking in that it cannot give a good picture of memory and  judgment processes integrated together

- MDM is a multiple-trace memory model that tries explain  representativeness and availability broadly in terms of memory. It says a memory trace is created for each event experienced  (instead of updating). A memory probe is created to access a  memory and when a trace has a weak similarity to the probe,  then the “echo” is weak and vise versa. As a result, this theory  proposes that when we make judgments of likelihood, we use  the “echo intensity” from the memory traces of MDM.  

- Example: In tackling the Linda problem with MDM, we would  create memory probes for the information given on Linda.  When judging the probability for Linda is a bank teller, there is  not as much echo intensity for bank teller b/c the memory  probes created seem to match feminist more, but when we  judge the probability of the conjunction that Linda is a feminist  and bank teller, there is more echo intensity.  

- In other words, a conjunctive probe will give a higher echo  intensity for A∩ B than for A and B alone because it will

combine the echo intensities of both A and B.

- Therefore, bias can arise from the strength of encoding  memories

- We don’t know how accurate MDM is in modeling memory, so  we don’t know how capable it is in explaining judgment  phenomena.  

- There are other theories that say highly distinct memories are  encoded as separate memory traces and similar experiences  are assimilated.

- Remember that there are other theories for how we make  judgments in addition to heuristics and MDM.

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