Reason, Passion, & Cognition, Week 3 Notes
Reason, Passion, & Cognition, Week 3 Notes 88-120
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This 8 page Class Notes was uploaded by Monica Chang on Saturday September 17, 2016. The Class Notes belongs to 88-120 at Carnegie Mellon University taught by Julie Downs in Fall 2016. Since its upload, it has received 26 views. For similar materials see Reason, Passion, and Cognition in Social & Decision Sciences at Carnegie Mellon University.
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Date Created: 09/17/16
Week 3 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: Case 1: If someone gives you $1000 and tells you 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: risk seeking preference. 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 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. Low probabilities overweighed, high probabilities underweighted Example: Lotteries: 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 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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