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W11 Decision Making - Coggle Diagram

- - - - outcomes that achieve individual goals
    - - people often do not follow decision making as specified in expected utility theory
    - - :one: Told participants they earn $1 each time they draw red bean :red_circle:
        
        :two: Asked them to choose small bowl or large bowl
        
        Small Bowl has 1 :red_circle: 9 :white_circle:
        
        Large Bowl has 7 :red_circle: 93 :white_circle:
      - Results
        
        many chose large bowl with 7% possibility
        
        felt that chance was higher with more red beans even though they knew probability was lower
        
        influenced by factors other than knowledge of probability
  - - - Anxious
        
        more likely to avoid decisions with potentially large negative consequences
      - Optimism
        
        more likely to ignore negative info and focus on positive info
        
        too much can lead to poor decision making
    - - Factors
        
        Expected Emotions
        
        emotions that people predict will occur after a particular outcome
        
        Coin EXP
        
        :one: Given $5 and told they would either win $5 or lose $3 based on coin flip
        
        :two: rated current happiness and predicted happiness changes in response to either outcome
        
        :three: completed 10min filler task after coin flip and rated actual happiness
        
        predicted decrease in happiness from losing $3 much greater than increase from gaining $5
        
        actual decrease much lesser than predicted decrease
        
        actual increase slightly lesser than predicted increase
        
        1 more item...
        
        predictions do not account for coping mechanisms used to manage negative emotions in failure
      - tendency to avoid taking risks
  - - - Presenting Choices (Organ Donor)
        
        Opt-in
        
        active step required (signing donor card) to being organ donor
        
        low rates (28% in USA)
        
        Opt-out
        
        everyone consents to being organ donor otherwise opting not to be
        
        high rates (>99% in France, Belgium)
      - tendency to do nothing when faced with decision
    - - decisions influenced by how choices are stated or framed
      - New Disease EXP
        
        :one: told of new disease that is expected to kill 600 people
        
        :two: gave one group choice between Program A or B
        
        Program A :arrow_right: save 200 :ambulance:
        
        Program B :arrow_right: 33% chance all saved :ambulance: and 66% chance none are saved :ambulance:
        
        :two: gave another group choice between Program C or D
        
        Program C :arrow_right: 400 :skull_and_crossbones: will die
        
        Program D :arrow_right: 33% chance none die and 66% chance all die :skull_and_crossbones:
        
        Results
        
        given AB choice, 72% chose A
        
        risk aversion strategy
        
        chose option with a guarantee of more lives saved
        
        gain framing :arrow_right: saving lives :ambulance:
        
        given CD choice 78% chose D
        
        risk taking strategy
        
        chose option with chance of reducing deaths
        
        loss framing :arrow_right: deaths :skull_and_crossbones:
        
        gain framing usually :arrow_right: risk averse strategies and loss framing usually :arrow_right: risk taking strategies
  - - - :one: proposer receives sum of money and makes offer to responder on how to split
        
        :two: responder accepts :check::arrow_right: money split as proposed
        
        :two: responder rejects :red_cross: :arrow_right: no one gets money
      - Utility Theory
        
        rational response is to accept any offer as long as greater than 0
        
        if you refuse you get nothing
      - Unfair Computer EXP
        
        :one: play ultimatum game with 10 different :silhouette: and 10 with :computer:
        
        :two: :silhouette: and :computer: gave fair or unfair offers as determined by experimenters
        
        Results
        
        more likely to reject low offers from :silhouette: than :computer:
        
        felt angry at unfair offer
        
        right anterior insula
        
        3x more active when rejecting offer vs accepting offer
        
        responders with higher activation to unfair offers rejected more unfair offers
        
        insula associated with negative emotions (pain, hunger, disgust, anger)
        
        PFC
        
        same activity when accepting or rejecting offers
        
        may activate to regulate decision making process
        
        weighing choices (emotional vs rational) to determine best option
        
        rejecting :arrow_right: PFC rejecting offer to pursue emotional goal of punishing unfairness
      - Ultimatum with TMS EXP
        
        :one: one group had PFC temporarily deactivated by PFC while other group went through "sham" control
        
        Results
        
        both groups rated low offers as unfair
        
        PFC deactivated group more likely to accept unfair offers
        
        PFC important in making cognitively demanding decision of rejecting unfair offer
      - Non-WEIRD study
        
        US participants offer amounts on extreme high end of spectrum for Dictator and Ultimatum games
        
        rejection of hyperfair offers (60% to 100%) occurs in China, Russia, Sweden, Germany
        
        US responders have higher threshold for accepting offer
        
        smaller scale societies made lower offers without being rejected
        
        community size predicts rejection rates
- - - - statements begin with All, No, Some
      - Issues
        
        VALID form but FALSE premises
        
        Syllogism 2
        
        syllogism valid but untrue
        
        TRUE premises but INVALID form
        
        Students & Tired people
        
        may fall for it due to belief bias
        
        Students & Millionaires
      - Belief Bias
        
        syllogism being valid if conclusion is plausible regardless of validity
        
        Syllogism 2 conclusion "some students are irritable" is believable
        
        more likely to be considered valid
        
        conclusion "all birds have four legs" not plausible
        
        more likely to be considered invalid
        
        EXP
        
        :one: read valid or invalid syllogisms with believable or unbelievable conclusions
        
        :two: indicate whether syllogism is valid
        
        Results
        
        Valid syllogism
        
        80% accepted if conclusion is believable
        
        56% accepted if conclusion unbelieveable
        
        Invalid syllogism
        
        71% accepted if conclusion is believable
      - Mental Model
        
        specific situation represented in person's mind to help determine syllogism validity in deductive reasoning
        
        :one: Create a model of situation
        
        :two: Generate tentative conclusions about model
        
        :three: Look for exceptions that falsify model and modify model accordingly
        
        :four: Determine validity when exceptions cannot be found and model matches conclusion
        
        Venn Diagram Representation
        
        Principle
        
        conclusion valid when cannot be refuted by any possible model of premises
        
        Benefits
        
        assess syllogism validity without training in rules of logic
        
        generates testable predictions
        
        syllogisms requiring more complex models are harder
    - - form of the syllogism means that conclusion logically follows from premises
    - - ABC format useful for comparing forms
    - - first premise has form "If p then q"
        
        Modus Ponens
        
        2nd premise :arrow_right: p
        
        Conclusion :arrow_right: q
        
        identified by 97% as valid
        
        Modus Tollens
        
        2nd premise :arrow_right: not q (didn't do well)
        
        Conclusion :arrow_right: not p. (didn't study)
        
        identified by only 60% as valid
        
        Syllogism 3
        
        2nd premise :arrow_right: q (did well)
        
        Conclusion :arrow_right: p (studied)
        
        identified by 40% as invalid
        
        Syllogism 4
        
        2nd premise :arrow_right: not p (didn't study)
        
        Conclusion :arrow_right: not q (didn't do well)
        
        identified by 40% as invalid
        
        people judge syllogisms validity when real-life examples used compared to abstract symbols
        
        Wason Four Card Problem
        
        Original EXP
        
        :one: Rule is "If vowel on one side => even number on other side"
        
        :two: indicate cards to turn over to verify rule
        
        Results
        
        53% of participants indicated E
        
        1 more item...
        
        46% indicated 4
        
        1 more item...
        
        4% indicated 7
        
        1 more item...
        
        each card has letter on 1 side and number on other side
        
        Beer Drinking age EXP
        
        :one: Rule is "If person is drinking beer, must be over 19 years old"
        
        :two: indicate cards to turn over to verify rule is being followed
        
        Results
        
        73% indicated correct answer (beer & 16 yrs old)
        
        none answered letter number version correctly
        
        Explanations
        
        2 more items...
      - If I study, I will get an A
        
        I studied
        
        Therefore, i get a good grade
- - - - evaluating syllogisms under time pressure increases belief bias effect
- - - - Strong inductive arguments :arrow_right: more likely to be true
      - Weak inductive arguments :arrow_right: less likely to be true
    - - Representativeness of Observations
        
        how well do observations about a category represent all members of that category
        
        crows being black in Pittsburgh and Washington
      - Quality of Observations
        
        scientific observations that support sun rising in Tucson
      - Number of Observations
        
        Europe crows are gray with black tails and wings
        
        sun rising in Tucson supported by large number of observations
    - - predicting future based on past observations
      - observe that prof uses MCQs a lot in exams and induces that his exams will all have MCQs
    - - Availability Heuristic
        
        events that are more easily recalled judged as more probable than events less easily recalled
        
        R in Words EXP
        
        :one: participants asked whether more words have "r" in 1st or 3rd position
        
        70% responded 1st position but there are 3x more 3rd position words
        
        easier to think of 1st position words than 3rd position
        
        availability heuristic may mislead when less frequently occurring events stand out in memory
        
        cloudy and there is a smell in air
        
        availability heuristic suggests it may rain due to many past observations
        
        Illusory Correlations
        
        relationship between two events that seems to exist when relationship is weak or absent
        
        Jack Grealish wearing old boots in premier league playoffs
        
        related to
        
        Stereotypes
        
        oversimplified generalisation of groups or classes of people that is usually negative
        
        may direct attention to negative stereotypical behaviours
        
        stereotypical behaviours more accessible by availability heuristic
        
        1 more item...
        
        Draw Scientist EXP
        
        most children draw men when asked to draw scientists
        
        Women talk excessively stereotype
      - Representativeness Heuristic
        
        likelihood of instance being member of category based on degree instance resembles properties typically associated with category
        
        Farmer Librarian EXP
        
        :one: Given description of Robert randomly selected from US population who wears :eyeglasses: and enjoys :book:
        
        :two: asked if Robert more likely to be librarian or farmer
        
        glasses and books associated with being librarian (illusory conjunction)
        
        ignored base rate
        
        US govt statistics in 2016 show there are 20x more farmers than librarians
        
        Base Rate
        
        relative proportion of different classes in population
        
        Engineer Lawyer EXP
        
        :one: participants given either base rate info or base rate info + descriptive info
        
        :two: asked to evaluate likelihood of individual being engineer
        
        Results
        
        estimated likelihood at 30% if only base rate info given
        
        greatly increased estimate if descriptive info provided
        
        people disregard base rate info when descriptive info available
        
        Conjunction Rule
        
        Bank teller EXP
        
        :one: ”Imagine a 31-year old called Linda, she cares about discrimination and social justice”
        
        :two: asked which statement more likely
        
        a) Linda is a bank teller
        
        b) Linda is a feminist bank teller
        
        correct answer is a) but 85% chose b)
        
        representation heuristic --> saw Linda description as more representative of b) than a)
        
        violated conjunction rule
        
        probability of conjunction of two things (A & B) never higher than each individual thing (A or B)
        
        Law of Large Numbers
        
        large numbers always more representative of population
        
        Hospital EXP
        
        :one: large hospital => 45 births daily and small hospital => 15 births daily
        
        :two: Which hospital has more days where 60% or more births are :male_sign:
        
        22% said large :hospital:, 22% said small :hospital:, 56% said no diff
        
        correct answer is small :hospital:
        
        based on inverse of law of large numbers (small samples are less representative)
      - shortcuts, rules of thumb that help generalise from specific observations to broader judgements
        
        likely to provide correct answer to problem
      - reduce cognitive load
      - may introduce cognitive biases
    - - Myside Bias
        
        evaluating evidence such that it is biased towards own opinions and attitudes
        
        Capital Punishment EXP
        
        :one: Differentiated supporters and upposers of capital punishment using questionnaire
        
        :two: presented with research that either provided or didn't provide evidence for capital punishment as murder deterrent
        
        article supporting capital punishment as murder deterrent rated as convincing by supporters and unconvincing by opposers
        
        attended to info aligned w their beliefs and ignored info that contradicted beliefs
      - Confirmation Bias
        
        *looking for info that conforms to hypothesis and ignoring info that contradicts it*
        
        broader than myside bias which is limited to opinions and attitudes
        
        Three Numbers EXP
        
        :one: given 3 numbers and told they conform to a simple rule
        
        :two: guess rule by writing out 3 number sets and receive feedback on whether sets fit rule
        
        :three: announce rule once confident enough
        
        many guessed "increments of 2" which was wrong :red_cross:
        
        correct rule :check: was "increasing magnitude"
        
        many only wrote sets that confirmed hypothesis (confirmation bias)
        
        few guessed correct rule
        
        wrote sets to disconfirm current hypotheses
        
        tested multiple hypotheses
      - Echo Chamber
        
        choosing to spend time with people that share your beliefs
        
        result of confirmation bias + myside bias
    - - Backfire Effect
        
        support for viewpoint becomes stronger when presented with facts opposing viewpoint
        
        WMD EXP
        
        :one: identified political inclinations of participants
        
        :two: given mock news story describing WMDs in Iraq
        
        :three: 1/2 received a correction saying that no WMDs were found in Iraw
        
        :four: asked how much they agree that Iraq had WMDs but hid them right before USA invasion
        
        Results
        
        liberals that received correction more likely to disagree
        
        correction was able to reduce WMD misinformation
        
        moderate liberals and centrists that received correction agreed as much as those w/o correction
        
        conservatives more likely to believe statement if issued correction
        
        backfire effect