Bounded rationality Flashcards

1
Q

what is bounded rationality =

A

a human decision making process in which people seek a decision that will be good enough rather than optimal

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2
Q

what is a rational decision maker

A
  • have consistent transitive preferences
  • maximise their utility
  • use all information to make decisions
  • obey Bayes rules and the laws of probability
  • description invariance - answers remain unchanged by a transformation
  • emotions dont affect logical thinking
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3
Q

why are probabilities important in decision making

A

when making decisions - required judgement of probabilities is needed
- people make decisions based on their perceived probabilities

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4
Q

what do rational people do with probabilities - when forming beliefs

A
  • they obey the law of probabilities
  • have all information
  • use Bayes rule to asses probabilities of events
  • bayes rule uses conditional probability
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5
Q

Casscells (1978)

A
  • do humans apply Bayes rule
  • asked 60 harvard medical students - a question to solve the conditional probability
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6
Q

results from getting students to use Bayes

A
  • half got the obviously wrong answer
  • only 18% got the right answer
  • people ignore Bayer rule and ignore base rates
  • peoples subjective beliefs are very different from the real probabilities - overestimating
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7
Q

How are people bounded rationally?

A

they use heuristics rather than making fully rational choices (rational = use Bayes theorem when forming probabilistic judgements)
- people use mental shortcuts that dont overload cognitive thinking to simplify problems

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8
Q

what are the 2 models of thinking

A

system 1 = quick and intuitive thinking, effortless, difficult to control, silly mistakes = intuition

system 2 = consious reasoning, slow, calculating, effortful, complex = reasoning

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9
Q

why does system 1 override 2

A

system 1 is fast - easier to reach a conclusion = plausible answer - comes quickly to mind

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10
Q

what are the 3 reasons system 1 intutions come to mind fast

A
  1. accessibility
    - information is readiliy accessible - come to mind fast
    - if it comes faster than working out hard calculations
    - depends on context and reference of things
  2. perceptions
    - system 1 is stronger than system 2 - think answers that come naturally are right
  3. priming
    - subconscious clues that hint us to give a certain answer = primes only work on system 1
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11
Q

instead of using Bayes rule people use heuristics to …

A

make judgements and estimates of probabilities because
- simple cognitive complexity
- speed up decision making
- but LEAD TO SYSTEMATIC BIASES

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12
Q

what are the three important types of heuristics

A
  1. representativeness
    - the probability of x belonging to y is judged on the basis of how similar x is to y
  2. availability
    - probability is estimated by how easy it comes to mind
  3. anchoring
    - a quantity is estimated by starting from a convenient anchoring and adjusting appropriately from there
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13
Q

Kahneman (1973)

A
  • peoples rankings of probability and similarity turn out to be the same - they use similarity as a proxy for probabilistic thinking
  • 3 groups
    1. rank 9 degrees by frequencies of students (elicit base rates) - education 20%
    2. gave a summary of Tom - asked students how similar Tom is to the typical student of each degree - computer science highest
    3. how likely is Tom to be a student of each subject rank - computer science highest
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14
Q

what are the results of getting to rank students into degrees

A
  • they ignore the base rates that they think 20% of students choose education
  • instead peoples probability judgement are the same as there similarity scale
  • system 1 mistake - easy to use stereotypes instead of working out answer
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15
Q

what are 3 other biases related to representativeness = kahnemann

A
  1. insesitivity to sample size
    - the likelihood of a sample result by the similarity of the results to the properties of the population
  2. misconception of chance
    - law of small numbers - think that population expected values will show in small samples
  3. regression to the mean
    - outliers move back towards the mean in next trials - they draw incorrect inferences
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16
Q

example of insensitivity to sample size

A
  • large hospital = 45 babies
  • small hospital = 15 babies
  • which hospital would you expect more boys to be born
  • right answer = small hospital - wrong answer = about the same
  • as sample size grows mean gets closer to theoretical mean
17
Q

example of misconception of chance

A
  • people expect sequences of events that will represent the essential characteristic of large sequence
  • HHHTTT < HHTHTT
  • lottery - given numbers drop off
  • store is lucky - more people get tickets
18
Q

examples of regression to the mean

A
  • if extreme variable is observed in first measurment it will tend to be closer to average in second measurement
  • people think previous trials are representative of further trials
  • if my child does bad on test and i punish then they do good
  • if my child does good i reward and then they do bad
19
Q

availability heuristic example

A
  • frequency substituted with how easy they come to mind
  • ease of recall used to estimate probabilities
20
Q

anchoring example

A
  • will wrongly adjust probabilities according to anchor
  • acts as a prime
  • anchoring as priming = swade you to answer according to the prime - system 1 problem