Clare 2

• heuristics for judgments (Kahneman & Tversky)

  mental shortcuts or rules of thumb that people use to make judgments and decisions quickly and efficiently

  
  

  sometimes lead to biases/ systematic errors in judgment
• intuition vs logic


  argue we have two separate systems in minds

  
  

  system 1 - heuristics to quickly produce intuitive answer

  
  

  system 2 - time and working memory to slowly produce logical answer
• types of heuristics and biases

  - representativeness

  - availability

  - anchoring
• conjunction fallacy


  The co-occurrence of two instances is more likely than a single one.

  
  

  eg feminist bank teller instead of regular bank teller
• representativeness heuristic


  judging likelihood of things in terms of how well they seem to represent/ match stereotypes

  
  

  (may lead us to ignore other relevant information)
• representativeness and misconceptions of chance


  lottery ticket - ordered sequence vs random sequence

  
  

  Representative heuristic: we estimate the second as being more probable
• availability heuristic


  making a decision based on the answer that most easily comes to mind, assume it's most common

  
  

  eg shark attack > falling coconut

  
  

  social media has a big impact
• availability influences belief (Carroll 1978)


  imagine either Gerald Ford or Jimmy Carter winning the presidential election.

  ford group - 60% believed he would actually win

  carter group - 55%

  
  

  imagination increases availability of event, increased judgments of likelihood
• anchoring adjustment


  tendency for people to be influenced by previously existing value or starting point to make a decision

  
  

  typically don't adjust answer enough so judgments are closer to anchor than should be

  
  

  (eg is river longer or shorter than 500/5000 miles? A: 2300)
• bayes theorem


  The probability of an event occurring based upon other event probabilities.
• base rate neglect


  The tendency to ignore information about general principles/ base rate information in favour of very specific but vivid information.

  
  

  (eg proportion of green and blue cabs)
• recognition heuristic


  when comparing two alternatives, if one is recognized and the other is not, the recognized alternative is inferred to be of higher value, or more likely to be correct
• recognition heuristic example (Goldstein & Gigerenzer 2002)


  which city has larger population, san diego or san antonio

  62% californian correct

  100% of german correct

  
  

  many germans recognised san diego but not antonio, americans knew both so more likely to fail
• Gigerenzer rationality and judgment


  we have an adaptive toolbox that contains fast and frugal heuristics

  
  

  these allow us to solve real world adaptive problems quickly and well

  
  

  we're rational in the sense that we have evolved decision heuristics
• Kahneman & Tverksy rationality and judgment


  our use of heuristics often leads to irrational judgments inconsistent with normative theories

  
  

  judgments are made intuitively and rapidly, but may be overridden with additional processing
• normative theories


  Theories that propose what humans ought to value/ what is right and wrong
• recognising patterns (Pinker 2012)


  one image randomly generated

  one pattern from nature

  
  

  we try to come up with theories why we think which is which

  
  

  (eg. glow worms want to group together, although they actually try to create space and even distribution)
• gambler's fallacy


  the belief that after a streak of events, the opposite becomes more likely
• survey of basketball fans (Gilovich et al 1985)


  91% agree that player has better chance of making shot after making 3 last shots vs missing 3 last shots

  
  

  after making shot, probability he will make next = 61%

  
  

  after missing shot, probability he will make next = 42%
• gamblers fallacy and winning streaks in sport


  After a series of the same outcome

  
  

  With gambling - we expect the next outcome to be different

  
  

  With sports - we expect the next outcome to be the same
• why is gambling and sports different


  We expect a random sequence to contain more variation than it actually does

  
  

  We try to construct explanations for a run

  eg in sports we say a person is hot, we use that explanation to predict the next outcome

  
  

  If we can't, eg for coin tosses, we expect that it can't continue and the next outcome will be different
• online gambling (Xu & Harvey 2014)


  mean chance of winning when on a winning streak = higher

  
  

  more cautious = higher chance of winning

  more risky = higher chance of losing

  
  

  self fulfilling prophecy
• how do we make predictions summary

  - probability

  - rely on past experience

  
  

  but sometimes see pattern in things that are random

  can lead us to wrong predictions
• how do we make judgments summary

  - use heuristics of representativeness, availability and anchors

  
  

  - good at recognising patterns and making predictions