College 3 Flashcards
Life is unpredictabe
We are dependent on estimates and rules of thumb.
Our intuition isn’t always right.
We don’t apply statistics & math correctly.
Life is like a box of chocolates; you never know which one you’re gonna get.
When do we notice a decision the most?
When it goes wrong (e.g., a shipwreck or after a breakup you think “how did I now see this before”)
Biases used in decisions and judgments
Bias in finding information.
–> In line with theory (tunnel theory)
Bias in sample.
–> E.g., small size, selective sample (e.g., only friends)
Bias in base rates.
Bias in using or “averaging” the information.
–> You gather a lot of information but use statistics wrong
Biases in seeing co-variation.
–> Illusory correlation
Biases in ‘weighing’ the data.
The law of large numbers
The more you repeat an experiment, the more likely the outcome of this experiment is in line with the true expected value.
Outcomes become closer to the expected value with more trials.
Gambler’s fallacy
If a particular event occurs more frequently than normal during the past, it is less likely to happen in the future, even when events are statistically independent.
Regression effect
Regression to the mean does exist, but…
Extreme effect will, on average, be less extreme at another point in time. With more observations it will more likely be less extreme.
Regression to the mean
Effect from the theory of large numbers.
Effect applies to stable context.
In unstable context, extreme observation can be indicative of change (e.g., new chef).
Whether you punish or praise someone that comes late to their job/school doesn’t work, because it is random. You don’t have as much of an effect as you think you have.
Anchoring and adjustment
People give too much weight to the first bit of data for their quantitative estimates.
Anchoring
Assimilation of quantitative estimates to an available comparison figure.
- Also, with numbers that are irrelevant to the decision.
Explanations
- Initial hypothesis: answer = anchoring value; then people adjust too little.
- Anchors make different types of information accessible, that are then used in the judgement.
Lexical Decision Task
Task
- Task to measure cognitive accessibility
- Existing word => “yes” button (as fast as possible)
- Non-existing word => “no” button (as fast as possible)
Findings
- More accessible words are recognized more quickly as being existing words.
- In the 20,000 Euros condition participants recognized cheaper car brands (Opel, Golf) faster.
o Greater chance that this information is retrieved from memory and used to make an estimate.
o This is why the estimate was lower. - In the 40,000 Euros condition participants recognized expensive car brands (BMW, Mercedes) faster
o Etc.
What helps against anchors?
Shaking your head helps a bit.
Leaving the negotiation (not too sure if this’ll work).
Availability heuristic
The more vivid, the more you overestimate it.
- Easier to come up with example of event => event is estimated to be more probable.
Subjective ease counts, not number of retrieved examples.
- Experiment where subjects had to give examples of assertiveness 12 vs. 6. The people that had to come up with 6 examples were found to think of themselves as more assertive than people that had to come up with 12 examples. This is probably because it’s hard to come up with 12 examples.
Often leads to good estimates but familiarity and vividness of information can bias estimates.
Simulation heuristic
The generation (mental simulation) of events.
- Guides expectations, motivation, behaviour
Particularly with missed opportunities.
Counterfactual thinking.
- Simulation of alternative results (“if only I had…”)
- The easier it is => the greater the disappointment
Experiment by Medvec, Madey & Gilovic (1995) on medal winners
Participants rated emotions of medal winners during prize ceremonies
- Bronze winners look happier than silver winners (i.e., “gold losers”)
- Silver winners can imagine what it would have been like to win (1 step), bronze winners can image what it would have been like to not have been on the podium at all (1 step).
Representation heuristic
The more characteristics A (librarian) shares with B (Joris his behaviour), the more likely people think that A and B are associated.
- B can be the consequence of A
o Because he is a librarian he likes to read - B can be an exemplar of category A
o Boring, tidy and enjoying reading have a lot in common with a librarian. Observed probability is greater that Joris works in a library than in construction.
