Reasoning and Decision Making Flashcards
Decision making
Making choices between alternatives
Reasoning
Process of drawing conclusions
Inductive reasoning
Arriving at conclusions about what is probably true, based on evidence
Uses heuristics
Deductive reasoning
Following logic to assess validity of a statement
Definitely correct, no need for experience
How strength of inductive reasoning is determined
High representativeness of observations: strong evidence
How much one instance fits with another instance
Representativeness heuristic
Judgments based on how much one event resembles another event
Probability that A is a member of class B can be determined by how well the properties of A resemble the properties commonly associated with class B
Example: meet Steve exercise (Steve sounds more like a librarian than a salesman, but Steve isn’t necessarily a librarian)
Base rate
Relative proportion of different classes in the population
People ignore this, leading to error in their reasoning
Meet Steve example: people forget that there are more salesmen in the population than librarians (therefore, it is more likely that Steve is a salesman)
Conjugation rule
Likelihood of 2 events occurring together cannot be higher than probability of either event occurring alone
Meet Linda example: Linda sounds like a feminist, but it is more likely that she is just a bank teller (more bank tellers in population than feminist bank tellers)
Law of large numbers
The larger the number of individuals randomly drawn from a population, the more representative the resulting group will be of the entire population
A few observations don’t correlate to a large number of observations
Example: kidney cancer (incidence is both highest and lowest in rural, sparsely populated, and traditionally Republican states: population is small, so individuals can greatly affect incidence)
Availability heuristic
Those instances that come to mind most readily are judged to be the most common
Example: people think of plane crashes more than car crashes, but there are far more car crashes than plane crashes
Illusory correlations
Imaginary relationships
Lead to stereotypes and superstitions
Economic utility theory
If people are rational and have all the relevant information, they will make a decision which results in the maximum expected utility
People don’t always use this approach (people choose a 50% chance of winning $500 over a 25% chance of winning $1100)
Can’t use theory when payoff can’t be calculated
Framing effects
People can be influenced to make a certain decision based on the way that it is framed
Example: “gun violence” rather than “gun control” or “gun rights” rather than “gun control”
Opting in versus opting out (more effort to opt in or out, so more people pick default option; reason why organ donation is so low in US, which has an opt in policy)
Ultimatum game
Proposer (human or computer) and responder
Proposer has money and must split it
Responder must decide on whether to take offer (if not, no one gets anything)
People are more likely to take bad offers ($9 to $1) if computer than if human (feeling of unfairness associated with human)
How an increase in choices affects decision making
More choices -> greater feeling of overwhelm -> lessened ability to make decisions
Why computers make different decisions than people
Computers don’t have emotions: make rational decisions
People have emotions: don’t always make rational decisions (don’t always maximize benefit)
Predicting emotions
Points game: people overestimate effects of winning (keep all 5 points) and effects of losing (lose 3 points, leaving you with 2) on happiness levels
People overestimate the influence of emotions (especially negative ones)
Justifying decisions
People use emotions to justify decisions
Trip example: people choose to buy trip package after finding out that they passed or failed test (justification: reward or opportunity to release stress), but delay making decision when it isn’t clear whether they passed or failed test
Confirmation bias
Tendency to look for information that conforms to our hypotheses and to overlook information that argues against it
Relationship between validity and truth
Validity doesn’t always equal truth
Conditional syllogism
Form of deductive reasoning that contains two premises and a conclusion and the first premise has the form “If… then…”
First “if” term: antecedent
Second “then” term: consequent
Example: “If you hit a glass with a feather (antecedent), it will break (consequent).”
Affirming the antecedent
If p, then q. P. Therefore, q.
Example: “Lisa hit the glass with a feather. Therefore, the glass broke.”
Valid syllogism: follows logical rule
Denying the consequent
If p, then q. Not q. Therefore, not p.
Example: “The glass didn’t break. Therefore, it wasn’t hit with a feather.”
Valid syllogism
Affirming the consequent
If p, then q. Q. Therefore, p.
Example: “The glass broke. Therefore, it was hit with a feather.”
Invalid syllogism: other things could break glass
Denying the antecedent
If p, then q. Not p. Therefore, not q.
Example: “Lisa didn’t hit the glass with a feather. Therefore, the glass didn’t break.”
Invalid syllogism: glass could still break from something else
Wason task
4 cards: each has a letter on one side and a number on the other
Task: which cards need to be turned over to test the rule
Rule: if there is a vowel on one side, there is an even number on the other
E, K, 4, 7
Must check E and 7 (affirming antecedent and denying consequent)
4% of people get this task right
Falsification principle
To test a rule, it is necessary to look for situations that falsify that rule
How everyday experiences affect deductive reasoning
We perform better on tasks that match our everyday experience
Amended Wason task: cards say beer, soda, 24 years old, 16 years old
73% of people can get it right (need to turn over beer and 16 years old)
