Chapter 13- Decision making Flashcards
Reasoning
The process of drawing conclusions- the cognitive processes by which people start with information and come to conclusions that go beyond that information
Inductive reasoning
Reasoning based on observations, examples, or patterns to arrive at a general conclusion. You use data to draw specific observations, and our past experiences are used as data to make decisions. These conclusions are probably, but not definitely, true. Ex- , building scientific theories based on empirical data
Deductive reasoning
Reasoning based on facts, rules, definitions, or properties to arrive at a conclusion. Starts with a general statement or hypothesis, then examines the possibilities to reach a specific conclusion. Conclusion logically follows from premises, going from general to specific. Ex- exploring predictions of hypotheses made by theories in scientific experiments. Involves series of statements called syllogisms.
Inductive reasoning factors (3)
- Representativeness- how likely is it that your observation generalizes
- Number of Observations- how replicable are your observations
- Quality of Evidence- how valid are your observations
Heuristic
Informal strategy or approach that works under some circumstances, but is not guaranteed to yield the correct answer. It’s like a rule of thumb or Mental shortcut. Advantage- saves resources at the cost of accuracy, allow fast decision making. Disadvantage- it can be misleading, can cause inaccurate decisions
Availability heuristic
Estimates are influenced by the ease with which
relevant examples can be remembered. Items that come more easily to memory are assumed to be more likely (plane crashes come to mind more easily than car crashes).
Illusory correlation
Bias in the judgment of the frequency with which two events co-occur. Based on the strength of associative bond- when the association is strong one is likely to conclude that the events have been frequently paired. Strong associates will be judged to have occurred together frequently. Extremely resistant to contradictory data
Representativeness heuristic
An estimate of the probability is determined by one of two features. Items that resemble expectations are assumed to be more likely. Tendency to ignore probability and attend to features that align with representations. If Event A is more representative than Event B there is à tendency to judge Event A is more probable than event B. Farmer vs librarian example
Base rates
In the Robert example, participants ignore the base rates of farmers and librarians in the population. There are 5x or more Farmers than Librarians in the US. Therefore, it is much more likely that Robert was a farmer (remember that he was randomly chosen from the population)
Conjunction fallacy
Conjunction: two things (events) happen together. The probability of events A & B occurring together
CANNOT BE HIGHER than the probability of either A or B event
Syllogisms
A form of deductive reasoning. Syllogisms consist of three statements: first two statements (premises) are taken to be true (accept them as given). Third statement is the conclusion based on the first two statements. Includes categorical and conditional syllogism.
Categorical syllogism
All three statements (premises and conclusions) start with either ALL, NO, or SOME. Both premises are taken to be true, the conclusion follows logically, so the
conclusion is valid
Need to consider 2 dimensions to evaluate a syllogism
- Is the argument valid? (In other words, is the form of the argument correct?)
- Is the argument sound (i.e. true)? In other words, is the argument valid
and are its premises true
Validity
Depends on the form of the syllogism, which determines whether the conclusion follows from the two premises
Truth (or soundness)
Refers to the content
of the premises, which have to be evaluated to determine whether they are consistent with the facts.
