PSC 1 Lecture 10: Lecture 10: Reasoning, Judgment and Decision-Making
Lecture 10: Reasoning, Judgment and Decision-Making
Reasoning, Judgment and Decision-Making
• Classic trolley problem
• How do people make decisions?
• Are people rational?
o We are rational if decisions are based on logic, reasoning, consistent etc.
o Emotion is considered less rational
o One's beliefs are consistent with the reasons for having those beliefs.
o One's decisions are consistent with one's reasons for those decisions.
• Are people consistent?
• Are people good at estimating probability?
• Are normal people rational?
o Normal people are not always rational
• We make decisions even when deliberating and without complete information, we take
decisions under uncertainty.
o E.g. Picking restaurants
o Thus, we need to be good with probabilities.
• Meteor is not going to hit the restaurant. That should not affect our decision.
• Some decisions are made automatically (like a reflex)
o If a ball is coming my way, but I'm going to move without a thought.
• Some decisions are made by the "heuristics"
o Heuristics help us make decisions quickly
o But reduces rationality and accuracy of the decision.
• Some decisions are made after exhaustive reasoning.
o Also called "algorithmic reasoning"
Bayesian statistical inference
• Assume;
o You have a test for HIV
o The test is 98% accurate
o The incidence of HIV is 1 in 1000
• Question: if someone tests positive for HIV, what is the true probability that they have
HIV?
o 98 sick people and 1998 healthy people het positive test result.
o 98 sick + 1998 healthy = 2096 total
o So probability of sick given positive = 98/2096 = 0.0468*100=4.68
o P(sick/positive) = 4.7%
Gamblers' fallacy
• Prior result affects probability of future events (even when events are independent).
• E.g. Dr. J (Julius Irving )
o Dr. J sinks 50% of his shots overall
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Document Summary
Picking restaurants: thus, we need to be good with probabilities, meteor is not going to hit the restaurant. That should not affect our decision: some decisions are made automatically (like a reflex) Bayesian statistical inference: assume, you have a test for hiv, the test is 98% accurate, the incidence of hiv is 1 in 1000, question: if someone tests positive for hiv, what is the true probability that they have. Hiv: 98 sick people and 1998 healthy people het positive test result, 98 sick + 1998 healthy = 2096 total, so probability of sick given positive = 98/2096 = 0. 0468*100=4. 68, p(sick/positive) = 4. 7% Gamblers" fallacy: prior result affects probability of future events (even when events are independent), e. g. Dr. j (julius irving : dr. j sinks 50% of his shots overall. Availability heuristics: perception of frequency depends on availability, e. g.
