Decision Making & Reasoning

Decision Making:

e.g. choosing a college --

 

 

 

 

• loss aversion:

e.g. toss a coin -- you'll get $20 if it comes up heads, but lose $10 if it comes up tails. Accept?

 

 

 

• perceived justification:

e.g. Tversky and Shafir (1992) - told participants to imagine they had the chance to buy a very cheap holiday in Hawaii, but the special offer expired tomorrow.

Task: to choose one of the following 3 options.

1. buy the holiday

2. do not buy the holiday

3. pay a $5 non-refundable fee to retain the opportunity to buy the holiday the day after tomorrow.

3 groups of participants:

Group A:

Group B:

Group C:

 

Results:

Group A:

 

Group B:

 

Group C:

 

 

Conclusion: perceived justification affects the decision

Group A:

 

Group B:

 

Group C:

 

• subject to biases -- we often do not make objective decisions

 

• Confirmation bias:

 

 

e.g. politicians

 

 

e.g. medical diagnosis

 

 

• Belief Perseverance:

 

e.g. Madonna

e.g. 1st year at college

 

 

 

• Heuristics in Decision Making:

 

• Representativeness Heuristic:

 

 

e.g. eating at one restaurant

 

 

e.g. description of a person: skilled at carpentry, proficient at wrestling, owns a pet snake, knows how to repair motorcycles, and has a police record.

 

More likely a man or a woman?

More likely a salesman or in a motorcycle gang?

 

• let's estimate there are 10,000 members of motorcycle gangs and 100 million salesmen

 

 

 

 

 

 

e.g. Study by Kahneman & Tversky (1973). Subjects given a description to read and told that it was 1 of 100 description (actually the same one was used for all subjects).

 

Group A:

 

Group B.

 

 

Description: "Jack is a 45-year-old man. He is married and has 4 children. He is generally conservative, careful, and ambitious. He shows no interest in political and social issues and spends most of his free time on his many hobbies which include home carpentry, sailing, and mathematical puzzles."

 

Task: Subjects asked if it was more likely the description was for a lawyer or an engineer.

 

Results:

 

 

 

 

 

 

• Availability Heuristic:

 

 

e.g. if a word of 3 or more letters is picked at random from an English text, is it more likely it will have an "r" as the 1st letter or an "r" as the 3rd letter?

 

 

 

e.g. how likely is it you'll be the victim of a crime?

 

 

e.g. likelihood of dying in a car crash or a plane crash?

 

 

 

• Framing Effects in Decision Making:

 

 

e.g. deciding which course to enroll in.

"Dr. Smith fails 10 percent of his students."

"Dr. Smith passes 90 percent of his students."

 

 

e.g. more likely to recommend a medical treatment to another person when told it has a "50 percent success rate" than a "50 percent failure rate".

 

e.g. rate meat more highly when labeled "75 percent lean" than when labeled "25 percent fat".