1 Reasoning and Decision Making

2 Thinking  Ways of thinking Analysis – breaking down a large complex problem into smaller simpler problems Synthesis – combining two or more concepts into a complex form Divergent thinking – generating many ideas or possible solutions to a problem Convergent thinking – choosing the best solution or idea of a possible many

3 Categories of thinking processes  Problem solving – developing a solution to a problem situation  Judgments and decision making – involves making choices  Reasoning – drawing conclusions given specific information  Creativity – production of original thoughts and ideas

4 Reasoning

5 Two basic processes in reasoning  1.A process that uses existing knowledge to reason or make decisions about new situations and information acquired during new experiences. Top-down process Errors can lead to top-down errors  2.A process that determines what new information is relevant to reasoning and decision making Confirmation bias

6 Reasoning and Logic  Two forms to be covered: Syllogisms – a 3-statement logical form, the 1 st two parts state premises or statements assumed to be true, and the 3 rd part is a conclusion based on those premises Conditional reasoning – a logical determination of whether evidence supports, refutes, or is irrelevant to the stated if-then relationship

7 Syllogisms  Abstract: All members of category A are members of category B. All members of category B are members of category C Therefore, all members of category A are members of category C  More concrete example: All psychology students are intelligent All intelligent people are rich Therefore all psychology students are rich  Use of a Venn diagram to determine accuracy of conclusion

8 Conditional Reasoning  An if – then statement where the if part is the antecedent and the then statement is the consequence If the antecedent is true, the consequence is true, or If the antecedent exists, the consequence exists Two types of valid inferences  Modus ponens  Modus tollens

9 Modus Ponens  Affirming the antecedent to be true  Valid inference: If a person is intelligent, then they are rich. Mary is intelligent, she is rich  Invalid inference: negating the antecedent Mary is not intelligent, she is not rich. Wrong  An easier example: If one kills a lawyer, then she is dead. Valid: John killed a lawyer, she is dead Invalid: John did not kill a lawyer, she is not dead

10 Modus Tollens  Concerned with the consequence – works opposite to modus ponens  If you kill a lawyer, then she will be dead Invalid inference – confirming the consequence  The lawyer is dead, therefore you killed her Valid inference – negating the consequence  The lawyer is not dead, therefore you didn’t kill her

11 Other examples  If one is intelligent, then one is rich  1.John is rich, therefore he is intelligent  Invalid – not all rich people are intelligent  2.John is not rich, therefore he is not intelligent  Valid  3.John is intelligent; he is rich  Valid  4.John is not intelligent; he is not rich  Invalid- you do not have to be intelligent to be rich

12 Problem with the confirmation bias  Problem is we tend to want to affirm or deny the antecedent and ignore the consequence  Example: Wasson card problem Test rule :If a card has a vowel on one side, then it has to have an even number on the other side. 2 nd rule: If a letter is sealed, then it has to have a 50cent stamp

13 Problem with the confirmation bias

14 Decisions and Judgments

15 Decisions under situations of certainty  You have all the necessary information to make a correct decision  Frequently studied – decisions about physical differences Our decisions about which stimulus is the brightest, smallest, heaviest, etc. depends upon factors other than the physical difference between them Example: The determination of which of 2 lights is brightest depends upon the physical difference, but also the absolute brightness of the light, the brightness of the background, and how long the lights were visible

16 Distance or discrimination effect  The greater the distance or difference between two stimuli being compared, the faster the decision about their differences  Symbolic distance effect – comparisons between two symbols that represent two stimuli – like drawings Differs from distance effects in that it requires semantic and other memory processes  Semantic contiguity effect

17 Examples

18 Judgment and decision making in situations of uncertainty  The individual is not given all the information necessary to be certain of the answer and has to use previously acquired knowledge  Primary problem: lack of knowledge and misinterpretation

19 Utility Maximization Theory  Humans attempt to make decisions that provide us with the maximum gain  Subjective utility theory – modification that takes into consideration that humans are not always objective, but take consider subjective factors

20 Examples of Subjective factors  Satisficing – we do not always pursue the optimal decision, but accept one that is adequate  Immediate benefit versus delayed reward – discounting delayed rewards  The way the problem is framed (presented) is important

21 Example 1 of framing  You go to New York and decide to go to a Broadway play. You buy a ticket for $100 in the morning, but when you go to the theater that evening, you discover you have lost the ticket. You have plenty of money to buy another one: do you?  You go to New York and decide to go to a Broadway play and tickets cost $100. You go to the theater that evening and when you start to pay for your ticket, you discover you have lost $100. You have plenty of money to buy a ticket: do you?

22 Example 2 of framing  Subjects has to make 2 decisions:  Decision 1: A. A sure gain of $240 or B 25% chance of winning $1000 and a 75% chance of winning nothing  Decision 2: C. A sure loss of $750 or D. 75% chance of losing$1000 and 25% chance of losing nothing

23 Possible outcomes  A and C: A sure loss of $510  B and D: 75% chance of losing $1000 and only a 25% chance of winning – not good odds  A and D: $240 - $1000 = -$760 $240 - $0 = +$240  B and C: $ $750 = +$250 $0 - $750 = -$750

24 Use of algorithms  A specific solution procedure that if used correctly guarantees a correct solution  Identify all possible solutions and try each one until you find the one that works  The use of Algorithms is not “trial and error”  Addressed in more detail in problem solving

25 Heuristics  A rule of thumb strategy – usually a short cut that generally works in most situations, but doesn’t guarantee a correct solution

26 The Representative Heuristic  Definition: a judgment rule in which an estimate of probability or likelihood of an event is determined by one of two features: How similar the event is to the population of events it came from, or Whether the event seems similar to the process that produced it

27 Examples  A town has 2 hospitals. In 1, about 45 babies are born each day, and only 15 are born in the other each day. On the average 50% of all babies are boys. Though not necessarily on every day. Across 1 year the hospitals recorded the number of days on which 60% or more of the babies born were males.  Which hospital had more of these days or were they have the same number of these days?

28 Example 2  You flip a coin 6 times. Given that flipping a fair coin is random ( a chance or a head or tail). Which of the following outcomes is most likely or probable? A.HHTHTT B.HHHTTT  Both are equally likely the probability is same on each toss.

29 Example 3 – the use of stereotypes  There are 100 people in a room, 70 of them are lawyers, 30 are engineers.  Bill is randomly selected from this room. What is the probability he is a lawyer?  Dick is a 30-year-old man. He is married with no children. A man of high ability and high motivation he promises to be very successful. He is well liked by his colleagues.  Jack is 45-years-old, and married with 4 children. He tends to be conservative, careful, and ambitious. He shows little interest in political and social interests, and enjoys carpentry, sailing, and mathematical puzzles.

30 Ignoring Base Rates  Why are more graduates first-born than second-born?  Why do more hotel fires start on the 1 st ten floors than the second ten floors  In baseball why are more runners thrown out by pitchers on 1 st base than 2 nd base?  Frank is a meek and quiet man whose only hobby is playing chess. He was near the top of his college class and majored in philosophy. Is he a librarian or a business man?  You’ve watched a coin toss come up heads 5 times in a row. If you bet $100 on the next toss, would you choose heads or tails?

31 Availability Heuristic  A judgment rule in which one’s estimates are influenced by the ease with which relevant examples can be remembered  General world knowledge Are there more words in the English language that begin with “R” or have an “R” as the 3 rd letter? GM sells more Chevrolets than Cadillacs. For every Cadillac it sells how many Chevrolets does it sell?

32 Other availability heuristic biases  Familiarity Bias – Tversky and Kahneman (1973) Subjects given list of 39 names, 19 women’s names and 20 names of men Group 1 asked to recall all the names on the list; group 2 asked to determine if the list had more women’s names or men’s names  Salience and vividness biases

33 Simulation heuristic  A judgment rule that involves a mental construction or imagining of outcomes, a forecasting of how some event will turn out or how it might have turned out differently under another set of circumstances Undoing heuristic Hindsight bias Blaming the victim

34 Blaming the victim  Paul normally leaves work at 5:30 and drives directly home. One day, while following his routine, Paul is broadsided by a driver who violated a stop sign and is seriously injured.  Paul, feeling restless at work, leaves early to see a movie. He is broadsided by a driver who violated a stop sign and is seriously injured.  Paul receives an emergency call to return home. While driving home, Paul is broadsided by a driver who violated a stop sign and is seriously injured.

35 Limited knowledge as a limitation in reasoning  People who keep pushing an elevator button to make it come faster  Naïve physics understanding principles of motion  Limitations in processing resources  What is the answer to 8X7X6X5X4X3X2X1  What is the answer to 1X2X3X4X5X6X7X8

36 Group decision making  3 frequent errors Group think Incremental-decision making Content error

37 Development of reasoning in young adults  Relativistic reasoning  Dialectic reasoning  Systematic reasoning