11-decisionmaking-NC.ppt [Uyumluluk Modu]...• The order of presentation for these two groups had a...
Transcript of 11-decisionmaking-NC.ppt [Uyumluluk Modu]...• The order of presentation for these two groups had a...
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DEDUCTIVEREASONING
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Deductive Reasoning
• Reaching a conclusion from some given premises.– All pop-stars are stupidp p p– Merve is a popstar
Th f M i t pid– Therefore, Merve is stupid
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Conditional Reasoning
• Deductive validity– You can reach deductively valid conclusions that
are completely untrue with respect to the worldp y p– People are more likely mistakenly to accept an
illogical argument as logical if the conclusion isillogical argument as logical if the conclusion is factually true
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Conditional Reasoning
• Modus ponensThe reasoner affirms the antecedent– The reasoner affirms the antecedentIf p then qppq
• Example– All apples are fruits– This is an apple
Th f thi i f it– Therefore, this is a fruit
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Conditional Reasoning
• Modus tollensThe reasoner denies the consequent– The reasoner denies the consequentIf p then qnon qqnon p
• Example– All apples are fruits– This is not a fruit
Th f thi i t l– Therefore, this is not an apple
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Conditional Reasoning
• Deductive fallacies– Denying the antecedent– Affirming the consequentAffirming the consequent
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Conditional Reasoning
• Denying the andecedentIf p then qIf p then qnot pnot q (invalid)q ( )
• Example– All apples are fruits– This is not an apple– Therefore, this is not a fruit (invalid)
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Conditional Reasoning
• Affirming the consequentIf p then qIf p then qqp (invalid)p ( )
• Example– All apples are fruits– This is a fruit– Therefore, this is an apple (invalid)
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Wason Selection TaskWason Selection Task
Which cards do you need to turn over to obtain conclusive evidence of the following rule: gA card with a vowel on it will have an even number on the other sidenumber on the other side
E K 4 7E K 4 7
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Wason Selection TaskC fi i BiConfirmation Bias
Answer:E – affirming the antecedent7 – denying the consequenty g q
E – 89%7 25%7 – 25%K – 16%4 – 62%
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DECISION MAKING
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Decision Making
• An interdisciplinary field– Economics– Political SciencePolitical Science– Consumer Research
S i l– Sociology– Medicine– Psychology
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Decision Making
• We use heuristics in making decisions– Heuristics: general strategies that typically
produce correct solutionsp• Heuristics sometimes lead us errors an biases
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Decision Making
Heuristics and Biases• Amos Tversky and Daniel Kahneman
People may be far more likely to make decisions– People may be far more likely to make decisions based on biases and heuristics (short-cuts) than earlier decision making research has suggestedearlier decision-making research has suggested
– These mental shortcuts lighten the cognitive load f ki d i i b t th l ll fof making decisions, but they also allow for a
much greater chance of error
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Representativeness Heuristic
Judgments strategy in which we make estimates on howJudgments strategy in which we make estimates on how similar (or representative) an event is to its population.
Coin toss: Which is more likely to occur?HHHHHTTTTTHTHTHTTHHT
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Representativeness Heuristic
• Judge probability of an event based on how it hmatches a prototype
• Can be accurate • Can also lead to errors• Most will overuse representativeness• Most will overuse representativeness
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Availability Heuristic
In the English language, are there more words beginning with the letter K or more words with K in the third position?
People often report 2 x as many wordsPeople often report 2 x as many words beginning with K
But there are many more words with K in the 17
third position than in the first.
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Anchoring-and-Adjustment• Participants asked to calculate in 5 secs the• Participants asked to calculate in 5 secs the
answer to one of the following problems:1 2 3 4 5 6 7 8= 512– 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8= 512
– 8 x 7 x 6 x 5 x 4 x 3 x 2 x 1= 2,250• The order of presentation for these two groups
had a significant impact on their estimates• The correct answer, in both cases, is 40,320!
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A h i d Adj H i iAnchoring-and-Adjustment Heuristic
• Begin by guessing a first approximation (an g y g g pp (anchor)
• M k dj t t t th t b th• Make adjustments to that number on the basis of additional information
• Often leads to a reasonable answer• Can lead to errors in some cases• Can lead to errors in some cases
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Overconfidence
• Overconfidence– And individual’s overvaluation of her or his own
skills, knowledge, or judgmentsg j g– People tend to overestimate the accuracy of their
judgmentsjudgments– Example:
• When people were 100% confident in their answers• When people were 100% confident in their answers, they were right only 80% of the time
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Illusory Correlations
• An illusory correlation is a perceived relationship that does not in fact existthat does not in fact exist
• Illusory correlations are formed by the pairing of two distinctive events– Redelmeier and Tversky (1996) – 18 arthritis patients observed over 15 monthsp– The weather was also recorded– Most of the patients were certain that their p
condition was correlated with the weather– The actual correlation was close to zero
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Framing• Suppose you have invested in stock equivalent to the sum of
$60,000 in a company that just filed a claim for bankruptcy. They offer two alternatives in order to save some of the invested money:
• Positive Framing• Positive Framing– If Program A is adopted, $20,000 will be saved (certain
outcome))– If Program B is adopted, there is a 1/3 probability that $60,000
will be saved and a 2/3 probability that no money will be saved ( i k )(risky outcome)
• Negative FramingIf pr r m A i d pt d $40 000 ill b l t ( rt i t m )– If program A is adopted $40,000 will be lost (certain outcome)
– If program B is adopted, there is a 1/3 probability that no money will be lost, and 2/3 probability that $60,000 will be
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money will be lost, and 2/3 probability that $60,000 will be lost (risky outcome)
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Results
• Positive Framing– 78% choose program A– 22% choose program B22% choose program B
• Negative Framing– 22% choose program A– 78% choose program B
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