1. Why dumb people do smart things… and vice versa.
Heuristics and Biases
Why dumb people do smart things… and vice versa.

2. Overview Context Heuristics as biases (defects)
Availability
Anchoring and adjustment
Representativeness (Revisited)
Heuristics as intelligence
Recognition
“Fast and frugal”
Tit-for-tat (social heuristics
Conclusion

3. Context Rational Choice Theory Utility Theory Probability Theory
Bounded Rationality

4. Rational Choice Theory
von Neumann & Morgenstern (1947) attempted to remove psychological assumptions from the theory of decision making:
Individuals have precise information about the consequences of their actions
Individuals have sufficient time and capability to weigh alternatives
All decisions are “forward looking” (e.g., the “sunk-cost fallacy”)
“Game theory” is RCT in practice

5. Utility & Probability Theory
UT determines how preferences are determined within RCT
A response to the St. Petersburg Paradox (1734)
Strictly construed, UT assigns a common currency (utiles) to disparate outcomes
Probabilistic reasoning
Under uncertainty, the “value” of a choice is the expected value of probabilistic outcomes
Savage (1972) formalized the conjunction of UT and Bayesian probabilistic reasoning

6. Bounded Rationality
Acknowledges several limitations of UT and RCT as descriptive models of individual choice
People lack:
Perfect information of outcomes and probabilities
Consistent utility functions across domains
Time and cognitive capabilities to comprehensively enact the prescriptions of UT and RCT
Experts and everyday decision makers are error-prone
Useful in the domain of problem-solving
Simon (1955) argued for satisficing
Individuals make decisions that are “good enough” considering the costs of decision-making, the specific goal, and cognitive limitations

7. Why heuristics? RCT & UT are terrible descriptive models in many cases
Bounded Rationality’s limitations are insufficient to explain human behavior
Sometimes information is insufficient even for BR
Many judgments are not goal-directed or encased in problem-solving tasks
Human errors are systematic
Discovering heuristic rules of judgment can explain these systematic errors

8. Heuristics
Substitute easy questions for difficult ones (attribute substitution)
Defined heuristic rules specify the substitution
Allow judgment and decision making in cases where specific and accurate solutions are either unknown or unknowable
Availability, anchoring and adjustment, and representativeness are frequently considered “metaheuristics” since they engender many specific effects

9. Heuristics as error-generators
How smart people do dumb things
Kahneman, Slovic, & Tversky (1974)
Availability
Anchoring and adjustment
Representativeness

10. Availability Heuristic
“The ease with which instances or occurrences can be brought to mind” motivates judgment
Retrievability of instances
“Were there more males or females on a given list?”
Effectiveness of a search set
“Do more English words begin with r or have r as the third letter?”
Biases of imaginability in ad hoc categories
“Which is larger: 10 C 2 or 10 C 8?”
Illusory correlation
Chapman & Chapman (1969)

11. Anchoring and adjustment
Insufficient adjustment
Estimates are made by starting from an initial value (anchor) and adjusting to yield a final answer
A Demonstration:

12. Condition 1
1×2×3×4×5×6×7×8 = ?

14. Condition 2
8×7×6×5×4×3×2×1 = ?

16. Anchoring and adjustment
Insufficient adjustment
Condition 1: 1×2×3×4×5×6×7×8
Mean answer: 512
Condition 2: 8×7×6×5×4×3×2×1
Mean answer: 2250

17. Anchoring and adjustment
Correct answer: 8! =
1×2×3×4×5×6×7×8 =
8×7×6×5×4×3×2×1 =
40,320

18. Anchoring and adjustment
Biases in the evaluation of conjunctive and disjunctive events
Probability of conjunctive events overestimated
Probability of disjunctive evens underestimated
Anchoring in the assessment of subjective probability distributions
Variance of estimated probability distributions narrower than actual probability distributions
Common to naïve and expert respondents

19. Representativeness
Likelihood of a condition is judged by similarity to a condition, mitigating factors notwithstanding
Insensitivity to prior probability of outcomes
“Imagine a group of (70/30) lawyers and (30/70) engineers.”
“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 quite successful in his field. He is well-liked by his colleagues.”
Participants judged Dick to be equally likely to be an engineer regardless of prior probability condition
Base rate neglect

20. Representativeness Insensitivity to sample size
A certain town is served by two hospitals. In the larger hospital about 45 babies are born each day, and in the smaller hospital about 15 babies are born each day. As you know, about 50 percent of all babies are boys. However, the exact percentage varies from day to day…
For a period of 1 year, each hospital recorded the days on which more than 60 percent of the babies born were boys. Which hospital do you think recorded more such days?
The larger hospital (21)
The smaller hospital (21)
About the same (within 5% of each other) (53)

21. Representativeness Misconceptions of chance Conjunctive fallacy
“The law of large numbers applies to small numbers as well.”
Expert researchers select sample sizes too small to fairly test hypotheses (Cohen, 1969)
Conjunctive fallacy
Linda!
Robust effect:
Linda is more likely to be a bank teller than she is to be a feminist bank teller, because every feminist bank teller is a bank teller, but some women bank tellers are not feminists, and Linda could be one of them

22. Representativeness Conjunctive fallacy Linda! Robust effect:
1. Linda is more likely to be a bank teller than she is to be a feminist bank teller, because every feminist bank teller is a bank teller, but some women bank tellers are not feminists, and Linda could be one of them
2. Linda is more likely to be a feminist bank teller than she is likely to be a bank teller, because she resembles an active feminist more than she resembles a bank teller
65% of participants selected argument 2

23. Representativeness Misconceptions of Regression to the Mean
Why flight instructors conclude that criticism is more effective than praise

24. Heuristics as intelligence
How dumb people do smart things:
when less (information) is more

25. Recognition Heuristic
Who will win in the soccer match: Manchester United vs. Shrewsbury Town? (Ayton & Onkal, 1997)
Which has a greater population: San Diego or San Antonio? (Goldstein & Girgerenzer, 2002)
Turkish participants as accurate as British in the former; German participants more accurate than American in the latter

26. “Fast and Frugal” Ta Heart-attack patient
Standard, multivariate patient-interview vs.
Limited-information decision tree of 3 yes/no Q’s.
Decision tree “more accurate in classifying risk than complex statistical methods”
(Breiman et al., 1993) , in
(Todd and Gigerenzer, 1999) Ta

27. “Tit-for-tat” Simple game-theory strategy:
In the first round: always cooperate
Subsequently:
Remembers partner’s (opponent’s) one (!) previous response
Reciprocates previous response
This simple cooperation heuristic bested many highly sophisticated algorithms that based their decisions on high memory of partner’s actions and intense computational machinery (Axelrod, 1984)
Interacts well with other tit-for-tat machines as well
“Why selfish people do nice things” – a simple heuristic for social cooperation

28. Heuristics: bias or intelligence
Bias View
limited decision-making methods that people often misapply to situations where UT, RCT, and PT should be applied instead
instantaneous responses based on attribute substitution – switch hard questions for easy ones
sources of predictable error and underperformance
Intelligence View
“intelligent behavior” need not be computationally expensive
frugal representations and response mechanisms are more tractable and plausible
simple rules can generate rich cognitive and social effects