Lecture 5-6

Question 1

Why study normative models?

Answer 1

We need to understand optimization to try to approximate it. Also helps us understand decision making. Easier to understand than descriptive as they are consistent and follow rules.

Question 2

Why study normative models?

Answer 2

We need to understand optimization to try to approximate it. Also helps us understand decision making. Easier to understand than descriptive as they are consistent and follow rules.

Question 3

Objective standards

Answer 3

Physical, biological, economic criteria

Question 4

Subjective standards

Answer 4

Happiness, well-being, utility, reference points, multiple goals

Question 5

Constrained optimization

Answer 5

Decision making in the real world must be understood as constrained optimization. There are physical constraints (engineering), logical constraints (axioms of normative decision making), cognitive and emotional constraints (descriptive decisions)

Question 6

Perceptions of randomness

Answer 6

People see patterns when there is actually randomness - confirmation bias

Coincidences seem extremely unlikely due to the fact that we tend to focus on what is the same, not what is different. Confirmation bias. We also tend to focus on the particular (such as names) instead of focusing on the coincidence of an unspecified match of the same category. Latter is much more likely - see birthday problem.

Randomness is also mistaken for patterns in the lab - correct guesses make participants superstitious and more likely to choose the alternative the next time.

Question 7

Gambler’s Fallacy

Answer 7

Idea that probability is self correcting - after many heads, it has to be tails. Reverse Gambler’s Fallacy - superstition that something has to keep on happening. Of course, sometimes events are not independent - earthquakes become increasingly likely, for example, the longer it’s been since previous one.

Question 8

Law of small numbers

Answer 8

In a small sample, if there are streaks then we believe that something non-random is happening even if it is random.

Question 9

Why do people tend to see patterns in randomness?

Answer 9

Rewards for recognizing real patterns might be greater than penalties for imagining patterns.

Question 10

Illusory correlation

Answer 10

Important to view ratios when comparing, not just the numbers themselves.

Question 11

Post-hoc fallacy

Answer 11

If event B closely follows event A, we often think A caused B. That’s why there are people who believe vaccinations are harmful. Temporal connections are compelling; we often ignore many events between A and B. We pay more attention to what is there than what isn’t there

Question 12

Positive vs. negative evidence

Answer 12

We tend to pay more attention to positive evidence than negative evidence. Confirmation bias

Question 13

Third factor

Answer 13

Mistaken causation b/c ignore possible presence of a third factor related to both.

Question 14

Correlation vs. causation

Answer 14

Correlation is just when A is related to B, can be with or without causation. There may be third variables causing correlation. For causation, one must precede the other in time.

Question 15

Objective standards

Answer 15

Physical, biological, economic criteria

Question 16

Subjective standards

Answer 16

Happiness, well-being, utility, reference points, multiple goals

Question 17

Constrained optimization

Answer 17

Decision making = objective function + constraints

Decision making in the real world must be understood as constrained optimization. There are physical constraints (engineering), logical constraints (axioms of normative decision making), cognitive, emotional, and socialconstraints (descriptive decisions)

Question 18

Perceptions of randomness

Answer 18

People see patterns when there is actually randomness - confirmation bias

Coincidences seem extremely unlikely due to the fact that we tend to focus on what is the same, not what is different. Confirmation bias. We also tend to focus on the particular (such as names) instead of focusing on the coincidence of an unspecified match of the same category. Latter is much more likely - see birthday problem.

Randomness is also mistaken for patterns in the lab - correct guesses make participants superstitious and more likely to choose the alternative the next time.

Question 19

Availability heuristic

Answer 19

Things that are more easily available in memory, primed, vivid, or easier to think of (more examples) are judged as occurring more frequently. This is problematic in news as we can bring to mind catastrophic events more - plane travel seems riskier. Being killed by a shark seems more likely than being killed by falling airplane parts, although the latter is 30x as likely

Pros: many times, frequent events are easier to recall

Cons: Ease of recall often driven by factors other than frequency. Selective exposure.

Question 20

Law of small numbers

Answer 20

In a small sample, if there are streaks then we believe that something non-random is happening even if it is random.

Question 21

Why do people tend to see patterns in randomness?

Answer 21

Rewards for recognizing real patterns might be greater than penalties for imagining patterns.

Question 22

Illusory correlation

Answer 22

Important to view ratios when comparing, not just the numbers themselves.

Question 23

Post-hoc fallacy

Answer 23

If event B closely follows event A, we often think A caused B. That’s why there are people who believe vaccinations are harmful. Temporal connections are compelling; we often ignore many events between A and B. We pay more attention to what is there than what isn’t there

Question 24

Positive vs. negative evidence

Answer 24

We tend to pay more attention to positive evidence than negative evidence. Confirmation bias

Question 25

Third factor

Answer 25

Mistaken causation b/c ignore possible presence of a third factor related to both.

Question 26

Correlation vs. causation

Answer 26

Correlation is just when A is related to B, can be with or without causation. There may be third variables causing correlation. For causation, one must precede the other in time.

Question 27

Maximizing vs. satisficing

Answer 27

Maximizing - normative - find best outcome regardless of time and energy spent
Satisficing - descriptive - finding first outcome above certain criterion (good enough)

We satisfice to save time or when a decision is relatively unimportant

Question 28

System 1 vs. System 2

Answer 28

System 1 - hot system - emotion, associations, memory. Fast and automatic. Associated with satisficing

System 2 - cold system - reason, calculations, abstract and logical. Slow and effortful

Question 29

Cognitive heuristics

Answer 29

Mental shortcuts or rules of thumb that take less effort than deliberating using system 2, and that usually give us decent results.

Question 30

Pros and cons of heuristics

Answer 30

Advantages: less complexity, functional, fast, usually provides decent results
Disadvantages: possible biases or errors, usage often unconscious, errors may persist even when we are aware

Question 31

Anchoring & adjustment heuristic

Answer 31

When we estimate unknown quantity, we start with convenient initial value (anchor) and shift upwards or downwards (adjust). Anchor may be a known value, a primed value, a calculated value, or a given value. We do this even if the anchor is obviously extreme or irrelevant.

Pros: anchor is often a good starting point, and adjustment gets us close enough

Cons: easy to weigh anchors too heavily and adjust insufficiently. We can be manipulated. Combat by generating equally extreme alternative anchors.

Question 32

Availability heuristic

Answer 32

Things that are more easily available in memory or easier to think of (more examples) are judged as occurring more frequently. This is problematic in news as we can bring to mind catastrophic events more - plane travel seems riskier. Being killed by a shark seems more likely than being killed by falling airplane parts, although the latter is 30x as likely

Question 33

Representativeness heuristic

Answer 33

Probability of Event A is evaluated by how much A resembles B; if A is like B then A should be categorized in the same way as B. This is why we sometimes think probability of a compound event (that matches things we have seen in real life) greater than probability of element. Adding details increases representativeness

Pros: similarity often predicts group membership

Cons: ignores prior probabilities, similarities can be illusory, stereotyping.