Decision Making Pt.2 Flashcards

1
Q

Heuristics

A

Mental short-cuts that allow us to skip careful deliberation to draw an inference

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Bias

A

Deviations from rationality (errors) that are caused by using heuristics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Availability Heuristic

A

A heuristic in which we estimate the probability of an event based on the ease at which it can be brought to mind

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Availability Heuristic

A

People rate tornadoes as the larger cause of death, though it kills 20x less people than asthma. This is because it is easier to bring to mind deaths by tornadoes than asma.
This is an example of…

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Affect Heuristic

A

The tendency to overestimate the risk of an event that generates strong emotional responses

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Effect Heuristic

A

-People rate sharks as one of the most dangerous animals, especially after being exposed to media about shark attacks
-Yet people are much more likely to die from bees or wasps than sharks
This is an example of…

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Representative Heuristic

A

People tend to make inferences on the basis that small samples resemble the larger population they were drawn from
-People base their judgements of group membership based on similarity

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Base Rate Neglect

A

When you fail to use information about the prior probability of an event to judge the likelihood of an event
-Despite Tom’s profile, the overall likelihood of him being a psychology student is much higher than him being a student of mathematics because of the base rate!

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Conjunction Fallacy

A

False belief that the conjunction of 2 conditions is more likely than either single condition
-The likelihood of an event is always higher than the likelihood of that event and something else

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Anchoring

A

The tendency for people to overweight initial information when making decisions
-Particularly important for designing self-report scales
-Initial question influences answer you will get

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Anchoring

A

Big # = primed to think of big #’s
Small # = primed to think of small #’s

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Regression Toward the Mean

A

When a process is somewhat random (i.e. weak correlation), extreme values will be closer to the mean (i.e. less extreme) when measured a second time
-This is not a fluke; this is a statistical necessity
-1st time= a lot of variability, by chance
-2nd time= more observations, average out more, general regression towards everage

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Bounded Rational

A

The theory that humans are rational relative to environmental constraints (e.g. time pressure) and individual constraints (e.g. working memory, attention)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

-People are satisficers: we look for solutions that are ‘‘good enough’’
-‘‘Making do’’ with the limitations we have as humans
-Although heuristics sometimes provide incorrect answers and lead to biases; they also work

A

Under the bounded rational view…

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Ecological Rationality

A

-the view proposed by Gerd Gigerenzer (1999) which seens heuristics not as a ‘‘good enough’’ approach to solving a problem but as the optimal approach

-A heuristic is the best solution to a particular problem; what is the best way for me to answer this question, given all of my constraints

-Given the right environment, a heuristic can be better than optimization than other complex strategies (that would require a lot of time)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Ecological Rationality

A

You are faced with a very difficult math problem and you choose to ask chatgpt (heuristic) instead of dwelling on it (complex strategy)
This is an example of…

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

the limitations we face
-but can sometimes produce correct responses (ecological rationality)

A

Heursitics and biases arise from…

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

biases

A

Applying heuristics too often can lead to…

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

Perceptual Decision Making

A

-Objective (externally defined) criterion for making your choice
-Are the dots moving left or right?
-There is a correct answer
-Simplest type of decisions

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

Value-based Decision Making

A

-Subjective (internally defined) criterion for making your choice
-Do I want cake or ice cream for dessert?
-Depends on motivational state and goal
-There is not external correct response

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

Decision Under Risk

A

-Decisions when outcomes are uncertain
-Ambiguity: when you have incomplete information out the consequences
-Many of our decisions in life fall under this category
-We try to make the best decision even though outcome is unsure

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

Risky Decision-Making

A

-When outcomes are uncertain, we still need to decide
-Extremes in risk taking (high or low) can be very harmful (e.g. stagnant living, addiction)
-Risks can be frames as gains or losses
-Most people are risk averse (people tend to avoid risk)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

Risk Attitude Profiles

A

For each decision and decision-maker, we can describe the ‘‘risk profile’’
-What do people tend to like?

24
Q

Risk Premium

A

Difference between expected gains of a risky option and a certain option

25
Risk Averse
-Decision maker has positive risk premium -Need a chance at winning a lot more than a certain option to select the risky option -If they were to pick the risky option, they need a lot more
26
Risk Neutral
-Decision maker has zero risk premium -No difference in the options
27
Risk Seeking
-Decision maker has negative risk premium -Doesn't need the chance at winning more than the certain option to gamble -They just like the risk
28
Classical Economic Theory
-Explained rational choice in terms of expected value -The rational thing to do is choose the option that maximizes expected value -Can account for individual's risk preferences -We do not follow expected value -The question for behaviour economist then is not how should people act, but how do people act?
29
Gain Framing
-Program A: 200 lives could be definitely be saved -Program B: Even if the risky option doesn't work out, those are 200 lives I could have saved!
30
Loss Framing
-Program A: I am for sure killing 400 people -Program B: If the risky option does work out, nobody will die!
31
The Framing Effect
The display of inconsistent risk preferences depending on the framing (loss vs. gains) of the problem
32
People are risk-averse when the options are described as gains
-They prefer the sure thing and go for safety -The cup is half full- do I need more?
33
People are risk-seeking when the options are described as losses
-They can tolerate an uncertain thing and risk a loss -The cup is half empty- don't take any more away!
34
Endowment Effect
The tendency to ascribe higher value to objects people own or possess compared to identical objects they do not own -Another case in which the framing of the decision matters -Higher worth because you have it -Lower worth because you don't have it
35
Prospect Theory
A psychological theory that explains how people make decisions under uncertainty -Describes how people do act; not how people should act -The birth of behavioural economics
36
Two Major Features of Prospect Theory
1. Shape of utility function (losses vs. gains) 2. Shape of probability weighting function (unlikely vs. likely events)
37
Utility
-Subjective value assigned to an object (i.e. satisfaction) -Context dependent -Assigned to a monetary amount as a function of someone's current state (reference point) and not in absolute value -Deviations from the reference point will determine risk preference -Anchor and adjustment heuristic
38
Utility Function
Describes how people map money to utility
39
The Insights of Prospect Theory
1. The extra utility earned from gaining a dollar is larger when you only have $1 vs. when you have $1M 2. Utility for losses versus gain is asymmetrical -Steeper for losses than gain -$1 lost hurts more than $1 earned -losses loom larger than gains
40
Utility Function
-Being given 1 pizza slice is worth more to you when you had none then when you have 12 -The gain in utility for receiving a slice of pizza is less than the loss (disutility) after giving a slice away
41
Probability Weighting
-According to PT, people do not treat probabilities in an objective manner -What causes more deaths: dying in a car crash or dying of cancer -Unlikely events are overestimated -Likely events are underestimated -Availability of an option changes the perceived frequency of occurence
42
The Fourfold Pattern
-High probability + Losses = risk-seeking -High probability + Gains = risk-averse -Low probability + Losses = risk-averse -Low probability + Gains = risk-seeking
43
how people behave
Prospect theory describes...
44
how people treat gains & losses
The utility function describes...
45
how people understand likelihoods
Probability weighting function describes...
46
Dual-Process Theory
The broad scientific theory that humans posses 2 systems for making decisions -We are somewhat between the 2
47
System 1
-Fast, effortless, automatic, inuitive, emotional -Heuristics and biases -Limbic system
48
System 2
-Slow, deliberate, effortful, explicit, logical -Rational choice -Frontal cortex
49
How Emotions Affect Choice
-Participants read newspaper stories designed to induce positive (happy) or negative (sad) affect -Then, the participants estimated frequencies of death for various causes -Participants provided higher estimates of death frequency when people were in a negative mood compared to a positive mood
50
Prediction Error (PE)
The difference between what you predicted would happen and what actually happened
51
Positive Prediction Error
Unexpectedly good outcome -make us happier
52
Negative Prediction Error
Unexpectedly bad outcome -make us sadder
53
risky decision-making -E.g. when people are happy, they are more likely to gamble
Changes in mood predict...
54
inhibition and reduces learning from error
Systems linked to anxiety leads to...
55
exploration and learning from errors
Systems linked to curiosity leads to...