Probability Judgement

Question 1

Normative, Descriptive, Prescriptive

Answer 1

Normative is how people should make decisions according to theory of rationality.

Descriptive is the way people actually tend to behave

Prescriptive how we might use knowledge to help people make better decisions

Question 2

Coherence

Answer 2

Internal consistency between your judgements

Question 3

Correspondence

Answer 3

Accuracy with respect to world

Question 4

Money Pump Problem

Answer 4

Thought experiment, shows preferences can be intransitive - not coherent.

If I would pay more ££ for A then B, and Pay more ££ for B then C, BUT would pay more for C then A - could theoretically create a ‘money pump’

Question 5

Expected Value

Answer 5

Expected Value =(O1 * P1) + (O2 * P2)

Question 6

St Peters Paradox Paradox

Answer 6

Shows failure of expected Value to capture how people actually make decisions

Toss coin, every time lands on tails, double prize - starting with £2 - How much would you be willing to pay to play?

Paradoxical as has infinite expected value

Question 7

Expected Utility

Answer 7

Utility doesn’t equal value

For example the utility of money decreases as £ increases

Question 8

Bounded Rationality Theory

Answer 8

How people actually make decisions
Based on imperfect knowledge
Context sensitivity
“satisficing” - adequate rather than optimal

Herbert Simon - economic decision making

Question 9

Alias Paradox

Answer 9

Choice problem showing inconsistency of observed choices with predictions of expected utility

In first gamble less risky choice is preferred over a higher expected utility, while in the second gamble a higher expected utility is preferred over a less risky choice.

Question 10

Brunswick Lens Model

Answer 10

Indirect interaction with objects / events
Internal perceptions of external events mediated through sense organs

“Lense of Cues”

Question 11

Multiple Cue Probability Learning

Answer 11

Experimental Paradigm for learning relationships between cues and events

• Learning over many trials, hypothesis testing
• Learning to to Predict outcome
• Cues in environment vary in validity

Question 12

Key processes in making judgments

Answer 12

1 - Discovering Information
2 - Acquiring & searching through Information
3 - Combining information
4 - Feedback

Question 13

1 - Discovering Information

Answer 13

• Identification of new valid predictive cues
• Incorporation into mental model

Klayman (1988)

Question 14

2 - Acquiring Information

Answer 14

*When information is costly DM needs to balance against improved Decision accuracy

People tend to use under optimal samples, why? costs, working memory, amplification effect?

Question 15

2 - Searching for Information

Answer 15

• Alternative based strategy - consider each option in turn

* Consider by attribute (easier elimination)

Question 16

Elimination by Aspects Model (EBA)

Answer 16

A theory of choice - presented by Tversky (1972)

Identify a dimension, alternatives that don’t posses this are eliminated
Choose aspects acording to weight

Question 17

Compensatory versus non-compensatory stratagies

Answer 17

Compensatory strategies allow trade off between attributes, ie a high score in a lower weighted attribute can compensate for low score in a more important attribute.
Conflict confronting, e.g Weighted Additive strategies.

** Linear Model as all info included

Non compensatory strategies do not allow trade off - e.g if most important attribute scores low, that alternative will not be chosen - eg lexicographic. (Conflict avoiding)

Question 18

3 - Combining Information

Answer 18

How process information to make decision?

Include all cues? Weight information same?

Compensatory or non-compensatory?

Question 19

Proper Linear models versus bootstrap model

Answer 19

Proper linear models -> optimized mathematically

Bootstrap model -> Formalise judgers use of cues, express judgment policy in model

Question 20

Should wherever possible a human judge be replaced with a machine?

Answer 20

• Statistical models routinely outperform humans
• Statistical model always arrive at same judgment given a data set
• Computers process all of the data
• Humans prone to bias (confirmation, availability)
• • BUT
• humans good at identifying & quantifying cues
• still need experts to provide information

Question 21

Recognition Heuristic

Answer 21

If one of two things recognised and not the other - infer the recognized one have a higher value with respect to criterion

Question 22

Maximising Expected Utility

Answer 22

Assessing decisions on states, outcomes, acts

EU = S Utility (outcome ij) * Probability (statej)

Four axioms of MEU Theory

Question 23

Four axioms of expected Utility

Answer 23

1 - cancellation
2 - transitivity
3 - Dominance
4 - Invariance

Question 24

1) Cancellation Axiom

Answer 24

If states of world give same outcome - regardless of choice, can eliminate from decision

E.g broken by Ellsberg paradox

Question 25

2) Transitive axiom

Answer 25

Preferences should be transitive

LInked to money pump

Question 26

3) Dominance

Answer 26

If A is better than B is 1 respect and at least as good as in all other - A should always be preferred

Question 27

4) Invariance

Answer 27

Given the same information we should make same decisions regardless of how presented

Question 28

Prospect Theory

Answer 28

People evaluate gambles in terms of losses of gains according to a reference point

Concave function of value - losses loom larger than gains

Reference point open to manipulation