Decision Under Risk 2 Flashcards

1
Q

Who developed prospect theory?

A

2 Nobel prize winning psychologists including Daniel Kahneman, yet they won their Nobel Prizes in economics!

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2
Q

What are the main components which comprise Prospect Theory?

A

Editing
Value Function
Weighting Function
Explaining the anomolies

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3
Q

What is the expected utility of a prospect with payoffs xi given by?

A

EU= ΣipiU(y+xi)

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4
Q

Prospect Theory generalises EUT so that (edited) prospects are evaluated according to…

A
EUprospect =Σw(pi)V(y+xi-r)
Where w(p) = decision weights 
V() is a value function
r is a reference level (if r=y then there is no role for initial income)
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5
Q

If an individual with initial income £2 faces the prospect (-1,0.5;1,0.5), under Prospect Theory, what are their two outcomes?

A

V(-1) and V(1)

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6
Q

In prospect theory if the currrent asset= the reference then what do we think in terms of?

A

Purely gains and losses.

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7
Q

What is the editing process called a combination?

A

Where prospects are simplified by combining the probabilities with identical outcomes.

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8
Q

What is the editing process called a segregation?

A

Some prospects contain a riskless component that is segregated from the risky component.

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9
Q

Under segregation, what would (£300,0.8; £200, 0.2) change to?

A

£200+ (£100, 0.2; £0, 0.2)

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10
Q

What is the editing process called cancellation?

A

The discarding of components that are shared by the offered prospects.

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11
Q

What does the value function look like?

A

Like an S shape

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12
Q

What is the reference level?

A

The neutrality point- something you would consider as neither a gain or a loss.

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13
Q

Is the S symmetrical?

A

No, it goes further low more than it does high- due to loss aversion.

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14
Q

What is loss aversion?

A

The idea that losses loom larger than gains- so therefore the value function is steeper for losses.

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15
Q

What is diminishing sensitivity?

A

Marginal utility falls as we move further away from the reference point- we are more sensitive the closer we are to the reference point.

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16
Q

What is the relevance of the left side of the graph being convex and the right side being concave?

A

it indicates that people switch their risk preference around, depending on whether they’re thinking about an outcome, as a gain (concave) or whether they’re thinking about an outcome as a loss (convex).

17
Q

Are decision weights directly observed?

A

No, “decision weights are inferred from choices between prospects. Decision weights are not probabilities: they do not o bey the probability axioms and they should not be interpreted as measures of degree or belief.”- Kahneman and Tverksy.

18
Q

Do we generally overweight or underweight vanishingly small probabilities (e.g. winning the National Lottery)?

A

Overweight

19
Q

Why do we not always overweight vanishingly small probabilities?

A

Because sometimes we just ignore the,, eg we don’t focus on the low odds of a bridge collapsing, we just walk across it. This makes the value function very difficult to work out at such low levels of actual probability.

20
Q

According to the Weighting Function, do people mainly underweight or overweight probabilities?

A

Underweight

21
Q

According to subcertainty- what do decision weights add up to?

A

Less than 1.

22
Q

What does Allais’ paradox imply?

A

Subcertainty.

23
Q

Why does the Prospect Theory give us Reflection Effect?

A

Due to diminishing sensitivity.

24
Q

What can the Isolation Effect by explained by>

A

The Segregation operation during the editing phase of Prospect Theory.

25
Q

Why can’t the full version of Prospect Theory account for Event Splitting Effects?

A

Because the two events with a payoff of b would be combined during the Combination operation during the editing phase.

26
Q

How can we then explain Event Splitting through Prospect Theory?

A

By ignoring the Combination operation, in what we called “stripped down Prospect Theory”.

27
Q

How can stochastic dominance be violated?

A

If the indirect effect is negative and outweighs the direct effect.