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MT6 Decisions under Risk Notes

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These notes were prepared based on the lectures and supplemented by information from textbooks and tutorials where parts of the lecture were unclear. Graphs, equations, and bullet-point explanations included. Prepared by a first class Economics and Management student for the FHS Microeconomics pape...

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  • June 27, 2024
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MT6 Decisions under Risk
Lecture 15: Decisions under Risk
Outline:
 Lotteries
 Expected Value (EV)
 Expected Utility (EU) hypothesis
 Allais paradox

Lotteries
 We want to study decision making when wealth, consumption etc. are stochastic (random)
rather than deterministic
Simple lotteries
 Motivating story
o A trader (Ann) owns goods at home worth a total of $4,000 and in addition possesses
$12,000 worth of commodities in a foreign country. She wants to bring her commodities
home on a single ship. Experience tells her that one in four ships perishes.
o We can describe Ann’s wealth as a simple lottery:


 A simple lottery is a probability distribution over a fixed set of outcomes:
o
o xi ∈ X. X being the set of outcomes
o pi ≥ 0 is the probability of outcome xi, ∑pi = 1
Compound lotteries
 Motivating story
o Bob also owns $4,000 at home and $12,000 overseas, but his foreign goods are in a
country where a revolt may take place before he can ship his commodities home.
o It is equally likely that his foreign goods will be confiscated or not before they can be
shipped. Once his ship sails Bob will face the same risks as Ann.
o We can describe Bob's wealth as a compound lottery:
 Bob’s wealth is a lottery in which one of the outcomes is a lottery



 Where (same lottery as Ann's)
 A compound lottery is an object
o Li are simple or compound lotteries
o pi ≥ 0 is the probability of Li occurring, ∑pi = 1
o Example: “0.5L1 + 0.5L2” for “if Heads then L1 if Tails then L2”
Reduction of compound lotteries
 Every compound lottery can be reduced to a simple lottery by enumerating all possible prizes
and their corresponding probabilities. Example: Bob's wealth
o Bob’s final wealth is 4K with probability ½ + ½ * ¼ = 5/8
o Bob’s final wealth is 16K with probability ½ * ¾ = 3/8

, o
 Assumption in decision making under risk: decision makers consider compound lotteries in their
reduced form (I.e. they are rational)
Plotting lotteries
 Either fix the pi’s and plot the xi’s or fix the xi’s and plot the pi’s
o Lotteries are conveniently plotted either in outcome-space for given probabilities, or in
probability-space for fixed outcomes
 Binary outcomes, fixed probabilities
o Consider a binary outcome (accident does or does not happen) occurring with fixed
probabilities: Pr[accident] = p
o Let xN denote wealth if no accident, x A wealth if accident occurs




o
 L: Ann’s initial position (16K if no accident, 4K if accident)
 L': Ann sells her foreign goods for ¾ × 12 = 9K before shipping (wealth is 9K + 4K
= 13K regardless of whether accident occurs)- fair insurance
 L'': Pirate with initial wealth of 2K who recovers half of Ann’s shipment when it
is lost (6K + 2K = 8K)
o Note we cannot plot Bob’s wealth here: his probabilities are different, but probability of
accident in this picture is fixed
o We can draw indifference curves in this space
 Three fixed outcomes, variable probabilities
o Fix x1 ≺ x2 ≺ x3 (e.g., increasing final wealth levels); then the lottery L = [p 1, p2, p3; x1, x2,
x3] is represented as a point in the (p 1, p3) plane




o
 This is referred to as Marschak–Machina triangle
 3 points of the triangle correspond to the 3 outcomes (x 1, x2, x3): origin is x2
(since p1 = p3 = 0, so p2 = 1), and x3 at p3 = 1, x1 at p1 = 1
 Lottery L (at the origin) is median outcome x 2 for sure
 Lottery L' puts probability ¾ on the worst (x 1) and ¼ on the best (x3) outcome
 Points on the hypotenuse have p2 = 0, p1 + p3 = 1

,  Lottery L'' puts probability 1/3 on each outcome
 Notice p2 can be inferred from the 2 other coordinates (since they sum
to 1)
 These lotteries have nothing to do with the ones on previous slide
o All points in the triangle correspond to lotteries with different probabilities of outcomes
(x1, x2, x3)

Expected value
 The simplest approach to evaluate lotteries is to take the mathematical expected value


o
 Example of Ann

o
o Suppose Ann is willing to accept $8,500 for her foreign goods before shipping. This may
not be irrational: by doing so she will have $12,500 for sure and “no mental anguish”
o Risk averse individuals may rationally take a discount
o People neutral towards risk would take EV
 St Petersburg paradox
o A convincing example of how the EV of a lottery may fail to capture how humans
evaluate risky bets is the following puzzle:
 Toss a coin and continue to do so until it comes up Heads, then stop. You win £1
if it is Heads on the first throw, £2 if Heads on the second throw, £4 if on the
third, etc: with each additional throw the amount you get paid is doubled. How
much is this gamble worth to you?
o Most people would pay around £8 or £10 to play this gamble
o However, the expected value of your gain is infinitely large!



 Expected value to expected utility
o It appears that individuals often require a “discount” when asked to pay for the
expected value of a gamble (nothing irrational, just aversion to risk)
o Formalization proposed by Bernoulli:
 Individuals evaluate a lottery according to the mathematical expectation of the
utility of final wealth
 The utility of the final wealth is a concave function of wealth,
 Eg. u(x) = ln(x)
 The certainty equivalent (CE) of a stochastic final wealth (lottery) is the
deterministic final wealth whose utility is the same as the expected utility of the
lottery, and it is typically less than the EV
 Expected utility in the St Petersburg paradox
o Your utility in the St Petersburg gamble, given initial wealth w, is

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