An individual purchases a dozen eggs and must take them home. Although making trips home is costless, there is a 50 percent chance that all of the eggs carried on any one trip will be broken during the trip. The individual considers two strategies: Strategy 1: Take all 12 eggs in one trip. Strategy 2: Take two trips with 6 in each trip. a. List the possible outcomes of each strategy and the probabilities of these outcomes. Show that on the average, 6 eggs will remain unbroken after the trip home under either strategy. b. Develop a graph to show the utility obtainable under each strategy. Which strategy will be preferable? c. Could utility be improved further by taking more than two trips? How would this possi bility be affected if additional trips were costly?

Short Answer

Expert verified
Answer: Both strategies have an average of 6 unbroken eggs. However, preferences depend on an individual's utility function and their attitudes towards risk. Risk-averse individuals may prefer Strategy 2 (two trips with 6 eggs each), while risk-seeking individuals may prefer Strategy 1 (one trip with 12 eggs).

Step by step solution

01

Strategy 1: One Trip with 12 Eggs

In this strategy, there are two possible outcomes: 1. All eggs are unbroken, which has a probability of 50% (\(0.5\)). 2. All eggs are broken, which has a probability of 50% (\(0.5\)).
02

Strategy 2: Two Trips with 6 Eggs Each

In this strategy, there are four possible outcomes: 1. All eggs are unbroken on both trips, which has a probability of 25% (\(0.5 \times 0.5 = 0.25\)). 2. All eggs are unbroken on the first trip and all broken on the second trip, which has a probability of 25% (\(0.5 \times 0.5 = 0.25\)). 3. All eggs are broken on the first trip and all unbroken on the second trip, which has a probability of 25% (\(0.5 \times 0.5 = 0.25\)). 4. All eggs are broken on both trips, which has a probability of 25% (\(0.5 \times 0.5 = 0.25\)).
03

Average Number of Unbroken Eggs

For Strategy 1: - \(0.5 \times 12 + 0.5 \times 0 = 6\) unbroken eggs on average. For Strategy 2: - \(0.25 \times 12 + 0.25 \times 6 + 0.25 \times 6 + 0.25 \times 0 = 6\) unbroken eggs on average. Under either strategy, the average number of unbroken eggs is 6. #b. Graph to show the utility under each strategy# Since this is a theoretical exercise, I will describe how to develop a graph instead: 1. On the horizontal axis, represent the number of unbroken eggs ranging from 0 to 12. 2. On the vertical axis, represent the utility. 3. Plot the possible outcomes and their probabilities for each strategy, such as \;(0, 0.5)\; and \;(12, 0.5)\; for Strategy 1 and \;(0, 0.25)\;, \;(6, 0.5)\;, \;(12, 0.25)\; for Strategy 2. The strategy that will be preferable depends on the individual's personal preferences and their utility function. If the individual has a risk-averse attitude, they may prefer Strategy 2 since it offers a 50% chance of having 6 unbroken eggs. If the individual is more risk-seeking, they may prefer Strategy 1 since it has a 50% chance of having all 12 unbroken eggs. The preference can be determined by comparing the different combinations of unbroken eggs and their respective probabilities. #c. Utility improvement with more than two trips# To determine if taking more than two trips could improve utility, we would analyze additional strategies, such as taking three trips with 4 eggs each or four trips with 3 eggs each. We must compute the possible outcomes and probabilities for these new strategies and calculate their average number of unbroken eggs. If additional trips were costly, an individual's utility function would need to take the cost into account. The utility for each strategy would diminish due to the increased cost, shifting the preferences. The individual may prefer taking fewer trips to save costs, even if it means a higher chance of broken eggs. In this case, the individual would need to weigh the additional cost against the benefits of potentially having more unbroken eggs.

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Most popular questions from this chapter

In some cases individuals may care about the date at which the uncertainty they face is resolved. Suppose, for example, that an individual knows that his or her consumption will be 10 units today \((Q)\) but that tomorrow's consumption \(\left(\mathrm{C}_{2}\right)\) will be either 10 or \(2.5,\) depending on whether a coin comes up heads or tails. Suppose also that the individual's utility function has the simple Cobb-Douglas form \\[U\left(Q, C_{2}\right)=V^{\wedge} Q.\\] a. If an individual cares only about the expected value of utility, will it matter whether the coin is flipped just before day 1 or just before day \(2 ?\) Explain. More generally, suppose that the individual's expected utility depends on the timing of the coin flip. Specifically, assume that \\[\text { expected utility }=\mathbf{f}^{\wedge}\left[\left(\mathrm{fi}\left\\{17\left(\mathrm{C}, \mathrm{C}_{2}\right)\right\\}\right) \text { ) } \text { ? } |\right.\\] where \(E_{x}\) represents expectations taken at the start of day \(1, E_{2}\) represents expectations at the start of day \(2,\) and \(a\) represents a parameter that indicates timing preferences. Show that if \(a=1,\) the individual is indifferent about when the coin is flipped. Show that if \(a=2,\) the individual will prefer early resolution of the uncertainty - that is, flipping the coin at the start of day 1 Show that if \(a=.5,\) the individual will prefer later resolution of the uncertainty (flipping at the start of day 2 ). Explain your results intuitively and indicate their relevance for information theory. (Note: This problem is an illustration of "resolution seeking" and "resolution-averse" behavior. See D. M. Kreps and E. L. Porteus, "Temporal Resolution of Uncertainty and Dynamic Choice Theory," Econometrica [January \(1978]: 185-200\).

Ms. Fogg is planning an around-the-world trip on which she plans to spend \(\$ 10,000\). The utility from the trip is a function of how much she actually spends on it \((Y),\) given by \\[ U(Y)=\ln Y \\] a. If there is a 25 percent probability that Ms. Fogg will lose \(\$ 1000\) of her cash on the trip, what is the trip's expected utility? b. Suppose that Ms. Fogg can buy insurance against losing the \(\$ 1000\) (say, by purchasing traveler's checks) at an "actuarially fair" premium of \(\$ 250 .\) Show that her expected utility is higher if she purchases this insurance than if she faces the chance of losing the \(\$ 1000\) without insurance. c. What is the maximum amount that Ms. Fogg would be willing to pay to insure her \(\$ 1000 ?\)

Blue-eyed people are more likely to lose their expensive watches than are brown-eyed people. Specifically, there is an 80 percent probability that a blue-eyed individual will lose a \(\$ 1,000\) watch during a year, but only a 20 percent probability that a brown-eyed person will. Blue-eyed and brown-eyed people are equally represented in the population. a. If an insurance company assumes blue-eyed and brown-eyed people are equally likely to buy watch-loss insurance, what will the actuarially fair insurance premium be? b. If blue-eyed and brown-eyed people have logarithmic utility-of-wealth functions and cur rent wealths of \(\$ 10,000\) each, will these individuals buy watch insurance at the premium calculated in part (a)? c. Given your results from part (b), will the insurance premiums be correctly computed? What should the premium be? What will the utility for each type of person be? d. Suppose that an insurance company charged different premiums for blue-eyed and brown-eyed people. How would these individuals' maximum utilities compare to those computed in parts (b) and (c)? (This problem is an example of adverse selection in insurance.)

Investment in risky assets can be examined in the state-preference framework by assuming that \(W^{*}\) dollars invested in an asset with a certain return, \(r,\) will yield \(\mathrm{W}^{*}(\mathrm{l}+r)\) in both states of the world, whereas investment in a risky asset will yield \(\mathrm{W}^{*}\left(1+r_{g}\right)\) in good times and \(\left.\mathrm{W}^{*}\left(\mathrm{l}+r_{b}\right) \text { in bad times (where } r_{g}>r>r_{b}\right)\) a. Graph the outcomes from the two investments. b. Show how a "mxied portfolio" containing both risk-free and risky assets could be illustrated in your graph. How would you show the fraction of wealth invested in the risky asset? c. Show how individuals' attitudes toward risk will determine the mix of risk- free and risky assets they will hold. In what case would a person hold no risky assets? d. If an individual's utility takes the constant relative risk aversion form (Equation 8.62 ), ex plain why this person will not change the fraction of risky asset held as his or her wealth increases.

Show that if an individual's utility-of-wealth function is convex (rather than concave, as shown in Figure 8.1 ), he or she will prefer fair gambles to income certainty and may even be willing to accept somewhat unfair gambles. Do you believe this sort of risk-taking behavior is common? What factors might tend to limit its occurrence?

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