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Chapter 14

Department

EconomicsCourse Code

EC260Professor

Olivia Ozlem MestaChapter

14This

**preview**shows pages 1-3. to view the full**9 pages of the document.**Chapter 14: Risk Analysis

Risk and Probability

-We define risk as a hazard or chance of experiencing a loss

-We define probability as the likelihood or chance that an event will take place

-If R is the number of times we repeat an experiment (i.e., rolling a single dice) and r is the

number of times the outcome A occurs, then the probability of A is defined as:

P(A) = r/R

-In rolling a single die, the probability of obtaining any of the six possible outcomes will be 1/6

— this is called the frequency definition of probability

-Since it is sometimes impossible or unrealistic to repeat an experiment over and over again,

managerial economists use a subjective definition of probability

-According to this definition, the probability of a certain outcome is the degree of confidence a

manager has that the outcome will occur

-The probabilities of all possible outcomes must sum to one

Probability Distributions and Expected Values

-When all possible outcomes are listed and a probability of occurrence is assigned to each

possible outcome, the resulting table is called a probability distribution

Only pages 1-3 are available for preview. Some parts have been intentionally blurred.

-In general, we have the following expression for the expected value of profit:

-Where π is the profit associated with the ith possible outcome, Pi is the probability of the ith

outcome occurring, and N is the number of possible outcomes

Comparisons of Expected Profits

Road Map to Decision Making

-A decision tree is a diagram managers can use to help them visualize what strategies to pursue

-At each point where the firm must make a decision, a series of branches represents the choices

they can make (a decision fork) — indicated by a small square

-At each point where chance or the actions affect the firm’s strategy, a series of branches

represents the possible outcomes (a chance fork)

Only pages 1-3 are available for preview. Some parts have been intentionally blurred.

Expected Value of Perfect Information

-Imperfect or incomplete information is one source of uncertainty

-Sometimes managers can purchase information to remove some uncertainty

-The value of perfect information should be equal to the increase in expected profit resulting

from having this additional information

-It is much harder to place a value on less than perfect information

A Utility Approach to Measuring Attitudes Towards Risk

-Up to this point, we have assumed that managers facing a risky decision will want to

maximize expected profit

-Some managers might not want to face the risk of a loss and might be inclined to choose a

less-profitable but certain outcome

-It is possible to construct a utility function for a manager that measures their attitude towards

risk

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