05 Jun uestion Learning Objectives
uestion
Learning Objectives
After reading Chapter 15 and working the problems for Chapter 15 in the textbook and in this Workbook, you should be able to:
! Distinguish between decision making under uncertainty and under risk.
! Compute the expected value, variance, standard deviation, and coefficient of variation of a probability distribution.
! Apply the expected value rule, the mean-variance rules, and the coefficient of variation rule to make decisions under risk.
! Define three risk preference categories: risk averse, risk neutral, and risk loving, and relate these attitudes toward risk to the shape of the utility of profit curve.
! Find the optimal level of a risky activity (when the variances of marginal benefit and marginal cost are constant) by setting E(MB) = E(MC).
! Apply (1) the maximax rule, (2) the maximin rule, (3) the minimax regret rule, and
(4) the equal probability rule to make decisions under uncertainty.
Essential Concepts
1. Conditions of risk occur when a manager must make a decision for which the outcome is not known with certainty. Under conditions of risk, the manager can make a list of all possible outcomes and assign probabilities to the various outcomes. Uncertainty exists when a decision maker cannot list all possible outcomes and/or cannot assign probabilities to the various outcomes.
2. In order to measure the risk associated with a decision, the manager can examine several characteristics of the probability distribution of outcomes for the decision. A probability distribution is a table or graph showing all possible outcomes or payoffs for a decision and the probability that each outcome will occur.
3. In order to measure the risk associated with a decision, several statistical charac-teristics of the probability distribution can be employed:
a. The expected value (or mean) of a probability distribution is
n
E(X) = Expected value of X=! pi Xi
i=1
where Xi is the it h outcome of a decision, pi is the probability of the it h
Chapter 15: Decisions Under Risk and Uncertainty
323
outcome, and n is the total number of possible outcomes in the probability distribution. The expected value of a distribution does not give the actual value of the random outcome, but rather indicates the “average” value of the outcomes if the risky decision were to be repeated a large number of times.
b. The variance (a measure of absolute risk) of a probability distribution measures the dispersion of the outcom
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