Week 11 A

# Week 11 A - COMM 295 November 16, 2010 Decision Making with...

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COMM 295 November 16, 2010 Decision Making with Uncertainty: Part 1

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Topics Examined • Concept of uncertainty in decision making • Probability • Calculating and using expected value • Objective measures of risk • Aversion to risk • Decision making with expected utility • Marginal utility and risk tolerance • Certainty equivalent and risk premium
Uncertainty • Virtually all decisions in the real world are made with some degree of uncertainty • Students are uncertain about future jobs when making decisions about education • Firms are uncertain about future prices when making investment decisions • Managers are uncertain about an employee’s work ability when making hiring decisions • Firms are uncertain about the cost of an incumbent firm when entering an industry

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How do you? • Choose which queue to stand in at MacDonalds? • Choose whether to accept or turn down a summer job when other applications are outstanding? • Decide whether to quit a \$40,000/year job to invest \$30,000 in a MBA • Decide whether to purchase a monthly cell phone plan, or a pay-as-you-go plan?
Uncertainty and Probability Uncertainty (or risk) is present when there is more than one possible outcome for a decision Probability of an outcome is the odds or chance the outcome will occur • Objective probability is equivalent to expected frequency (e.g., flip a coin) • Objective probability can be estimated from data using past frequency (e.g., fraction of rainy days during past year) • Subjective probability (e.g., your judgment that central bank will raise interest rates)

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Probability Distribution Probability distribution: a list of the possible outcomes of an unknown event and their respective probabilities. • These probabilities must sum to one • We will assume that it is possible to write down a complete probability distribution – Identify all possible outcomes – Attach economic values to each outcome (V i ) – Identify the probability of occurrence for each outcome (P i )
E.g., Future Revenues for New Product Sales Revenue (V i ) Probability (p i ) 50,000 0.10 200,000 0.25 500,000 0.40 750,000 0.20 1,000,000 0.05 1.00

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Expected Value (EV) • The mean or expected value is: the weighted average of the outcomes, with the probabilities of each outcome serving as the respective weights • Suppose a random variable has n possible outcomes V i , which occur with probability p i , then EV =p i V 1 + p 2 V 2 + … + p n V n
Expected Value vs Actual Outcome • The expected value of an event is seldom equal to the actual outcome of an event • Suppose you are paid \$1 if a flipped coin is heads and \$3 if a flipped coin is tails • There is a 50% chance of either heads or tails • Your expected payment is .5(1) + .5(3) = \$2 • Your actual payment is either \$1 or \$3, but is never \$2

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Expected Value as Long-Run Average • The expected value represents a long-run average outcome if the economic situation repeats itself for a large number of times • Suppose the coin game of previous slide is repeated twenty times • Suppose you earn: \$3, \$3, \$1, \$1, \$1, \$3, \$3,
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## This note was uploaded on 01/03/2011 for the course COMM 290 taught by Professor Brian during the Winter '09 term at UBC.

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Week 11 A - COMM 295 November 16, 2010 Decision Making with...

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