Case

Here we use the Thompson Lumber Company case as an example to illustrate these decision theory steps. John Thompson is the founder and president of Thompson Lumber Company, a profitable firm located in Portland, Oregon.

Step 1

The problem that John Thompson identifies is whether to expand his product line by manufacturing and marketing a new product, backyard storage sheds.

Step 2

* The second step is to list the alternative. * Thompson’s second step is to generate alternatives that are available to him .In decision theory the alternative is a course of action or strategy that the decision maker can choose .According to him his alternatives are to construct: 1• a large new plant to manufacture the storage sheds 2• a small plant, or

3• no plant at all

* So, the decision makers should try to make all possible alternatives ,on some occasion even the least important alternative might turn out to be the best choice.

Step 3

* Third step is to identify possible outcomes. * The criteria for action are established at this time. According to Thompson there are two possible outcomes: the market for the storage sheds could be favorable means there is a high demand of the product or it could be unfavorable means that there is low demand of the product. * Optimistic decision makers tend to ignore bad outcomes; where as pessimistic managers may discount a favorable outcome. If you don’t consider all possibilities, it will be difficult to make a logical decision, and the result may be undesirable. * There may be some outcomes over which the decision maker has little or no control is known as states of nature.

Step 4

* Fourth step is to list payoffs. * This step is to list payoff resulting from each possible combination of alternatives and outcomes. Because in this case he wants to maximize his profits, he use profits to evaluate each consequences .Not every decision, of course, can be based on money alone – any appropriate means of measuring benefit is acceptable. In decision theory we call such payoff or profits conditional values.

Step 5 & 6

* The last two steps are to select and apply the decision theory model. * Apply it to the data to help make the decision. Selecting the model depends on the environment in which you are operating and the amount of risk and uncertainty involved. * Decision Table with condition values for Thompson

TYPES OF DECISION MAKING ENVIRONMENTS

* The types of decisions people make depends on how much knowledge or information they have about the situation. There are three kind of decision making environments:

* Decision making under certainty.

* Decision making under risk.

* Decision making under uncertainty.

Decision Making Under Certainty

* Here the decision makers know about the certainty of consequences every alternative or decision choice has. * Naturally they will choose the alternative that will result in the best outcome. * Example: Let’s say that you have $10000 to invest for a period of one year. And you have two alternatives either to open a savings account paying 6% interest and another is investing in Govt. Treasury Bond paying 10% interest. If both the investments are secure and guaranteed, the best alternative is to choose the second investment option to gain maximum profit.

Decision Making Under Risk

* Here the decision Maker knows about the several possible outcomes for each alternative and the probability of occurrence of each outcome. * Example: The probability of being dealt a club is 0.25. The probability of rolling a 5 on die is 1/6. * In the decision making under risk, the decision maker usually attempts to maximize his or her expected well being. Decision theory models for business problems in this in this environment typically employ two equivalent criteria: maximization of expected monetary value and minimization of expected loss. * Expected monetary value is the weighted value of possible payoffs for each alternative

Decision Making under Uncertainty

* Here there are several outcomes for each alternative, and the decision maker does not know the probabilities occurrences of various outcomes. * Example The probability that a Democrat/Republican will be the President of a country 25 Years from now is not known. * The criteria that is covered in this section as follows:

1 – Maximax • this criterion find the alternative that maximizes the maximum payoffs or consequence for every alternative. Here we first locate the maximum payoff with every alternative and then pick that alternative with the maximum number. This is also known as optimistic decision criterion.

* Maximin • this criterion finds the alternative that maximizes the minimum payoff or consequence for every alternative. Here we first locate the minimum outcome within every alternative and then pick that alternative with maximum number. This is called as pessimistic decision criterion. * Criterion of Realism: Also called as weighted average, is a compromise between an optimistic and a pessimistic decision. Let the coefficient of realism is ‘a’ selected. The coefficient is between 0 and 1. When ‘a’ is close to 1, the decision maker is optimistic about the future. When ‘a’ is close ‘0’ the decision maker is pessimistic. It helps the decision maker to build feelings about relative optimism and pessimism. * Weighted average =a (maximum in row) + (1-a)(minimum in row). * Equally likely (Laplace)-one criterion that uses all the payoffs for each alternative is the equally likely also called Laplace decision criterion. This is to find alternative with highest payoff. * Minimax Regret • the final decision criterion that we discuss is based on opportunity loss or regret.

Expected Value of Perfect Information

* Formula

EVPI = A – B

A = expected value with perfect information

B = expected value without perfect information

Calculation of (A) value:

A = the best of each outcome x their prob.

The best of outcomes:

Best outcome= (100,000) (30,000)

A= 0.6 x 100,000 + 0.4 x 30,000 = 72,000

Calculation of (B) value:

B = we select the max value of each given below

Outcome of each event:

0.6(50000) + 0.4 (30,000)= 42,000

0.6(100,000 -0.4(40,000)= 44,000

0.6(30,000) + 0.4(10,000)= 20,000

The max value for all computed value = 44,000

EVPI = A – B

= 72,000 – 44,000

= 28,000

Expected Opportunity Loss

The expected opportunity loss is the expected value of the regret for each decision (Minimax)

EOL (Apartment) = $50,000(.6) + 0(.4) = 30,000

EOL (Office) = $0(.6) + 70,000(.4) = 28,000

EOL (Warehouse) = $70,000(.6) + 20,000(.4) = 50,000

Marginal Analysis

* Most of our decisions are made following our “marginal analysis” of costs and benefits * To achieve a given outcome we often have to make a choice from among alternative means; we normally try to make the “least costly” choice among the available means * Sometimes our decisions result in benefits as well as costs; * How much food should you buy?

* How many years of schooling should you have?

* How many hours should you work?

* How many workers should you hire?

* How much should save/invest?

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