Decision Model Theory Essay

CaseHere we use the Thompson Lumber Company case as an example to illustrate these finality theory feels. John Thompson is the fo under and hot seat of Thompson Lumber Company, a profitable firm located in Portland, Oregon.Step 1The problem that John Thompson identifies is whether to expand his product line by manufacturing and merchandise a new product, backyard storage sheds.Step 2* The second step is to list the selection. * Thompsons second step is to generate alternatives that are available to him .In ratiocination theory the alternative is a course of action or strategy that the finish maker can choose .According to him his alternatives are to construct 1 a large new plant to manufacture the storage sheds 2 a sm each plant, or3 no plant at all* So, the decision makers should try to make all feasible alternatives ,on whatever occasion even the least important alternative might turn out to be the outgo choice.Step 3* Third step is to identify possible end points. * Th e criteria for action are established at this time. According to Thompson on that point are two possible gists the market for the storage sheds could be favorable nitty-gritty 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 discouraged managers may discount a favorable outcome. If you dont consider all possibilities, it bequeath be difficult to make a logical decision, and the result may be undesirable. * in that respect 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 severally possible combination of alternatives and outcomes. Because in this case he wants to maximize his profits, he use profits to evaluate each consequences .Not all decision, of course, can be based on money alone any appropriate means of measuring benefit is acceptable. In decision theory we birdsong 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. * ending Table with condition values for ThompsonTYPES OF DECISION MAKING ENVIRONMENTS* The types of decisions people make depends on how much knowledge or education they have about the situation. There are three kind of decision making environments* ending making under certainty.* ending 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. * deterrent example Lets say that you have $10000 to invest f or a purpose of one year. And you have two alternatives either to open a savings account paying 6% interest and another is investing in Govt. exchequer Bond paying 10% interest. If both the investments are secure and guaranteed, the best alternative is to choose the second investment option to gain utmost 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 occupy 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 alternativeDecision Making u nder Uncertainty* Here there are several outcomes for each alternative, and the decision maker does not know the probabilities occurrences of several(a) 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 cover in this section as follows1 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 token(prenominal) 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 compro mise 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 mourning.Expected Value of Perfect Information* FormulaEVPI = A BA = expected value with perfect informationB = expected value without perfect informationCalculation of (A) valueA = the best of each outcome x their prob.The best of outcomesBest outcome= (100,000) (30,000)A= 0.6 x 100,000 + 0.4 x 30,000 = 72,000Calculation of (B) valueB = we select the max value of each given belowOutcome of each typeface0.6(50000) + 0.4 (30,000)= 42,0000.6(100,000 -0.4(40,000)= 44,0000.6(30,000) + 0.4(10,000)= 20,000The max value for all computed value = 44,000EVPI = A B= 72,000 44,000= 28,000Expected Opportunity LossThe expected opportunity loss is the expected value of the regret for each decision (Minimax)EOL (Apartment) = $50,000(.6) + 0(.4) = 30,000EOL (Office) = $0(.6) + 70,000(.4) = 28,000EOL (Warehouse) = $70,000(.6) + 20,000(.4) = 50,000Marginal 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 galore(postnominal) years of schooling should you have?* How many hours sho uld you work?* How many workers should you hire?* How much should save/invest?