Fluent Essays

The discussion requires a minimum of 300 words, 3 scholarly sources, including the textbook. Make sure that you use APA style with your references. Under no circumstances use any direct quotes. Any directly quoted or copied material will result in a zero for the assignment. Let’s be sure to write it in own work 100\% and give appropriately when using someone’s else work. Reference for textbook attached: Thomas, C. R., & Maurice, S. C. (2010). Managerial economics: Foundations of business analysis and strategy (10th ed.). New York: McGraw-Hill/Irwin. U.S. Supreme Court Justice Stephen Breyers book Breaking the Vicious Circle: Toward Effective Risk Regulation examines governments role in controlling and managing the health risks society faces from exposure to environmental pollution. One major problem examined in the book is the cleanup of hazardous waste sites. Justice Breyer was extremely critical of policymakers who wish to see waste sites 100 percent clean. Explain, using the theory of optimization and a graph, the circumstances under which a waste site could be made too clean. You may either describe your graph in detail or post a photo of your graph for the class to see. Good answers are dispassionate and employ economic analysis. 1,500 word count and there is a total of 8 questions each (not including in-text citation and references as the word count), a minimum of 4 scholarly sources are required in APA format. For the 4 scholarly sources, one from the textbook that’s posted below and the other two from an outside source . Let’s be sure to write it in own work 100\% and give appropriately when using someone’s else work. Under no circumstances use any direct quotes. Any directly quoted or copied material will result in a zero for the assignment. Reference for textbook attached: Thomas, C. R., & Maurice, S. C. (2010). Managerial economics: Foundations of business analysis and strategy (10th ed.). New York: McGraw-Hill/Irwin. I think you will find Unit 8 to be one of the more straightforward units we have had in the course. Everything that you have learned in the previous units has led up to being able to quickly and easily do a marginal analysis. Had we covered this unit earlier, you would have found it much more difficult. On the Complete, please be sure to explain in detail how you arrived at each answer. Just filling in the blanks is not sufficient, and you will still need to do some research to meet your word count requirement. Feel free to research examples of marginal analyses in any area of study that you find interesting. Use the graph below to answer the following questions: a) At 200 units of the activity, marginal benefit is $__________ and marginal cost is $__________. b) Adding the 200th unit of the activity causes net benefit to __________ (increase, decrease) by $__________. c) At 700 units of the activity, marginal benefit is $__________ and marginal cost is $__________. d) Subtracting the 700th unit of the activity causes net benefit to __________ (increase, decrease) by $__________. e) The optimal level of the activity is __________units. At the optimal level of the activity, marginal benefit is $__________ and marginal cost is $__________. Give an example from your own business-related experience of a situation in which a marginal analysis could be conducted. Explain how you would find and recommend the optimal level of activity for the situation. mba506_unit8.pdf Unformatted Attachment Preview Chapter 3 Marginal Analysis for Optimal Decisions R I C A R After reading this chapter, you willD be able to: 3.1 Define several key concepts and terminology. , 3.2 Use marginal analysis to find optimal activity levels in unconstrained maximization problems and explain why sunk costs, fixed costs, and average costs are irrelevant for decision A making. 3.3 Employ marginal analysis to D find the optimal levels of two or more activities in constrained maximization and minimization problems. R I E aking optimal decisions about the levels of various business activities is an essential skill forNall managers, one that requires managers to analyze benefits and costs to make the best possible decision under N a given set of circumstances. When Ford Motor Company began producing a E redesigned and reengineered Explorer, Ford’s CEO decided that the first 5,000 M units rolling off assembly lines would not be delivered immediately to Ford dealer showrooms, even as potential buyers waited anxiously to get the new model. Instead, all of the new 1 vehicles were parked in lots outside factories while quality control engineers9examined 100 of them for defects in assembly and workmanship. The intense, 24-hour-a-day inspection process continued for three months, delaying the 0launch of the highly profitable new Explorer. While no one could blame Ford’s 2 executives for wanting to minimize costly product recalls, many auto industry analysts, car buyers, and owners of Ford T Ford was undertaking too much quality dealerships nonetheless thought control. Ford’s CEO assured his S critics that choosing to add three months of quality control measures was optimal, or best, under the circumstances. Apparently, he believed the benefit of engaging in three months of quality control effort (the savings attributable to preventing vehicle recalls) outweighed 88 tho21901_ch03_088-120.indd 88 8/11/15 4:36 PM C H A P T E R 3   Marginal Analysis for Optimal Decisions   89 the cost of the additional quality control measures (the loss and delay of profit as 5,000 new Explorers spent three months in factory parking lots). As you can see, Ford’s CEO, weighing costs and benefits, made a critical ­decision that three months, rather than two months or four months, and a sample of 100 vehicles, rather than 50 vehicles or 300 vehicles, were the optimal or best levels of these two quality control decisions for launching the redesigned E ­ xplorer. We don’t have enough information about Ford’s costs and benefits of quality control to tell you whether the CEO succeeded in making the optimal ­decision for Ford. We can, however, R tell you that one year later Consumer Reports still ranked the overall reliability of Ford automobiles dead last and Ford had a new CEO. I effort would have been optimal. Perhaps more quality control A manager’s decisionC is optimal if it leads to the best outcome under a given set of circumstances. Finding the best solution involves applying the fundamental A theory developed in this chapter. These analytical principles of optimization principles, which economists refer to as “marginal analysis,” turn out to be R nothing more than a formal presentation of commonsense ideas you already apply, D it, in your everyday life. Marginal analysis supplies probably without knowing the fundamental logic for , making optimal decisions. Managers benefit from understanding marginal analysis because it enables them to make better decisions while avoiding some rather common errors in business decision making. The idea behind marginal A analysis is this: When a manager contemplates whether a particular business activity needs adjusting, either more or less, to reach D the best value, the manager needs to estimate how changing the activity will affect both the benefits the firmRreceives from engaging in the activity and the costs the firm incurs from engaging I in the activity. If changing the activity level causes benefits to rise by more than costs rise, or, alternatively, costs to fall by more than benefits fall, then the netEbenefit the firm receives from the activity will rise. The manager should continueNadjusting the activity level until no further net gains are possible, which means the activity has reached its optimal value or level. N 1, managers face two general types of decisions: As mentioned in Chapter ­routine business practice E or tactical decisions and strategic decisions that can alter the firm’s competitive environment. Marginal analysis builds the essential foundation for making everyday business decisions, such as choosing the number of workers to hire, 1 the amount of output to produce, the amount to spend on advertising, and so on. While strategic decision making relies heavily 9 on concepts from game theory, strategic analysis nevertheless depends indirectly on optimal decision making as the means for computing or forecasting the payoffs 0 under various strategy options. 2 T 3.1 CONCEPTS AND TERMINOLOGY Optimizing behavior on S the part of a decision maker involves trying to maximize objective function The function the decision maker seeks to maximize or minimize. tho21901_ch03_088-120.indd 89 or minimize an objective function. For a manager of a firm, the objective function is usually profit, which is to be maximized. For a consumer, the objective function is the satisfaction derived from consumption of goods, which is to be maximized. 8/11/15 4:36 PM 90  C H A P T E R 3   Marginal Analysis for Optimal Decisions maximization problem An optimization problem that involves maximizing the objective function. minimization problem An optimization problem that involves minimizing the objective function. activities or choice variables Variables that determine the value of the objective function. discrete choice ­variables A choice variable that can take only specific integer values. continuous choice variables A choice variable that can take any value between two end points. unconstrained ­optimization An optimization problem in which the decision maker can choose the level of activity from an unrestricted set of values. constrained ­optimization An optimization problem in which the decision maker chooses values for the choice variables from a restricted set of values. tho21901_ch03_088-120.indd 90 For a city manager seeking to provide adequate law enforcement services, the objective function might be cost, which is to be minimized. For the manager of the marketing division of a large corporation, the objective function is usually sales, which are to be maximized. The objective function measures whatever it is that the particular decision maker wishes to either maximize or minimize. If the decision maker seeks to maximize an objective function, the optimization problem is called a maximization problem. Alternatively, if the objective function is to be minimized, the optimization problem is called a minimization problem. As a general rule, when the objective R function measures a benefit, the decision maker seeks to maximize this benefit and is solving a maximization problem. I a cost, the decision maker seeks to minimize When the objective function measures this cost and is solving a minimization C problem. The value of the objective function is determined by the level of one or more A example, the value of profit depends on the activities or choice variables. For number of units of output produced R and sold. The production of units of the good is the activity that determines the value of the objective function, which in this Dcontrols the value of the objective function by case is profit. The decision maker choosing the levels of the activities , or choice variables. The choice variables in the optimization problems discussed in this text will at times vary discretely and at other times vary continuously. A discrete choice variable can take on only specified A integer values, such as 1, 2, 3, . . . , or 10, 20, 30, . . . Examples of discrete choice variables arise when benefit and cost data D are presented in a table, where each row represents one value of the choice R of discrete choice variables will be presented variable. In this text, all examples in tables. A continuous choice variable can take on any value between two end I points. For example, a continuous variable that can vary between 0 and 10 can E or any one of the infinite number of values take on the value 2, 2.345, 7.9, 8.999, between the two limits. Examples N of continuous choice variables are usually presented graphically but are sometimes shown by equations. As it turns out, the N in the discrete and continuous cases. optimization rules differ only slightly In addition to being categorized E as either maximization or minimization problems, optimization problems are also categorized according to whether the decision maker can choose the values of the choice variables in the objective function from an unconstrained 1 or constrained set of values. Unconstrained optimization problems occur when a decision maker can choose any level of 9 to maximize the objective function. In this activity he or she wishes in order chapter, we show how to solve only 0 unconstrained maximization problems since all the unconstrained decision problems we address in this text are maximization 2 problems. Constrained optimization problems involve choosing the levels of T or minimize the objective function subject two or more activities that maximize to an additional requirement or constraint that restricts the values of A and B S that can be chosen. An example of such a constraint arises when the total cost of the chosen activity levels must equal a specified constraint on cost. In this text, we examine both constrained maximization and constrained minimization problems. 8/11/15 4:36 PM C H A P T E R 3   Marginal Analysis for Optimal Decisions   91 marginal analysis Analytical technique for solving optimization problems that involves changing values of choice variables by small amounts to see if the objective function can be further improved. Now try Technical Problem 1. As we show later in this chapter, the constrained maximization and the constrained minimization problems have one simple rule for the solution. Therefore, you will only have one rule to learn for all constrained optimization problems. Even though there are a huge number of possible maximizing or minimizing decisions, you will see that all optimization problems can be solved using the single analytical technique, mentioned at the beginning of this chapter: marginal analysis. Marginal analysis involves changing the value(s) of the choice variable(s) by a small amount to see if the objective function can be further increased (in the case ofRmaximization problems) or further decreased (in the case of minimization problems). If so, the manager continues to make incremental adjustmentsI in the choice variables until no further improvements are possible. Marginal C analysis leads to two simple rules for solving optimization problems, one for unconstrained decisions and one for constrained decisions. We turn first A to the unconstrained decision. R D Any activity that decision , makers might wish to undertake will generate both 3.2 UNCONSTRAINED MAXIMIZATION net benefit The objective function to be maximized: NB 5 TB 2 TC. benefits and costs. Consequently, decision makers will want to choose the level of activity to obtain the maximum possible net benefit from the activity, where the net benefit (NB) associated A with a specific amount of activity (A) is the difference between total benefit (TB) and total cost (TC) for the activity D NB 5 TB 2 TC R Net benefit, then, serves as the objective function to be maximized, and the amount of activity, A, representsI the choice variable. Furthermore, decision makers can choose any level of activity E they wish, from zero to infinity, in either discrete or continuous units. Thus, we are studying unconstrained maximization in this section. N The Optimal Level of Activity (A*) N We begin the analysis ofEunconstrained maximization with a rather typical set of total benefit and total cost curves for some activity, A, as shown in Panel A of Fig­ure 3.1. Total benefit increases with higher levels of activity up to 1,000 units of activity (point G); then total 1 benefit falls beyond this point. Total cost begins at a value of zero and rises continuously as activity increases. These “typical” curves 9 rules for finding the best solution to all such unconallow us to derive general strained problems, even0though specific problems encountered in later chapters sometimes involve benefit and cost curves with shapes that differ somewhat from 2 those shown in Panel A. For example, total benefit curves can be linear. Total cost curves can be linear or even T S-shaped. And, as you will see in later chapters, total cost curves can include fixed costs when they take positive values at zero units of S activity. In all of these variations, however, the rules for making the best decisions do not change. By learning how to solve the optimization problem as set forth in Figure 3.1, you will be prepared to solve all variations of these problems that come later in the text. tho21901_ch03_088-120.indd 91 8/11/15 4:36 PM 92  C H A P T E R 3   Marginal Analysis for Optimal Decisions F I G U R E 3.1 The Optimal Level of Activity TC Total benefit and total cost (dollars) 4,000 RD I C D9 A R NB* 5 $1,225 D , B 2,310 2,000 C 1,085 1,000 B9 C9 200 350 5 A* Net benefit (dollars) Panel A — Total benefit and total cost curves 1,225 1,000 Panel B — Net benefit curve tho21901_ch03_088-120.indd 92 M A D R600 700 Level of I activity (A) E N N E 1,000 c99 600 0 TB F 3,000 0 G 200 350 5 A* 1d 99 f 99 9 0600 Level of activity (A) 2 T S 1,000 NB 8/11/15 4:36 PM C H A P T E R 3   Marginal Analysis for Optimal Decisions   93 I L L U S T R AT I O N 3 . 1 Is Cost–Benefit Analysis Really Useful? We have extolled the usefulness of marginal analysis in optimal decision making—often referred to as cost– benefit analysis—in business decision making as well as decision making in everyday life. This process involves weighing the marginal benefits and marginal costs of an activity while ignoring all previously incurredR or sunk costs. The principal rule is to increase the levelI of an activity if marginal benefits exceed marginal costs C and decrease the level if marginal costs exceed marginal benefits. This simple rule, however, flies in the faceA of many honored traditional principles such as “Never R give up” or “Anything worth doing is worth doing well” or “Waste not, want not.” So you might wonder D if cost–benefit analysis is as useful as we have said it is. It is, at least according to an article in The Wall , Street Journal entitled “Economic Perspective Produces Steady Yields.” In this article, a University of Michigan research team concludes, “Cost–benefit analysis pays A off in everyday living.” This team quizzed some of D the university’s seniors and faculty members on such questions as how often they walk out on a bad movie, R refuse to finish a bad novel, start over on a weak term paper, or abandon a research project that no longer I looks promising. They believe that people who cut optimal level of ­activity The level of activity that maximizes net benefit (A*). their losses this way are following sound economic rules: calculating the net benefits of alternative courses of action, writing off past costs that can’t be recovered, and weighing the opportunity to use future time and effort more profitably elsewhere.a The findings: Among faculty members, those who use cost–benefit reasoning in this fashion had higher salaries relative to their age and departments. Economists were more likely to apply the approach than professors of humanities or biology. Among students, those who have learned to use cost–benefit analysis frequently are apt to have far better grades than their SAT scores would have predicted. The more economics courses the students had taken, the more likely they were to apply cost–benefit analysis outside the classroom. The director of the University of Michigan study did concede that for many Americans cost–benefit rules often appear to conflict with traditional principles such as those we previously mentioned. Notwithstanding these probable conflicts, the study provides evidence that decision makers can indeed prosper by following the logic of marginal analysis and cost–benefit analysis. “Economic Perspective Produces Steady Yield,” The Wall Street Journal, March 31, 1992. a E N The level of activity that maximizes net benefit is called the optimal level of N from other levels of activity with an asterisk: A*. In ­activity, which we distinguish Panel A of Figure 3.1, netEbenefit at any particular level of activity is measured by the vertical distance between the total benefit and total cost curves. At 200 units of activity, for example, net benefit equals the length of line segment CC9, which happens to be $1,000 as shown in Panel B at point c”. Panel B of Figure 3.1 shows 1 the net benefit curve associated with the TB and TC curves in Panel A. As you can 9 see from examining the net benefit curve in Panel B, the optimal level of activity, 0reaches its maximum value. At 350 units in Panel A, the A*, is 350 units, where NB vertical distance between TB and TC is maximized, and this maximum distance is 2 $1, 225 (5 NB*).1 1 T S the optimal activity level was 700 units since two curves in You might, at first, have thought Panel A of Figure 3.1 intersect at point F, and this situation frequently identifies “correct” answers in economics. But, as you can see in Panel B, choosing 700 units of activity creates no more net benefit than choosing to do nothing at all (i.e., choosing A 5 0) because total benefit equals total cost at both zero and 700 units of activity. tho21901_ch03_088-120.indd 93 8/11/15 4:36 PM 94  C H A P T E R 3   Marginal Analysis for Optimal Decisions Now try Technical Problem 2. Two important observations can now be made about A* in unconstrained maximization problems. First, the optimal level of activity does not generally result in maximization of total benefits. In Panel A of Figure 3.1, you can see that total benefit is still rising at the optimal point B. As we will demonstrate later in this book, for one of the most important applications of this technique, profit maximization, the optimal level of production occurs at a point where revenues are not maximized. This outcome can confuse managers, especially ones who believe any decision that increases revenue should be undertaken. We will have much more to say about this later in the Rtext. Second, the optimal level of activity in an unconstrained maximization problem does not result in minimization of total cost. In Panel A, you can easily verify Ithat total cost isn’t minimized at A* but rather at zero units of activity. C Finding A* in Figure 3.1 seems easy enough. A decision maker starts with the A in Panel A and subtracts the total cost curve total benefit and total cost curves from the total benefit curve to construct the net benefit curve in Panel B. Then, R the decision maker chooses the value of A corresponding to the peak of the net D wonder why we are going to develop an benefit curve. You might reasonably alternative method, marginal analysis, for making optimal decisions. Perhaps , the most important reason for learning how to use marginal analysis is that economists regard marginal analysis as “the central organizing principle of economic theory.”2 The graphical A derivation of net benefit shown in Figure 3.1 serves only to define and describe the optimal level of activity; it does not explain D why net benefit rise ... Purchase answer to see full attachment