Simon Decision Making

Home [pic]http://jayhanson. us/america. htm [pic] Decision Making and Problem Solving by Herbert A. Simon and Associates Associates: George B. Dantzig, Robin Hogarth, Charles R. Piott, Howard Raiffa, Thomas C. Schelling, Kennth A. Shepsle, Richard Thaier, Amos Tversky, and Sidney Winter. Simon was educated in political science at the University of Chicago (B. A. , 1936, Ph. D. , 1943).He has held research and faculty positions at the University of California (Berkeley), Illinois Institute of Technology and since 1949, Carnegie Mellon University, where he is the Richard King Mellon University Professor of Computer Science and Psychology. In 1978, he received the Alfred Nobel Memorial Prize in Economic Sciences and in 1986 the National Medal of Science. Reprinted with permission from Research Briefings 1986: Report of the Research Briefing Panel on Decision Making and Problem Solving  © 1986 by the National Academy of Sciences. Published by National Academy Press, Washington, DC.Intr oduction The work of managers, of scientists, of engineers, of lawyers–the work that steers the course of society and its economic and governmental organizations–is largely work of making decisions and solving problems. It is work of choosing issues that require attention, setting goals, finding or designing suitable courses of action, and evaluating and choosing among alternative actions. The first three of these activities–fixing agendas, setting goals, and designing actions–are usually called problem solving; the last, evaluating and choosing, is usually called decision making.Nothing is more important for the well-being of society than that this work be performed effectively, that we address successfully the many problems requiring attention at the national level (the budget and trade deficits, AIDS, national security, the mitigation of earthquake damage), at the level of business organizations (product improvement, efficiency of production, choice of investments), and at the level of our individual lives (choosing a career or a school, buying a house).The abilities and skills that determine the quality of our decisions and problem solutions are stored not only in more than 200 million human heads, but also in tools and machines, and especially today in those machines we call computers. This fund of brains and its attendant machines form the basis of our American ingenuity, an ingenuity that has permitted U.S. society to reach remarkable levels of economic productivity. There are no more promising or important targets for basic scientific research than understanding how human minds, with and without the help of computers, solve problems and make decisions effectively, and improving our problem-solving and decision-making capabilities.In psychology, economics, mathematical statistics, operations research, political science, artificial intelligence, and cognitive science, major research gains have been made during the past half ce ntury in understanding problem solving and decision making. The progress already achieved holds forth the promise of exciting new advances that will contribute substantially to our nation's capacity for dealing intelligently with the range of issues, large and small, that confront us.Much of our existing knowledge about decision making and problem solving, derived from this research, has already been put to use in a wide variety of applications, including procedures used to assess drug safety, inventory control methods for industry, the new expert systems that embody artificial intelligence techniques, procedures for modeling energy and environmental systems, and analyses of the stabilizing or destabilizing effects of alternative defense strategies. Application of the new inventory control techniques, for example, has enabled American corporations to reduce their inventories by hundreds of millions of dollars since World War II without increasing the incidence of stockouts. ) Some o f the knowledge gained through the research describes the ways in which people actually go about making decisions and solving problems; some of it prescribes better methods, offering advice for the improvement of the process.Central to the body of prescriptive knowledge about decision making has been the theory of subjective expected utility (SEU), a sophisticated mathematical model of choice that lies at the foundation of most contemporary economics, theoretical statistics, and operations research. SEU theory defines the conditions of perfect utility-maximizing rationality in a world of certainty or in a world in which the probability distributions of all relevant variables can be provided by the decision makers. In spirit, it might be compared with a theory of ideal gases or of frictionless bodies sliding down inclined planes in a vacuum. ) SEU theory deals only with decision making; it has nothing to say about how to frame problems, set goals, or develop new alternatives. Prescri ptive theories of choice such as SEU are complemented by empirical research that shows how people actually make decisions (purchasing insurance, voting for political candidates, or investing in securities), and research on the processes people use to solve problems (designing switchgear or finding chemical reaction pathways).This research demonstrates that people solve problems by selective, heuristic search through large problem spaces and large data bases, using means-ends analysis as a principal technique for guiding the search. The expert systems that are now being produced by research on artificial intelligence and applied to such tasks as interpreting oil-well drilling logs or making medical diagnoses are outgrowths of these research findings on human problem solving.What chiefly distinguishes the empirical research on decision making and problem solving from the prescriptive approaches derived from SEU theory is the attention that the former gives to the limits on human ratio nality. These limits are imposed by the complexity of the world in which we live, the incompleteness and inadequacy of human knowledge, the inconsistencies of individual preference and belief, the conflicts of value among people and groups of people, and the inadequacy of the computations we can carry out, even with the aid of the most powerful computers.The real world of human decisions is not a world of ideal gases, frictionless planes, or vacuums. To bring it within the scope of human thinking powers, we must simplify our problem formulations drastically, even leaving out much or most of what is potentially relevant. The descriptive theory of problem solving and decision making is centrally concerned with how people cut problems down to size: how they apply approximate, heuristic techniques to handle complexity that cannot be handled exactly.Out of this descriptive theory is emerging an augmented and amended prescriptive theory, one that takes account of the gaps and elements of unrealism in SEU theory by encompassing problem solving as well as choice and demanding only the kinds of knowledge, consistency, and computational power that are attainable in the real world. The growing realization that coping with complexity is central to human decision making strongly influences the directions of research in this domain.Operations research and artificial intelligence are forging powerful new computational tools; at the same time, a new body of mathematical theory is evolving around the topic of computational complexity. Economics, which has traditionally derived both its descriptive and prescriptive approaches from SEU theory, is now paying a great deal of attention to uncertainty and incomplete information; to so-called â€Å"agency theory,† which takes account of the institutional framework within which decisions are made; and to game theory, which seeks to deal with interindividual and intergroup processes in which there is partial conflict of interest .Economists and political scientists are also increasingly buttressing the empirical foundations of their field by studying individual choice behavior directly and by studying behavior in experimentally constructed markets and simulated political structures. The following pages contain a fuller outline of current knowledge about decision making and problem solving and a brief review of current research directions in these fields as well as some of the principal research opportunities. Decision Making SEU THEORY The development of SEU theory was a major intellectual achievement of the first half of this century.It gave for the first time a formally axiomatized statement of what it would mean for an agent to behave in a consistent, rational matter. It assumed that a decision maker possessed a utility function (an ordering by preference among all the possible outcomes of choice), that all the alternatives among which choice could be made were known, and that the consequences of choosin g each alternative could be ascertained (or, in the version of the theory that treats of choice under uncertainty, it assumed that a subjective or objective probability distribution of consequences was associated with each alternative).By admitting subjectively assigned probabilities, SEU theory opened the way to fusing subjective opinions with objective data, an approach that can also be used in man-machine decision-making systems. In the probabilistic version of the theory, Bayes's rule prescribes how people should take account of new information and how they should respond to incomplete information. The assumptions of SEU theory are very strong, permitting correspondingly strong inferences to be made from them.Although the assumptions cannot be satisfied even remotely for most complex situations in the real world, they may be satisfied approximately in some microcosms–problem situations that can be isolated from the world's complexity and dealt with independently. For exam ple, the manager of a commercial cattle-feeding operation might isolate the problem of finding the least expensive mix of feeds available in the market that would meet all the nutritional requirements of his cattle.The computational tool of linear programming, which is a powerful method for maximizing goal achievement or minimizing costs while satisfying all kinds of side conditions (in this case, the nutritional requirements), can provide the manager with an optimal feed mix–optimal within the limits of approximation of his model to real world conditions. Linear programming and related operations research techniques are now used widely to make decisions whenever a situation that reasonably fits their assumptions can be carved out of its complex surround.These techniques have been especially valuable aids to middle management in dealing with relatively well-structured decision problems. Most of the tools of modern operations research–not only linear programming, but al so integer programming, queuing theory, decision trees, and other widely used techniques–use the assumptions of SEU theory. They assume that what is desired is to maximize the achievement of some goal, under specified constraints and assuming that all alternatives and consequences (or their probability distributions) are known.These tools have proven their usefulness in a wide variety of applications. THE LIMITS OF RATIONALITY Operations research tools have also underscored dramatically the limits of SEU theory in dealing with complexity. For example, present and prospective computers are not even powerful enough to provide exact solutions for the problems of optimal scheduling and routing of jobs through a typical factory that manufactures a variety of products using many different tools and machines.And the mere thought of using these computational techniques to determine an optimal national policy for energy production or an optimal economic policy reveals their limits. Co mputational complexity is not the only factor that limits the literal application of SEU theory. The theory also makes enormous demands on information. For the utility function, the range of available alternatives and the consequences following from each alternative must all be known.Increasingly, research is being directed at decision making that takes realistic account of the compromises and approximations that must be made in order to fit real-world problems to the informational and computational limits of people and computers, as well as to the inconsistencies in their values and perceptions. The study of actual decision processes (for example, the strategies used by corporations to make their investments) reveals massive and unavoidable departures from the framework of SEU theory.The sections that follow describe some of the things that have been learned about choice under various conditions of incomplete information, limited computing power, inconsistency, and institutional co nstraints on alternatives. Game theory, agency theory, choice under uncertainty, and the theory of markets are a few of the directions of this research, with the aims both of constructing prescriptive theories of broader application and of providing more realistic descriptions and explanations of actual decision making within U. S. economic and political institutions.LIMITED RATIONALITY IN ECONOMIC THEORY Although the limits of human rationality were stressed by some researchers in the 1950s, only recently has there been extensive activity in the field of economics aimed at developing theories that assume less than fully rational choice on the part of business firm managers and other economic agents. The newer theoretical research undertakes to answer such questions as the following: †¢ Are market equilibria altered by the departures of actual choice behavior from the behavior of fully rational agents predicted by SEU theory? Under what circumstances do the processes of competi tion â€Å"police† markets in such a way as to cancel out the effects of the departures from full rationality? †¢ In what ways are the choices made by boundedly rational agents different from those made by fully rational agents? Theories of the firm that assume managers are aiming at â€Å"satisfactory† profits or that their concern is to maintain the firm's share of market in the industry make quite different predictions about economic equilibrium than those derived from the assumption of profit maximization.Moreover, the classical theory of the firm cannot explain why economic activity is sometimes organized around large business firms and sometimes around contractual networks of individuals or smaller organizations. New theories that take account of differential access of economic agents to information, combined with differences in self-interest, are able to account for these important phenomena, as well as provide explanations for the many forms of contracts t hat are used in business.Incompleteness and asymmetry of information have been shown to be essential for explaining how individuals and business firms decide when to face uncertainty by insuring, when by hedging, and when by assuming the risk. Most current work in this domain still assumes that economic agents seek to maximize utility, but within limits posed by the incompleteness and uncertainty of the information available to them.An important potential area of research is to discover how choices will be changed if there are other departures from the axioms of rational choice–for example, substituting goals of reaching specified aspiration levels (satisficing) for goals of maximizing. Applying the new assumptions about choice to economics leads to new empirically supported theories about decision making over time. The classical theory of perfect rationality leaves no room for regrets, second thoughts, or â€Å"weakness of will. It cannot explain why many individuals enroll in Christmas savings plans, which earn interest well below the market rate. More generally, it does not lead to correct conclusions about the important social issues of saving and conservation. The effect of pensions and social security on personal saving has been a controversial issue in economics. The standard economic model predicts that an increase in required pension saving will reduce other saving dollar for dollar; behavioral theories, on the other hand, predict a much smaller offset. The empirical evidence indicates that the offset is indeed very small.Another empirical finding is that the method of payment of wages and salaries affects the saving rate. For example, annual bonuses produce a higher saving rate than the same amount of income paid in monthly salaries. This finding implies that saving rates can be influenced by the way compensation is framed. If individuals fail to discount properly for the passage of time, their decisions will not be optimal. For example, air conditioners vary greatly in their energy efficiency; the more efficient models cost more initially but save money over the long run through lower energy consumption.It has been found that consumers, on average, choose air conditioners that imply a discount rate of 25 percent or more per year, much higher than the rates of interest that prevailed at the time of the study. As recently as five years ago, the evidence was thought to be unassailable that markets like the New York Stock Exchange work efficiently–that prices reflect all available information at any given moment in time, so that stock price movements resemble a random walk and contain no systematic information that could be exploited for profit.Recently, however, substantial departures from the behavior predicted by the efficient-market hypothesis have been detected. For example, small firms appear to earn inexplicably high returns on the market prices of their stock, while firms that have very low price-earnings ra tios and firms that have lost much of their market value in the recent past also earn abnormally high returns. All of these results are consistent with the empirical finding that decision makers often overreact to new information, in violation of Bayes's rule.In the same way, it has been found that stock prices are excessively volatile–that they fluctuate up and down more rapidly and violently than they would if the marke t were efficient. There has also been a long-standing puzzle as to why firms pay dividends. Considering that dividends are taxed at a higher rate than capital gains, taxpaying investors should prefer, under the assumptions of perfect rationality, that their firms reinvest earnings or repurchase shares instead of paying dividends. (The investors could simply sell some of their appreciated shares to obtain the income they require. The solution to this puzzle also requires models of investors that take account of limits on rationality. THE THEORY OF GAMES In ec onomic, political, and other social situations in which there is actual or potential conflict of interest, especially if it is combined with incomplete information, SEU theory faces special difficulties. In markets in which there are many competitors (e. g. , the wheat market), each buyer or seller can accept the market price as a â€Å"given† that will not be affected materially by the actions of any single individual.Under these conditions, SEU theory makes unambiguous predictions of behavior. However, when a market has only a few suppliers –say, for example, two–matters are quite different. In this case, what it is rational to do depends on what one's competitor is going to do, and vice versa. Each supplier may try to outwit the other. What then is the rational decision? The most ambitious attempt to answer questions of this kind was the theory of games, developed by von Neumann and Morgenstern and published in its full form in 1944. But the answers provided by the theory of games are sometimes very puzzling and ambiguous.In many situations, no single course of action dominates all the others; instead, a whole set of possible solutions are all equally consistent with the postulates of rationality. One game that has been studied extensively, both theoretically and empirically, is the Prisoner's Dilemma. In this game between two players, each has a choice between two actions, one trustful of the other player, the other mistrustful or exploitative. If both players choose the trustful alternative, both receive small rewards. If both choose the exploitative alternative, both are punished.If one chooses the trustful alternative and the other the exploitative alternative, the former is punished much more severely than in the previous case, while the latter receives a substantial reward. If the other player's choice is fixed but unknown, it is advantageous for a player to choose the exploitative alternative, for this will give him the best outc ome in either case. But if both adopt this reasoning, they will both be punished, whereas they could both receive rewards if they agreed upon the trustful choice (and did not welch on the agreement).The terms of the game have an unsettling resemblance to certain situations in the relations between nations or between a company and the employees' union. The resemblance becomes stronger if one imagines the game as being played repeatedly. Analyses of â€Å"rational† behavior under assumptions of intended utility maximization support the conclusion that the players will (ought to? ) always make the mistrustful choice. Nevertheless, in laboratory experiments with the game, it is often found that players (even those who are expert in game theory) adopt a â€Å"tit-for-tat† strategy.That is, each plays the trustful, cooperative strategy as long as his or her partner does the same. If the partner exploits the player on a particular trial, the player then plays the exploitative strategy on the next trial and continues to do so until the partner switches back to the trustful strategy. Under these conditions, the game frequently stabilizes with the players pursuing the mutually trustful strategy and receiving the rewards. With these empirical findings in hand, theorists have recently sought and found some of the conditions for attaining this kind of benign stability.It occurs, for example, if the players set aspirations for a satisfactory reward rather than seeking the maximum reward. This result is consistent with the finding that in many situations, as in the Prisoner's Dilemma game, people appear to satisfice rather than attempting to optimize. The Prisoner's Dilemma game illustrates an important point that is beginning to be appreciated by those who do research on decision making. There are so many ways in which actual human behavior can depart from the SEU assumptions that theorists seeking to account for behavior are confronted with an embarrassment o f riches.To choose among the many alternative models that could account for the anomalies of choice, extensive empirical research is called for–to see how people do make their choices, what beliefs guide them, what information they have available, and what part of that information they take into account and what part they ignore. In a world of limited rationality, economics and the other decision sciences must closely examine the actual limits on rationality in order to make accurate predictions and to provide sound advice on public policy.EMPIRICAL STUDIES OF CHOICE UNDER UNCERTAINTY During the past ten years, empirical studies of human choices in which uncertainty, inconsistency, and incomplete information are present have produced a rich collection of findings which only now are beginning to be organized under broad generalizations. Here are a few examples. When people are given information about the probabilities of certain events (e. g. , how many lawyers and how many en gineers are in a population that is being sampled), and then are given some additional information as to which of the vents has occurred (which person has been sampled from the population), they tend to ignore the prior probabilities in favor of incomplete or even quite irrelevant information about the individual event. Thus, if they are told that 70 percent of the population are lawyers, and if they are then given a noncommittal description of a person (one that could equally well fit a lawyer or an engineer), half the time they will predict that the person is a lawyer and half the time that he is an engineer–even though the laws of probability dictate that the best forecast is always to predict that the person is a lawyer.People commonly misjudge probabilities in many other ways. Asked to estimate the probability that 60 percent or more of the babies born in a hospital during a given week are male, they ignore information about the total number of births, although it is evi dent that the probability of a departure of this magnitude from the expected value of 50 percent is smaller if the total number of births is larger (the standard error of a percentage varies inversely with the square root of the population size). There are situations in which people assess the frequency of a class by the ease with which instances can be brought to mind.In one experiment, subjects heard a list of names of persons of both sexes and were later asked to judge whether there were more names of men or women on the list. In lists presented to some subjects, the men were more famous than the women; in other lists, the women were more famous than the men. For all lists, subjects judged that the sex that had the more famous personalities was the more numerous. The way in which an uncertain possibility is presented may have a substantial effect on how people respond to it.When asked whether they would choose surgery in a hypothetical medical emergency, many more people said tha t they would when the chance of survival was given as 80 percent than when the chance of death was given as 20 percent. On the basis of these studies, some of the general heuristics, or rules of thumb, that people use in making judgments have been compiled—heuristics that produce biases toward classifying situations according to their representativeness, or toward judging frequencies according to the availability of examples in memory, or toward interpretations warped by the way in which a problem has been framed.These findings have important implications for public policy. A recent example is the lobbying effort of the credit card industry to have differentials between cash and credit prices labeled â€Å"cash discounts† rather than â€Å"credit surcharges. † The research findings raise questions about how to phrase cigarette warning labels or frame truth-in-lending laws and informed consent laws. METHODS OF EMPIRICAL RESEARCH Finding the underlying bases of hu man choice behavior is difficult.People cannot always, or perhaps even usually, provide veridical accounts of how they make up their minds, especially when there is uncertainty. In many cases, they can predict how they will behave (pre-election polls of voting intentions have been reasonably accurate when carefully taken), but the reasons people give for their choices can often be shown to be rationalizations and not closely related to their real motives. Students of choice behavior have steadily improved their research methods. They question respondents about specific situations, rather than asking for generalizations.They are sensitive to the dependence of answers on the exact forms of the questions. They are aware that behavior in an experimental situation may be different from behavior in real life, and they attempt to provide experimental settings and motivations that are as realistic as possible. Using thinking-aloud protocols and other approaches, they try to track the choice behavior step by step, instead of relying just on information about outcomes or querying respondents retrospectively about their choice processes.Perhaps the most common method of empirical research in this field is still to ask people to respond to a series of questions. But data obtained by this method are being supplemented by data obtained from carefully designed laboratory experiments and from observations of actual choice behavior (for example, the behavior of customers in supermarkets). In an experimental study of choice, subjects may trade in an actual market with real (if modest) monetary rewards and penalties.Research experience has also demonstrated the feasibility of making direct observations, over substantial periods of time, of the decision-making processes in business and governmental organizations–for example, observations of the procedures that corporations use in making new investments in plant and equipment. Confidence in the empirical findings that have been accumulating over the past several decades is enhanced by the general consistency that is observed among the data obtained from quite different settings using different research methods.There still remains the enormous and challenging task of putting together these findings into an empirically founded theory of decision making. With the growing availability of data, the theory-building enterprise is receiving much better guidance from the facts than it did in the past. As a result, we can expect it to become correspondingly more effective in arriving at realistic models of behavior. Problem Solving The theory of choice has its roots mainly in economics, statistics, and operations research and only recently has received much attention from psychologists; the theory of problem solving has a very different history.Problem solving was initially studied principally by psychologists, and more recently by researchers in artificial intelligence. It has received rather scant attention f rom economists. CONTEMPORARY PROBLEM-SOLVING THEORY Human problem solving is usually studied in laboratory settings, using problems that can be solved in relatively short periods of time (seldom more than an hour), and often seeking a maximum density of data about the solution process by asking subjects to think aloud while they work.The thinking-aloud technique, at first viewed with suspicion by behaviorists as subjective and â€Å"introspective,† has received such careful methodological attention in recent years that it can now be used dependably to obtain data about subjects' behaviors in a wide range of settings. The laboratory study of problem solving has been supplemented by field studies of professionals solving real-world problems–for example, physicians making diagnoses and chess grandmasters analyzing game positions, and, as noted earlier, even business corporations making investment decisions.Currently, historical records, including laboratory notebooks of s cientists, are also being used to study problem-solving processes in scientific discovery. Although such records are far less â€Å"dense† than laboratory protocols, they sometimes permit the course of discovery to be traced in considerable detail. Laboratory notebooks of scientists as distinguished as Charles Darwin, Michael Faraday, Antoine-Laurent Lavoisier, and Hans Krebs have been used successfully in such research. From empirical studies, a description can now be given of the problem-solving process that holds for a rather wide range of activities.First, problem solving generally proceeds by selective search through large sets of possibilities, using rules of thumb (heuristics) to guide the search. Because the possibilities in realistic problem situations are generally multitudinous, trial-and-error search would simply not work; the search must be highly selective. Chess grandmasters seldom examine more than a hundred of the vast number of possible scenarios that confro nt them, and similar small numbers of searches are observed in other kinds of problem-solving search.One of the procedures often used to guide search is â€Å"hill climbing,† using some measure of approach to the goal to determine where it is most profitable to look next. Another, and more powerful, common procedure is means-ends analysis. In means-ends analysis, the problem solver compares the present situation with the goal, detects a difference between them, and then searches memory for actions that are likely to reduce the difference.Thus, if the difference is a fifty-mile distance from the goal, the problem solver will retrieve from memory knowledge about autos, carts, bicycles, and other means of transport; walking and flying will probably be discarded as inappropriate for that distance. The third thing that has been learned about problem solving–especially when the solver is an expert–is that it relies on large amounts of information that are stored in me mory and that are retrievable whenever the solver recognizes cues signaling its relevance.Thus, the expert knowledge of a diagnostician is evoked by the symptoms presented by the patient; this knowledge leads to the recollection of what additional information is needed to discriminate among alternative diseases and, finally, to the diagnosis. In a few cases, it has been possible to estimate how many patterns an expert must be able to recognize in order to gain access to the relevant knowledge stored in memory. A chess master must be able to recognize about 50,000 different configurations of chess pieces that occur frequently in the course of chess games.A medical diagnostician must be able to recognize tens of thousands of configurations of symptoms; a botanist or zoologist specializing in taxonomy, tens or hundreds of thousands of features of specimens that define their species. For comparison, college graduates typically have vocabularies in their native languages of 50,000 to 200 ,000 words. (However, these numbers are very small in comparison with the real-world situations the expert faces: there are perhaps 10120 branches in the game tree of chess, a game played with only six kinds of pieces on an 8 x 8 board. One of the accomplishments of the contemporary theory of problem solving has been to provide an explanation for the phenomena of intuition and judgment frequently seen in experts' behavior. The store of expert knowledge, â€Å"indexed† by the recognition cues that make it accessible and combined with some basic inferential capabilities (perhaps in the form of means-ends analysis), accounts for the ability of experts to find satisfactory solutions for difficult problems, and sometimes to find them almost instantaneously.The expert's â€Å"intuition† and â€Å"judgment† derive from this capability for rapid recognition linked to a large store of knowledge. When immediate intuition fails to yield a problem solution or when a prospec tive solution needs to be evaluated, the expert falls back on the slower processes of analysis and inference. EXPERT SYSTEMS IN ARTIFICIAL INTELLIGENCE Over the past thirty years, there has been close teamwork between research in psychology and research in computer science aimed at developing intelligent programs. Artificial intelligence (AI) research has both borrowed from and contributed to research on human problem solving.Today, artificial intelligence is beginning to produce systems, applied to a variety of tasks, that can solve difficult problems at the level of professionally trained humans. These AI programs are usually called expert systems. A description of a typical expert system would resemble closely the description given above of typical human problem solving; the differences between the two would be differences in degree, not in kind. An AI expert system, relying on the speed of computers and their ability to retain large bodies of transient information in memory, wil l generally use â€Å"brute force†Ã¢â‚¬â€œsheer omputational speed and power–more freely than a human expert can. A human expert, in compensation, will generally have a richer set of heuristics to guide search and a larger vocabulary of recognizable patterns. To the observer, the computer's process will appear the more systematic and even compulsive, the human's the more intuitive. But these are quantitative, not qualitative, differences. The number of tasks for which expert systems have been built is increasing rapidly. One is medical diagnosis (two examples are the CADUCEUS and MYCIN programs).Others are automatic design of electric motors, generators, and transformers (which predates by a decade the invention of the term expert systems), the configuration of computer systems from customer specifications, and the automatic generation of reaction paths for the synthesis of organic molecules. All of these (and others) are either being used currently in professional or industrial practice or at least have reached a level at which they can produce a professionally acceptable product. Expert systems are generally constructed in close consultation with the people who are experts in the task domain.Using standard techniques of observation and interrogation, the heuristics that the human expert uses, implicitly and often unconsciously, to perform the task are gradually educed, made explicit, and incorporated in program structures. Although a great deal has been learned about how to do this, improving techniques for designing expert systems is an important current direction of research. It is especially important because expert systems, once built, cannot remain static but must be modifiable to incorporate new knowledge as it becomes available.DEALING WITH ILL-STRUCTURED PROBLEMS In the 1950s and 1960s, research on problem solving focused on clearly structured puzzle-like problems that were easily brought into the psychological laboratory and that were within the range of computer programming sophistication at that time. Computer programs were written to discover proofs for theorems in Euclidean geometry or to solve the puzzle of transporting missionaries and cannibals across a river. Choosing chess moves was perhaps the most complex task that received attention in the early years of cognitive science and AI.As understanding grew of the methods needed to handle these relatively simple tasks, research aspirations rose. The next main target, in the 1960s and 1970s, was to find methods for solving problems that involved large bodies of semantic information. Medical diagnosis and interpreting mass spectrogram data are examples of the kinds of tasks that were investigated during this period and for which a good level of understanding was achieved. They are tasks that, for all of the knowledge they call upon, are still well structured, with clear-cut goals and constraints.The current research target is to gain an understanding of proble m-solving tasks when the goals themselves are complex and sometimes ill defined, and when the very nature of the problem is successively transformed in the course of exploration. To the extent that a problem has these characteristics, it is usually called ill structured. Because ambiguous goals and shifting problem formulations are typical characteristics of problems of design, the work of architects offers a good example of what is involved in solving ill-structured problems.An architect begins with some very general specifications of what is wanted by a client. The initial goals are modified and substantially elaborated as the architect proceeds with the task. Initial design ideas, recorded in drawings and diagrams, themselves suggest new criteria, new possibilities, and new requirements. Throughout the whole process of design, the emerging conception provides continual feedback that reminds the architect of additional considerations that need to be taken into account.With the cur rent state of the art, it is just beginning to be possible to construct programs that simulate this kind of flexible problem-solving process. What is called for is an expert system whose expertise includes substantial knowledge about design criteria as well as knowledge about the means for satisfying those criteria. Both kinds of knowledge are evoked in the course of the design activity by the usual recognition processes, and the evocation of design criteria and constraints continually modifies and remolds the problem that the design system is addressing.The large data bases that can now be constructed to aid in the management of architectural and construction projects provide a framework into which AI tools, fashioned along these lines, can be incorporated. Most corporate strategy problems and governmental policy problems are at least as ill structured as problems of architectural or engineering design. The tools now being forged for aiding architectural design will provide a basis for building tools that can aid in formulating, assessing, and monitoring public energy or environmental policies, or in guiding corporate product and investment strategies.SETTING THE AGENDA AND REPRESENTING A PROBLEM The very first steps in the problem-solving process are the least understood. What brings (and should bring) problems to the head of the agenda? And when a problem is identified, how can it be represented in a way that facilitates its solution? The task of setting an agenda is of utmost importance because both individual human beings and human institutions have limited capacities for dealing with many tasks simultaneously. While some problems are receiving full attention, others are neglected.Where new problems come thick and fast, â€Å"fire fighting† replaces planning and deliberation. The facts of limited attention span, both for individuals and for institutions like the Congress, are well known. However, relatively little has been accomplished toward analy zing or designing effective agenda-setting systems. A beginning could be made by the study of â€Å"alerting† organizations like the Office of Technology Assessment or military and foreign affairs intelligence agencies.Because the research and development function in industry is also in considerable part a task of monitoring current and prospective technological advances, it could also be studied profitably from this standpoint. The way in which problems are represented has much to do with the quality of the solutions that are found. The task of designing highways or dams takes on an entirely new aspect if human responses to a changed environment are taken into account. (New transportation routes cause people to move their homes, and people show a considerable propensity to move into zones that are subject to flooding when partial protections are erected. Very different social welfare policies are usually proposed in response to the problem of providing incentives for economi c independence than are proposed in response to the problem of taking care of the needy. Early management information systems were designed on the assumption that information was the scarce resource; today, because designers recognize that the scarce resource is managerial attention, a new framework produces quite different designs. The representation or â€Å"framing† of problems is even less well understood than agenda setting.Today's expert systems make use of problem representations that already exist. But major advances in human knowledge frequently derive from new ways of thinking about problems. A large part of the history of physics in nineteenth-century England can be written in terms of the shift from action-at-a-distance representations to the field representations that were developed by the applied mathematicians at Cambridge. Today, developments in computer-aided design (CAD) present new opportunities to provide human designers with computer-generated representat ions of their problems.Effective use of these capabilities requires us to understand better how people extract information from diagrams and other displays and how displays can enhance human performance in design tasks. Research on representations is fundamental to the progress of CAD. COMPUTATION AS PROBLEM SOLVING Nothing has been said so far about the radical changes that have been brought about in problem solving over most of the domains of science and engineering by the standard uses of computers as computational devices.Although a few examples come to mind in which artificial intelligence has contributed to these developments, they have mainly been brought about by research in the individual sciences themselves, combined with work in numerical analysis. Whatever their origins, the massive computational applications of computers are changing the conduct of science in numerous ways. There are new specialties emerging such as â€Å"computational physics† and â€Å"computa tional chemistry. Computation–that is to say, problem solving–becomes an object of explicit concern to scientists, side by side with the substance of the science itself. Out of this new awareness of the computational component of scientific inquiry is arising an increasing interaction among computational specialists in the various sciences and scientists concerned with cognition and AI. This interaction extends well beyond the traditional area of numerical analysis, or even the newer subject of computational complexity, into the heart of the theory of problem solving.Physicists seeking to handle the great mass of bubble-chamber data produced by their instruments began, as early as the 1960s, to look to AI for pattern recognition methods as a basis for automating the analysis of their data. The construction of expert systems to interpret mass spectrogram data and of other systems to design synthesis paths for chemical reactions are other examples of problem solving in s cience, as are programs to aid in matching sequences of nucleic acids in DNA and RNA and amino acid sequences in proteins.Theories of human problem solving and learning are also beginning to attract new attention within the scientific community as a basis for improving science teaching. Each advance in the understanding of problem solving and learning processes provides new insights about the ways in which a learner must store and index new knowledge and procedures if they are to be useful for solving problems. Research on these topics is also generating new ideas about how effective learning takes place–for example, how students can learn by examining and analyzing worked-out examples. Extensions of TheoryOpportunities for advancing our understanding of decision making and problem solving are not limited to the topics dealt with above, and in this section, just a few indications of additional promising directions for research are presented. DECISION MAKING OVER TIME The time dimension is especially troublesome in decision making. Economics has long used the notion of time discounting and interest rates to compare present with future consequences of decisions, but as noted above, research on actual decision making shows that people frequently are inconsistent in their choices between present and future.Although time discounting is a powerful idea, it requires fixing appropriate discount rates for individual, and especially social, decisions. Additional problems arise because human tastes and priorities change over time. Classical SEU theory assumes a fixed, consistent utility function, which does not easily accommodate changes in taste. At the other extreme, theories postulating a limited attention span do not have ready ways of ensuring consistency of choice over time. AGGREGATIONIn applying our knowledge of decision making and problem solving to society-wide, or even organization-wide, phenomena, the problem of aggregation must be solved; that is, way s must be found to extrapolate from theories of individual decision processes to the net effects on the whole economy, polity, and society. Because of the wide variety of ways in which any given decision task can be approached, it is unrealistic to postulate a â€Å"representative firm† or an â€Å"economic man,† and to simply lump together the behaviors of large numbers of supposedly identical individuals.Solving the aggregation problem becomes more important as more of the empirical research effort is directed toward studying behavior at a detailed, microscopic level. ORGANIZATIONS Related to aggregation is the question of how decision making and problem solving change when attention turns from the behavior of isolated individuals to the behavior of these same individuals operating as members of organizations or other groups.When people assume organizational positions, they adapt their goals and values to their responsibilities. Moreover, their decisions are influenc ed substantially by the patterns of information flow and other communications among the various organization units. Organizations sometimes display sophisticated capabilities far beyond the understanding of single individuals. They sometimes make enormous blunders or find themselves incapable of acting.Organizational performance is highly sensitive to the quality of the routines or â€Å"performance programs† that govern behavior and to the adaptability of these routines in the face of a changing environment. In particular, the â€Å"peripheral vision† of a complex organization is limited, so that responses to novelty in the environment may be made in inappropriate and quasi-automatic ways that cause major failure. Theory development, formal modeling, laboratory experiments, and analysis of historical cases are all going forward in this important area of inquiry.Although the decision-making processes of organizations have been studied in the field on a limited scale, a great many more such intensive studies will be needed before the full range of techniques used by organizations to make their decisions is understood, and before the strengths and weaknesses of these techniques are grasped. LEARNING Until quite recently, most research in cognitive science and artificial intelligence had been aimed at understanding how intelligent systems perform their work.Only in the past five years has attention begun to turn to the question of how systems become intelligent–how they learn. A number of promising hypotheses about learning mechanisms are currently being explored. One is the so-called connexionist hypothesis, which postulates networks that learn by changing the strengths of their interconnections in response to feedback. Another learning mechanism that is being investigated is the adaptive production system, a computer program that learns by generating new instructions that are simply annexed to the existing program.Some success has been achi eved in constructing adaptive production systems that can learn to solve equations in algebra and to do other tasks at comparable levels of difficulty. Learning is of particular importance for successful adaptation to an environment that is changing rapidly. Because that is exactly the environment of the 1980s, the trend toward broadening research on decision making to include learning and adaptation is welcome. This section has by no means exhausted the areas in which exciting and important research can be launched to deepen understanding of decision making and problem solving.But perhaps the examples that have been provided are sufficient to convey the promise and significance of this field of inquiry today. Current Research Programs Most of the current research on decision making and problem solving is carried on in universities, frequently with the support of government funding agencies and private foundations. Some research is done by consulting firms in connection with their d evelopment and application of the tools of operations research, artificial intelligence, and systems modeling.In some cases, government agencies and corporations have supported the development of planning models to aid them in their policy planning–for example, corporate strategic planning for investments and markets and government planning of environmental and energy policies. There is an increasing number of cases in which research scientists are devoting substantial attention to improving the problem-solving and decision-making tools in their disciplines, as we noted in the examples of automation of the processing of bubble-chamber tracks and of the interpretation of mass spectrogram data.To use a generous estimate, support for basic research in the areas described in this document is probably at the level of tens of millions of dollars per year, and almost certainly, it is not as much as $100 million. The principal costs are for research personnel and computing equipment, the former being considerably larger. Because of the interdisciplinary character of the research domain, federal research support comes from a number of different agencies, and it is not easy to assess the total picture.Within the National Science Foundation (NSF), the grants of the decision and management sciences, political science and the economics programs in the Social Sciences Division are to a considerable extent devoted to projects in this domain. Smaller amounts of support come from the memory and cognitive processes program in the Division of Behavioral and Neural Sciences, and perhaps from other programs. The â€Å"software† component of the new NSF Directorate of Computer Science and Engineering contains programs that have also provided important support to the study of decision making and problem solving.The Office of Naval Research has, over the years, supported a wide range of studies of decision making, including important early support for operations researc h. The main source of funding for research in AI has been the Defense Advanced Research Projects Agency (DARPA) in the Department of Defense; important support for research on applications of A1 to medicine has been provided by the National Institutes of Health. Relevant economics research is also funded by other federal agencies, including the Treasury Department, the Bureau of Labor Statistics, and the Federal Reserve Board.In recent years, basic studies of decision making have received only relatively minor support from these sources, but because of the relevance of the research to their missions, they could become major sponsors. Although a number of projects have been and are funded by private foundations, there appears to be at present no foundation for which decision making and problem solving are a major focus of interest. In sum, the pattern of support for research in this field shows a healthy diversity but no agency with a clear lead responsibility, unless it be the rathe r modestly funded program in decision and management sciences at NSF.Perhaps the largest scale of support has been provided by DARPA, where decision making and problem solving are only components within the larger area of artificial intelligence and certainly not highly visible research targets. The character of the funding requirements in this domain is much the same as in other fields of research. A rather intensive use of computational facilities is typical of most, but not all, of the research. And because the field is gaining new recognition and growing rapidly, there are special needs for the support of graduate students and postdoctoral training.In the computing-intensive part of the domain, desirable research funding per principal investigator might average $250,000 per year; in empirical research involving field studies and large-scale experiments, a similar amount; and in other areas of theory and laboratory experimentation, somewhat less. Research Opportunities: Summary T he study of decision making and problem solving has attracted much attention through most of this century. By the end of World War II, a powerful prescriptive theory of rationality, the theory of subjective expected utility (SEU), had taken form; it was followed by the theory of games.The past forty years have seen widespread applications of these theories in economics, operations research, and statistics, and, through these disciplines, to decision making in business and government. The main limitations of SEU theory and the developments based on it are its relative neglect of the limits of human (and computer) problem-solving capabilities in the face of real-world complexity. Recognition of these limitations has produced an increasing volume of empirical research aimed at discovering how humans cope with complexity and reconcile it with their bounded computational powers.Recognition that human rationality is limited occasions no surprise. What is surprising are some of the forms t hese limits take and the kinds of departures from the behavior predicted by the SEU model that have been observed. Extending empirical knowledge of actual human cognitive processes and of techniques for dealing with complexity continues to be a research goal of very high priority. Such empirical knowledge is needed both to build valid theories of how the U. S. society and economy operate and to build prescriptive tools for decision making that are compatible with existing computational capabilities.The complementary fields of cognitive psychology and artificial intelligence have produced in the past thirty years a fairly well-developed theory of problem solving that lends itself well to computer simulation, both for purposes of testing its empirical validity and for augmenting human problem-solving capacities by the construction of expert systems. Problem-solving research today is being extended into the domain of ill-structured problems and applied to the task of formulating proble m representations.The processes for setting the problem agenda, which are still very little explored, deserve more research attention. The growing importance of computational techniques in all of the sciences has attracted new attention to numerical analysis and to the topic of computational complexity. The need to use heuristic as well as rigorous methods for analyzing very complex domains is beginning to bring about a wide interest, in various sciences, in the possible application of problem-solving theories to computation.Opportunities abound for productive research in decision making and problem solving. A few of the directions of research that look especially promising and significant follow: †¢ A substantially enlarged program of empirical studies, involving direct observation of behavior at the level of the individual and the organization, and including both laboratory and field experiments, will be essential in sifting the wheat from the chaff in the large body of theor y that now exists and in giving direction to the development of new theory. Expanded research on expert systems will require extensive empirical study of expert behavior and will provide a setting for basic research on how ill-structured problems are, and can be, solved. †¢ Decision making in organizational settings, which is much less well understood than individual decision making and problem solving, can be studied with great profit using already established methods of inquiry, especially through intensive long-range studies within individual organizations. The resolution of conflicts of values (individual and group) and of inconsistencies in belief will continue to be highly productive directions of inquiry, addressed to issues of great importance to society. †¢ Setting agendas and framing problems are two related but poorly understood processes that require special research attention and that now seem open to attack. These five areas are examples of especially promisi ng research opportunities drawn from the much larger set that are described or hinted at in this report.The tools for decision making developed by previous research have already found extensive application in business and government organizations. A number of such applications have been mentioned in this report, but they so pervade organizations, especially at the middle management and professional levels, that people are often unaware of their origins. Although the research domain of decision making and problem solving is alive and well today, the resources devoted to that research are modest in scale (of the order of tens of millions rather than hundreds of millions of dollars).They are not commensurate with either the identified research opportunities or the human resources available for exploiting them. The prospect of throwing new light on the ancient problem of mind and the prospect of enhancing the powers of mind with new computational tools are attracting substantial numbers of first-rate young scientists. Research progress is not limited either by lack of excellent research problems or by lack of human talent eager to get on with the job. Gaining a better understanding of how problems can be solved and decisions made is essential to our national goal of increasing productivity.The first industrial revolution showed us how to do most of the world's heavy work with the energy of machines instead of human muscle. The new industrial revolution is showing us how much of the work of human thinking can be done by and in cooperation with intelligent machines. Human minds with computers to aid them are our principal productive resource. Understanding how that resource operates is the main road open to us for becoming a more productive society and a society able to deal with the many complex problems in the world today. [pic]

Monopoly as a source of market failure Essay

Abtsract. Environmental problems also occur when one of the participants in an exchange of property rights is able to exercise an inordinate amount of power over the outcome. This can occur, for example, when a product is sold by a single seller, or monopoly. A firm that has no competitors in its industry is called a monopoly. Monopolies are not all evil. Neither are they utterly good. Monopolies are much maligned because their profit incentive leads them to raise prices and lower output in order to squeeze more money out of consumers. As a result, governments typically go out of their way to break up monopolies and replace them with competitive industries that generate lower prices and higher output. Our study examines Arcelor-Mittal: the uncontrolled growth of this steel giant often at the expense of peoples’ health in a rapidly globalizing world has given people all around the world common cause for resistance. We have focused on Arcelor-Mittal Temirtau Kazakhstan which as we think is the best example of monopoly of market failure. Our paper work on â€Å"Monopoly as a source of market failure† explores global steel giant’s environmental and social impacts in 2008-2009 that have emerged from the Environmental&Natural Resource Economics. First, we provide the background information about the theory of natural monopoly as a source of market failure. Then we show the certain case of such monopoly – ArcelorMittal Temirtau Kazakhstan. Our research analysis is divided to two parts: background information and social&environmental impacts of global steel giant’s work in our homeland. Considering the situation and the current conditions of Arcelor-Mittal we then provide following solutions to the company that have to be implemented in order to enable it to overcome and or limit the potential problems in the foresseable future. This topic is very crucial and relevant not just only for our country to be mentioned and finally to be solved but also for the whole world as Arcelor-Mittal is operating worldwide. However it still neither has taken into account the seriousness of the problems that it has induced to the environment nor all of the responsibility. Introduction: The rise of a steel giant. We are all shareholders, maybe not in the company, but 1 / 13 indeed in our environments, and shareholders of corporations such as ArcelorMittal need to be aware of this reality. Company shareholders are often blinded by the glossy reports, company greenwash and figures detailing rising profits. This paper work seeks to create a new awareness amongst ArcelorMittal’s shareholders, and calls on them to act on the evidence presented. Many perceive the rise of Mittal Steel – now ArcelorMittal – from a small mill to a global steel giant as one of the great wonders of the business world. The success of the company has coincided with the exploitation of weaker national laws and political wrangling. In the last three decades Mittal has bought up old, run-down state-owned steel factories in places like Trinidad, Mexico, Poland, Czech Republic, Romania, South Africa and Algeria. The cost of Mittal Steel’s success has largely been paid by the communities living and working near the company’s plants. Mittal Steel has a global reputation for prioritising productivity over the environment, communities and fair labour practices in countries where it operates steel mills, such as Romania, Poland, Czech Republic South Africa, Kazakhstan and the United States, in spite of frequent company statements about its attention to and investment in these areas. No longer can they be uninformed shareholders reaping annual profits. They need to accept responsibility for the negative impacts their investments have on peoples’ lives along with accepting the profits they reap on their shares. It is critical to understand that the local injustices presented in the report will not just ‘go away’. They need careful deliberation and shareholder resolutions for ethical investment that calls for improved operations on the ground in order to deliver environmental justice to local people. Economic monopolies have existed throughout much of human history. In ancient and medieval times dire scarcity of resources was common and affected the lives of most human beings. When resources are extremely scarce, little room exists for a multiplicity of producers for many products and services. Monopoly is a well-defined market structure where there is only one seller who controls the entire market supply, as there are no close substitutes for his product and there are no barriers to the entry of rival producers. However in this dynamically changing world there is no such situation where the commodity does not have a substitute. So for a monopoly to be effective there must be no practical substitutes for the product or service sold, and no serious threat of the entry of a competitor into the market. This enables the seller (â€Å"monopolist†) to control the price. The term monopolist is derived from the Greek word â€Å"mono†, meaning â€Å"single†, and â€Å"polist† meaning seller. Thus the monopolist may be defined as the sole seller of a product which has no close substitutes. At the beginning we state the background information about the theory of natural monopoly as a source of market failure. Then we show the certain case of such monopoly – ArcelorMittal Temirtau Kazakhstan. Our research analysis is divided to two parts: background information and social&environmental impacts of global steel giant’s work in our homeland. Considering the situation and the current conditions of Arcelor-Mittal we then provide following solutions to the company that have to be implemented in order to enable it to overcome and or limit the potential problems in the foresseable future. The Theory of Natural Monopoly. Market failure occurs when resources are misallocated, or allocated inefficiently. There are five important sources of market failure, each of which results from the failure of one of the assumptions basic to the perfectly competitive model. Each also points to a potential role for government in the economy. One of the causes of market failure is imperfect competition, particularly monopolies. An imperfectly competitive market is one where the assumption of many buyers and sellers does not hold. These types of market organizations include monopoly, monopsony, oligopoly, and monopolistic competition. The operations of monopoly or natural monopoly often result in misuse of market power and inefficient allocation of resources, which reduce community welfare. For this reason, governments generally regulate monopoly and enforce laws preventing cartels. This type is a major rationale for a comprehensive competition policy. A monopoly is a market with one seller and many buyers. A monopoly may exist because of special 2 / 13 government regulation or because the monopolist is the sole owner of a resource (due to a patent or some other reason). A monopoly has the following characteristics: †¢There is only one producer in the market †¢They sell a single product with no close substitutes †¢Monopolies are price makers. The monopolies demand curve is the market demand curve; therefore the firm can sell the product at a higher price but only if it reduces output. It has control over the price or quantity sold, but not both. †¢There are very strong barriers to entry. This might include: High capital costs; High ‘sunk’ costs. Sunk costs are those which cannot be recovered if the firm goes out of business, such as advertising costs – the greater the sunk costs the greater the barrier. Technological knowledge, when one firm acquires the technological know-how that other firms do not have Patents and copyrights, protecting other firms from copying their product; Government regulations and restrictions; The monopoly can execute predatory pricing which involves dropping price very low in a ‘demonstration’ of power and to put pressure on existing or potential rivals and/or limit pricing. Limit pricing is a specific type of predatory pricing which involves a firm setting a price just below the average cost of new entrants – if new entrants match this price they will make a loss! A natural monopoly. A natural monopoly is a firm that can supply a good or service to an entire market at a lower price than if there were two or more firms. It has some similarities to a monopolist. It is an imperfect competitor, the sole producer in a market, and able to retain this position because of barriers to entry, such as government regulation, technological leadership or large start-up capital, It is able to restrict output in order to increase price and earn supernormal profits. However, a natural monopoly has a downward-sloping average cost curve (AC) over the relevant range of outputs, which results from economies of scale. Economies of scale develop in the long run, which is a period of time when all inputs are variable and the constraints imposed by diminishing returns no longer apply. The graph below shows the long run as being made up of a series of short-run periods, shown as a series of short-run AC en shown together illustrate economies of scale. Figure 1. Economies of scale. Source Senior Economics Workbook: NCEA Level 3. Geoff Evans, Ben Cahill, John Rogers. Pearson Education New Zealand Limited, 2005. Chapter 10. Page 93. A â€Å"natural monopoly† because it is economically efficient for there to only be one supplier. The following diagram can help to illustrate just why: Figure 2. A natural monopoly. Source Senior Economics Workbook: NCEA Level 3. Geoff Evans, Ben Cahill, John Rogers. Pearson Education New Zealand Limited, 2005. Chapter 10. Page 109. Given the downward sloping supply curve, and ignoring the demand curve for a minute, having an equilibrium at point E1, which gives us price P1. We could assume that this is a monopoly equilibrium, where Q1 represents the entire size of the market – it represents everybody who wants to buy the good. But in the case of a duopoly market, where there are two suppliers, we could assume that each seller in the market has exactly half of the market. This corresponds to the equilibrium E2 on the above diagram, which gives us quantity Q2 and price P2. We can assume the Q2 = 0. 5 x Q1, and that each of the two firms supplies Q2 of the good in question. And here a major problem arises. If we have one firm only, the marginal cost of supply is P1, which is lower than the duopoly price, P2. This means that having two firms in a market ends up with the firms having to charge a higher price than if only one firm existed. In this case, it is efficient, or â€Å"natural†, for there to only be one firm in 3 / 13 the market. This is why declining-marginal-cost industries are called natural monopolies. Because natural monopolies tend to be utilities, which are services like gas, electricity, water and telephones, which the public generally holds to be necessities of life, we are not comfortable allowing these firms to charge monopoly prices (i. e. , the pricing where MR = MC). Because these are staples or necessities, the demand curve for these goods is very inelastic – it is very steep. This means that the monopolist price would be much higher than the free-market price, and a large volume of people would be denied basic necessities of life. Instead, we use the power of government to regulate prices in these markets. The normal avenue for regulation of natural monopolies is the public utilities commission. These exist at the state-level in the United States, and at the national level in many other countries. Utilities commissions are given the task of making sure that utility companies make enough money to stay in business, but not enough to enjoy monopoly profits. They make sure that everybody is served, and served well, in theory. Since utilities are monopolies that are not subject to market forces and competition, they have little pressure to be responsive to market forces, which means that they do not have to treat their customers well, because their customers do not have the ability to switch to a different supplier. The costs of monopoly: †¢Less choice. Clearly, consumers have less choice if supply is controlled by a monopolist – for example, the Post Office used to be monopoly supplier of letter collection and delivery services across the UK and consumers had no alternative letter collection and delivery service. †¢High prices. Monopolies can exploit their position and charge high prices, because consumers have no alternative. This is especially problematic if the product is a basic necessity, like water. †¢Restricted output Monopolists can also restrict output onto the market to exploit its dominant position over a period of time, or to drive up price. †¢Less consumer surplus A rise in price or lower output would lead to a loss of consumer surplus. Consumer surplus is the extra net private benefit derived by consumers when the price they pay is less than what they would be prepared to pay. Over time monopolist can gain power over the consumer, which results in an erosion of consumer sovereignty. †¢Asymmetric information There is asymmetric information – the monopolist may know more than the consumer and can exploit this knowledge to its own advantage. †¢Productive inefficiency Monopolies may be productively inefficient because there are no direct competitors a monopolist has no incentive to reduce average costs to a minimum, with the result that they are likely to be productively inefficient. †¢Allocative inefficiency Monopolies may also be allocatively inefficient – it is not necessary for the monopolist to set price equal to the marginal cost of supply. In competitive markets firms are forced to ‘take’ their price from the industry itself, but a monopolist can set (make) their own price. Consumers cannot compare prices for a monopolist as there are no other close suppliers. This means that price can be set well above marginal cost. †¢Net welfare loss Even accounting for the extra profits derived by a monopolist, which can be put back into the economy when profits are distributed to shareholders, there is a net loss of welfare to the community. Welfare loss is the loss of community benefit, in terms of consumer and producer surplus, that occurs when a market is supplied by a monopolist rather than a large number of competitive firms. 4 / 13. †¢Monopoly welfare loss A ‘net welfare loss’ refers any welfare gains less any welfare loses as a result of an economic transaction or a government intervention. Using ‘welfare analysis’ allows the economist to evaluate the impact of a monopoly. †¢Less employment Monopolists may employ fewer people than in more competitive markets. Employment is largely determined by output – the more output a firm produces the more labour it will require. As output is lower for a monopolist it can also be assumed that employment will also be lower. The benefits of monopoly:Monopolies can provide certain benefits, including: †¢Exploit economies of scale As we have already mentioned above, the natural monopoly exploits economies of large scale. This means that it can produce at low cost and pass these savings on to the consumer. However, there would be little incentive to do this and the savings made might be used to increase profits or raise barriers to entry for future rivals. †¢Dynamic efficiency Monopolists can also be dynamically efficient – once protected from competition monopolies may undertake product or process innovation to derive higher profits, and in so doing become dynamically efficient. It can be argued that only firms with monopoly power will be in the position to be able to innovate effectively. Because of barriers to entry, a monopolist can protect its inventions and innovations from theft or copying. †¢Avoidance of duplication of infrastructure The avoidance of wasteful duplication of scarce resources – if the monopolist is a ‘natural monopoly’ it can be argued that competitive supply would be wasteful. Natural monopolies include gas, rail and electricity supply. A natural monopoly occurs when all or most of the available economies of scale have been derived by one firm – this prevents other firms from entering the market. But having more than one firm will mean a wasteful duplication of scarce resources. †¢Revenue Monopolists can also generate export revenue for a national economy. A single firm may gain from economies of scale in its own domestic economy and develop a cost advantage which it can exploit and sell relatively cheaply abroad. Remedies for monopoly:If a monopolist can gain a foothold in a market it becomes very difficult for new firms to enter, with the result that the price mechanism is restricted from doing its job. Resources cannot be allocated to where they are most needed because the monopolist can erect barriers to other firms. These barriers will not ‘naturally’ come down. The failure of markets to ‘self regulate’ is at the heart of monopoly as a ‘market failure. There are a number of ways in which the negative effects of monopoly power can be reduced: Regulation of firms who abuse their monopoly power. This could be achieved in a number of ways, including: †¢Price controls Setting price controls. For example, the current UK competition regulator, the Office of Fair Trading (OFT), has developed a system of price ‘capping’ for the previously state owned natural monopolies like gas and water. This price capping involves tying prices to just below the current general inflation rate. The formula, RPI – X, is used, where the RPI (the Retail Price Index) is the chosen index of inflation and ‘X’ is a level of price reduction agreed between the regulator and the firm, based on expected efficiency gains. †¢Prohibiting mergers Prohibiting mergers – in the UK the Competition Commission can prohibit mergers between firms that create a combined market share of 25% or more if it believes that the merger would be against the ‘public interest’. In making their judgement, the ‘public interest’ takes into account the effect of the merger on jobs, prices and the level of competition. †¢Breaking up the monopoly Breaking up the monopoly into several smaller firms. For example regulators in the EU are currently 5 / 13 investigating potential abuse of market dominance by Microsoft, which is under threat of being broken up into two companies – one for its operating systems and the other for software. †¢NationalisationBringing the monopoly under public control – which is referred to as ‘nationalisation’. The ultimate remedy for an abusive monopoly is for the State to take a controlling interest in the firm by acquiring over 50% of its shares, or to take it over completely. The monopolist can still be run along commercial lines, but be made to operate as though the market were competitive. †¢Deregulation In those cases where a monopolist is already State controlled, such as the Post Office, it may be necessary to engage in deregulation to enable it to become more efficient. Deregulation could be used to bring down barriers to entry and open up a previously state controlled industry to competition, as has happened with the British Telecom and British Rail monopolies. This may help encourage new entrants into a market. Do Monopolies Undermine The Environment? As monopoly and natural monopoly tend to have a perpetual ownership of a scarce resource, they do not only ‘tie-up’ the existing scarce resources making it difficult for new entrants to exploit these resources, but also they often cause some environmental problems. Furthermore for many skeptics of the environmental benefits of market economies it seems that the fear of monopoly control over natural resources is one of their greatest concerns as well. The reality is actually much more complicated, because of the following: 1. Most natural resource industries are not controlled by monopolies, and are in fact characterized by a high degree of competitiveness. Agriculture, forestry, and fishing industries are almost everywhere characterized by markets with hundreds or thousands of players, some of them big but with plenty of smaller players as well. While limited degrees of market power exist in some of these industries in some areas, on the whole they are actually some of the more competitive industries in the world. Even energy and mineral industries are fairly competitive and where they are not they are characterized by oligopoly structures, almost never a monopoly. 2. Monopolies restrict output and raise the price of goods above their marginal costs (which leads to a loss of social welfare), which is why economists (mostly) consider them bad. But from an environmental perspective, they may actually be quite good since they lead to lower resource use and higher prices. For example, if oil was a completely competitive market the price would be lower and we would burn even more of it than if OPEC kept the price artificially high! The problem the environmentalist faces is not that monopolies keep prices high and limit output (that’s called conservation), but that this has a regressive effect and hurts the poor. (By the way, this is one of the biggest issues that confront environmentalists more generally, who for the most part would like to see resource prices rise. ). 3. As to examples where monopolies restrict R&D or limit technological innovation, there certainly are examples of this, but in general, the profit motive is sufficient to overcome this. Bottom line: the cheap prices of resources are the greatest threat to advances in efficiency and monopolies lead us in the opposite direction. 4. There are examples of what economists call â€Å"natural monopolies† where fixed costs are so high that only one company can be profitable providing a given service in a given region; examples are water, telecommunications, and electricity (imagine if every provider of water had to build their own pipe system? ). In cases where natural monopolies arise it is much more efficient for society to grant the company limited monopoly rights and regulate them. These are often called public utilities and abound in America (PG&E is my public utility in CA). The problem with public utilities is that often the regulators force them to charge very low prices that favor consumers but again lead to increased uses of resource; that is, if the monopolies were unregulated we would see lower resource use. 5. Let us not forget that the biggest monopolies in the history of humanity are state-owned. The monopolies in the former Soviet Union were certainly the biggest ever (and the worst environmental 6 / 13 offenders the world has ever known), and even today state-run monopolies for all sorts of resources (primarily oil, gas, and telecommunications) abound. Almost without fail, they are characterized by high prices, poor service, and abysmal environmental records. 6. Since competitive markets are one of the foundations of a prosperous economy, market-based societies have developed various forms of anti-trust legislation to ensure relatively high degrees of competitive in most markets. Laws regulating market share, anti-competitive pricing, etc. are commonplace in all of the advanced market systems, and have a relatively good record of success. Probably the greatest success has been in the telecommunications industry where deregulation has led to real price declines of almost 95% in telecommunications fees over the past 25 years. (Examples of the failure of states to break up monopolies abound in Latin America, particularly in telecom. I have written about how the Telmex in Mexico is one of the most egregious examples of robbing from the poor to give to the rich and how it is a great impediment to Mexico’s economic development. What the Mexiccam telecommunications industry desperately needs is more market-based competition to break Telmex’s grip, but unfortunately, due to immense corruption the average Mexican must continue to spend large shares of their meager earnings on phone calls. ) 7. Probably the biggest pro-competition policy is free trade and globalization. The greatest threats to regional and national monopolies come from trade from abroad and the innovation that trade accelerates. Contrary to popular wisdom, globalization does not increase the power of corporations over individuals, but just the reverse; people can shift their business to the other companies more easily as their choices increase. If you doubt this, just look at how lists of the â€Å"Fortune 500† companies continually shift every few years, and even more so in this more globalized age. In summary, while economists have long ago identified the pros and cons of monopolies, how they interact with environmental outcomes is not entirely straight-forward. What is obvious is that in non market-based economies we witness the worst forms of monopoly abuse and the resulting environmental degradation. ArcelorMittal: Going nowhere slowly. Background. ArcelorMittal Temirtau Kazakhstan(formerly Mittal Steel Temirtau, Ispat Karmet and Karaganda Metallurgical Plant). Arcelor Mittal Temirtau (AMT), founded in 1950, is one of the largest integrated steel plants in the world. The steel plant, along with all its infrastructure facilities, captive coal, iron ore and power plant, was acquired by ArcelorMittal – then Ispat – from the Kazakhstan government in 1995. Located in the city of Temirtau, population 170 000, in the Karaganda Region of Central Kazakhstan, it covers about 5 000 hectares and has a steel-making capacity of about 5. 5 million tonnes per annum. AMT operates eight coal mines in the region, producing a total of 12 202 million tonnes of coal in 2007. In the same year AMT’s output of rolled steel was 3. 581 million tonnes. The plant exports about 90 percent of its output, mostly to Russia, Iran and China. The towns of Temirtau and Karaganda as well as the surrounding area (about 1 million people) indirectly depend on the plant, which used to account for nearly 10 percent of Kazakhstan’s GDP . As of 2006 it employed 55 000 people and generated 4 percent of the country’s GDP. Figure 3. ArcelorMittal Temirtau exports the majority of its steel output but local residents pay the costs. Photo by CEE Bankwatch Network. Table 1. Mittal’s plant in Temirtau has received several direct and indirect loans from IFIs in the last 12 years: Year1997 Financial InstitutionEBRD PurposeTo restore productive capacity and improve efficiency in the steel mill and coal mines; develop value-added, higher quality steel, and to implement three environmental action plans that would improve environmental and health & safety impacts and bring the company into compliance with World Bank environmental guidelines. AmountUSD 54 million 7 / 13 RecipientAMT (former Ispat Karmet Steel Works) Year1997 Financial InstitutionIFC. PurposeTo restore productive capacity and improve efficiency in the steel mill and coal mines; develop value-added, higher quality steel, and to implement three environmental action plans that would improve environmental and health & safety impacts and bring the company into compliance with World Bank environmental guidelines. AmountUSD 132. 5 million RecipientAMT (former Ispat Karmet Steel Works) Year1999 Financial InstitutionIFC PurposeTo support the development of small and medium enterprises directly or indirectly associated with AMT and/or to assist workers formerly employed by AMT and/or to provide for the growth of the private sector in the Karaganda region. AmountUSD. 2. 5 million RecipientIndirect financial help to AMT through Kazkommertsbank. Year2001 Financial InstitutionIFC PurposeTo stimulate the relationship between the large corporate sector (in this case AMT) and the private SME sector. AmountUSD 3. 4 million equity investments. RecipientAMT. Year2004 Financial InstitutionIFC corporate loanPurposeTo enable LNM to improve the environmental performance of its present and future subsidiaries and bring them up to World Bank Group and/or European Union standards; – to assist LNM in creating and maintaining an environmental and worker health and safety system on a corporate wide level, to bring all its current and future operations in compliance with WB and/or EU standards;- to rehabilitate, dbottleneck and provide working capital and cash support to LNM’s present and future subsidiaries.      

Blood Sports (Debate) Essay Example for Free

Blood Sports (Debate) Essay Blood sports should not be banned; whatever problems there are with the sport can be fixed with reforms. The World Health Organization has called for tighter regulation, including â€Å"Simple rules, such as requiring medical clearance, national passports to prevent players from fighting under more than one name, restricting fights for fixed periods after knockouts, requiring that ringside physicians be paid by the state and not the promoter, and making sure that the players are aware of the potential long-term consequence of blood sports, may help protect them to some degree. †The Australian Medical Association additionally â€Å"recommends that media coverage should be subject to control codes similar to those which apply to television screening of violence. †Finally, the World Medical Association suggests that all matches should have a ring physician authorized to stop the fight at any time. It has been reported that no safety regulations would be effective if head blows remain – however such authors incorrectly apportion blame on boxing for a group of diseases known as Parkinson’s syndrome. Blood sports can result in chronic traumatic neurological conditions if fighters are not well matched, and fight without regulations in regard to their exposure. Boxing cannot cause Parkinson’s disease or other conditions such as Alzheimer’s disease as those are genetic conditions – so to include them together as one set of conditions is incorrect and misleading. About 80% of deaths are caused by head, brain, and neck injuries, so the removal of the head as a scoring region may make a huge difference to the injury outcomes for this sport. However it would also change the very nature of the sport; and may mean people won’t participate in it. Ultimately, governments should do what they can to make blood sports as safe as possible, without losing the essence of the sport or banning it entirely. ————————————————- (Banning blood sports would force people to channel their aggression into more harmful, violent activities) There is no conclusive scientific evidence linking increased contact sport participation with being more violent in social settings. Such statements make it sound as thought we would have not violence in society if all contact sport was removed – and we all know that is untrue. Blood sports isn’t about violent aggression, it is about controlled aggression – this is very different to violent behaviors. In a report on â€Å"violent† sports in schools, conducted by the Lance Armstrong Foundation, a martial-arts instructor explained, â€Å"Contact and combat sports allow students to deal with their aggression in a safe environment, rather than in the context of the classroom or school hallway. †This type of outlet is not only important for youth, but for adults as well. Jason Brick said, â€Å"Positive Views on Violence In Sports,† Live strong, January 7, 2011, accessed July 13, 2011, With /proposition (The Effect of blood sports on the viewers) Blood Sports have been around for decades. Viewing violence generally triggers or serves in the increase of aggression of an individual. Sports such as wrestling (smack down) and Ultimate Fighter Competition (UFC) are bloody sports and have mostly negative effects on those who watch them. The objective of these two sports is to beat an individual into unconsciousness, make them tap out by inflicting pain, if none of these is accomplished within a time frame, the match is to be stopped and the judges decide who wins. Many children, teenagers, and even adults tend to try and imitate a knock out or combos that were seen performed at one of these fights onto an individual in an uncontrolled environment whether it is their sibling, friend, coworker, or a stranger for different reasons that includes but is not limited to a misunderstanding or horse playing. Watching this sport leaves the viewer psychologically aggressive. For example, if someone watches a match and gets into a fight with another person later on, that person is more likely to use a technique he saw during the fight, and since there is no referee to stop the fight in case of suffocation or tap-out, the victim is more likely to bleed, pass out or even dies. During the 1980’s, two men were in a bar discussing the Marvin Haggler and Sugar Ray Leonard fight that had occurred several days before, and in the process on trying to show exactly how one of the punch landed, both men went outside, drawing a crowd with them. The demonstration turned tragic when one of the men landed a punch to the jaw of the other, and such was the power of the blow, that the victim fell, hit his head on the pavement and started to bleed, and had to be buried a few weeks later. Seeing and permitting violence to be seen makes it seem normal and legal when in fact it is not normal and it is horrible, but here is where lies another problem which is called desensitization. Many years ago when a horrible scene was about to be portrayed on your television set, there would first appear a window saying ‘the images that you are about to see might injure the sensibility of certain people’ or words to that effect. Well, have you noticed that now they no longer even bother showing that little window? It’s as if the media know that human kind are used to everything by now. That nothing is going to affect them that much. So what does this show? It shows that us human beings are getting desensitized to everything and when that happens it also means that we don’t get so emotional about anything anymore and so consequently don’t fight any more either in order to strive for a change. We have all come to a point where nothing moves us that much anymore. (Pain and Injury as the Price of blood sports) Many people think about sports in a paradoxical way: They accept violence in sports, but the injuries caused by that violence make them uneasy. They seem to want violence without consequences— like the ?ctionalized violence they see in the media and video games in which characters engage in brutality without being seriously or permanently injured. However, blood sports are real, and it causes real pain, injury, disability, and even death (Dater, 2005; Farber, 2004; Leahy, 2008; Rice, 2005; Smith, 2005b; Young, 2004a). Ron Rice, an NFL player whose career ended when he tackled an opponent, discusses the real consequences of blood sports. The brutal body contact of the tackle left him temporarily paralyzed and permanently disabled. He remembers that â€Å"before I hit the ground, I knew my career was over. . . . My body froze. I was like a tree that had been cut down, teetering, then crashing, unable to break my fall. † Research on pain and injury among athletes helps us understand that blood sports have real consequences. Studies indicate that professional sports involving brutal body contact and borderline violence are among the most dangerous workplaces in the occupational world. The same could be said about high-pro? le power and performance intercollegiate sports in which 80 percent of male and female athletes sustain at least one serious injury while playing their sports and nearly 70 percent are disabled for two or more weeks. Research shows a close connection between dominant ideas about masculinity and the high rate of injuries in many sports. Ironically, some power and performance sports are organized so that players feel that their manhood is up for grabs. Men who de? ne masculinity in terms of physically dominating others often use violence in sports as an expression of this code of manhood. Until they critically examine issues related to gender and the organization of their sports, they will mistakenly de? ne violence as a source of rewards rather than a source of chronic pain and disabilities that constrain and threaten their lives. Blood Sports (Debate). (2017, Jun 01).

A critical analysis of the globalization strategy of a multinational Essay

A critical analysis of the globalization strategy of a multinational company (Coca-Cola) - Essay Example While conceding to the fact that internationalisation is a risky endeavour, international business theory has proposed a number of risk-minimising strategies and a set of recommendations for the constructive exploitation of globalisation for the purposes of profit maximisation. Needless to say, while some corporations have successfully implemented these recommendations and have substantially expanded their markets and financial returns as a result, others have not. This research looks at one of the corporations which has successfully reaped the rewards of globalisation: Coca-Cola. Drawing on international business theory, the study engages in a critical analysis of Coca-Cola's external and internal environments for the purposes of shedding light on its corporate strategy and the uncovering the determinants of its success. The analyses, which utilise Porter's Five Forces, SWOT and PEST, indicates that Coca Cola's success is a direct outcome of an internationalisation strategy which is deeply considerate of the particularities and peculiarities of the various national markets within which it operates. 1 Introduction Multinational corporations are popularly regarded as the primary beneficiaries of globalisation. In his defence and justification of this claim, Wartick and Wood (2006) highlight the immediate correlation between the removal of barriers to international trade and foreign direct investment and the growth and expansion of the global activities of multinational companies. While not disputing this claim, the fact is that multinational companies are not simply the primary beneficiaries of globalisation but the purveyors of globalisation. In other words, globalisation was spearheaded by globally-minded, expansionist corporations such as Coca-Cola. Indeed, as Wartick and Wood (2006) argue, corporations such as Coca-Cola, McDonald's, Phillip Morris, Nestle and several others globalised business through international expansion via mergers, acquisitions and franchises, prior to the inception of globalisation. The implication here is that Coca-Cola, among others played a seminal role in the glo balisation of the international economy and, indeed, designed and pursued global business strategies prior to the passage and subsequent enforcement of WTO rules. This perspective on the role of multinational companies in the globalisation process can be validated through a brief, albeit critical, review of the implications of multinationals. Understanding the role of multinationals in globalisation and the degree to which, if at all, globalisation impacted the strategies of MNC, is contingent upon knowing the meaning of MNC. Gershon (1997, p. 3) offers a very precise and concise definition of the concept, writing that a multinational corporation is "a nationally based company with overseas operations in two or more countries" (Gershon, 1997, p. 3). As may have been inferred from the introductory paragraph and as most are well aware of, multinational corporations are a significant part of the contemporary global economy and, without any doubt, its primary players. The power which multinationals command and the extent of their influence on the economy, whether at the national, regional or global level, is explicitly explained in Jacoby's (1984, p. 5) description of the multinational cor