Systems of Inquiry Essay Example

Systems of Inquiry Essay Example Systems of Inquiry Essay Systems of Inquiry Essay The first three of these activitiesfixing agendas, setting goals, and designing actionsare usually called problem solving; the last, evaluating and choosing, is usually called decision making. This system of inquiry should be performed effectively (Simon, et al., 1986).The basic framework to be used is the determination of the quality of our decisions and problem solutions through the abilities and skills of the human resource in the organization and the tools and machines available like computers. Maximization of the human resource and the use of tools and machine may reach remarkable levels of economic productivity. The targets for this system of inquiry is understanding how human minds, with and without the help of computers, solve problems and make decisions effectively, and improving problem-solving and decision-making capabilities. Some of the knowledge and data that will be gained through this research describes the ways in which people in the organization actually go about making decisions and solving problems, adopt better methods and offer advice for the improvement of the process (A Roundtable Discussion: Knowledge and the New Organization, 2006).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. subjective expected utility 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. subjective expected utility theory deals only with decision making; it has nothing to say about how to frame problems, set goals, or develop new alternatives (Simon, et al., 1986).Prescriptive theories of choice such as subjective expected utility 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 (Buchanan and Smith, 1988).What chiefly distinguishes the empirical research on decision making and problem solving from the prescriptive approaches derived from subjective expected utility theory is the attention that the former gives to the limits on human rationality. 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 (Simon, et al., 1986).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 (Nicholas, 1998).The growing realization that coping with comp lexity 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 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 str uctures (Simon, et al., 1986).This system will be adopted since in this system all the alternatives among which choice could be made will be known, and that the consequences of choosing each alternative could be ascertained. It is assumed that a subjective or objective probability distribution of consequences was associated with each alternative. It will make use of the subjective expected utility theory. By admitting subjectively assigned probabilities, subjective expected utility 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, Bayess rule prescribes how people should take account of new information and how they should respond to incomplete information.Through this sytem, strong inferences can be made. Although the assumptions cannot be satisfied even remotely for most complex situations in the real world, they may be satisfied approximately in some microcosmsproblem situations that can be isolated from the worlds complexity and dealt with independently. For example, 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 mixoptimal within the limits of approximation of his model to real world conditions. Linear programming and related operations research techniques can be used 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 (Simon, et al., 1986).Other tools of modern operations research that can be used adide from linear programming, are integer programming, queuing theory, decision trees, and other widely used techniques. 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 (Simon, et al., 1986).Decision-making and 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 can be used dependably to obtain data about subjects behaviors in a wide range of settings. The laboratory study of decision-making and proble m solving has been supplemented by field studies of professionals solving real-world problems. Currently, historical records, including laboratory notebooks of scientists, are also being used to study decision-making and problem-solving processes in scientific discovery (Simon, et al., 1986).These systems can be used by the students or management people in the company. They may question respondents about specific situations, rather than asking for generalizations. They ones conducting this system should be sensitive to the dependence of answers on the exact forms of the questions. They should be aware that behavior in an experimental situation may be different from behavior in real life. They may also attempt to provide experimental settings and motivations that are as realistic as possible. Using thinking-aloud protocols and other approaches, they can try to track the choice behavior step by step, instead of relying just on information about outcomes or querying respondents retrosp ectively about their choice processes (Hofer, 2004).The code will be implemented through finding the underlying bases of human choice behavior. Although not always easy, try to provide veridical accounts of how decision-makers make up their minds, especially when there is uncertainty. In many cases, predict how they will behave but the reasons people give for their choices can often be shown to be rationalizations and not closely related to their real motives (Simon, et al., 1986).Possible reaction that will be generated from the code from employees is that the employees may find that 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 pol icy reveals their limits (Currently skimming chapter: Report of the Research Briefing Panel on Decision Making and Problem Solving, 1986).This system may also make enormous demands on information. For the utility function, the range of available alternatives and the consequences following from each alternative must all be known. The employees may find this system as not fitting real-world problems aside from the informational and computational limits of people and computers and the inconsistencies in their values and perceptions (Simon, et al., 1986).The effect that the code would have on the organization is that the code would provide explanations for the many forms of decisions that has to be made in the 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. It assumes that economic agents seek to maximize uti lity, but within limits posed by the incompleteness and uncertainty of the information available to them (Currently skimming chapter: Report of the Research Briefing Panel on Decision Making and Problem Solving, 1986).Decision-making and problem-solving relies on large amounts of information that are stored in memory and that are retrievable whenever the maker / 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. In applying knowledge of decision making and problem solving to society-wide, or even organization-wide, phenomena, the problem of aggregation must be solved. Methodologies must be found to extrapolate from theories of individual decision processes to the net effects as a whole. Because of the wide variety of ways in which any given decision task can be approached, it is unrealistic to postulate a representative firm or an economic man, and to simply lump together the behaviors of large numbers of supposedly identical individuals. Solving the aggregation problem becomes more important (Simon, et al., 1986).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 performance programs that govern behavior and to the adaptability of these routines in the face of a changing environment. In particular, the peripheral vision of a complex organization is limited, so that responses to novelty in the environment may be made in inappropri ate and quasi-automatic ways that cause major failure (Simon, et al., 1986).