Elements for a Theory of Decision in Uncertainty (Applied Optimization)

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Data becomes information, when it becomes relevant to your decision problem. Information becomes fact, when the data can support it.

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Facts are what the data reveals. However the decisive instrumental i. Fact becomes knowledge, when it is used in the successful completion of a decision process. Once you have a massive amount of facts integrated as knowledge, then your mind will be superhuman in the same sense that mankind with writing is superhuman compared to mankind before writing. The following figure illustrates the statistical thinking process based on data in constructing statistical models for decision making under uncertainties.

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The above figure depicts the fact that as the exactness of a statistical model increases, the level of improvements in decision-making increases. That's why we need probabilistic modeling. Probabilistic modeling arose from the need to place knowledge on a systematic evidence base. This required a study of the laws of probability, the development of measures of data properties and relationships, and so on. Statistical inference aims at determining whether any statistical significance can be attached that results after due allowance is made for any random variation as a source of error.

Intelligent and critical inferences cannot be made by those who do not understand the purpose, the conditions, and applicability of the various techniques for judging significance. Knowledge is more than knowing something technical. Knowledge needs wisdom. Wisdom is the power to put our time and our knowledge to the proper use.

Wisdom comes with age and experience. Wisdom is the accurate application of accurate knowledge and its key component is to knowing the limits of your knowledge. Wisdom is about knowing how something technical can be best used to meet the needs of the decision-maker.

Elements for a Theory of Decision in Uncertainty | Jaime Gil-Aluja | Springer

Wisdom, for example, creates statistical software that is useful, rather than technically brilliant. For example, ever since the Web entered the popular consciousness, observers have noted that it puts information at your fingertips but tends to keep wisdom out of reach. Considering the uncertain environment, the chance that "good decisions" are made increases with the availability of "good information. One may ask, "What is the use of decision analysis techniques without the best available information delivered by Knowledge Management?

However, for private decisions one may rely on, e. Moreover, Knowledge Management and Decision Analysis are indeed interrelated since one influences the other, both in time, and space. The notion of "wisdom" in the sense of practical wisdom has entered Western civilization through biblical texts. In the Hellenic experience this kind of wisdom received a more structural character in the form of philosophy.

In this sense philosophy also reflects one of the expressions of traditional wisdom. Making decisions is certainly the most important task of a manager and it is often a very difficult one. The Decision-Making Process: Unlike the deterministic decision-making process, in the decision making process under uncertainty the variables are often more numerous and more difficult to measure and control.

However, the steps are the same. They are: Simplification Building a decision model Testing the model Using the model to find the solution It is a simplified representation of the actual situation It need not be complete or exact in all respects It concentrates on the most essential relationships and ignores the less essential ones. It is more easily understood than the empirical situation and, hence, permits the problem to be more readily solved with minimum time and effort.

It can be used again and again for like problems or can be modified. Fortunately the probabilistic and statistical methods for analysis and decision making under uncertainty are more numerous and powerful today than even before. The computer makes possible many practical applications. A few examples of business applications are the following: An auditor can use random sampling techniques to audit the account receivable for client.

A plant manager can use statistic quality control techniques to assure the quality of his production with a minimum of testing or inspection. A financial analyst may use regression and correlation to help understand the relationship of a financial ratio to a set of other variables in business. A market researcher may use test of significant to accept or reject the hypotheses about a group of buyers to which the firm wishes to sell a particular product.

A sale manager may use statistical techniques to forecast sales for the coming year. Further Readings: Berger J. Corfield D. Matt, Eds.

DECISION-MAKING UNDER UNCERTAINTY - Quantitative Techniques for management

It is intended for decision makers in companies, in non-profit organizations and in public administration. Lapin L. Lindley D. Pratt J. Raiffa, and R. Press S. Comparing and contrasting the reality of subjectivity in the work of history's great scientists and the modern Bayesian approach to statistical analysis. Tanaka H. Decision Analysis: Making Justifiable, Defensible Decisions Decision analysis is the discipline of evaluating complex alternatives in terms of values and uncertainty.

Decision Theory Under Uncertainity Practically Solved Example IN HINDI By JOLLY COACHING

Values are generally expressed monetarily because this is a major concern for management. Furthermore, decision analysis provides insight into how the defined alternatives differ from one another and then generates suggestions for new and improved alternatives. Numbers quantify subjective values and uncertainties, which enable us to understand the decision situation.

These numerical results then must be translated back into words in order to generate qualitative insight. Humans can understand, compare, and manipulate numbers. Therefore, in order to create a decision analysis model, it is necessary to create the model structure and assign probabilities and values to fill the model for computation. This includes the values for probabilities, the value functions for evaluating alternatives, the value weights for measuring the trade-off objectives, and the risk preference. Once the structure and numbers are in place, the analysis can begin. Decision analysis involves much more than computing the expected utility of each alternative.

If we stopped there, decision makers would not gain much insight. We have to examine the sensitivity of the outcomes, weighted utility for key probabilities, and the weight and risk preference parameters. As part of the sensitivity analysis, we can calculate the value of perfect information for uncertainties that have been carefully modeled. There are two additional quantitative comparisons.

The first is the direct comparison of the weighted utility for two alternatives on all of the objectives. The second is the comparison of all of the alternatives on any two selected objectives which shows the Pareto optimality for those two objectives. Complexity in the modern world, along with information quantity, uncertainty, and risk, make it necessary to provide a rational decision making framework. The goal of decision analysis is to give guidance, information, insight, and structure to the decision-making process in order to make better, more 'rational' decisions.

A decision needs a decision maker who is responsible for making decisions.


This decision maker has a number of alternatives and must choose one of them. The objective of the decision-maker is to choose the best alternative. When this decision has been made, events that the decision-maker has no control over may have occurred. Each combination of alternatives, followed by an event happening, leads to an outcome with some measurable value.

Managers make decisions in complex situations. Decision tree and payoff matrices illustrate these situations and add structure to the decision problems. Forman E. Gigerenzer G. Manning N. Patz A. Vickers G. Von Furstenberg G. Elements of Decision Analysis Models The mathematical models and techniques considered in decision analysis are concerned with prescriptive theories of choice action.

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This answers the question of exactly how a decision maker should behave when faced with a choice between those actions which have outcomes governed by chance, or the actions of competitors. Decision analysis is a process that allows the decision maker to select at least and at most one option from a set of possible decision alternatives. There must be uncertainty regarding the future along with the objective of optimizing the resulting payoff return in terms of some numerical decision criterion.

The elements of decision analysis problems are as follow: A sole individual is designated as the decision-maker. For example, the CEO of a company, who is accountable to the shareholders. A finite number of possible future events called the 'States of Nature' a set of possible scenarios. They are the circumstances under which a decision is made. The states of nature are identified and grouped in set "S"; its members are denoted by "s j ".

Elements for a Theory of Decision in Uncertainty (Applied Optimization) Elements for a Theory of Decision in Uncertainty (Applied Optimization)
Elements for a Theory of Decision in Uncertainty (Applied Optimization) Elements for a Theory of Decision in Uncertainty (Applied Optimization)
Elements for a Theory of Decision in Uncertainty (Applied Optimization) Elements for a Theory of Decision in Uncertainty (Applied Optimization)
Elements for a Theory of Decision in Uncertainty (Applied Optimization) Elements for a Theory of Decision in Uncertainty (Applied Optimization)
Elements for a Theory of Decision in Uncertainty (Applied Optimization) Elements for a Theory of Decision in Uncertainty (Applied Optimization)

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