File Name: decision making under certainty uncertainty and risk .zip
Decisions are made under the condition of certainty when the manager has perfect knowledge of all the information needed to make a decision. This condition is ideal for problem solving. The challenge is simply to study the alternatives and choose the best solution.
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Log In Sign Up. Download Free PDF. Decision-making under risk and uncertainty and its application in strategic management Journal of Business Economics and Management, Download PDF. A short summary of this paper. Decision-making under risk and uncertainty and its application in strategic management. IntroductionDecision-making problems are very common in our lifes. In business and economics, people are always making decisions.
Sometimes, these decisions are very relevant because they involve huge amounts of money macro decisions and sometimes they simply represent a simple action with almost no cost micro decisions. In our economy, the politicians make macroeconomic decisions that require a lot of efforts in order to be properly assessed. At the same time, we can also find microeconomic decisions made by an individual consumer when dealing with the usual actions of his life like the decision of selecting an apartment or a car, or more simply the selection of a product in a supermarket.
Therefore, it is clear that decision processes are always present in our World. Moreover, we can find more complex decisions based on our intuition or on the nature of the universe we are living in such as the unconsciousness decisions people are making when selecting the appropriate words when having a conversation with other people.
Usually, these decisions are made with the intuition and with some partial information people have. However, implicitly there is a mathematical or statistical model that represents the problem that should be able to provide the optimal decision according to the interests of the decision-maker.
Sometimes, our intuition finds this decision but sometimes not. In this context, through decades, scientists have been looking for the optimal model for making decisions. Unfortunately, our world is strongly affected by different types of uncertainty.
Therefore, it is not easy to find the optimal choice because in a lot of problems the decisions are made under uncertainty. Some of them have focussed on decision-making under risk environments Yager Yager , That is, when we have some kind of uncertainty but we can assess it with probabilistic information. Some others have analyzed decision-making under uncertainty.
That is, when we can not assess the uncertainty with probabilities. Therefore, we have to use more subjective methods such as the use of the ordered weighted averaging OWA operator Yager It is an aggregation operator that provides a parameterized family of aggregation operators between the minimum and the maximum. It unifies the classical decision-making methods under uncertainty optimistic criteria, Wald, Laplace and Hurwicz Luce, Raiffa in one single formulation that includes these methods as particular cases of a more general framework.
Since its appearance, the OWA operator has been studied by a lot of authors Beliakov et al. It provides a more general reordering process of the information to be aggregated by using order inducing variables. Thus, we can consider more complex attitudinal characters that include psychological and personal factors in the analysis.
Wei bWei , c considered the use of different types of fuzzy information and harmonic means. It unifies the probability and the OWA operator considering the degree of importance that each concept has in the aggregation. It is worth noting that in the literature we find some previous studies that presented different ways for unifying these concepts such as the immediate probability Engemann et al.
Moreover, some other models focussed on the unification between the weighted average and the OWA operator such as the hybrid average Xu, Da and the weighted OWA operator Torra However, it is easy to use the weighted average as a probability.
Therefore, it is straightforward to extend these models in a probabilistic framework. However, only the POWA operator is able to consider the degree of relevance of each concept in the analysis.
The objective of this paper is to present a new approach for dealing with risk and uncertain environments in the same formulation. It is an aggregation operator that unifies the probability and the IOWA operator in the same formulation and considering the degree of importance of each concept in the aggregation.
Moreover, it provides a parameterized family of aggregation operators between the minimum and the maximum. Furthermore, it also considers complex reordering processes that permit to assess complex attitudinal characters of the decision-maker. It includes a wide range of particular cases including the maximum, the minimum, the arithmetic mean, the probabilistic maximum, the probabilistic minimum, the probabilistic aggregation, the OWA operator, the IOWA operator and the POWA operator.
We study the applicability of this approach and we see that it is very broad because all the previous studies the use the probability and the OWA operator can be revised and extended with this new approach. Moreover, in case the classical approach is enough, we can always reduce the new model to the classical one because it is included as a particular case. Thus, we can see that we can extend this approach in statistics and in all the disciplines that use statistical techniques based on the probability and the OWA operator such as decision theory, economics, soft computing, engineering and physics.
We briefly present some basic examples in statistics by using the IPOWA operator in the variance, the covariance, the Pearson coefficient and in a simple linear regression model. With this approach we can unify decision-making problems under risk and under uncertainty in the same formulation and considering the degree of importance that each approach has in the specific problem considered.
Thus, we can provide a more general framework for decision-making. We also study some other approaches based on the use of "ex-ante" and "ex-post" decisions and situations with imprecise information.
We focus on a multi-person decision-making problem in strategic management regarding the selection of the optimal strategy for a company. It is an aggregation operator with similar properties than the IPOWA operator that can assess the opinion of several persons in the analysis. The application in strategic management shows that the decision-maker gets a better representation of the problem because he can assess risk and uncertain environments in the same formulation and select the alternative in closest accordance with his interests.
Note that strategic management problems are a key issue in decision-making because we can formulate strategies in different fields. Especially, in business decision-making it is very useful because the enterprises need to formulate the optimal strategies in order to success in the development of the company. This paper is organized as follows. In Section 5 we analyze the applicability of the IPOWA operator and in Section 6 the application of multi-person decision-making problems in strategic management.
In Section 7 we summarize the main conclusions of the paper. The OWA operatorThe OWA operator Yager is an aggregation operator that provides a parameterized family of aggregation operators between the minimum and the maximum. It can be defined as follows. One of the key aspects of the OWA operator in decision-making under uncertainty is that it unifies the classical decision-making methods in one single formulation.
Thus, the optimistic criteria, the pessimistic or Wald criteria, the Laplace criteria and the Hurwicz criteria are particular cases of the OWA operator. Note that different properties can be studied such as the distinction between descending and ascending orders, different measures for characterizing the weighting vector and other families of OWA operators.
Its main difference is that the reordering step is not developed with the values of the arguments a i. In this case, the reordering step is developed with order inducing variables. The IOWA operator also includes as particular cases the maximum, the minimum and the average criteria. The IOWA operator is also monotonic, bounded, idempotent and commutative. It is defined as follows. It also uses order inducing variables in order to represent the reordering process from a general point of view.
Its main advan-tage is that it can unify the probability and the IOWA operator in the same formulation and considering the degree of importance of each concept in the aggregation. Thus, we can use the objective information of the problem and the attitudinal character of the decision maker in the same formulation.
Thus, we get the following. With Eq. Obviously, we get the same results with both formulas. Note that if the weighting vector of the OWA or the probability is not normalized, i. Note that in the literature we may find other models that deal with probabilities and OWA operators in the same formulation. The main approach is the concept of immediate probability Engemann et al.
Its main disadvantage is that it can not represent the degree of importance of each concept in the aggregation process. In the following, we briefly present its definition when using induced aggregation operators. Therefore, they can also be extended for situations with the OWA operator and probabilities assuming that for some situations the WA can be seen as a probability. However, these and other approaches are useful for some particular situations but they do not seem to be so complete than the IPOWA because they cannot unify them considering different degrees of importance to each case.
Each case is a particular attitude of the decision maker that is useful in some specific situations. Remark 3. Moreover, we can consider different types for each of case.
Thus, we can consider a wide range of alternatives. In Table 1, we present some of these cases. Therefore, we see that the applicability is incredibly broad because all the previous models, theories, etc. Therefore, this new model always includes the classical approach. However, we believe that in the future there will be a need to produce various degrees of underestimated and overestimated results because they will provide more complete information in the analysis.
Using the model presented in this paper, we can vary the degree of importance of these concepts depending on the particular problem we are analyzing. This will allow us to consider situations where the probability or the OWA is more relevant than the other concept.
In the following, we mention some of the main research application areas. Within each field, there are many potential applications. For example, we can implement it in linear and multiple regressions. We can also extend it to probability theory and a lot of other related areas such as hypothesis testing and inference statistics.
Moreover, we can apply it in neural network theory, evolutionary computation, probabilistic reasoning and chaotic computing. In summary, any current or future research that uses either the IOWA or the PA can be revised and extended by using this new approach.
Decision Making faces 3 particular conditions they are; 1 uncertainty, 2 certainty, and 3 risk. These conditions determine the probability of an error in decision making. Under conditions of certainty, the manager has enough information to know the outcome of the decision before it is made. This money is kept in a savings account at a local bank that pays 7. Half of the money will be drawn out next month and the rest when the job is completed in 90 days.
Virtually all decisions are made in an environment of at least some uncertainty. However, the degree will vary from relative certainty to great uncertainty. There are certain risks involved in making decisions. In a situation involving certainty, people are reasonably sure about what will happen when they make a decision. The information is available and is considered to be reliable, and the cause and effect relationships are known. In a situation of uncertainty, on the other hand, people have only a meager data base, they do not know whether or not the data are reliable, and they are very unsure about whether or not situation may change.
Post a comment Semoga perkongsian ilmu ini diberkati. Explain the difference between decision-making under certainty, risk and uncertainty. Decision making is a process of identifying problems and opportunities and choosing the best option among alternative courses of action for resolving them successfully. Usually, there are three different conditions under which decisions are made; these conditions are explained as follow:. Conditions under certainty are which the decision maker has full and needed information to make a decision.
A condition of certainty exists when the decision-maker knows with reasonable certainty what the alternatives are, what conditions are associated with each.
Information, S. Decision theory represents a general approach to decision making. It is suitable for a wide range of operations management decisions. Among them are capacity planning, product and service design, equipment selection, and location planning.
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Код страны - 1. Действительно хорошая новость. ГЛАВА 54 - Пусти .
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