1. Assume a firm owns a small airplane worth $700,000. Assume initially that the
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Question
1. Assume a firm owns a small airplane worth $700,000.
Assume initially that the plane is subject to the risk of physical damage. They believe that the probability of loss is 3%. Also assume that when a loss occurs, it will be a total loss. Assume this firm%u2019s marginal tax rate is 35%.
The firm has four current risk management options to use to manage this risk.
[1] The firm can purchase full insurance for the risk of physical damage/destruction to this plane for a premium of $22,000.
[2] The firm is also considering retention as an alternative to full insurance.
[3] The firm is considering a loss control measure (LC) to use in conjunction with both retention and full insurance.
The cost of loss control is $3000
The impact of loss control is to reduce the probability of loss from 3% to 2%.
The insurer agrees to reduce the insurance premium from $22,000 to $17,000 if/when the loss control measure is introduced.
a. Construct an after tax loss matrix. Assume that the firm%u2019s marginal tax rate is 35%.
b. Suppose the risk manager wants to minimize expected loss as her decision rule. What risk management alternative does she choose? Show all expected loss calculations and work and explain why the risk manager chooses the option.
c. Assume that the risk manager has a worry value (WV) equal to $5,000 for retention. Assume also that the WV for retention falls to $4,000 when the probability of the loss falls due to the loss control measure, (i.e., the WV for retention with loss control is now equal to $4,000.)
If the risk manager decides to minimize TOTAL COST, what risk management alternative does she choose? Show all total cost calculations and work and explain why the risk manager selects the option.
Explanation / Answer
Rules of thumb, intuition, tradition, and simple financial analysis are often no longer sufficient for addressing such common decisions as make-versus-buy, facility site selection, and process redesign. In general, the forces of competition are imposing a need for more effective decision making at all levels in organizations.
Decision analysts provide quantitative support for the decision-makers in all areas including engineers, analysts in planning offices and public agencies, project management consultants, manufacturing process planners, financial and economic analysts, experts supporting medical/technological diagnosis, and so on and on.
Progressive Approach to Modeling: Modeling for decision making involves two distinct parties, one is the decision-maker and the other is the model-builder known as the analyst. The analyst is to assist the decision-maker in his/her decision-making process. Therefore, the analyst must be equipped with more than a set of analytical methods.
Specialists in model building are often tempted to study a problem, and then go off in isolation to develop an elaborate mathematical model for use by the manager (i.e., the decision-maker). Unfortunately the manager may not understand this model and may either use it blindly or reject it entirely. The specialist may feel that the manager is too ignorant and unsophisticated to appreciate the model, while the manager may feel that the specialist lives in a dream world of unrealistic assumptions and irrelevant mathematical language.
Such miscommunication can be avoided if the manager works with the specialist to develop first a simple model that provides a crude but understandable analysis. After the manager has built up confidence in this model, additional detail and sophistication can be added, perhaps progressively only a bit at a time. This process requires an investment of time on the part of the manager and sincere interest on the part of the specialist in solving the manager's real problem, rather than in creating and trying to explain sophisticated models. This progressive model building is often referred to as the bootstrapping approach and is the most important factor in determining successful implementation of a decision model. Moreover the bootstrapping approach simplifies otherwise the difficult task of model validating and verification processes.
What is a System: Systems are formed with parts put together in a particular manner in order to pursuit an objective. The relationship between the parts determines what the system does and how it functions as a whole. Therefore, the relationship in a system are often more important than the individual parts. In general, systems that are building blocks for other systems are called subsystems
The Dynamics of a System: A system that does not change is a static (i.e., deterministic) system. Many of the systems we are part of are dynamic systems, which are they change over time. We refer to the way a system changes over time as the system's behavior. And when the system's development follows a typical pattern we say the system has a behavior pattern. Whether a system is static or dynamic depends on which time horizon you choose and which variables you concentrate on. The time horizon is the time period within which you study the system. The variables are changeable values on the system.
In deterministic models, a good decision is judged by the outcome alone. However, in probabilistic models, the decision-maker is concerned not only with the outcome value but also with the amount of risk each decision carries
As an example of deterministic versus probabilistic models, consider the past and the future: Nothing we can do can change the past, but everything we do influences and changes the future, although the future has an element of uncertainty. Managers are captivated much more by shaping the future than the history of the past.
Uncertainty is the fact of life and business; probability is the guide for a "good" life and successful business. The concept of probability occupies an important place in the decision-making process, whether the problem is one faced in business, in government, in the social sciences, or just in one's own everyday personal life. In very few decision making situations is perfect information - all the needed facts - available. Most decisions are made in the face of uncertainty. Probability enters into the process by playing the role of a substitute for certainty - a substitute for complete knowledge.
Probabilistic Modeling is largely based on application of statistics for probability assessment of uncontrollable events (or factors), as well as risk assessment of your decision. The original idea of statistics was the collection of information about and for the State. The word statistics is not derived from any classical Greek or Latin roots, but from the Italian word for state. Probability has a much longer history. Probability is derived from the verb to probe meaning to "find out" what is not too easily accessible or understandable. The word "proof" has the same origin that provides necessary details to understand what is claimed to be true.
Probabilistic models are viewed as similar to that of a game; actions are based on expected outcomes. The center of interest moves from the deterministic to probabilistic models using subjective statistical techniques for estimation, testing, and predictions. In probabilistic modeling, risk means uncertainty for which the probability distribution is known. Therefore risk assessment means a study to determine the outcomes of decisions along with their probabilities.
Decision-makers often face a severe lack of information. Probability assessment quantifies the information gap between what is known, and what needs to be known for an optimal decision. The probabilistic models are used for protection against adverse uncertainty, and exploitation of propitious uncertainty.
Difficulty in probability assessment arises from information that is scarce, vague, inconsistent, or incomplete. A statement such as "the probability of a power outage is between 0.3 and 0.4" is more natural and realistic than their "exact" counterpart such as "the probability of a power outage is 0.36342."
It is a challenging task to compare several courses of action and then select one action to be implemented. At times, the task may prove too challenging. Difficulties in decision making arise through complexities in decision alternatives. The limited information-processing capacity of a decision-maker can be strained when considering the consequences of only one course of action. Yet, choice requires that the implications of various courses of action be visualized and compared. In addition, unknown factors always intrude upon the problem situation and seldom are outcomes known with certainty. Almost always, an outcome depends upon the reactions of other people who may be undecided themselves. It is no wonder that decision-makers sometimes postpone choices for as long as possible. Then, when they finally decide, they neglect to consider all the implications of their decision.
Emotions and Risky Decision: Most decision makers rely on emotions in making judgments concerning risky decisions. Many people are afraid of the possible unwanted consequences. However, do we need emotions in order to be able to judge whether a decision and its concomitant risks are morally acceptable. This question has direct practical implications: should engineers, scientists and policy makers involved in developing risk regulation take the emotions of the public seriously or not? Even though emotions are subjective and irrational (or a-rational), they should be a part of the decision making process since they show us our preferences. Since emotions and rationality are not mutually exclusive, because in order to be practically rational, we need to have emotions. This can lead to an alternative view about the role of emotions in risk assessment: emotions can be a normative guide in making judgments about morally acceptable risks.
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