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In general, the forces of competition are imposing a need for more effective decision making at all levels in organizations. 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.

Tools for Decision Analysis

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.

Unfortunately the manager may not understand this model and may either use it blindly or business presentation maker free download 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 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.

Many of the systems we are part of are dynamic systems, which are they change over time. 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 modelsa 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.

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 factorsas 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.

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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.

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