Posted: December 28th, 2014

W5 D566 "Heuristic and Model Based Decisions, Simulations and Searches"

W5 D566 “Heuristic and Model Based Decisions, Simulations and Searches”

Project description
Write a one page essay using proper APA format, citations and use 3 sources ( one good source is Business Intelligence and Analytics tenth edition by Ramesh Sharda,

Dursun Delen and Efraim Turban).
1.Excel is probably the most popular spreadsheet software for PCs. Why? What can we do with this package that makes it so attractive for modeling efforts?
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Added on 28.12.2014 13:16
Chapter 9

CHAPTER OVERVIEW

In this chapter, we describe selected techniques employed in prescriptive analytics. We present this material with a note of caution: Modeling can be a very difficult

topic and is as much an art as a science. The purpose of this chapter is not necessarily for you to master the topics of modeling and analysis. Rather, the material is

geared toward gaining familiarity with the important concepts as they relate to DSS and their use in decision making. It is important to recognize that the modeling we

discuss here is only cursorily related to the concepts of data modeling. You should not confuse the two. We walk through some basic concepts and definitions of

modeling before introducing the influence diagrams, which can aid a decision maker in sketching a model of a situation and even solving it. We next introduce the idea

of modeling directly in spreadsheets. We then discuss the structure and application of some successful time-proven models and methodologies: optimization, decision

analysis, decision trees, and analytic hierarchy process.

DECISION SUPPORT SYSTEMS MODELING

Many readily accessible applications describe how the models incorporated in DSS contribute to organizational success. Simulation models can enhance an organizations

decision-making process and enable it to see the impact of its future choices. Properly constructed models can predict future disaster and provide enough forewarning

to effectively mitigate the situation: as long as the company makes the necessary changes. Modeling is a key element in most DSS and a necessity in a model-based DSS.

There are many classes of models, and there are often many specialized techniques for solving each one.

Modeling is not an exact science. There are many challenges that can impact the validity and outcomes of DSS models. These include proper framing of the modeled issue,

proper variable identification, and accurate forecasting. Further complications can arise based on the type of model that is selected. Figure 1 illustrates various

(common) modeling categories.

Figure 1 illustrates various modeling categories

STRUCTURE OF MATHEMATICAL MODELS FOR DECISION SUPPORT

All quantitative models are typically made up of four basic components: result variables, decision variables, uncontrollable variables and intermediate result

variables. Mathematical relationships link these components together. In non-quantitate models, the relationships are symbolic or qualitative. The results of decisions

are determined based on the decision made, the factors that cannot be controlled by the decision maker, and the relationship among the variables.

Fig 2 The General Structure of a Quantitative Model.
Result (Outcome) Variables: reflect the level of effectiveness of a system.
Decision Variables: describe alternative courses of action.
Uncontrollable Variables, or Parameters: factors that affect the result variables but are not under the control of the decision maker.
Intermediate Result Variables: reflect intermediate outcomes in mathematical models.

CERTAINTY, UNCERTAINTY, AND RISK

Part of Simons decision-making process described in Chapter 2 involves evaluating and comparing alternatives. During this process, it is necessary to predict the

future outcome of each proposed alternative. Decision situations are often classified on the basis of what the decision maker knows (or believes) about the forecasted

results. Customary categories of knowledge range from Complete knowledge to complete ignorance.

Fig 3 The Zones of Decision Making

In decision making under certainty, it is assumed that complete knowledge is available so that the decision maker knows exactly what the outcome of each course of

action will be. It may not be true that all of the outcomes are 100% known, but there is enough certainty that raises the confidence of the decision maker to a very

comfortable threshold. When considering the other end of the spectrum, uncertainty, the decision maker must consider several outcomes and what could be possible for

each. Uncertainty simply means that insufficient information is available, yet a decision must be made. Finally, decision making under risk is one in which the

decision maker must consider several possible outcomes for each alternative and weight the probability of occurrence. Generally decisions under risk conditions should

also include contingency plans to mitigate any adverse effects that may occur from one of the possible outcomes.

DECISION MODELING WITH SPREADSHEETS

Models can be developed and implemented in a variety of programming languages and systems. The spreadsheet is clearly the most popular (and cost effective) end-user

modeling tool. The built-in functions of spreadsheets include many powerful financial, statistical, and mathematical capabilities. In conjunction with standard

capabilities, many third-party add-ins provide advanced solutions including linear and non-linear optimization of mathematical modeling, as well as pre-programed

functions that mimic neural networks and other high-level specialized calculation packages. By combining all of these functions, spreadsheets offer important “what-if”

analysis capabilities that are flexible and easy to implement. Once the models are developed and vetted, more permanent versions can be incorporated into the DS

Systems.

MULTIPLE GOALS, SENSITIVITY ANALYSIS, WHAT-IF ANALYSIS, AND GOAL SEEKING

The analysis of management decisions aims to provide the best possible solutions. Unfortunately, managerial problems are seldom evaluated with a single simple goal.

Instead, managers (and companies) want to attain simultaneous goals. And sometimes these different goals may conflict with one another, as different stakeholders can

have different ambitions. Therefore, it is often necessary to analyze each alternative in light of how it impacts or serves several different objectives.

Certain difficulties arise when analyzing multiple goals:
It is usually difficult to obtain an explicit statement of the organizations goals.
The decision maker may change the importance assigned to specific goals over time or for different decision scenarios.
Goals and sub-goals are viewed differently at various levels of the organization.
The relationship between alternatives and their role in determining goals may be difficult to quantify.
Complex problems are solved by groups of decision makers. Each of these decisions have a different agenda.
Participants assess the importance (priorities) of the various goals differently.

DECISION ANALYSIS WITH DECISION TABLES AND DECISION TREES

Decision situations that involve a finite and usually not too large number of alternatives are modeled through an approach called decision analysis. Using this

approach, the alternatives are listed in a table or graph, with their forecasted contributions to the goal(s) and the probability of obtaining the contribution. These

can be evaluated to select the best alternative.

Two common methods of organizing the information required to make decisions are decision tables and decision trees. Decision tables conveniently organize information

and knowledge in a systematic, tabular manner to prepare it for analysis. And decision trees show the relationships of the problem graphically and can be used to

analyze complex situations in a compact form. Both methods have their merits and drawbacks, and a good manager should be familiar with both.

Chapter 9

CHAPTER OVERVIEW

In this chapter, we describe selected techniques employed in prescriptive analytics. We present this material with a note of caution: Modeling can be a very difficult

topic and is as much an art as a science. The purpose of this chapter is not necessarily for you to master the topics of modeling and analysis. Rather, the material is

geared toward gaining familiarity with the important concepts as they relate to DSS and their use in decision making. It is important to recognize that the modeling we

discuss here is only cursorily related to the concepts of data modeling. You should not confuse the two. We walk through some basic concepts and definitions of

modeling before introducing the influence diagrams, which can aid a decision maker in sketching a model of a situation and even solving it. We next introduce the idea

of modeling directly in spreadsheets. We then discuss the structure and application of some successful time-proven models and methodologies: optimization, decision

analysis, decision trees, and analytic hierarchy process.

DECISION SUPPORT SYSTEMS MODELING

Many readily accessible applications describe how the models incorporated in DSS contribute to organizational success. Simulation models can enhance an organization�s

decision-making process and enable it to see the impact of its future choices. Properly constructed models can predict future disaster and provide enough forewarning

to effectively mitigate the situation: as long as the company makes the necessary changes. Modeling is a key element in most DSS and a necessity in a model-based DSS.

There are many classes of models, and there are often many specialized techniques for solving each one.

Modeling is not an exact science. There are many challenges that can impact the validity and outcomes of DSS models. These include proper framing of the modeled issue,

proper variable identification, and accurate forecasting. Further complications can arise based on the type of model that is selected. Figure 1 illustrates various

(common) modeling categories.

Figure 1 illustrates various modeling categories

STRUCTURE OF MATHEMATICAL MODELS FOR DECISION SUPPORT

All quantitative models are typically made up of four basic components: result variables, decision variables, uncontrollable variables and intermediate result

variables. Mathematical relationships link these components together. In non-quantitate models, the relationships are symbolic or qualitative. The results of decisions

are determined based on the decision made, the factors that cannot be controlled by the decision maker, and the relationship among the variables.

Fig 2 The General Structure of a Quantitative Model.
�Result (Outcome) Variables: reflect the level of effectiveness of a system.
�Decision Variables: describe alternative courses of action.
�Uncontrollable Variables, or Parameters: factors that affect the result variables but are not under the control of the decision maker.
�Intermediate Result Variables: reflect intermediate outcomes in mathematical models.

CERTAINTY, UNCERTAINTY, AND RISK

Part of Simon�s decision-making process described in Chapter 2 involves evaluating and comparing alternatives. During this process, it is necessary to predict the

future outcome of each proposed alternative. Decision situations are often classified on the basis of what the decision maker knows (or believes) about the forecasted

results. Customary categories of knowledge range from Complete knowledge to complete ignorance.

Fig 3 The Zones of Decision Making

In decision making under certainty, it is assumed that complete knowledge is available so that the decision maker knows exactly what the outcome of each course of

action will be. It may not be true that all of the outcomes are 100% known, but there is enough certainty that raises the confidence of the decision maker to a very

comfortable threshold. When considering the other end of the spectrum, uncertainty, the decision maker must consider several outcomes and what could be possible for

each. Uncertainty simply means that insufficient information is available, yet a decision must be made. Finally, decision making under risk is one in which the

decision maker must consider several possible outcomes for each alternative and weight the probability of occurrence. Generally decisions under risk conditions should

also include contingency plans to mitigate any adverse effects that may occur from one of the possible outcomes.

DECISION MODELING WITH SPREADSHEETS

Models can be developed and implemented in a variety of programming languages and systems. The spreadsheet is clearly the most popular (and cost effective) end-user

modeling tool. The built-in functions of spreadsheets include many powerful financial, statistical, and mathematical capabilities. In conjunction with standard

capabilities, many third-party add-ins provide advanced solutions including linear and non-linear optimization of mathematical modeling, as well as pre-programed

functions that mimic neural networks and other high-level specialized calculation packages. By combining all of these functions, spreadsheets offer important “what-if”

analysis capabilities that are flexible and easy to implement. Once the models are developed and vetted, more �permanent� versions can be incorporated into the DS

Systems.

MULTIPLE GOALS, SENSITIVITY ANALYSIS, WHAT-IF ANALYSIS, AND GOAL SEEKING

The analysis of management decisions aims to provide the best possible solutions. Unfortunately, managerial problems are seldom evaluated with a single simple goal.

Instead, managers (and companies) want to attain simultaneous goals. And sometimes these different goals may conflict with one another, as different stakeholders can

have different ambitions. Therefore, it is often necessary to analyze each alternative in light of how it impacts or serves several different objectives.

Certain difficulties arise when analyzing multiple goals:
�It is usually difficult to obtain an explicit statement of the organization�s goals.
�The decision maker may change the importance assigned to specific goals over time or for different decision scenarios.
�Goals and sub-goals are viewed differently at various levels of the organization.
�The relationship between alternatives and their role in determining goals may be difficult to quantify.
�Complex problems are solved by groups of decision makers. Each of these decisions have a different agenda.
�Participants assess the importance (priorities) of the various goals differently.

DECISION ANALYSIS WITH DECISION TABLES AND DECISION TREES

Decision situations that involve a finite and usually not too large number of alternatives are modeled through an approach called decision analysis. Using this

approach, the alternatives are listed in a table or graph, with their forecasted contributions to the goal(s) and the probability of obtaining the contribution. These

can be evaluated to select the best alternative.

Two common methods of organizing the information required to make decisions are decision tables and decision trees. Decision tables conveniently organize information

and knowledge in a systematic, tabular manner to prepare it for analysis. And decision trees show the relationships of the problem graphically and can be used to

analyze complex situations in a compact form. Both methods have their merits and drawbacks, and a good manager should be familiar with both.

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