Chapter 4: Modeling and Analysis

Question 1

Certainty

Answer 1

A condition under which it is assumed that future values are known for sure and only one result is associated with an action.

Question 2

Complexity

Answer 2

A measure of how difficult a problem is in terms of its formulation for optimization, its required optimization effort, or its stochastic nature.

Question 3

Decision analysis

Answer 3

Methods for determining the solution to a problem, typically when it is inappropriate to use interval algorithms

Question 4

Decision table

Answer 4

Conveniently organized data in a tabular manner to prepare it for analysis.

Question 5

Decision tree

Answer 5

A graphical presentation of a sequence of interrelated decisions (nodes and roots) to be made under assumed risk.

Question 6

Decision variable

Answer 6

A variable in a model that can be changed and manipulated by the decision maker. Decision variables correspond to the decisions to be made.

Question 7

Dynamic modals

Answer 7

Models whose input data are changed over time e.g. 5-year profit projection.

Question 8

Environmental scanning and analysis

Answer 8

The monitoring, scanning, and interpretation of collected information.

Question 9

Forecasting

Answer 9

Predicting the future.

Question 10

Goal seeking

Answer 10

Asking a computer what values certain variables must have in order to attain desired goals.

Question 11

Heuristic programming

Answer 11

The use of heuristics in problem solving.

Question 12

Heuristics

Answer 12

Informal, judgmental knowledge of an application area that constitutes the rules of good judgment in the field. Heuristics also encompasses the knowledge of how to solve problems efficiently and effectively, how to plan steps in solving a complex problem, how to performance and so forth.

Question 13

Influence diagram

Answer 13

A graphical representation of a modal – a model of a model.

Question 14

Intermediate result variable

Answer 14

A variable that contains the values of intermediate outcomes in mathematical models.

Question 15

Linear programming

Answer 15

A mathematical model for the optimal solution of resource allocation problems. All relationship among the variables are linear.

Question 16

Mathematical (quantitative) model

Answer 16

A system of symbols and expressions representing a real situation

Question 17

Mathematical programing

Answer 17

An optimization technique for the allocation of resources subject to constraints.

Question 18

Model base management system

Answer 18

(MBMS) Software for establishing, updating, combining, and so on (e.g. managing) a DSS modal base.

Question 19

Multidimensional analysis

Answer 19

A modeling method the involves data analysis in several dimensions.

Question 20

Multiple goals

Answer 20

Refers to a decision in which alternatives are evaluated with several, sometimes conflicting, goals.

Question 21

Object orientated model base

management system

Answer 21

(OOMBMS) An MBMS constructed in an object orientated environment.

Question 22

Optimal solution

Answer 22

A best possible solution to a modeled problem.

Question 23

Parameter

Answer 23

(uncontrollable variable) A factor that affects the result of a decision but is not under the control of the decision maker, they can be internal or external.

Question 24

Quantitative software package

Answer 24

A preprogrammed (sometimes call a ready-made) model or optimization system. These packages sometimes serve as building blocks for other quantitative models.

Question 25

Relational model base management system

Answer 25

(RMBMS) A relational approach (as in relational databases) to the design and development of a model base management system.

Question 26

Result (outcome) variable

Answer 26

A variable that analyzes the result of a decision, usually one of the goals pf a decision-making problem.

Question 27

Risk

Answer 27

A probabilistic or stochastic decision situation.

Question 28

Risk analysis

Answer 28

A decision-making method that analyzes the risk associated with different alternatives. Also, known as calculated risk.

Question 29

Sensitivity analysis

Answer 29

A study of the effect of a change in one or more input variables on a proposed situation.

Question 30

Simulation

Answer 30

An imitation of reality.

Question 31

Static models

Answer 31

Models that describe a single interval of a situation.

Question 32

Uncertainty

Answer 32

In expert systems, a value that cannot be determined during consultation. Many expert systems can accommodate uncertainty; that is, they allow the user to indicate whether he or she does not know the answer.

Question 33

Visual interactive simulation

Answer 33

(VIS) A simulation approach used in the decision-making process that shows graphical animation in which systems and processes are presented dynamically to the decision maker. It enables visualization of the results of different potential actions.

Question 34

What-if analysis

Answer 34

A process that involves asking a computer what the effect of changing some input data or parameters would be.

Question 35

What is the difference between a static and a dynamic model

Answer 35

 Static models are a single event whereas dynamic models change over time
o Static models assume that data won’t change
o Dynamic models are important because they can model patterns over a given stretch of time
o Dynamic models can get increasingly more complex to the point that they cannot be solved

Question 36

Certainty, Uncertainty, and Risk

Answer 36

o Certainty assumes that complete knowledge of the problem is available to the decision maker
o Uncertainty considers situations in which many different outcomes are possible
o Risk is where the decision maker must consider the probability of many different outcome i.e. risk analysis

Question 37

MSS modeling with spreadsheets

Answer 37

 There are many different ways of showing and manipulating data
 The key takeaway from this section is that spreadsheets are a very popular way of modeling and analyzing data for the following reasons
o They incorporate many different functions into one tool
o Easy and familiar to most end-users
o They can be seamlessly integrated into other systems such as databases
o The user can easily construct static and dynamic models

Question 38

Decision analysis with decision trees and tables

Answer 38

 Decision tables are a fairly simple model that can easily be solved for certain and uncertain situations. They can also allow for probabilities to be added to account for risk
 Decision trees are an alternative to decision tables and can be used in a similar way. They do however show the relationships of the problem as roots and nodes

Question 39

The structure of mathematical models for decision support

Answer 39

	Mathematical models are made up of these basic components 
o	Result variable
o	Parameter 
o	Intermediate result variable 
o	And decision variables

Question 40

Mathematical programming optimization

Answer 40

 This chapter describes mathematical programming as a way of allocating resources among competing activities to optimize a goal (p. 153) one of the most common ways of doing so is through linear programming
 LP models consist of decision variables, and objective function, and constraints
 Lingo is a popular tool for quickly solving LP models
 LP typically assumes non-negative values for result variables

Question 41

Multiple goals, Sensitivity analysis, what-if analysis, goal seeking

Answer 41

 Multiple goals – This is where one attempts to solve several different and possibly conflicting problems to achieve simultaneous goals. Goal programming and A point system are some ways that one can solve these issues.
 Sensitivity analysis – This is where looks at the impact of changing parameters or input data has on the solution. This can be used for revising models, adding details, getting a better idea of external variables, and altering a system to make it more or less sensitive.
 The two types of sensitivity analysis are:
o Automatic
o Trial and error
 What-if analysis – This is a fairly self-explanatory analysis with the idea being what will happen if we change ______. The blank being an input value, parameter, or assumption.
 Goal seeking – this approach can be described as working a problem backward. One example that is common among retailers is how many products need to be sold to achieve annual revenue growth of _____%

Question 42

Problem solving search methods

Answer 42

 The book discusses several ways of problem-solving searching, they include:
o Algorithms – uses a step-by-step process (analytical)
o Blind searching – non-guided approaches to finding a possible solution
o Heuristic searching – finds rules to guide the searching

Question 43

Simulation

Answer 43

 In this section, the authors state that simulation is a way of experimenting using a computer based model.
 Some initial key takeaways from this section include:
o The idea of simulation is descriptive vs. normative – meaning that a simulation describes the system under different circumstances rather than attempting to find an optimal solution
o Some advantages are:
 The theory is straightforward
 The model is built from the perspective of the manager
 It can include real complexities of the problem
 It is the only DSS modeling method that can handle relatively unstructured problems
o Some disadvantages are:
 An optimal solution cannot be guaranteed
 The construction of the model can be slow and expensive
 The software can require special skills because of the complexity of the formal solution method
 The methodology of simulation is depicted in the graphic below

	Simulation types – see pg. 169 for more details 
o	Probabilistic simulation 
o	Time in/dependent simulations 
o	Object orientated simulations
o	Visual simulations