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mathematical (quantitative) models
a system of symbols and expressions representing a real situation

environmental scanning and analysis
the monitoring, scanning, and interpretation of collected information

influence diagrams
graphical models of mathematical models  can facilitate the variable identification process

forecasting (predictive analytics)
predicting the future

Decision Support Systems
DSS

MBMS (model base management systems)
used to aid in the management of models

DSS  use quantitative models
expert systems  use knowledgebased models
DSS uses mostly _____ models, whereas expert systems use _____ models in their applications.

1. optimization of problems with few alternatitives
2. optimization via algorithm
3. optimization via an analytic formula
4. simulation
5. heuristics
6. predictive models
7. other models
seven groups of DSS models

heuristics
model that includes finding a good enough solution using rules

optimization of problems with few alternatitives
model that infolves finding the best solution from a small number of alternatives

optimization via algorithm
model that involves finding the best solution from a large number of alternatives, using a stepbystep improvement process (linear and other mathematical programming models)

optimization via an analytic formula
model that involves finding the best solution in one step using a formula

simulation
model that involves finding a good enough solution or the best among the alternatives checked, using experimentation

predictive models
model that predicts the future for a given scenario

other models
model used to solve a whatif case, using a formula

management support system
MSS

multidimensional analysis (modeling)
involves data analysis in several dimensions  data are generally shown in a spreadsheet format

influence diagram
a graphical representation of a model  ie: a model of a model

result (or outcome) variables
decision variables
uncontrollable variables (and/or parameters)
intermediate result variables (not a "major" component)
 all linked by mathematical relationships
four basic components of a model

Decision Support System (DSS)
any system that is designed to help a decision maker make a better decision

a decision variable
A rectangle in an influence diagram represents ____.

an uncontrollable or intermediate variable
A circle in an influence diagram represents ____.

the result (outcome) variable; intermediate or final
An oval in an influence diagram represents ____.

the direction of influence (relationship)
A arrow in an influence diagram indicates ____.

mathematical relationships
What links the components of models?

result (outcome) variables
reflect the level of effectiveness of a system  ie: indicate how well the system performs or attains its goals

dependent variables
Result variables are also known as ______.

decision variables
describe alternative courses of action  ex: the amount to invest in bonds, people, times, & schedules

uncontrollable variables (fixed), parameters, or variables (can vary)
factors that affect the result variables but are not under the control of the decision maker

constraints
Variables that limit the decision maker form _____.

intermediate result variables
reflect intermediate outocomes in mathematical models  ie: not what you're looking for, but leads to what you're looking for

certainty
risk
uncertainty
categories of knowledge that decision situations can be grouped into

certainty model
 decision making model in which:
 complete knowledge is assumed
 all potential outcomes are known
 may yield the optimal solution

uncertainty model
 decision making model in which:
 there are several outcomes for each decision
 the probability of each outcome is known
 knowledge would lead to less uncertainty

risk analysis model (probablistic decision making)
 the decision making model in which the following are taken into consideration:
 probability of each of several outcomes occuring
 level of uncertainty >= expected value

decision making under certainty
it is assumed that complete knowldege is available so that the decision maker knows exactly what the outcome of each course of action will be  occurs most often with structured problems with short time horizons

decision making under uncertainty
decision maker considers situations in which several outcomes are possible for each course of action  decision makes does not know, or cannot estimate, the probability of occurrence

decision making under risk (ie: probabilistic or stochastic) decision making situation
the decision maker must consider several possible outcomes for each alternative, each with a given probability of occurrence  longrun probabilities that the given outcomes will occur are assumed to be known or can be estimated

assumed risk
The category of knowledge that most major business decisions are made under:

risk analysis (calculated risk)
a decisionmaking method that analyzes the risk (based on assumed known probabilities) associated with different alternatives

risk analysis (calculated risk)
can be performed by calculating the expected value of each alternative and selecting the one with the best expected value

spreadsheet
most popular enduser modeling tool:

mathematical programming
a family of tools designed to help solve managerial problems in which the decision maker must allocate scarce resources among competing activities to optimize a measurable goal

constraints
restriction of allocation by several limitations and requirements

optimal solution (best solution)
the solution for an alocation problem in which the degree of goal attainment associated with it is the highest

nonquantitative models (qualitative)
quantitative models
two types of mathematical models

nonquantitative models (qualitative)
captures symbolic relationships between decision variables, uncontrollable variables and result variables

quantitative models
mathematically links decision variables, uncontrollable variables , and result variables

objective function
a linear mathematical function that relates the decision variables to the goal, measures goal attainment, and is to be optimized

decision variables
variables in a linear programming (LP) problem whose values are unknown and are searched for

objective function coefficient
unit profit or cost coefficients indicating the contribution to the objective of one unit of a decision variable

constraints
expressed in the form of linear inequalities or equalities that limit resources and/or requirements

capacities
describe the upper and sometimes lower limits on the constraints and variables

input/output (technology) coefficients
indicate resource utilization for a decision variable

