Types of Mathematical Model. Iconic, analogical, symbolical, deterministic, stochastic, static and dynamic

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2. BUSINESS INTELLIGENCE
Types Of Mathematical Models
Mrs. Megha Sharma
M.Sc. Computer Science. B.Ed.
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3. Mathematical
Models
A mathematical model is a
description of a system using
mathematical concepts
and language. The process of
developing a mathematical model is
termed mathematical modeling
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4. Types Of Mathematical Models
ICONIC
STOCHASTIC
DETERMINISTIC
ANALGOICAL
SYMBOLICAL
DYNAMIC
STATIC
CHARACTERSTICS
TEMPORAL
DIMENSION
PROBABILISTIC
NATURE
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5. ICONIC
Physical replicas are referred to as Iconic Models
Iconic model is material representation of real system,
whose behaviour is follows for the purpose of
analysis.
E.g. Miniature model of airplane, car or bridge
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6. ANALOGICAL
 Analogical model are small physical system that have similar
characteristics and work line an original object or system.
 They are not replica of problem situation. Actual system is
complex and might not allow direct handling or manipulation
 E.g. Organization charts, showing structure authority and
responsibility relationship.
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7. SYMBOLICAL
Symbolic model is an abstract representation of a
real system.
It is intended to describe the behaviour of the
system through a series of symbolic variables,
numerical parameters and mathematical
relationships.
E.g. Graphs, Chart table.

8. DETERMINISTIC
All data inputs are supposed to be known a prior with
certainty.
In this model everything is predefined, and results are
uncertain. Beneficial for a variety of management
problems.
a deterministic system is a system in which no
randomness is involved in the development of future
states of the system.
A deterministic model will thus always produce the same
output from a given starting condition or initial state
It can help to reach the right business based on an ideal
customer profile.
E.g. Calculation to determine selling price if cost price is
200 and profit is 20%
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9. STOCHASTIC
A stochastic model is a tool for estimating probability
distributions of potential outcomes by allowing for random
variation in one or more inputs over time.
In this model some input information represents random
events and is therefore characterized by a probability
distribution.
In these models some inputs to the model are not known
with certainly.
Often used for strategic decision making involving an
organization relationship to its environment.
E.g. Predictive models, waiting line models.
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10. STATIC
A static model describes the static structure of the
system being modeled, which is considered less likely to
change than the functions of the system.
Static model consider a given system and the related
decision-making processes within one single temporal
stage.
E.g. Regression Models.
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11. DYNAMIC
The dynamic model represents the time–dependent
aspects of a system. It is concerned with the temporal
changes in the states of the objects in a system.
With a dynamic model processes are continuously
adjusted based on an analysis of context specific failures
and ensure that each step contributes to positive result.
E.g. An order interacts with inventory to determine
product availability.
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