Modeling and Simulation Body of Knowledge (M&SBOK)

Being prepared under the auspices of

the National Training Systems Association (NTSA)


M&S: Science/methodology - Models

1. Fundamental issues:

    - types of system problems concerning models

    - variables

    - complexity

    - modeling approaches for decomposition of problems

2. Modeling formalisms/schemas

    - conceptual modeling

    - taxonomies and ontologies of modeling formalisms

3. Model processing



1. Fundamental issues


Types of system problems concerning models


From a systemic point of view, simulation can be used to find the values of output, input, or state variables of a system; provided that the values of the two other types of variables are known.

In analysis problems, state variables are given (ie., the system is given); simulation can generate output trajectories for given initial conditions of the state variables and input trajectories.

In design problems, for pairs of input-output trajectories, a system has to be designed. In this case, simulation cen be iteratively used to find state values to satisfy given input-output trajectories.

In control problems, given the system (i.e., state variables) and their initial values, for desired output trajectories, necessary input trajectories (i.e., control) has to be generated.




The concept of “variable” is a very basic issue in M&S. As an example of the richness of even this very fundamental concept in M&S,  see a list which comprises almost 150 types of variables.

Input Variables: As another testimony of the richness of concepts in M&S, a special type of variable, i.e., “input variable” is elaborated and a taxonomy of input variables in conventional as well as artificial intelligence-directed and agent-directed simulation is provided in the following tables (Ören 2001). The two tables provide outlines of externally generated input variables and internally generated input variables.

Table - Types of Externally Generated Inputs

Mode of input

Type of Input




Passive acceptance of externally generated (exogenous) input

(imposed or forced input)



Type of access to input: values at input ports, coupling, argument passing, knowledge in a common area, message passing.

Nature of input:

- Data (facts)            

- Forced Events

- Sensation (converted sensory data: from analog to digital; single or multi sensor: sensor fusion). Sensory inputs include haptic inputs and  visual, auditory, and chemical sensation inputs).

- External goals (imposed goals)

Active perception of externally generated (exogenous) input)

(perceived input)

- Perception  (interpreted, sensory data and detected events) -- includes: decoding, selection (filtering), recognition, regulation

- Perceived goals

- Evaluated inputs

  -- evaluation of inputs (acceptability)

-- evaluation of source of inputs (reliability, credibility)

Table -  Types specification of Internally Generated Inputs

Mode of input

Type of Input

Active perception of internally generated input

- Introspection (perceived internal facts, events; or realization of lack of them)


Internal generation of :

- Anticipated facts and/or events

  (anticipatory systems)

- Questions

- Hypotheses  by:

 -- Data-driven reasoning

    (Expectation-driven reasoning)

    (Forward reasoning)

    (Bottom-up reasoning)

 -- Model-driven reasoning

- Goals


2. Modeling formalisms/schemas

Models: Some Taxonomies

Detailed taxonomies of simulation models exist – even since 1970s. The list of taxonomies developed by the author are available on the Web.

Some classifications are based on:

    - nature, existence & trajectory of variables

    - functional relationships of variables

    - formalisms used to describe the models

    - intended use

    - disposition of submodels

    - organization of submodels

    - goals to be pursued

(Taxonomies of models based on the above mentioned criteria will be posted.)


3. Model Processing


Model processing includes:

    - model analysis

    - model transformation

    - model synthesis/composition.


3.1 Model Analysis: The following two tables outline descriptive and evaluative types of model analysis, respectively.


Table - Types of Descriptive Model Analysis

(Model Characterization)


Model comprehensibility


Model documentation

- Static model documentation

- Dynamic model documentation

Model ventilation (to examine its assumptions, 

 deficiencies, limitations, etc.)

Model usability

Model referability

 - Model integrity

Model modifiability

- Model composability


Table -  Types of Evaluative Model Analysis

(Model Evaluation)


Evaluation with respect to:

Type of evaluation


A Modeling formalism

Consistency of

representation of the

- component model

- coupled model

- federated model

Model robustness


Another model

Structural model comparison

- model homomorphism

- model isomorphism

- model equivalencing

   -- for any two models

   -- for a simplified and original model

   -- for an elaborated and original model

Behavioral model comparison

   (Comparison of several  models within a given





Real system

Model qualification

- Model realism (veracity, verisimilitude)

  -- Adequacy of model structure

  -- Adequacy of model

     constants and parameters

       --- Model identification

       --- Model fitting

       --- Model calibration

- Model correctness analysis   

  -- Dimensional analysis    

Model validity

- Structural validity

- Replicative validity

- Predictive validity



Goal of the study

Model relevance

(For single models as well as federated models)

- Domain of intended Applications

  -- Appropriate use of a model

- Range of applicability of a model

Its technical system specification


Model verification  

  (comparison of a computerized model with its



3.2 Model Transformation includes:

    - model simplification

    - model pruning

    - model elaboration

    - model composition

    - model composability, as well as establishment of

    - model isomorphism

    - model homomorphism, and

    - model endomorphism.


Update Notice

Draft  version 4

updated and © by: Dr. Tuncer Ören - 2006-09-17

M&SNet – McLeod Modeling and Simulation Network

SITE – School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada

Note: I will be grateful if you would please report to me ( )

          any omission and/or error.