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.
Variables:
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
scenario)
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
specification)
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 (oren@site.uottawa.ca )
any omission and/or error.