Introduction
The present chapter addresses the question of building better models. This is crucial for coping with complexity in general and in particular for the management of dynamic systems (Schwaninger 2010). Both the epistemological and the methodological-technological aspects of model validation for the achievement of high-quality models are discussed. The focus is on formal models, i.e., those formulated in a stringent, logical, and mostly mathematical language.
The etymological root of “valid” is in the Latin word “validus,” which denotes attributes such as strong, powerful, and firm. A valid model, then, is well founded and difficult to reject because it accurately represents the perceived real system which it is supposed to reflect. This system can be either one that already exists or one that is being constructed, or even anticipated, by a modeler or a group of modelers.
The validation standards in System Dynamics are more rigorous than those of many other methodologies. Let...
Notes
- 1.
It is necessary to employ a double precision version of the software being utilized
Abbreviations
- Model/model system :
-
A model is a simplified representation of a real system. Models can be descriptive or prescriptive (normative). Their functions can be to enable explanation, anticipation, or design. A distinction used in this contribution is between causal and noncausal models, with System Dynamics models being of the former type. The term model system is used to stress the systemic character of a model; this serves to identify it as an organized whole of variables and relationships, on the one hand, and to distinguish it from the real system which is to be modeled, on the other.
- Model validity :
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A model’s property of reflecting adequately the system modeled. Validity is the main feature of model quality. It is a matter of degree, not a dichotomized property.
- Model purpose :
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The goal for which a model is designed or the function it is supposed to fulfill. The model purpose adheres closely to the end-model user or model owner. Model purpose is the criterion for the choice of a model’s boundary and design.
- Modeling process :
-
The process involving phases such as problem articulation, boundary selection, development of a dynamic hypothesis, model formulation, model testing, policy formulation, and policy evaluation (Sterman 2000). The modeling process is followed by model use and implementation, i.e., the realization of actions designed or facilitated by the use of the model.
- Validation process :
-
Validation is the process by which model validity is enhanced systematically. It consists in gradually building confidence in the usefulness of a model by applying validation tests as outlined in this chapter. In principle, validation pervades all phases of the modeling process and, in addition, reaches into the phases of model use and implementation.
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Appendix: Overview of the Tests Described in This Chapter
Appendix: Overview of the Tests Described in This Chapter
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1.
Tests of the Model-Related Context
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1.1.
Test of Model Framing
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1.2.
Issue Identification Test
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1.3.
Adequacy of Methodology Test
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1.4.
System Configuration Test
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1.5.
System Improvement Test
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1.1.
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2.
Tests of Model Structure
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2.1
Direct Structure Tests
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2.1.1
Structure Examination Test
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2.1.2
Parameter Examination Test
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2.1.3
Direct Extreme Condition Test
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2.1.4
Boundary Adequacy Structure Test
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2.1.5
Dimensional Consistency Test
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2.1.1
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2.2
Indirect Structure Tests
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2.2.1
Mass-Balance Check
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2.2.2
Indirect Extreme Condition Test
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2.2.3
Behavior Sensitivity Test
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2.2.4
Integration Error Test
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2.2.5
Boundary Adequacy Behavior Test/Boundary Adequacy Policy Test
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2.2.6
Loop Dominance Test
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2.2.1
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2.1
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3.
Tests of Model Behavior
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3.1.
Behavior Reproduction Tests
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3.1.1
Symptom Generation Test
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3.1.2
Frequency Generation and Phase Relationship Test
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3.1.3
Modified Behavior Test
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3.1.4
Multiple Modes Test
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3.1.5
Behavior Characteristic Test
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3.1.1
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3.2.
Behavior Anticipation Tests
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3.2.1
Pattern Anticipation Test
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3.2.2
Event Anticipation Test
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3.2.1
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3.3.
Behavior Anomaly Test
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3.4.
Family Member Test
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3.5.
Surprise Behavior Test
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3.6.
Turing Test
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3.1.
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Schwaninger, M., Groesser, S. (2016). System Dynamics Modeling: Validation for Quality Assurance. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27737-5_540-3
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System Dynamics Modeling: Validation for Quality Assurance- Published:
- 25 September 2018
DOI: https://doi.org/10.1007/978-3-642-27737-5_540-4
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System Dynamics Modeling: Validation for Quality Assurance- Published:
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DOI: https://doi.org/10.1007/978-3-642-27737-5_540-3