This document constitutes a working document and a methodological proposal for the verification and validation of simulation models based on the H-M-E approach. Its purpose is to describe a conceptual and technical framework that enables the analysis of a model’s internal coherence, structural adequacy, and degree of validity with respect to the objectives for which it was built.
The content of this document does not describe or specify any specific software application, nor should it be interpreted as a validation of any particular simulation tool. The proposed framework is intentionally independent of technology, implementation details, and execution environments in which models may be developed or evaluated.
Nevertheless, it is assumed that simulation tools adopting this approach — among them the GPSS-Plus simulation engine — will progressively incorporate the techniques, metrics, and mechanisms required to enable the practical application of this methodology. Such adoption does not imply exclusivity or dependency, nor does it condition the validity of the framework on a specific implementation.
The H-M-E model should therefore be understood as an open and revision-prone proposal, whose acceptance and evolution depend on its application across different domains, its use in diverse contexts, and critical evaluation by the community.
This document is published with the explicit purpose of being opinionable, criticizable, and debatable.
Classical approaches to verification and validation (V&V) of simulation models rely, either explicitly or implicitly, on a black-box conception: the model is interpreted as an opaque function that transforms a set of inputs into observable outputs, whose correctness is assessed by statistically comparing results against historical data or expert judgment.
This conception is adequate when the model approximates a pure, deterministic function. However, in the modeling of complex real-world systems, this situation is the exception rather than the rule. A simulation model rarely implements relationships of the form f(a,b); instead, it describes procedures that interact with a dynamic state, evolve over time, and depend on the concurrent behavior of other entities. The same procedure, invoked with identical parameters, will typically produce different results depending on time, context, and the system’s prior history.
In this scenario, the model ceases to be a functional “black box” and becomes a living environment whose meaning resides in its internal structure and execution dynamics. Validating outputs alone is therefore equivalent to ignoring the semantics of the process that generates them. Methods such as simulation Turing tests or direct KPI comparison may produce fortuitous validations: apparently correct results obtained for the wrong reasons.
In other words, a model that normally runs under certain circumstances cannot be statistically validated once those circumstances change. What is alive rarely encounters identical conditions twice.
Statistics are valid where phenomena are statistically repeatable. When the environment changes and that repeatability breaks down, the model ceases to be a black box by nature.
Simulation engines exist precisely because reality is not a pure function. And yet, they are validated as if they were.
1. Expert Judgment–Based Methods (“Face Validation”)
This is the most common approach.
2. Statistical and Black-Box Methods (Current Standard)
They focus on numbers, not on why those numbers arise.
3. Formal Methods and Logical Verification (Mathematical Rigor)
Used primarily in electronic chip design (Formal Verification).
All of these approaches suffer from common biases:
They attempt to validate the mental model before code exists. This is a purely theoretical exercise where experts “assume” the design will work.
They assume that if input distributions resemble historical data, the model is valid. They rarely question whether an observed Gaussian distribution might itself be a bias — perhaps the true distribution is triangular.
4. PROPOSED H-M-E Method (Semantic Consistency assisted by AI or non-expert auditors)
Based on the equivalence of three models: (H) History – (M) Model – (E) Execution.
Traditionally, verification and validation (V&V) were understood as:
1. Scope and Objective Definition
What system is being modeled?
Which Key Performance Indicators (KPIs) must the model predict?
2. Verification
Does the model do what it is supposed to do?
2.a Code/Logic Inspection
2.b Traceability Tests
2.c Boundary Condition Tests
3. Validation
Is the model an accurate representation of reality?
3.a Input Data Validation
3.b Output Data Validation (KPI comparison)
In summary
Traditional V&V assumes the model is a logical black box. What is never validated is semantic coherence between History ↔ Model ↔ Execution. The relevant question becomes: “Is what is inside the black box what we intended to create?”
The emergence of natural-language auditors (AI) enables the breakdown of structural limitations inherent to previous approaches.
The H-M-E method does not replace classical empirical or statistical validation; it incorporates them. Its objective is to act as a prior semantic pre-validation phase, ensuring that the meaning of the model is explicit, agreed upon, and verifiable before any comparison with reality.
Accordingly, a new DES verification and validation model is formulated, based on three levels of reality.
Should an existing H document intervene, given its potential conceptual errors?
Who defines KPIs, and when?
Who defines the study’s breadth (number of runs) validating route coverage?
Should comments and embedded documents in M be weighted or filtered during H* induction?
Should O* be textual or parametric?