OMG Human

What is Model-Based Systems Engineering (MBSE) in the context of human factors research?

Model-Based Systems Engineering represents a formalized application of modeling to support system requirements, design, analysis, verification, and validation activities beginning in the conceptual design phase and continuing throughout development. In human factors research, MBSE provides a structured approach to modeling human-system interactions.

A proper MBSE methodology is defined as a collection of related processes, methods, and tools used to support systems engineering with a model-based approach. According to the INCOSE MBSE Initiative survey, an effective methodology establishes quality decision-making procedures, structures research for easier data analysis, and addresses primary research questions with clarity and transparency .

The Object-Oriented Systems Engineering Method (OOSEM), one prominent MBSE methodology, supports integrated product development that improves communications across multidisciplinary teams working on human-system interfaces. OOSEM follows a recursive "Vee" lifecycle process model applied to multiple levels of the system hierarchy, making it particularly valuable when modeling complex human-machine interactions .

How does the Object Management Group (OMG) SysML support human-centered systems modeling?

The Systems Modeling Language (OMG SysML) provides researchers with a standardized visual modeling language that supports the specification, analysis, design, verification, and validation of complex systems that include human elements. SysML extends UML (Unified Modeling Language) with systems engineering features that better represent human-system interactions.

SysML enables researchers to create multiple integrated views of a system through specialized diagram types:

• Requirements diagrams: Document human needs and capability requirements

• Behavior diagrams: Model human interactions with the system

• Structure diagrams: Represent physical and logical interfaces between human operators and system components

• Parametric diagrams: Analyze performance aspects of human-system integration

According to the INCOSE MBSE Initiative, SysML serves as the predominant modeling language within methodologies like OOSEM, enabling systems engineers to "precisely capture, analyze, and specify the system and its components and ensure consistency among various system views" . This consistency is particularly valuable when modeling human factors, which often require integration across different disciplines.

What are the fundamental principles of experimental research design when studying human-system interactions?

Experimental research design for human-system interactions requires a framework of protocols and procedures created with a scientific approach using two sets of variables. As defined in experimental research literature, "the first set of variables acts as a constant, used to measure the differences of the second set" . When studying human factors, controlled variables often include system parameters, while measured variables focus on human performance or behavior.

Researchers should conduct experimental research in human-system interactions when:

• Time is an important factor in establishing a relationship between cause and effect

• There is invariable behavior between cause and effect

• The researcher wishes to understand the importance of cause and effect relationships

Effective experimental design in human factors research helps establish quality decision-making procedures, structures the research to facilitate data analysis, and addresses the main research question. To publish significant results, choosing a quality research design forms the foundation to build the research study, particularly critical when studying human-system interactions due to the inherent variability in human behavior .

How can researchers resolve contradictory data in human-systems integration models?

Resolving contradictory data in human-systems integration models requires a methodical approach that combines data validation, model refinement, and cross-disciplinary analysis. When faced with contradictory findings, researchers should:

• Verify data collection methodologies for potential biases or measurement errors

• Examine boundary conditions where contradictions appear to identify contextual factors

• Apply causal analysis techniques to determine limitations in current models

• Consider applying alternative MBSE methodologies that might better capture the complexity

The OOSEM approach provides a structured process for analyzing contradictory data through its "Analyze Stakeholder Needs" activity. This stage captures the "as-is" systems and enterprise, their limitations and potential improvement areas, using causal analysis techniques to determine limitations . This systematic analysis can help identify sources of contradiction in human factors data.

Additionally, researchers should consider employing the information model framework for Model-Driven System Design (MDSD) that illustrates relationships between different information types. This framework, illustrated in Figure 2-11 of the INCOSE survey, helps trace contradictions to their source by mapping relationships between requirements, behaviors, structures, and properties .

What methodological approaches best capture the complexity of human behavior in MBSE?

Capturing the complexity of human behavior in MBSE requires methodological approaches that accommodate both deterministic system behaviors and probabilistic human factors. Effective approaches include:

• Integrated Behavioral Modeling: Combining UML behavioral diagrams with human performance models that account for cognitive variability, attention allocation, and decision-making processes.

• Scenario-Based Modeling: As illustrated in the OOSEM methodology, use cases and scenarios capture functionality at the enterprise level . For human-centered systems, researchers should develop comprehensive scenarios that represent various user profiles, cognitive states, and environmental conditions.

• Multi-View Modeling: The INCOSE MBSE Initiative emphasizes that relationships between different model views must be maintained for consistency . When modeling human behavior, researchers should integrate physiological, cognitive, social, and environmental views.

• Iterative Validation: Human behavior models require frequent validation against empirical data. The recursive "Vee" process model in OOSEM supports this iterative validation at each level of the system hierarchy .

• Hybrid Modeling Approaches: Combining qualitative ethnographic approaches with quantitative systems models allows researchers to capture both structured system behaviors and nuanced human factors.

The foundation of OOSEM illustrates how behavioral analysis can be integrated with structural analysis and requirements engineering to create a holistic system model that accounts for human elements .

How do different MBSE methodologies compare when modeling cognitive processes?

When modeling cognitive processes, MBSE methodologies vary in their approaches, strengths, and limitations. Based on the INCOSE MBSE Initiative survey, we can compare key methodologies:

The OOSEM approach provides particularly useful modeling artifacts for cognitive processes through its "Define Logical Architecture" activity, which develops the logical decomposition of the system (including human components) and captures system behavior . This allows researchers to model cognitive processes as logical components with defined behaviors and interfaces.

What are the challenges in validating human-centered systems models?

Validating human-centered systems models presents unique challenges that require specialized methodological approaches. Key challenges include:

• Variability in Human Performance: Human behavior exhibits greater variability than mechanical or software systems, making validation against predetermined metrics difficult.

• Multi-Level Validation Requirements: As illustrated in the OOSEM activities (Figure 3-8 in the INCOSE survey), validation must occur at multiple levels from stakeholder needs to system verification .

• Integrated Validation: Human-centered validation must address not just individual components but emergent behaviors that arise from human-system interaction.

• Qualitative and Quantitative Alignment: Human factors often involve qualitative assessments that must align with quantitative system models.

• Evolution of Requirements: Human needs and expectations evolve over time, requiring continuous validation throughout the system lifecycle.

To address these challenges, researchers should employ the "Validate and Verify System" activity from OOSEM, which includes validating that the system satisfies stakeholder needs and verifying that the design satisfies requirements . For human-centered systems, this requires developing comprehensive validation scenarios that represent the full range of expected human variability and employing human-in-the-loop simulation for validation of cognitive and behavioral models.

How can researchers effectively translate between qualitative human factors data and quantitative systems models?

Translating between qualitative human factors data and quantitative systems models requires methodological bridges that preserve the richness of human experience while providing the precision needed for systems engineering. Effective translation approaches include:

• Structured Qualitative Coding: Develop systematic coding schemes for qualitative data that can be mapped to model parameters and variables.

• Mixed-Methods Integration Frameworks: Establish formal frameworks for integrating qualitative findings into quantitative models, similar to how the MDSD information model establishes relationships between different information types .

• Parameter Estimation Techniques: Use rigorous methods to derive quantitative parameters from qualitative observations, with appropriate uncertainty quantification.

• Contextual Variable Identification: Identify contextual variables from qualitative data that can serve as conditions or constraints in quantitative models.

• Behavioral Pattern Recognition: Apply pattern recognition techniques to identify recurring behaviors in qualitative data that can be formalized in behavioral models.

The OOSEM approach supports this translation through its emphasis on capturing stakeholder needs and converting them into system requirements . For human factors research, this process must be extended to include systematic methods for converting qualitative human needs and behaviors into quantifiable requirements and parameters.

Researchers should also consider employing open science approaches, where sharing data and methods enables broader community participation in connecting qualitative observations to quantitative models . This collaborative approach can be particularly valuable when addressing the complexity of human-systems integration.

How can research teams implement "People Also Ask" data mining to identify critical human factors research questions?

Implementing Google's "People Also Ask" (PAA) data mining for human factors research requires a methodical approach that leverages search engine data to identify research priorities. This approach helps researchers identify what questions are being frequently asked about human-systems integration and where knowledge gaps exist.

The PAA feature provides an accordion list of questions other people have asked about a particular search query, offering researchers insights into what aspects of human-systems integration are most confusing or important to practitioners . Researchers can leverage this data by:

• Systematically collecting PAA questions related to human factors in systems engineering

• Categorizing questions by research domain (cognitive, physical, organizational)

• Analyzing question patterns to identify knowledge gaps and common misconceptions

• Prioritizing research questions based on frequency and relevance to theoretical advancement

As noted in SEO literature, PAA questions are primarily determined through machine learning that observes user tendencies and draws connections between different topics and search questions . This makes PAA data particularly valuable for identifying emerging research needs that may not yet be reflected in formal academic literature.

What are effective strategies for integrating experimental research design with model-based systems engineering?

Integrating experimental research design with MBSE requires methodologies that bridge empirical data collection with formal modeling approaches. Effective strategies include:

• Model-Driven Experimental Design: Use system models to identify critical variables, interactions, and boundary conditions that should be tested experimentally.

• Experiment-Informed Modeling: Design experimental protocols specifically to generate data needed for model parameterization, validation, and refinement.

• Unified Traceability Framework: Establish bidirectional traceability between experimental findings and model elements, similar to the information model for MDSD shown in the INCOSE survey .

• Iterative Model-Experiment Cycles: Implement formal cycles where models inform experiments and experimental results refine models, similar to the recursive "Vee" process in OOSEM .

• Cross-Disciplinary Teams: Form integrated teams with expertise in both experimental design and systems modeling to ensure methodological alignment.

The experimental research design framework emphasizes that "time is an important factor in establishing a relationship between the cause and effect" , which aligns with the temporal aspects of behavioral modeling in MBSE. Researchers should leverage this alignment to create experiments that explicitly test the temporal dynamics represented in their models.

How should researchers approach the "dark matter" of human-systems integration in MBSE?

The concept of "dark matter" in human-systems integration refers to aspects of human behavior and cognition that remain poorly understood or inadequately modeled in current MBSE approaches. Drawing a parallel to the "dark kinases" described in pharmaceutical research , researchers should approach these understudied areas through:

• Systematic Identification: Catalog aspects of human-systems integration that are currently underrepresented in models but potentially significant for system performance.

• Open Science Initiatives: Establish collaborative frameworks for sharing human factors data and modeling approaches, similar to the approach used for sharing kinase inhibitors as chemical tools .

• Cross-Domain Tool Development: Create modeling tools specifically designed to address understudied aspects of human behavior, making them freely available to researchers.

• Integration of Multiple Perspectives: Combine insights from cognitive science, anthropology, psychology, and systems engineering to illuminate the "dark matter" of human-systems integration.

• Targeted Research Programs: Design research initiatives specifically focused on understudied aspects of human behavior in systems contexts.

The integration of open science and human-systems chemogenomics (broadly defined as the systematic mapping of human behaviors to system responses) can support the study of many new potential modeling approaches by the scientific community, just as the pharmaceutical industry's open science initiative supported new drug target discovery .