Issuu

HUMAN PERFORMANCE MODELING

FOR MARITIME DESIGN AND OPERATIONS

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HUMAN PERFORMANCE MODELING

CONTENTS Introduction.........................................................................................................3 What Human Performance Models Do.....................................................4 What an HPM Output Looks Like...............................................................5 How Other Industries Use HPMs.................................................................6 Why Maritime Needs HPMs Now................................................................8 The Cost of Getting It Wrong...................................................................... 11 What Maritime Already Has......................................................................... 11 A Path Forward................................................................................................ 12 Conclusion.......................................................................................................... 13 References.......................................................................................................... 15

INTRODUCTION Alarms sound. Conditions change. Decisions must be made in seconds. Whether on a manned bridge or in a remote operations center, human performance remains one of the most important factors in maritime safety. As the industry adopts new technologies, design questions are becoming more difficult: How quickly can an operator respond? How small can a bridge team be without increasing risk? How many autonomous vessels can one remote operator safely supervise before workload becomes a liability? These are not hypothetical questions. They are design decisions that shipbuilders, operators and Maritime Autonomous Surface Ships (MASS) developers are making right now. Today, most of these decisions are informed by expert judgment, ergonomic checklists or simulator trials. These methods are valuable, but they share a common limitation: they typically occur after major design commitments have already been made, or after systems are already in operation. By that point, fixing a human performance problem means costly rework, schedule delays or operational workarounds. Maritime does not need to invent human performance modeling (HPM). It needs to operationalize proven HPM methods for defined maritime use cases such as bridge staffing, remote operations, automation oversight and human-machine interface design, using a framework that is credible, practical and proportionate to risk. This challenge is not unique to maritime. Other high-reliability industries faced the same challenge

Human performance models are simulations that predict how people will perform in complex systems before those systems are built.

decades ago and have developed a solution: human performance models. HPMs are simulations that predict how people will perform in complex systems before those systems are built. The defense, aerospace and nuclear industries use HPMs to evaluate crew sizing, workload, function allocation and automation interaction as part of the design process. These models have changed design requirements, prevented costly rework and improved safety. Historically, the human element in system design has been assessed through qualitative data, subjective feedback and observation. While these methods provide useful insight, they can be difficult to quantify, difficult to repeat and difficult to defend when making the case for design changes. HPMs fill that gap by providing measurable, repeatable, scenario-specific predictions of human performance that decision-makers can act on.

HUMAN PERFORMANCE MODELING

WHAT HUMAN PERFORMANCE MODELS DO

One widely used approach for predicting workload

HPM encompasses a range of analytical and

helps identify when multiple tasks compete for the

computational methods used to represent, predict

same human processing channel at the same time,

and evaluate how people perform tasks and interact

increasing the likelihood of overload [Zhang et

with systems [Card et al., 1986]. Approaches range

al., 2024]. This multi-channel approach reflects an

from task-based analyses to sophisticated computer

important operational reality: two tasks may each

simulations, allowing organizations to assess the

be manageable on their own but become difficult to

impact of staffing [Sebok & Plott, 2010], automation

sustain when performed together if they rely on the

[Huey et al., 1990], interface design and operational

same processing resources.

concepts before implementation. By modeling human tasks, decisions, workload and interactions, HPM provides quantitative measures and actionable insights into system performance, safety and mission effectiveness.

is the VACP method, which assesses visual, auditory, cognitive and psychomotor demands. The method

CHALLENGES THAT HPMS CAN ADDRESS INCLUDE: • Task conflicts and delays: When competing demands require an operator’s attention simultaneously, this may potentially cause delays,

HPMS CAN BE USED TO PREDICT AND EVALUATE: • Task timing: How long it takes a crew to complete

omissions or degraded performance [Wickens, 1991]. • Staffing sufficiency: Whether available personnel

a procedure or respond to an event and how

can safely and effectively accomplish required

performance changes under different operating

functions across a range of scenarios.

conditions. • Workload: The cognitive, visual, auditory and

• Situational awareness support: How well displays, alerts and information architectures support

physical demands placed on operators, identifying

operators in perceiving, understanding and

when workload approaches or exceeds sustainable

anticipating system states.

levels.

• Error-likely conditions: Where mistakes, delays or missed actions are most likely to occur, and the operational factors that contribute to those risks. By quantifying the human element, HPM enables decision-makers to evaluate operational concepts, optimize staffing, assess emerging technologies and identify performance risks long before they appear in the real world. HPMs are strongest for structured, procedural and repeatable activities, and their outputs are generally more reliable for comparing design alternatives than for predicting absolute real-world performance. Subject matter expert input and workload ratings introduce subjectivity, which should be managed through structured elicitation methods and sensitivity analysis. HPM results should support, not replace, simulator trials, operational validation and expert review. Model validity is also scenario-specific; a model validated for one operational context should not be assumed valid for another without additional review. These characteristics make HPMs a powerful decision-support tool, but only as one component of a broader human factors engineering process.

Setting appropriate expectations is essential. HPM is not a replacement for human factors engineering, which encompasses methods such as task analysis, ergonomic assessment and user-centered design. It is not a substitute for simulator validation, which provides empirical data from real operators. It is also not a universal predictor of human error, particularly for knowledge-based and diagnostic tasks. Recognizing these boundaries is essential to using HPMs effectively and credibly.

WHAT AN HPM OUTPUT LOOKS LIKE HPMs produce quantitative data that can be compared across alternative designs, staffing concepts, procedures, or levels of automation. Depending on the modeling approach, outputs may include task timelines, workload profiles, staffing utilization, task delays, performance variability and mission outcomes [Wickens et al., 2007]. The goal is not to treat illustrative values as precise predictions, but to compare concepts consistently across repeated simulations. For example, consider a simplified bridge crew workload analysis during a port approach scenario.

METRIC

4-PERSON CREW

3-PERSON CREW

DIFFERENCE

18.4

27.6

50%

Tasks delayed per scenario

0.3

4.7

1,467%

Tasks dropped per scenario

0.0

1.2

—

61%

53%

62.4 minutes

68.9 minutes

Standard deviation 4.1

Standard deviation 7.3

Mean peak workload for officer of the watch (OOW)

Runs where OOW exceeded overload threshold Scenario completion time

10%

Crew Comparison (100 simulation runs per variant) Values are illustrative and provided for demonstration purposes.

Even in this simplified example, reducing bridge

When interpreting workload outputs, it is useful to

staffing increases operator workload, delays task

distinguish between workload amount and workload

execution, raises the likelihood of overload and

intensity. The workload amount reflects the total

lengthens scenario completion time. Rather than

demand for a set of tasks. Workload intensity reflects

relying on assumptions or subjective assessments, HPM

that demand relative to the time available to complete

provides quantitative evidence that allows stakeholders

it. The same total workload spread across a full watch

to evaluate tradeoffs and identify performance risks

presents a very different challenge than the same

before implementation.

workload compressed into a ten-minute port approach

HUMAN PERFORMANCE MODELING

by showing how demand concentrates or spreads

DEFENSE: CREW SIZING AND FUNCTION ALLOCATION

across the operational timeline, making it possible to

The United States (U.S.) Department of Defense has

phase. HPM workload timelines capture this distinction

identify not just whether workload is high, but when and for how long. This ability to compare alternatives in a repeatable, data-driven manner is one of the primary strengths of HPM. It enables organizations to assess concepts, procedures, staffing models and system designs before hardware is built; software is deployed, or operators enter a simulator.

incorporated HPMs into the MANPRINT (Manpower and Personnel Integration) process for decades [Booher & Hewitt, 1990]. These models are routinely used to assess crew sizing, workload, function allocation, training requirements and automation concepts during system acquisition. In one widely cited example, task-network modeling was used to evaluate two- and three-soldier crew concepts for a mounted combat platform. The analysis

HOW OTHER INDUSTRIES USE HPMS HPM is not an emerging or experimental practice. For decades, defense, aerospace and nuclear power industries have used HPMs to inform decisions involving staffing, automation, operator interfaces and system design. In many cases, these models have

predicted unacceptable workload levels for the twoperson crew across much of the mission profile. Based in part on these findings, the Army adopted a threeperson crew requirement for the system [Mitchell et al., 2003]. This demonstrates how HPMs can inform major design decisions before systems are operationalized.

influenced requirements, reduced development risk

The Army Research Lab has also used HPM to address

and provided quantitative evidence before costly

reduced manning issues for naval ship operations

prototypes or operational testing were conducted.

[Archer et al., 1996]. Similar approaches have been applied extensively to unmanned aerial vehicle (UAV) operations, where HPMs are used to determine how many systems a single operator can supervise and to identify the workload conditions that lead to degraded performance [Humann & Spero, 2018]. These challenges closely resemble those emerging in maritime remote operations centers and autonomous vessel supervision. The principal takeaway for maritime stakeholders is that the underlying human performance challenges associated with reduced crew sizes, remote operations and multi-vessel oversight have already been addressed in the defense sector. Mature methods, assessment frameworks and validation processes are available and can be leveraged immediately. The opportunity is not to reinvent these approaches, but to tailor them to the unique operational demands, mission profiles and workforce configurations of the maritime domain.

AEROSPACE: DISPLAY DESIGN AND SITUATIONAL AWARENESS National Aeronautics and Space Administration (NASA) developed integrated HPM environments that combine task modeling, cognitive architecture and 3D visualization to evaluate crew station designs before building physical prototypes [Gore, 2011]. These

tools simulate how operators scan displays, allocate

Attention-based HPM methods can augment current

attention, manage workload, maintain situational

practices and offer a way to compare alternative

awareness and make errors.

layouts quantitatively before hardware is committed.

A key capability is the modeling of attention

NUCLEAR: VALIDATION AND REGULATORY INTEGRATION

allocation by predicting where operators will look and what information they will notice based on measurable display properties (salience, effort to shift gaze) and task characteristics (information update frequency, task importance) [Santiago-Espada et al., 2011]. This allows designers to compare alternative display layouts and alarm configurations based on predicted operator awareness, not just compliance with ergonomic guidelines. By predicting where operators focus their attention and how quickly they recognize critical information, aerospace HPMs allow designers to compare display concepts before constructing physical prototypes. This reduces development costs while improving situational awareness and operator effectiveness. For maritime, this means HPMs can evaluate not only staffing levels, but also whether bridge displays, alarm configurations and MASS remote operations center interfaces actually support operator awareness under realistic conditions. Display design decisions currently rely on ergonomic guidelines and expert review.

The nuclear industry provides perhaps the most relevant example for maritime adoption. Recognizing the growing use of HPMs in staffing and human reliability analyses, the U.S. Nuclear Regulatory Commission (NRC) developed formal guidance governing model verification, validation and application [O’Hara, 2009]. Nuclear experience demonstrated that HPMs can reliably predict procedural and rule-based activities when properly validated against empirical data. Equally important, it highlighted where models are less effective, particularly for complex diagnostic and judgment-intensive tasks. This balanced understanding of both capabilities and limitations has enabled HPMs to be used as a decision-support tool rather than a replacement for human testing. The NRC’s verification and validation framework provides a proven template that could be adapted for maritime applications.

HUMAN PERFORMANCE MODELING

Across defense, aerospace and nuclear power, HPMs have matured from research tools into practical

WHY MARITIME NEEDS HPMS NOW

engineering tools. These industries use HPMs to

Maritime automation is not just adding new equipment

evaluate staffing levels, assess automation concepts,

to the bridge; rather, it is fundamentally changing

optimize operator interfaces and identify performance

the role of the human operator. As vessels become

risks long before systems reach operational testing.

increasingly automated, operators are shifting from

The challenges now emerging in maritime operations

direct control to supervisory oversight, where they

include reduced crewing, increasing automation

must monitor automated systems, maintain situational

and remote vessel supervision. These challenges are

awareness, detect anomalies and intervene when

the types of opportunities HPMs were developed to

automation reaches its limits.

address. This transition introduces well-known human factors For maritime, the nuclear lesson is less about the

challenges, including automation complacency,

modeling tool and more about credibility: HPM

reduced situational awareness, skill degradation,

adoption requires explicit verification, validation and

automation surprise and vigilance loss during extended

defined limits of use. Without a structured verification

monitoring periods. These risks emerge from the

and validation (V&V) process, HPM outputs are difficult

interaction between human operators and complex

to defend in design reviews, regulatory submissions

systems and cannot be adequately assessed through

or safety cases. Establishing a maritime-specific V&V

checklists or expert opinion alone. They require a

framework is a prerequisite for HPMs to be accepted as

systematic approach that predicts how people will

engineering evidence.

perform under realistic operational conditions.

Taken together, these examples show that HPMs are

These drivers also intersect with the maritime industry’s

not speculative tools. They are established engineering

primary operational governance framework: the Safety

methods whose relevance increases when systems

Management System (SMS). The International Safety

become more automated, staffing becomes leaner and

Management (ISM) Code requires maritime companies

assurance expectations become more demanding.

to establish an SMS covering hazard identification,

risk assessment, safe operating procedures, adequate

Answering these questions also requires defining

manning, emergency preparedness and continuous

what “acceptable” performance looks like for each

improvement. HPMs can strengthen these processes

situation. Research across multiple industries has

by providing scenario-specific workload predictions,

shown that there is no single workload threshold above

supporting procedure evaluations and helping identify

which all operators fail and below which all operators

human performance risks before incidents occur.

succeed. Human operators adapt their strategies,

Framing HPMs as strengthening existing SMS processes, rather than creating new ones, may be the most practical path to adoption.

THE QUESTIONS ARE GETTING HARDER The design and operational questions facing the maritime industry are becoming more complex: • How many vessels can a remote operator supervise? The answer depends on traffic density, automation reliability, alarm frequency, communication delays and human cognitive limits. • Can bridge crews be reduced without increasing risk? The answer depends on task demands, timing, workload distribution and the crew’s ability to manage concurrent activities. • Will a new bridge display improve

reprioritize tasks and adjust their approach to maintain performance across a range of workload conditions, until the limits of their flexibility are reached, and performance degrades sharply. This means that workload limits should be set based on the criticality of the task being evaluated. A safety-critical function such as collision avoidance demands a more conservative threshold than a routine administrative task. HPMs support this approach by allowing analysts to apply different performance criteria to different parts of the operational scenario and evaluate the consequences quantitatively. These questions cannot be answered reliably through intuition alone; they require structured evidence generated through established methods. HPMs provide a means to generate evidence early in the design process, when changes remain practical and affordable.

performance? The answer depends on how effectively operators can locate, interpret and act on critical information. • Can an operator regain situational awareness quickly enough during a takeover from an autonomous system? The answer depends on how information is presented, the complexity of the situation and the time available to respond.

HUMAN PERFORMANCE MODELING

THE REGULATORY ENVIRONMENT IS EVOLVING Emerging frameworks for Maritime Autonomous Surface Ships (MASS) increasingly emphasize the ability of human operators, whether aboard the vessel or ashore, to maintain situational awareness, respond effectively and assume control when required [IMO, 2026]. The challenge is not recognizing the importance of the human element; it is demonstrating that human performance requirements have been met. HPM offers a structured, repeatable and defensible method for generating evidence and evaluating alternative designs before entering service. Organizations that can support design and operational decisions with quantitative human performance data will be better positioned than those relying solely on qualitative assessments and expert judgment. As assurance frameworks develop, stakeholders may increasingly need to demonstrate not just that a system functions technically, but that operators can monitor it, understand it and intervene effectively under realistic conditions. Specifically, emerging evidence expectations may include demonstrations of takeover capability within defined time windows, workload acceptability across the range of credible operating conditions, situational awareness support under degraded or high-traffic scenarios, staffing sufficiency during abnormal and emergency conditions and alarm management performance under realistic alarm loads. HPMs provide a structured method for generating this evidence across far more scenarios and conditions than simulator trials alone can practically cover. Organizations that establish HPM capabilities now will be better positioned to meet these expectations as regulatory and classification frameworks mature.

THE COST OF GETTING IT WRONG Human-system integration problems are among the

As maritime systems become more automated, more

most expensive and disruptive deficiencies to correct

connected and more dependent on human oversight,

after deployment. A bridge layout issue discovered

the ability to predict human performance before

during sea trials, a manning concept that proves

implementation will become increasingly valuable. HPM

unsustainable in operation or an automation design

provides a practical way to make those predictions and

that creates unexpected operator workload can

reduce risk before it reaches the bridge, the operations

require costly redesigns, retraining and operational

center or the fleet.

workarounds. HPMs allow these risks to be identified when they are still inexpensive to address. Modifying a task model or evaluating an alternative staffing concept during design takes days. Redesigning hardware, procedures or operational concepts after deployment can take months and cost millions.

WHAT MARITIME ALREADY HAS The maritime industry is not starting from zero. Rather,

• Transferable methods from other industries: Task-

many of the building blocks needed to support HPM

network models, workload prediction methods,

already exist across industry guidance, research

cognitive architectures and attention-allocation

programs and operational practice.

models have been successfully applied in defense, aerospace and naval operations and are readily

• Human-centered design guidance: Frameworks such

adaptable to commercial maritime applications.

as the ABS Guidance Notes on Ergonomic Design of Navigation Bridges provide established principles

These efforts demonstrate that the underlying

for function allocation, workload management,

concepts, methods and tools are already available.

situational awareness and bridge design.

However, what remains absent is a cohesive framework

• Maritime human factors research: European initiatives including CASCADe, CyClaDes and FAROS

that integrates them into a practical maritime methodology.

have explored cognitive modeling, crew-centered

Maritime lacks a common approach for selecting

design, human reliability and the human implications

modeling methods, establishing model credibility,

of increasing automation.

validating predictions, defining acceptable use cases,

• MASS-focused studies: Academic and industry research continues to advance understanding of seafarer workload, cognitive demands, remote operations and human-system interaction in MASS concepts. • Modeling tools and prototypes: Open-source tools such as SimManning have demonstrated the feasibility of applying discrete-event simulation and other modeling methods to maritime manning and workload assessments.

incorporating HPM results into design reviews and decision-making. Establishing that framework would allow the industry to move beyond isolated studies and toward the systematic use of HPMs as an engineering and assurance tool. Such a framework would not require inventing new modeling techniques. Rather, it would adapt proven practices from defense, aerospace and nuclear power to the unique operational, regulatory and safety challenges of the maritime domain.

HUMAN PERFORMANCE MODELING

A PATH FORWARD

Priority applications include:

A phased approach is recommended to integrate

• Manning and staffing analysis

HPM into maritime design and operations. While the

• MASS remote operations center design

industry already possesses many of the technical building blocks, broader adoption will require common

• Bridge and control room display evaluation

practices, shared resources and a clear understanding

• Function allocation between humans and automation

of where HPMs can provide the greatest value. As

• Procedure development and assessment

the industry moves toward adoption, HPMs should be integrated into existing safety management processes rather than treated as a standalone method. Organizations can begin by mapping HPM outputs to specific SMS processes: workload and error-likelihood data into risk assessments, task timing into procedure evaluations and performance risk identification into

• Human reliability analysis • Integrated system validation

2. ESTABLISH A VERIFICATION AND VALIDATION FRAMEWORK For HPM outputs to be trusted and used in design,

continuous improvement cycles.

operations and decision-making, the industry will

1. ADOPT HPMS AS A DESIGN-SUPPORT METHOD

validation. Experience from defense, aerospace and

HPMs should be used as a quantitative complement to established human factors engineering practices. Rather than replacing task analysis, ergonomic assessments, or simulator testing, HPMs extend these methods by providing predictive insights early in the design process.

need a structured approach to model verification and nuclear power demonstrates that model credibility depends on clearly defining a model’s intended use, verifying that it has been correctly implemented and validating its predictions against empirical data where appropriate. Developing maritime-specific guidance for HPM verification and validation will be an important step toward broader adoption and acceptance.

3. DEVELOP MARITIME-SPECIFIC RESOURCES Broader adoption will require a common set of maritime resources that support model development, calibration and validation, including: • Task libraries: Standardized representations of bridge, engine room, cargo and remote operations tasks • Scenario templates: Representative operational scenarios that enable consistent and repeatable analyses • Referent databases: Collections of workload, timing, performance and human reliability data that support model calibration and validation These shared resources would reduce development effort, improve consistency and increase confidence in model results across the industry.

4. APPLY HPMS TO MASS ASSURANCE Evidence from HPMs can help demonstrate that human operators, whether aboard ships or ashore, can maintain situational awareness, manage workload and execute effective intervention when required.

HPMs can provide quantitative evidence supporting the

design human-centered systems, assess automation

design and evaluation of remote operations centers,

strategies and identify performance risks long before

takeover procedures, alarm management concepts,

they appear in sea trials or operational service. Not all

staffing levels and human-automation interfaces across

HPM applications are equally mature or equally ready

a range of operating conditions.

for maritime adoption. A tiered approach can help organizations prioritize:

CONCLUSION HPM is not a new technology. It is a mature and proven methodology that has been used for decades in defense, aerospace and nuclear power to evaluate human-system performance, reduce design risk and support critical engineering decisions. Today, the maritime industry faces many of the same challenges that drove HPM adoption in those sectors: increasing automation, evolving operator roles, reduced manning and growing expectations for demonstrating human-system performance before deployment. The modeling methods, tools and validation practices needed to address these challenges already exist. What has been missing is a maritime-specific framework that brings them together into a coherent and practical approach. The opportunity for applying HPM applications in the maritime industry is significant. HPMs can help maritime stakeholders evaluate staffing concepts,

NEAR-TERM, PRACTICAL STARTING POINTS: • Manning and staffing analysis • Bridge and control room workload evaluation • Procedure assessment under normal and emergency conditions

MEDIUM-TERM APPLICATIONS: • Interface and alarm management assessment • Remote operator workload distribution and multivessel supervision analysis

LONGER-TERM AND ADVANCED APPLICATIONS: • Situational awareness prediction using attentionbased models • Takeover readiness evaluation for complex autonomous operations • Knowledge-based decision modeling using cognitive architectures

HUMAN PERFORMANCE MODELING

Starting with near-term applications allows the

This white paper represents a first step toward

industry to build experience, develop maritime-

establishing a maritime-specific framework. ABS

specific resources and establish credibility before

invites shipowners, operators, designers, technology

progressing to more complex modeling approaches.

developers, regulators, researchers and classification

By moving critical human factors questions earlier

societies to participate in building a maritime HPM

in the design process, HPMs can reduce uncertainty,

capability that is credible, practical and fit for purpose.

lower development costs and improve the safety and

The industry’s most important future decisions will

effectiveness of future maritime systems.

increasingly depend not only on what technology can

The maritime industry’s challenge is no longer recognizing that the human element matters. The challenge is evaluating it early enough, rigorously enough and consistently enough to influence design before risk becomes expensive to correct. HPM offers a practical way to do that.

do, but on how people perform when working with it. HPM provides a means to evaluate that interaction before those decisions become costly to change.

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Mitchell, D. K., Samms, C. L., Henthorn, T., & Wojciechowski,

Navigation Bridges.

J. Q. (2003). Trade study: A two-versus three-soldier crew

Archer, R. D., Lewis, G. W., & Lockett, J. (1996, October). Human performance modeling of reduced manning concepts for Navy ships. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 40, No. 19, pp. 987991). Sage CA: Los Angeles, CA: SAGE Publications. Booher, H. R., & Hewitt, G. M. (1990). MANPRINT tools and techniques. In MANPRINT: An approach to systems integration (pp. 343-390). Dordrecht: Springer Netherlands. Card, S., MORAN, T., & Newell, A. (1986). The model human processor- An engineering model of human performance. Handbook of perception and human performance., 2(45–1), 1-35. Gore, B. F. (2011). Man–machine integration design and analysis system (MIDAS) v5: Augmentations, motivations and directions for aeronautics applications. In Human Modelling in Assisted Transportation: Models, Tools and Risk Methods (pp. 43-54). Milano: Springer Milan. Huey, B. M., Kruser, D. S., & Baron, S. (Eds.). (1990).

for the mounted combat system (MCS) and other future combat system platforms. Army Research Laboratory, Human Research & Engineering Directorate, Aberdeen Proving Ground. O’Hara, J.M. (2009). Applying Human Performance Models to Designing and Evaluating Nuclear Power Plants: Review Guidance and Technical Basis. Brookhaven National Laboratory / U.S. Nuclear Regulatory Commission. Santiago-Espada, Y., Myer, R. R., Latorella, K. A., & Comstock Jr, J. R. (2011). The multi-attribute task battery ii (matb-ii) software for human performance and workload research: A user’s guide (No. L-20031). Sebok, A., & Plott, B. (2010). Staffing levels: Methods for assessing requirements. In Simulator-based Human Factors Studies Across 25 Years: The History of the Halden ManMachine Laboratory (pp. 181-196). London: Springer London. Wickens, C.D. (1991). Processing Resources and Attention. In Damos, D. (Ed.), Multiple Task Performance. Taylor & Francis.

Quantitative modeling of human performance in complex,

Wickens, C. D., Sebok, A., Bagnall, T., & Kamienski, J. (2007,

dynamic systems. National Academies Press.

October). Modeling of situation awareness supported by

Humann, J., & Spero, E. (2018, April). Modeling and simulation of multi-UAV, multi-operator surveillance systems. In 2018 Annual IEEE International Systems Conference (SysCon) (pp. 1-8). IEEE. International Maritime Organization. (2026). International Code of Safety for Maritime Autonomous Surface Ships (MASS Code). Maritime Safety Committee, 111th Session. London: IMO.

advanced flight deck displays. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 51, No. 12, pp. 794-798). Sage CA: Los Angeles, CA: Sage Publications. Zhang, Y., Wen, H., Dai, X., Liang, J., Xu, Z., Xue, K., & Yang, M. (2024). Research on modeling in operator mental workload based on VACP method. Quality and reliability engineering international, 40(1), 19-33.

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Human Performance Modeling