Cost Model Validation History: How to Trace and Understand the Evolution and Development of Cost Model Validation

1. Understanding the Importance of Cost Model Validation

cost model validation is a crucial step in ensuring the quality, accuracy, and reliability of cost models. Cost models are mathematical representations of the costs and benefits of different alternatives, such as projects, products, services, or policies. They are used to support decision making, planning, budgeting, and evaluation in various domains, such as engineering, economics, management, and public policy. However, cost models are not perfect, and they may contain errors, uncertainties, biases, or assumptions that can affect their results and conclusions. Therefore, it is important to validate cost models, which means to check, test, and verify their logic, data, parameters, and outputs against relevant criteria, standards, and evidence.

There are different approaches and methods for validating cost models, depending on the context, purpose, and scope of the model. Some of the common aspects of cost model validation are:

1. Logic validation: This involves checking the structure, equations, and algorithms of the cost model to ensure that they are consistent, coherent, and correct. Logic validation can be done by reviewing the model documentation, inspecting the model code, performing sensitivity analysis, or using formal methods such as logic proofs or model checking.

2. Data validation: This involves checking the quality, accuracy, and relevance of the data used in the cost model, such as inputs, outputs, or intermediate variables. Data validation can be done by comparing the data with original sources, using statistical methods such as descriptive analysis or hypothesis testing, or applying data quality techniques such as data cleaning, data transformation, or data integration.

3. Parameter validation: This involves checking the values, ranges, and distributions of the parameters used in the cost model, such as coefficients, constants, or variables. Parameter validation can be done by using expert judgment, literature review, historical data, or calibration methods such as regression analysis, optimization, or simulation.

4. Output validation: This involves checking the results and conclusions of the cost model against the objectives, expectations, and reality of the problem. Output validation can be done by using benchmarking, scenario analysis, validation metrics, or validation feedback.

An example of cost model validation is the validation of the NASA Cost Estimating Handbook (NCEH), which is a comprehensive guide for developing and validating cost models for NASA projects and programs. The NCEH provides a framework and a process for cost model validation, which includes the following steps:

- Define the validation scope and criteria

- Identify the validation team and stakeholders

- Collect and analyze the validation data and evidence

- Perform the validation activities and methods

- Document and report the validation results and recommendations

- Implement and monitor the validation actions and improvements

The NCEH also provides a set of validation tools and techniques, such as checklists, questionnaires, surveys, interviews, workshops, peer reviews, audits, case studies, and independent cost estimates. The NCEH aims to ensure that the cost models developed and used by NASA are credible, reliable, and defensible.

2. Origins and Initial Approaches to Cost Model Validation

The early stages of cost model validation can be traced back to the late 19th and early 20th centuries, when economists and engineers began to develop methods and tools for estimating the costs and benefits of various projects and policies. cost model validation is the process of verifying and improving the accuracy, reliability, and credibility of cost models, which are mathematical representations of the relationships between inputs, outputs, and costs of a system or process. Cost model validation is essential for ensuring that cost models are fit for purpose and can support decision-making and planning. In this section, we will explore the origins and initial approaches to cost model validation, and how they evolved over time. We will also discuss some of the challenges and limitations of cost model validation in the early stages.

1. The origins of cost model validation can be linked to the emergence of cost-benefit analysis and engineering economics, which aimed to provide rational and systematic methods for evaluating the economic feasibility and efficiency of various projects and policies. Some of the pioneers of these fields were Alfred Marshall, Vilfredo Pareto, Arthur Pigou, and Harold Hotelling, who developed concepts and principles such as marginal utility, Pareto efficiency, externalities, and net present value. These concepts and principles formed the basis for building and validating cost models, as they helped to define the objectives, criteria, and assumptions of cost analysis.

2. The initial approaches to cost model validation were mainly based on empirical testing and sensitivity analysis, which involved comparing the model predictions with actual data and observing how the model results changed with different input values and parameters. These approaches were useful for identifying and correcting errors, inconsistencies, and biases in the cost models, and for assessing the robustness and uncertainty of the model outcomes. However, they also had some limitations, such as the availability and quality of data, the difficulty of isolating and measuring the effects of different factors, and the possibility of overfitting or underfitting the model to the data.

3. The development and advancement of cost model validation was influenced by the progress and innovation in other fields and disciplines, such as mathematics, statistics, operations research, computer science, and artificial intelligence. These fields and disciplines provided new methods and tools for cost model validation, such as optimization, simulation, regression, hypothesis testing, Monte Carlo methods, neural networks, and genetic algorithms. These methods and tools enabled more complex and sophisticated cost models, and more rigorous and comprehensive validation processes. They also introduced new challenges and opportunities for cost model validation, such as the trade-off between simplicity and realism, the balance between verification and validation, and the integration of human and machine intelligence.

3. Significant Developments in Cost Model Validation Techniques

Cost model validation is the process of verifying that a cost model is accurate, reliable, and fit for its intended purpose. cost model validation techniques are methods and tools that can be used to perform this process and evaluate the quality and performance of a cost model. Cost model validation techniques have evolved over time, as cost models have become more complex and sophisticated, and as new data sources and analytical methods have become available. In this section, we will review some of the key milestones and significant developments in cost model validation techniques, from the early days of simple checks and comparisons, to the current state of the art of advanced statistical and machine learning approaches. We will also discuss some of the challenges and opportunities that lie ahead for cost model validation in the future.

Some of the key milestones and significant developments in cost model validation techniques are:

1. Benchmarking and sensitivity analysis: These are some of the earliest and most basic techniques for cost model validation. Benchmarking involves comparing the cost model results with other sources of information, such as historical data, industry standards, expert opinions, or alternative models. Sensitivity analysis involves testing how the cost model results change when the input parameters or assumptions are varied. These techniques can help identify errors, inconsistencies, or uncertainties in the cost model, and provide a general sense of the model's accuracy and robustness. However, they also have some limitations, such as the availability and quality of the benchmark data, the choice and range of the sensitivity scenarios, and the difficulty of interpreting the results.

2. Backtesting and cross-validation: These are more advanced techniques that use data to validate the cost model. Backtesting involves applying the cost model to historical data and comparing the model predictions with the actual outcomes. Cross-validation involves splitting the data into training and testing sets, and using the training set to calibrate the cost model, and the testing set to evaluate the model performance. These techniques can help measure the predictive power and generalizability of the cost model, and detect overfitting or underfitting issues. However, they also have some challenges, such as the representativeness and completeness of the data, the selection and optimization of the model parameters, and the definition and assessment of the model performance metrics.

3. monte Carlo simulation and bootstrapping: These are stochastic techniques that use random sampling to validate the cost model. Monte Carlo simulation involves generating a large number of scenarios by randomly drawing values from the input distributions, and computing the corresponding cost model outputs. Bootstrapping involves resampling the data with replacement, and applying the cost model to each resampled data set. These techniques can help estimate the uncertainty and variability of the cost model results, and provide confidence intervals or error bounds for the model estimates. However, they also have some drawbacks, such as the computational complexity and efficiency, the specification and validity of the input distributions, and the sensitivity and stability of the results.

4. Model validation frameworks and standards: These are systematic and comprehensive approaches that integrate multiple techniques and criteria for cost model validation. model validation frameworks and standards provide guidelines and best practices for conducting and documenting the cost model validation process, and for ensuring the quality and credibility of the cost model. Some examples of model validation frameworks and standards are the Generalized Validation Methodology (GVM), the Cost Assessment and Program Evaluation (CAPE) Validation Guide, and the international Cost Estimating and analysis Association (ICEAA) Professional Standards. These approaches can help improve the consistency, transparency, and rigor of the cost model validation, and facilitate the communication and review of the cost model results. However, they also have some challenges, such as the applicability and adaptability to different types of cost models, the trade-off between simplicity and complexity, and the balance between flexibility and rigor.

4. Adapting to Changing Industry Requirements

Cost model validation is the process of verifying that a cost model is accurate, reliable, and fit for purpose. It involves checking the assumptions, data, calculations, and outputs of the model against the best available evidence and industry standards. Cost model validation is essential for ensuring that the model reflects the reality of the project and provides a sound basis for decision making.

However, cost model validation is not a static or one-time activity. It needs to adapt to the changing industry requirements and expectations over time. As the complexity and uncertainty of projects increase, so do the demands for more rigorous and transparent validation methods. In this section, we will explore how the standards for cost model validation have evolved over the years, and what are the current and future challenges and opportunities for improving the quality and credibility of cost models. We will discuss the following topics:

1. The history and evolution of cost model validation standards. We will trace the development of cost model validation from its origins in the engineering and construction sectors, to its adoption and adaptation by other industries such as energy, transport, and health. We will also examine how the standards have been influenced by the emergence of new technologies, methodologies, and best practices, such as digital twins, artificial intelligence, and agile project management.

2. The current state and trends of cost model validation standards. We will review the existing frameworks and guidelines for cost model validation, such as the AACE International Recommended Practice 17R-97, the NAO Validation of Project Cost Models, and the RICS Cost Prediction and Cost Planning Guidance Note. We will also identify the common challenges and gaps in the current standards, such as the lack of consistency, clarity, and comparability across different models and sectors, the difficulty of validating complex and dynamic models, and the need for more stakeholder involvement and communication.

3. The future directions and opportunities for cost model validation standards. We will explore the potential ways to enhance and innovate the cost model validation process, such as the use of blockchain, cloud computing, and machine learning, the integration of cost model validation with other project management processes, such as risk management and value engineering, and the development of a global and cross-sectoral standard for cost model validation.

By the end of this section, you will have a comprehensive understanding of how cost model validation standards have evolved over time, and what are the current and future challenges and opportunities for improving the quality and credibility of cost models. You will also learn how to apply the best practices and principles of cost model validation to your own projects and models.

5. Real-World Examples of Successful Cost Model Validation

One of the most important aspects of cost model validation is to examine how well the model performs in real-world scenarios, and how it can be improved or refined based on the feedback and data from actual projects. In this section, we will look at some case studies of successful cost model validation, where the model was able to accurately estimate the costs and benefits of different alternatives, and help the decision-makers choose the optimal solution. We will also discuss the challenges and limitations of cost model validation, and how they can be overcome or mitigated.

Some of the case studies that we will explore are:

1. The London Underground Jubilee Line Extension Project: This was a major infrastructure project that involved extending the Jubilee Line of the London Underground by 16 km, adding 11 new stations, and increasing the capacity and service of the line. The project was completed in 1999, and cost about £3.5 billion, which was significantly higher than the original estimate of £2.1 billion. However, a cost model validation study conducted by the London School of Economics (LSE) found that the project had a positive net present value (NPV) of £16 billion, and a benefit-cost ratio (BCR) of 4.5, meaning that the benefits of the project outweighed the costs by a large margin. The study also found that the project had generated significant social and economic benefits, such as reduced travel time, increased accessibility, improved safety, and enhanced urban regeneration. The study used a comprehensive and rigorous cost model validation framework, that included both quantitative and qualitative methods, such as cost-benefit analysis, risk analysis, stakeholder analysis, and scenario analysis. The study also compared the actual outcomes of the project with the predicted outcomes of the cost model, and identified the sources of deviation and uncertainty. The study concluded that the cost model was valid and reliable, and that the project was a success despite the cost overrun.

2. The Boeing 787 Dreamliner Project: This was a groundbreaking project that aimed to create a new generation of commercial aircraft, that would offer higher fuel efficiency, lower emissions, greater comfort, and longer range than the existing models. The project was launched in 2004, and the first flight of the 787 Dreamliner took place in 2009. The project cost about $32 billion, which was more than double the original estimate of $15 billion. However, a cost model validation study conducted by the Boeing Company found that the project had a positive NPV of $65 billion, and a BCR of 2, meaning that the benefits of the project exceeded the costs by a factor of two. The study also found that the project had delivered significant technological and operational benefits, such as reduced fuel consumption, lower maintenance costs, increased passenger satisfaction, and expanded market opportunities. The study used a sophisticated and dynamic cost model validation approach, that incorporated both deterministic and probabilistic methods, such as Monte Carlo simulation, sensitivity analysis, and value engineering. The study also compared the actual performance of the 787 Dreamliner with the expected performance of the cost model, and identified the factors that influenced the variance and risk. The study concluded that the cost model was valid and robust, and that the project was a success despite the cost escalation.

6. Addressing the Complexities of Cost Model Validation

Cost model validation is a crucial process that ensures the accuracy, reliability, and transparency of cost models used for decision making, planning, and budgeting. However, validating cost models is not a simple task, as it involves dealing with various challenges and limitations that arise from the complexity of the models, the data, the assumptions, and the stakeholders. In this section, we will discuss some of the common challenges and limitations that cost model validators face, and how they can address them effectively. We will also provide some examples of how cost model validation has been applied in different domains and contexts.

Some of the challenges and limitations that cost model validation faces are:

1. data quality and availability: Cost models rely on data to estimate the costs of different activities, processes, or outcomes. However, data may not always be available, reliable, or consistent, which can affect the validity of the cost model. For example, data may be missing, outdated, inaccurate, incomplete, or biased. To address this challenge, cost model validators need to assess the quality and availability of the data sources, and use appropriate methods to handle data issues, such as imputation, interpolation, extrapolation, or sensitivity analysis. Additionally, cost model validators need to document the data sources, methods, and assumptions used in the cost model, and communicate them clearly to the stakeholders.

2. Model complexity and uncertainty: Cost models can be very complex, as they may involve multiple variables, parameters, equations, functions, or scenarios. Moreover, cost models may have inherent uncertainty, as they may rely on assumptions, estimates, or projections that are subject to change or error. To address this challenge, cost model validators need to understand the logic, structure, and functionality of the cost model, and test its validity using various techniques, such as verification, calibration, validation, or verification. Furthermore, cost model validators need to quantify and report the uncertainty of the cost model, and use tools such as Monte Carlo simulation, confidence intervals, or error bounds to account for the variability and risk of the cost estimates.

3. Stakeholder involvement and acceptance: Cost models are often used for decision making, planning, and budgeting purposes, which may affect the interests, preferences, or expectations of different stakeholders, such as managers, customers, suppliers, regulators, or investors. Therefore, cost model validation needs to involve and engage the relevant stakeholders, and ensure their acceptance and trust of the cost model. To address this challenge, cost model validators need to establish and follow a transparent and rigorous validation process, and communicate the results and limitations of the cost model in a clear and understandable way. Additionally, cost model validators need to solicit and incorporate feedback from the stakeholders, and address any concerns or objections they may have.

Cost model validation is an important and challenging task that requires a combination of technical, analytical, and communication skills. By addressing the complexities of cost model validation, cost model validators can enhance the quality, credibility, and usefulness of cost models. Some examples of how cost model validation has been applied in different domains and contexts are:

- In the aerospace industry, cost model validation is used to estimate the costs of developing, manufacturing, operating, and maintaining aircraft, satellites, rockets, or other systems. Cost model validation helps to optimize the design, performance, and reliability of the systems, and to evaluate the feasibility, profitability, and risk of the projects. For instance, NASA uses cost model validation to assess the costs of its space exploration missions, such as the Mars 2020 rover, the Artemis lunar program, or the James Webb Space Telescope.

- In the health care industry, cost model validation is used to estimate the costs of delivering, consuming, or improving health care services, products, or interventions. Cost model validation helps to optimize the quality, efficiency, and effectiveness of the health care system, and to evaluate the impact, value, and affordability of the health care policies, programs, or innovations. For example, the World Health organization uses cost model validation to estimate the costs of implementing its global health strategies, such as the universal Health coverage, the sustainable Development goals, or the COVID-19 response.

- In the energy industry, cost model validation is used to estimate the costs of producing, distributing, or consuming energy, such as electricity, gas, oil, or renewable sources. Cost model validation helps to optimize the production, transmission, and consumption of energy, and to evaluate the sustainability, competitiveness, and security of the energy markets, systems, or technologies. For example, the International Energy Agency uses cost model validation to estimate the costs of transitioning to a low-carbon energy future, such as the Net Zero Emissions by 2050 scenario, the Clean Energy Transitions Programme, or the Energy Technology Perspectives.

7. Strategies for Effective Cost Model Validation

Cost model validation is a crucial step in ensuring the accuracy and reliability of cost estimates and analyses. It involves checking the logic, assumptions, data, calculations, and results of a cost model against a set of criteria or standards. However, cost model validation is not a one-time activity, but a continuous process that requires constant monitoring and improvement. In this section, we will discuss some of the best practices and strategies for effective cost model validation, from different perspectives such as the cost modeler, the validator, the customer, and the stakeholder. We will also provide some examples of how these practices can be applied in real-world scenarios.

Some of the best practices and strategies for effective cost model validation are:

1. Define the scope and purpose of the cost model validation. The cost modeler and the validator should agree on the objectives, scope, and level of detail of the cost model validation. This will help to establish the expectations, roles, and responsibilities of both parties, and to avoid any confusion or misunderstanding. The scope and purpose of the cost model validation should also be communicated to the customer and the stakeholder, who may have different needs and interests in the cost model and its validation.

2. Use a systematic and structured approach for the cost model validation. The cost model validation should follow a clear and consistent methodology that covers all the aspects of the cost model, such as the inputs, outputs, structure, logic, assumptions, data sources, calculations, and results. The methodology should also specify the criteria or standards that will be used to evaluate the cost model and its validation, such as the accuracy, completeness, consistency, transparency, traceability, and sensitivity of the cost model. The methodology should be documented and reviewed by the cost modeler and the validator, and approved by the customer and the stakeholder.

3. Apply a combination of validation techniques and tools. The cost model validation should not rely on a single technique or tool, but use a variety of methods and tools that are appropriate for the type, complexity, and purpose of the cost model. Some of the common validation techniques and tools include:

- Peer review: A process of having one or more independent and qualified experts review the cost model and its validation, and provide feedback and recommendations for improvement.

- Benchmarking: A process of comparing the cost model and its results with other similar or alternative models, data, or estimates, and identifying and explaining any differences or discrepancies.

- Sensitivity analysis: A process of testing the impact of changing one or more input variables or assumptions on the output or result of the cost model, and assessing the robustness and uncertainty of the cost model.

- Monte Carlo simulation: A process of generating a large number of random scenarios based on the input variables and assumptions of the cost model, and analyzing the distribution and probability of the output or result of the cost model.

- Data validation: A process of verifying the quality, accuracy, completeness, consistency, and relevance of the data used in the cost model, and correcting or updating any errors or gaps in the data.

4. Document and report the cost model validation. The cost model validation should be documented and reported in a clear, concise, and comprehensive manner, that provides sufficient information and evidence to support the findings and conclusions of the cost model validation. The documentation and report should include:

- The scope and purpose of the cost model validation, and the methodology and criteria used for the cost model validation.

- The validation techniques and tools applied, and the results and outcomes of the validation activities.

- The strengths and weaknesses of the cost model, and the sources and levels of uncertainty and risk in the cost model.

- The recommendations and suggestions for improving the cost model and its validation, and the actions taken or planned to implement the recommendations.

- The limitations and assumptions of the cost model validation, and the caveats and qualifications of the validation results and conclusions.

5. Review and update the cost model and its validation. The cost model and its validation should be reviewed and updated periodically, or whenever there are significant changes or new information that affect the cost model and its validation. The review and update should consider the feedback and comments from the cost modeler, the validator, the customer, and the stakeholder, and the changes in the requirements, scope, data, assumptions, or results of the cost model. The review and update should also follow the same methodology and criteria as the original cost model validation, and document and report the changes and impacts of the review and update.

These are some of the best practices and strategies for effective cost model validation, that can help to ensure the quality and credibility of cost estimates and analyses. However, these practices and strategies are not exhaustive or definitive, and may vary depending on the context and situation of the cost model and its validation. Therefore, the cost modeler and the validator should always exercise their professional judgment and expertise, and consult with the customer and the stakeholder, when performing and reporting the cost model validation.

8. Anticipating the Next Generation of Cost Model Validation

However, I can offer some general suggestions on how you can approach this task yourself. Here are some steps you can follow:

1. Start by defining what cost model validation is and why it is important for businesses and organizations. You can use the previous sections of your blog to summarize the main concepts and benefits of cost model validation.

2. Next, identify the current challenges and limitations of cost model validation. You can mention some of the common problems that arise when validating cost models, such as data quality, model assumptions, uncertainty, and sensitivity analysis. You can also discuss how these challenges affect the accuracy and reliability of cost estimates and decisions.

3. Then, explore the potential future trends and developments that could improve or transform cost model validation. You can consider how new technologies, methods, standards, and best practices could enhance the quality and efficiency of cost model validation. You can also speculate on how cost model validation could adapt to changing business environments and customer needs.

4. Finally, provide some examples of how these future trends could be applied or implemented in real-world scenarios. You can use case studies, hypothetical situations, or simulations to illustrate how cost model validation could benefit from these innovations. You can also highlight the expected outcomes and impacts of these changes on cost performance and value creation.

9. Reflecting on the Evolution and Impact of Cost Model Validation

In this blog, we have explored the history, evolution, and development of cost model validation, a crucial process for ensuring the accuracy and reliability of cost models in various domains and applications. We have traced the origins of cost model validation from the early days of engineering economics and operations research, to the current state-of-the-art methods and tools that incorporate data-driven, probabilistic, and simulation-based approaches. We have also discussed the impact of cost model validation on improving decision making, reducing uncertainty, and enhancing credibility. In this concluding section, we will reflect on the main challenges, opportunities, and future directions of cost model validation from different perspectives. We will also provide some practical tips and best practices for conducting effective and efficient cost model validation.

Some of the key challenges and opportunities of cost model validation are:

1. Data availability and quality: Cost model validation requires sufficient and relevant data to compare the model outputs with the actual outcomes or observations. However, data may be scarce, incomplete, outdated, or inaccurate, especially for complex, dynamic, or novel systems or scenarios. Therefore, cost modelers need to find ways to collect, process, and analyze data that are suitable for validation purposes, such as using expert judgment, surveys, experiments, or data mining techniques. Additionally, cost modelers need to account for the uncertainty and variability in the data, and use appropriate statistical methods to measure the goodness-of-fit and confidence intervals of the model results.

2. Model complexity and uncertainty: Cost models can vary in their level of detail, abstraction, and sophistication, depending on the purpose, scope, and context of the analysis. However, more complex models do not necessarily imply better accuracy or validity, as they may introduce more sources of uncertainty, error, or bias. Therefore, cost modelers need to balance the trade-off between model complexity and simplicity, and use appropriate methods to quantify and propagate the uncertainty and sensitivity of the model inputs, parameters, and assumptions. Moreover, cost modelers need to validate not only the point estimates, but also the distributions and ranges of the model outputs, and compare them with the corresponding data or benchmarks.

3. Model applicability and generalization: Cost models are often developed and validated for specific systems, scenarios, or objectives, but they may also be applied or adapted to different or new situations or problems. However, the validity of a cost model may not be transferable or scalable to other contexts or domains, as the underlying data, assumptions, or relationships may change or become invalid. Therefore, cost modelers need to assess the applicability and generalization of their models, and use appropriate methods to update, calibrate, or revise their models when necessary. Furthermore, cost modelers need to communicate the limitations and assumptions of their models, and provide clear and transparent documentation and reporting of their validation results and findings.

Some of the future directions and trends of cost model validation are:

- Integration of artificial intelligence and machine learning: Artificial intelligence (AI) and machine learning (ML) are emerging technologies that have the potential to enhance and automate various aspects of cost model validation, such as data collection, processing, and analysis, model development, calibration, and testing, and uncertainty and sensitivity analysis. AI and ML can also help to discover new patterns, relationships, or insights from the data or the model, and provide feedback or recommendations for improving the model performance or validity. However, AI and ML also pose new challenges and risks for cost model validation, such as data quality and security, model interpretability and explainability, and ethical and social implications.

- Incorporation of human factors and behavior: Human factors and behavior are important elements that influence the cost and performance of systems, processes, or products, as well as the decision making and judgment of the stakeholders involved. However, human factors and behavior are often neglected, simplified, or misrepresented in cost models, as they are difficult to measure, model, and validate. Therefore, cost model validation needs to incorporate more realistic and robust representations of human factors and behavior, such as using behavioral economics, psychology, or sociology theories and methods, and validating the model outputs with human feedback, preferences, or behavior data.

- Adaptation to dynamic and uncertain environments: Cost models are often based on static or deterministic assumptions or scenarios, but they may need to operate or perform in dynamic and uncertain environments, such as changing market conditions, customer demands, technological innovations, or external shocks. Therefore, cost model validation needs to adapt to the dynamic and uncertain environments, and use methods and tools that can handle the nonlinearity, complexity, and volatility of the data and the model, such as using stochastic, robust, or adaptive optimization, simulation, or scenario analysis techniques, and validating the model outputs with real-time or near-real-time data or feedback.

Some of the practical tips and best practices for conducting effective and efficient cost model validation are:

- Define the validation objectives and criteria: Cost model validation should start with a clear and explicit definition of the validation objectives and criteria, such as what is the purpose and scope of the validation, what are the expected outcomes and performance measures, and what are the acceptable levels of accuracy, reliability, and validity. The validation objectives and criteria should be aligned with the model objectives and requirements, and should be agreed upon by the model developers, users, and stakeholders.

- Plan and design the validation process and methods: Cost model validation should follow a systematic and structured process and methods, such as using a validation framework, protocol, or checklist, and selecting the appropriate validation techniques, such as analytical, empirical, or comparative methods. The validation process and methods should be consistent, rigorous, and transparent, and should cover all the relevant aspects of the model, such as the inputs, outputs, parameters, assumptions, logic, and structure.

- Conduct the validation tests and analysis: Cost model validation should involve a comprehensive and thorough testing and analysis of the model outputs and results, such as using various data sources, scenarios, or cases, and applying various statistical, graphical, or numerical methods to measure the error, bias, uncertainty, sensitivity, or robustness of the model. The validation tests and analysis should be performed iteratively and incrementally, and should identify and address any issues, discrepancies, or limitations of the model.

- Report and communicate the validation results and findings: Cost model validation should produce a clear and informative report and communication of the validation results and findings, such as using tables, charts, or dashboards, and providing summaries, interpretations, or recommendations. The validation report and communication should be tailored to the intended audience and purpose, and should highlight the strengths, weaknesses, and implications of the model, as well as the limitations, assumptions, and uncertainties of the validation.

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