How do you balance model fidelity and simplicity?

1 Model fidelity

Model fidelity refers to how closely a model or simulation represents the real system or phenomenon of interest. It can be measured by various criteria, such as physical, behavioral, functional, or data fidelity. High-fidelity models and simulations aim to capture the full complexity and dynamics of the system, while low-fidelity models and simulations simplify or omit some aspects of the system. The choice of model fidelity depends on several factors, such as the objectives, scope, resources, and assumptions of the modeling and simulation project.

2 Model simplicity

Model simplicity refers to how easy a model or simulation is to understand, communicate, and manipulate. It can be measured by various criteria, such as clarity, elegance, parsimony, or generality. Simple models and simulations aim to highlight the essential features and relationships of the system, while complex models and simulations include more details and variables that may obscure or complicate the system. The choice of model simplicity depends on several factors, such as the audience, context, format, and feedback of the modeling and simulation project.

3 Trade-offs and benefits

Balancing model fidelity and simplicity involves making trade-offs and weighing the benefits of each option. High-fidelity models and simulations may offer more realism, accuracy, and validity, but they may also require more time, data, and computational power. Low-fidelity models and simulations may offer more efficiency, flexibility, and insight, but they may also introduce more uncertainty, error, and bias. The optimal balance depends on the specific situation and criteria of the modeling and simulation project.

4 Best practices

To balance model fidelity and simplicity, you should define the purpose and scope of your model or simulation clearly and explicitly. Identify the key questions, variables, and parameters that are relevant to your model or simulation. When selecting the level of abstraction and detail, make sure it matches your purpose and scope. Utilizing multiple models or simulations with different levels of fidelity and simplicity can help you compare and contrast the results and assumptions. Validating and verifying your model or simulation against empirical data and expert knowledge is also important. Finally, document and communicate your model or simulation assumptions, limitations, and uncertainties.

5 Challenges and opportunities

Balancing model fidelity and simplicity is a complex process that requires facing various challenges and seizing opportunities to enhance modeling and simulation skills and outcomes. This includes dealing with incomplete, inconsistent, or uncertain data that could impact the model or simulation. Additionally, adapting to changing requirements, expectations, or feedback may require adjustments to the model or simulation. Exploring new methods, tools, or technologies can also help improve the model or simulation fidelity and simplicity. Moreover, collaborating with other stakeholders, experts, or users can provide different perspectives on the model or simulation fidelity and simplicity.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?