3. The Modeling Process

3.1. Start With a Modeling Question

Scientific modeling is done in support of answering some form of question. To help steer this process, it is helpful to organize modeling questions into three broad categories: explanation, prediction, and design 1.

3.1.1. Explanation

  • Can we provide a plausible explanation for some observed phenomenon?

  • Uncertainty matters because: Any model requires a set of assumptions, and a reasonable peer review will question those assumptions. Explicitly accounting for uncertainty helps build confidence that the author did not cherry-pick assumptions to match their desired conclusion.

  • Example: Explanation of the “crash” in count of a particular cell during HIV infection counter to the prevailing explanation at the time [Phi96].

3.1.2. Prediction

  • Can we predict an unobserved phenomenon?

  • Uncertainty matters because: No prediction will be 100% accurate; providing uncertainties associated predicted quantities will allow decision makers to plan accordingly. Furthermore, quantitatively attributing uncertainty in the output to the various inputs will guide the collection of future data, allowing for systematic and principled reduction of uncertainty.

  • Example: Climate change (includes both bounds and different scenarios)

3.1.3. Design

  • Can we interfere with a scenario to produce a desired outcome?

  • Example: Aircraft design; manufacturing variability and high-consequence failures means uncertainties cannot be ignored

3.2. Check the context

What are the consequences to making an incorrect conclusion?

  • If the consequences are innocuous, then a relatively simple model may be sufficient.

    • Explanation tends to be provisional, as it merely aims to provide a plausible answer.

    • A first-pass prediction or proof-of-concept prediction will tend to be rough in nature. If future modeling efforts are assumed to follow—and the rough prediction will not used for critical decisions—then a simple model may suffice. Note that the general public does not tend to appreciate this nuance!

  • If there are important consequences, then you need to be appropriately cautious in your methods.

    • Early comparative design decisions will be followed up with more detailed analysis. Modeling should be detailed enough to factor in the relevant aspects and support comparison, but not so detailed as to lead to “analysis paralysis.” TODO citation needed

    • Predictions that will be released to the public should be treated as high-consequence: TODO [Oneil16].

    • Design of a safety-critical system should be designed using the accepted best-practices in your field. For instance: Determination of allowable properties for aerospace materials follows a very formal distribution testing procedure [MMP08].

3.3. Gather Appropriate Information

(We can use summary statistics and distributional assumptions to make a rough model.)

(We can use data in large quantities to build a more detailed distribution model.)

(We can use physical principles to derive equations)

(We can use data in large quantities to fit an empirical equation)

3.4. Build the Model

(The rest of this book is about tools for building a model. A brief list of what we’ll learn: TODO)

3.5. Assess the Model

(While we make use of curiosity and skepticism throughout the modeling process, this is where we formally deploy mathematical techniques to put skepticism into practice.)

We use the AIAA definitions of verification and validation, stated below [AIA98]:


The process of determining that a model implementation accurately represents the developer’s conceptual description of the model and the solution to the model.


The process of determining the degree to which a model is an accurate representation of the real world from the perspective of the intended uses of the model.

Note that the definition of validation explicitly references the “intended uses of the model”; it is commonly accepted that the accuracy of a model can only be assessed in the context of an intended use.

3.6. Use the Model

(Once we have a verified and validated model, we can use it to help answer our question)

3.7. An Iterative Procedure

(You may pass through these phases multiple times)


I am indebted to my colleagues at Olin College, where the faculty have developed this three category framework in the first year course Modeling and Simulation of the Physical World.