Society of Actuaries (SOA) PA Practice Exam

In which situation would one prefer BIC over AIC?

1. When desiring a model with less interpretability

2. When construct a more complex model

3. When desiring a model with greater interpretability

4. When evaluating non-linear models

The correct answer is: When desiring a model with greater interpretability

Choosing Bayesian Information Criterion (BIC) over Akaike Information Criterion (AIC) is particularly advantageous when the goal is to obtain a model that emphasizes simplicity and greater interpretability. BIC incorporates a penalty for the number of parameters that grows with the sample size, which tends to favor more parsimonious models. This characteristic aligns well with the desire to create a model that is not only valid in terms of fit but also interpretable, ensuring that the model remains straightforward and practical for understanding and communicating the underlying processes it represents. When aiming for greater interpretability, it is essential to strike a balance between model complexity and comprehensibility. A model that is overly complex may fit the data closely but might be difficult to interpret and apply in practical terms. BIC’s greater penalty for complexity encourages the selection of models that are simpler and more understandable, which is often critical for effective decision-making and communication of results in applied settings. In contrast, other situations such as constructing a more complex model or evaluating non-linear models may not align with the key benefits of BIC. Seeking interpretability would generally favor a simpler model, thus reinforcing the preference for BIC in this context.