Avoiding Model Collapse: How Synthetic Data Can Help Models Stay Grounded in Reality

At Jigyasa Analytics, we’ve spent years building predictive models across industries—from credit risk to marketing—and one lesson has remained consistent: models are only as good as the data they’re trained on. As AI systems increasingly rely on synthetic data, a new challenge is emerging—model collapse—where models trained on other models’ outputs become progressively detached from real-world behavior.

This issue isn’t entirely new. In fact, it mirrors a problem we’ve seen repeatedly in model-based selection, especially in marketing and list rental modeling. When a model is trained on a population that was itself selected by a previous model, the resulting sample becomes biased. The new model performs well on the modeled population but fails to generalize to the true population. It’s a feedback loop that reinforces existing patterns while ignoring the diversity and complexity of the broader world.

The Risk of Recursive Modeling

Imagine a scenario where a model selects the top 10% of prospects from a previous campaign. These individuals are then used to train the next model. Over time, the training data becomes increasingly homogeneous—strong on certain features, weak or absent on others. The model learns to optimize for what it sees, not what exists. This recursive modeling leads to blind spots, missed opportunities, and ultimately, poor performance when deployed in the real world.

This is precisely the concern with synthetic data. If synthetic data is generated from models trained on biased or incomplete datasets, it risks amplifying those biases. Worse, if future models are trained on synthetic data alone, they may lose touch with the nuances of real-world behavior.

Synthetic Data as a Solution—Not the Problem

But synthetic data doesn’t have to be the villain in this story. In fact, when used thoughtfully, it can be a powerful tool to counteract model collapse.

Here’s how: synthetic data can be used to simulate the unmodeled population—the individuals who were excluded from previous model selections. For example, in a list rental scenario, if the original campaign only targeted retirees over 65, the model will never learn how younger individuals respond. By generating synthetic data that represents these missing segments, we can enrich the training set and build models that are more representative and robust.

This approach requires careful design. The synthetic data must be grounded in domain knowledge and exploratory analysis. At Jigyasa, we often begin by researching the product and its potential audience, then use automated exploratory algorithms to identify gaps in the data. These gaps—what’s missing—are often more telling than what’s present. Synthetic data can then be crafted to fill these gaps, ensuring that the model sees a fuller picture.

A Call for Thoughtful Modeling

As AI continues to evolve, the temptation to rely on synthetic data will grow. But we must resist the urge to treat it as a shortcut. Instead, we should view it as a strategic supplement—a way to correct for biases, not reinforce them.

The key lies in understanding the data, questioning its origins, and being intentional about what we include in our models. Whether we’re modeling for list rentals, credit risk, or any other domain, the principles remain the same: diverse, representative data leads to better models.

Model collapse is real, but it’s not inevitable. With thoughtful use of synthetic data and a commitment to understanding our samples, we can build models that stay grounded in reality—and deliver results that matter.