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AI Research
arXiv CS.AI·May 27·6 min read
Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice
TL;DR
Tabular foundation models (large neural networks trained on diverse datasets) predict choices accurately but violate economic principles—for example, raising prices sometimes increases predicted demand. Researchers built a two-stage adapter that constrains foundation model outputs to obey utility-maximization logic (the economic principle that consumers choose options maximizing their satisfaction).
✦ Why It Matters
Engineers can now deploy foundation models on economic tasks without sacrificing logical consistency or domain validity.
Key Takeaways
- Use two-stage adapters to enforce economic constraints on foundation model predictions for choice tasks.
- Validate that price-demand relationships follow economic logic before deploying models in production.
- Combine neural network accuracy with utility-maximization constraints for domain-critical applications.
- Test willingness-to-pay estimates for plausibility; negative values signal model violations requiring correction.
Full Summary
Tabular foundation models achieve strong accuracy on choice prediction tasks but produce economically invalid outputs. In discrete choice modeling, predictions should obey utility maximization: consumers select options that maximize their satisfaction given constraints.
Foundation models frequently violate this, generating negative willingness-to-pay estimates (what consumers will pay for goods) or predicting demand increases when prices rise. Researchers proposed a two-stage adapter embedding foundation model predictions within a utility-maximization framework.
Stage one estimates a standard choice model with parameters constrained to satisfy economic validity. Results show the adapter corrects violations while preserving predictive accuracy on benchmark datasets.
This approach bridges machine learning and economic theory, enabling deployment of foundation models in domains requiring logical consistency.