arXiv:2606.26432v1 Announce Type: new Abstract: Tabular foundation models achieve strong accuracy on choice prediction tasks, but their predictions often violate the economic logic those tasks require: raising a price can increase predicted demand, implied willingness-to-pay estimates are frequently negative or implausible, and unavailable alternatives receive nonzero probability. We propose a two-stage adapter that takes a foundation model's predicted choice probabilities as a precomputed feature and embeds them inside a multinomial logit's utility. In Stage 1, we fit the multinomial logit's
The proliferation of foundation models across various domains, including economics, necessitates methods to align their powerful predictive capabilities with established logical constraints.
This research addresses a critical limitation of AI in economic modeling, offering a path to integrate advanced AI predictions without violating fundamental economic principles, which is crucial for reliable decision-making.
Foundation models can now be applied to discrete-choice models with greater reliability and interpretability, as their predictions are constrained to adhere to economic logic.
- · Econometricians
- · Data scientists working with discrete choice
- · Businesses using AI for pricing and demand forecasting
- · AI models that solely rely on unconstrained predictions
- · Decision-makers who previously made choices based on economically illogical AI o
Foundation model predictions become more trustworthy and directly applicable in economic and business decision-making contexts.
Increased adoption of foundation models in fields requiring strict adherence to economic theory, leading to more robust analytical tools.
The development of a new generation of 'economically intelligent' AI systems that inherently understand and respect domain-specific structural guarantees.
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