Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guarantees
Making AI predictions follow economic rules without sacrificing accuracy
Foundation models predict choices well but often violate basic economics—suggesting that raising prices increases demand, or that unavailable options have some probability of being chosen. Researchers created a two-stage adapter that embeds foundation model predictions into an economic model while mathematically guaranteeing that the results follow economic logic, gaining an average 6.4 percentage point accuracy boost while maintaining 100% cost monotonicity.
Transportation agencies, retailers, and economists use choice models to estimate how people respond to prices and policies. An accurate model that also obeys economic logic is more trustworthy for real decisions—whether predicting traffic patterns after a toll increase or estimating consumer welfare. This method lets organizations use faster, more accurate foundation models without sacrificing the economic guarantees that justify policy reliance on their outputs.