Worker Utility as Hysteresis: A Preisach Model of Transaction Acceptance in Gig Labour Markets
2026-06-03 • Machine Learning
Machine Learning
AI summaryⓘ
The authors study how workers decide to accept or reject gig jobs, but they only see the final choice, not the workers' true feelings. They use a model called the Preisach hysteresis, which treats worker preferences as thresholds that create a gap between liking and disliking a job offer. They build a neural network to estimate these hidden preferences and combine it with another method to predict acceptance. Their model shows that lowering pay hurts completion rates more than raising pay helps, and it suggests wage adjustments that save money while keeping or improving how often jobs are accepted. Without modeling a zone where workers are unsure, these smart wage changes wouldn’t both be possible.
Preisach hysteresisworker utilitylatent preferencesneural networkmargin lossXGBoost classifieracceptance thresholdprice-to-threshold encodingJaccard indexROC AUC
Authors
Piotr Frydrych
Abstract
Worker utility is not observed -- only its consequence is. Each gig transaction produces a single bit: accepted or rejected. We argue this structure points directly to the Preisach hysteresis model as the natural representation of latent worker preferences. The Preisach operator models aggregate output as an integral over a population of binary threshold elements -- precisely the structure that emerges when heterogeneous workers each carry a private acceptance wage. We estimate two latent utility surfaces: acceptance utility U_1(X) and rejection utility U_0(X), via a dual-output neural network (shared layers 256->128, margin loss enforcing U_1 >= U_0). Classification reduces to the Preisach gap U_1(X) - U_0(X), passed into an XGBoost classifier alongside clip-stabilised price-to-threshold encodings. On 36,891 gig transactions, this pipeline achieves Jaccard = 0.827 and ROC AUC = 0.799. The price-to-threshold encoding accounts for +11.0 pp AUC over raw utility features. The model confirms the directional asymmetry hysteresis predicts: price decreases depress completion rates more than equivalent increases raise them. Applied to the full dataset, the model's recommendations simultaneously reduce the total wage bill by 21.3% and increase expected fill rate by 9.7 pp. For 74.2% of transactions, P(accept) already exceeds 0.80; reducing the wage keeps it above threshold (mean post-cut P = 0.972), releasing cost savings (median 31%). For the remaining 25.4%, a median 7% wage increase recovers +43 pp acceptance. A model without an explicit indifference zone cannot execute both moves simultaneously.