AI model predicts gig worker utility using hysteresis

Researchers have developed a novel Preisach hysteresis model to better understand worker acceptance in gig labor markets. This model, implemented using a dual-output neural network and an XGBoost classifier, analyzes transaction data to predict worker utility. The system achieved a Jaccard index of 0.827 and an ROC AUC of 0.799 on a dataset of 36,891 gig transactions. The model's findings indicate that price decreases have a more significant negative impact on completion rates than equivalent increases have positive impacts, leading to recommendations that could reduce wage bills by over 21% while increasing fill rates. AI

IMPACT Provides a new framework for optimizing gig economy platforms by modeling worker acceptance and wage dynamics.

RANK_REASON Academic paper detailing a novel modeling approach for gig labor markets. [lever_c_demoted from research: ic=1 ai=1.0]

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• dual-output neural network
• gig labour markets
• Piotr Frydrych Ph. D. Eng.
• Preisach hysteresis model
• XGBoost classifier

• paper
• other

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