用滞后模型分析零工市场接单行为，能同时降本提效。
Worker Utility as Hysteresis: A Preisach Model of Transaction Acceptance in Gig Labour Markets
- 基于普莱斯赫茨滞回模型建模工人隐含偏好，通过神经网络估计接受/拒绝效用面。
- 在3.6万笔交易上实现Jaccard 0.827、AUC 0.799，价格编码提升11个百分点性能。
- 揭示价格下调对完成率的伤害大于上调的收益，适合平台优化定价策略。
工人效用无法直接观测，仅能通过每笔零工交易的接受或拒绝结果推断。我们提出，这种二元输出结构天然契合普莱斯赫茨滞后模型。该模型将群体行为建模为多个二值阈值单元的积分，恰似不同工人拥有私有接受工资的场景。通过双输出神经网络（共享层256→128）估计接受效用U₁(X)与拒绝效用U₀(X)，并以边际损失强制U₁ ≥ U₀。分类基于二者之差（即普莱斯赫茨间隙）输入XGBoost，结合剪枝稳定的价格-阈值编码。在36,891笔交易上，该流程达到Jaccard 0.827、ROC AUC 0.799。价格编码相比原始效用特征提升11.0个百分点AUC。模型验证了滞回预测的方向不对称性：降价对完成率的抑制强于涨价带来的提升。应用于全数据集时，推荐策略使总工资支出减少21.3%，预期填单率提升9.7个百分点。74.2%的交易原本接受概率已超0.80，降薪后仍保持在0.972均值，平均节省31%成本；剩余25.4%需中位7%加薪，可恢复43个百分点接受率。无显式无差异区间的模型无法同时实现这两步操作。
原文摘要 · Abstract (English)
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.
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