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.

翻译：工人效用不可直接观测——只能从其结果推断。每笔零工交易仅产生一个二进制结果：接受或拒绝。我们认为这一结构直接指向Preisach迟滞模型，该模型能自然表征潜在的工作者偏好。Preisach算子将聚合产出建模为二进制阈值单元群体上的积分——这正是异质性工人各自持有私人接受工资时涌现的结构。我们通过双输出神经网络（共享层256→128，采用边际损失函数强制U_1 ≥ U_0）估计两个潜在效用曲面：接受效用U_1(X)和拒绝效用U_0(X)。分类问题简化为Preisach间隙U_1(X) - U_0(X)，该值结合经裁剪稳定化的价格-阈值编码后输入XGBoost分类器。在36,891笔零工交易上，该流程实现了Jaccard系数0.827和ROC AUC 0.799。价格-阈值编码相对于原始效用特征贡献了+11.0个百分点的AUC提升。模型验证了迟滞性预测的方向不对称性：价格下降对完成率的抑制效应大于同等幅度价格上涨的促进效应。将该模型应用于完整数据集后，其建议同时使总工资支出降低21.3%，并使预期填充率提高9.7个百分点。对于74.2%的交易，接受概率P(accept)已超过0.80；降低工资仍使其保持在阈值之上（降薪后平均P=0.972），从而释放成本节约（中位值31%）。对于剩余的25.4%交易，中位值7%的工资提升可恢复+43个百分点的接受率。不含明确无差异区的模型无法同时执行这两类操作。