电商算法不确定性下的劳动供给异化：一个基于行为经济学的理论框架

The Alienation of Labor Supply under E-Commerce Algorithm Uncertainty: A Theoretical Framework Based on Behavioral Economics

摘要: 平台经济的高速扩张使算法成为劳动组织的核心协调机制，但也将劳动者置于高度不确定的决策环境。本文基于行为经济学视角，构建了电商平台算法不确定性下劳动供给异化“算法不确定性–认知偏差–劳动异化”链式模型的理论框架，系统阐释电商平台如何通过“策略性不透明”触发并放大劳动者的损失厌恶、心理账户、过度自信，进而导致供给行为偏离理性路径。研究指出，算法管理虽提升平台运营效率，但高度动态与非透明特性带来收入、任务与规则的三重不确定性。通过前景理论、心理账户、过度自信等行为经济学概念，揭示了不确定性如何通过认知偏差扭曲劳动者决策，导致劳动供给异化，同时闭环反馈使异化状态自我强化，形成市场难以自纠的路径依赖。也为平台劳动治理提供了“降不确定–纠偏差–限干预”的政策切口，拓展了行为经济学在经济劳动研究中的应用边界。

Abstract: The rapid expansion of the platform economy has made algorithms the core coordinating mechanism for labor organization, yet it simultaneously exposes workers to a highly uncertain decision-making environment. Drawing on behavioral economics, this paper constructs a chain model of “algorithmic uncertainty—cognitive bias—labor-supply alienation” to explain how e-commerce platforms use “strategic opacity” to trigger and amplify workers’ loss aversion, mental accounting, and overconfidence, thereby pushing labor-supply behavior away from the rational path. The study argues that although algorithmic management improves platform efficiency, its highly dynamic and non-transparent characteristics generate triple uncertainty over income, tasks, and rules. Concepts from behavioral economics—prospect theory, mental accounting, and overconfidence—reveal how this uncertainty distorts workers’ decisions through cognitive biases and produces alienated labor supply. A closed-loop feedback process further reinforces the alienated state, creating a path dependence that the market cannot self-correct. The paper offers a policy entry point of “reduce uncertainty, correct biases, and limit interventions” for governing platform labor and extends the application of behavioral economics to labor studies in the digital economy.