Prospect Theory and Machine Learning-Based Goal Design in Flexible Labor Markets
Keywords:
prospect theory, goal design, flexible labor markets, machine learning, personalization, platform governanceAbstract
Flexible labor markets, typified by ride-hailing, food delivery, and freelance platforms, grant workers unprecedented autonomy over when and how much to work. Yet this autonomy shifts the burden of self-regulation onto individuals, making the design of effective earning and effort goals a central challenge for both worker welfare and platform sustainability. This paper develops an interdisciplinary framework that integrates prospect theory with machine learning to personalize goal recommendations in these environments. We argue that classical expected utility models fail to capture the reference-dependent and loss-averse preferences that dominate gig workers’ labor supply decisions. Drawing on the fourfold pattern of risk attitudes and the dynamics of reference point adaptation, we explore how behavioral goal design can improve decision quality, reduce churn, and align platform incentives with worker well-being. We then examine the machine learning architectures required to infer latent cognitive parameters and deliver personalized goals at scale, analyzing contextual bandits, meta-learning, and causal inference approaches. A central contribution is a system-level analysis of the trade-offs inherent in building such infrastructures: latency versus model sophistication, privacy preservation versus personalization depth, and fairness constraints versus efficiency gains. We discuss governance structures that must accompany algorithmic goal systems, including transparency mandates, auditability, and safeguards against exploitative framing. Through a deployment-oriented lens, we address sustainability challenges such as concept drift, engagement spirals, and the co-evolution of worker behavior and model recommendations. The paper concludes by outlining a research agenda that treats goal design as a socio-technical optimization problem, bridging behavioral economics, systems engineering, and policy design to create labor platforms that are simultaneously efficient, equitable, and human-centered.
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