Multi-Agent Reinforcement Learning for Labor Supply Optimization in Digital Platforms under Self-Set and Platform-Assigned Goals
Keywords:
multi-agent reinforcement learning, labor supply optimization, digital platforms, goal setting, platform governance, fairness, socio-technical systemsAbstract
Digital labor platforms increasingly mediate the supply of contingent work by matching independent workers with task demand through algorithmic allocation systems. These platforms face a fundamental tension between allowing workers to self-set their own labor goals and imposing platform-assigned targets to optimize aggregate output. This paper develops a multi-agent reinforcement learning framework to model and analyze labor supply optimization under both goal structures. We conceptualize each worker as an independent learning agent with private utility functions, while the platform acts as a centralized or decentralized goal-setting mechanism that shapes the reward environment. Through a system-level discussion, we examine how self-set goals foster worker autonomy and intrinsic motivation but may lead to suboptimal system-wide coordination, whereas platform-assigned goals can align individual behavior with global efficiency but risk fairness violations and worker disengagement. The architecture we propose integrates hierarchical reinforcement learning with meta-governance layers that dynamically adjust goal assignment based on worker state, platform congestion, and long-term sustainability metrics. We explore structural trade-offs between exploration and exploitation at both the agent and platform levels, and we draw parallels with other large-scale socio-technical systems such as ride-hailing fleets and crowd work markets. Governance implications are analyzed through the lenses of distributive justice, transparency, and algorithmic accountability. Furthermore, we discuss deployment challenges including computational scalability, communication overhead, and the need for robust mechanisms against adversarial worker behavior. Policy recommendations are offered for platform regulators and designers seeking to balance productivity with worker well-being. The paper concludes that hybrid goal structures, where platforms offer voluntary default targets while preserving opt-in self-set options, provide a promising middle ground that can be optimized using multi-agent reinforcement learning with appropriate fairness constraints.
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