Behaviorally Aligned Autonomous Agents for Platform Labor Markets: Integrating Goal-Setting Theory with Large Language Model Reasoning and Reinforcement Learning
Keywords:
autonomous agents, platform labor markets, goal-setting theory, large language models, reinforcement learning, behavioral alignment, socio-technical systems, algorithmic governanceAbstract
The rapid expansion of platform labor markets has created a pressing need for autonomous agents that can operate within complex, dynamic, and often precarious work environments. This paper proposes a novel framework for designing behaviorally aligned autonomous agents that integrate goal-setting theory with large language model reasoning and reinforcement learning. The framework addresses the fundamental tension between platform efficiency and worker welfare by embedding structured goal mechanisms that are informed by psychological research into human motivation and performance. We argue that existing reinforcement learning approaches, while powerful for optimizing instrumental objectives, often overlook the behavioral and cognitive dimensions that shape worker engagement and long-term sustainability. By incorporating goal-setting theory, which emphasizes the motivational effects of specific and challenging goals combined with feedback, our architecture enables agents to generate, pursue, and adapt goals in a manner that mirrors human self-regulation. Large language models provide the reasoning layer necessary for contextual interpretation, natural language communication, and dynamic goal reformulation, while reinforcement learning provides the sequential decision-making engine for efficient policy optimization. We examine the system-level architecture, structural trade-offs, governance challenges, deployment infrastructure, and policy implications of such an integrated approach. Key considerations include fairness in goal assignment, robustness to adversarial manipulation, alignment with worker autonomy, and the sustainability of platform ecosystems. The paper concludes with a research agenda for developing welfare-aware autonomous agents that balance productivity with human-centered design.
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