What question did this study set out to answer?

This work aims to explore modern strategies for optimizing labor resources through the collaboration of human and AI agents.

Synergistic Optimization of Human and Artificial Agents in Labor Resource Management in Hybrid Sociotechnical Systems

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Randomized trial explores human-AI collaboration for optimizing labor resources, suggesting new paradigms in management.

This work aims to explore modern strategies for optimizing labor resources through the collaboration of human and AI agents.
Developed algorithms for optimizing human-AI interaction to reduce personnel management costs while improving productivity.
Formulated mathematical models for task assignment, hierarchical management, and agent behavior.
Utilized game theory, queueing models, and machine learning to create a toolkit for managing workloads.
Integration of AI agents improved organizational efficiency compared to traditional methods with a focus on human-AI collaboration.
Model showed that dynamic architectures maintained global system stability despite task variability and uncertainty.
Incorporating behavior trees into agent models enhanced predictability, requiring synchronization with orchestrator policies.

Korolyov et al. (2026) studied this question.

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View Full Paper

Ask AI

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Authors

Randomized trial explores human-AI collaboration for optimizing labor resources, suggesting new paradigms in management.

This work aims to explore modern strategies for optimizing labor resources through the collaboration of human and AI agents.
Developed algorithms for optimizing human-AI interaction to reduce personnel management costs while improving productivity.
Formulated mathematical models for task assignment, hierarchical management, and agent behavior.
Utilized game theory, queueing models, and machine learning to create a toolkit for managing workloads.
Integration of AI agents improved organizational efficiency compared to traditional methods with a focus on human-AI collaboration.
Model showed that dynamic architectures maintained global system stability despite task variability and uncertainty.
Incorporating behavior trees into agent models enhanced predictability, requiring synchronization with orchestrator policies.

Korolyov et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark