Artificial Intelligence inspired methods for the allocation of common goods and services

Area of Science:

• Economic Systems
• Computational Economics
• Societal Planning

Background:

• The historical debate on resource allocation has largely favored market-based systems since 1991.
• Traditional economic planning faced challenges in efficiency and responsiveness.
• Previous models often operated on aggregate data, missing micro-level dynamics.

Observation:

• Current market systems face inherent inefficiencies in surplus allocation.
• Societal surplus allocation remains a persistent economic challenge.
• The collapse of the Soviet Union shifted allocation paradigms.

Findings:

• A novel automated planning system operating at the unit level (factories, citizens) is proposed.
• The system is theoretically viable and desirable as an alternative to market conditions.
• Experiments demonstrate the system's capacity to plan for 50,000 industrial and 5,000 final goods on commodity hardware.

Implications:

• This research bridges traditional planning with modern AI, suggesting new avenues for economic modeling.
• The proposed system offers a potential alternative to current market-based resource allocation.
• Further research can explore the integration of AI in large-scale economic planning.