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About this paper
Matthieu Mastio, Paul Saves, Benoit Gaudou, Nicolas Verstaevel
Researchers at IRIT developed a multi-agent model where firms use reinforcement learning to adapt pricing strategies in a spatial double-auction market, revealing how transport costs, disposal penalties, and firm density influence the emergence of industrial symbiosis and local circularity.
Key insights
- Decentralized firms utilizing adaptive reinforcement learning can collectively drive market prices and trade quantities towards stable approximate Nash equilibria in spatially constrained environments.
- Firm density is the most influential factor governing the emergence of local circularity and price stability, with its impact amplified by spatial compactness.
- Reducing transport costs and increasing disposal penalties are effective policy levers to enhance byproduct exchange, particularly critical for fostering symbiosis in less dense industrial areas.
Summary by alphaXiv.
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