Industrial symbiosis fosters circularity by enabling firms to repurpose residual resources, yet its emergence is constrained by socio-spatial frictions that shape costs, matching opportunities, and market efficiency. Existing models often overlook the interaction between spatial structure, market design, and adaptive firm behavior, limiting our understanding of where and how symbiosis arises. We develop an agent-based model where heterogeneous firms trade byproducts through a spatially embedded double-auction market, with prices and quantities emerging endogenously from local interactions. Leveraging reinforcement learning, firms adapt their bidding strategies to maximize profit while accounting for transport costs, disposal penalties, and resource scarcity. Simulation experiments reveal the economic and spatial conditions under which decentralized exchanges converge toward stable and efficient outcomes. Counterfactual regret analysis shows that sellers' strategies approach a near Nash equilibrium, while sensitivity analysis highlights how spatial structures and market parameters jointly govern circularity. Our model provides a basis for exploring policy interventions that seek to align firm incentives with sustainability goals, and more broadly demonstrates how decentralized coordination can emerge from adaptive agents in spatially constrained markets.

翻译：工业共生通过促使企业重新利用残余资源来促进循环性，但其涌现过程受到社会空间摩擦的制约，这些摩擦影响着成本、匹配机会和市场效率。现有模型往往忽视空间结构、市场设计与自适应企业行为之间的相互作用，限制了我们对于共生现象在何处及如何产生的理解。本文开发了一个基于主体的模型，其中异质企业通过空间嵌入的双重拍卖市场进行副产品交易，价格和数量从局部互动中内生涌现。借助强化学习，企业通过调整竞价策略以实现利润最大化，同时考虑运输成本、处置惩罚和资源稀缺性。仿真实验揭示了分散化交易向稳定高效结果收敛所需的经济与空间条件。反事实遗憾分析表明卖方策略趋近于近似纳什均衡，而敏感性分析则凸显了空间结构与市场参数如何共同调控循环性。本模型为探索旨在协调企业激励与可持续性目标的政策干预提供了基础，并在更广泛层面论证了空间约束市场中自适应主体如何实现分散化协调的涌现。