Adaptive Agents in Spatial Double-Auction Markets: Modeling the Emergence of Industrial Symbiosis

• URL: http://arxiv.org/abs/2512.17979v1
• Date: Fri, 19 Dec 2025 13:24:43 GMT
• Title: Adaptive Agents in Spatial Double-Auction Markets: Modeling the Emergence of Industrial Symbiosis
• Authors: Matthieu Mastio, Paul Saves, Benoit Gaudou, Nicolas Verstaevel,
• Abstract summary: Industrial symbiosis fosters circularity by enabling firms to repurpose residual resources.<n>Existing models often overlook the interaction between spatial structure, market design, and adaptive firm behavior.<n>We develop an agent-based model where heterogeneous firms trade byproducts through a spatially embedded double-auction market.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Coupling Agent-based Modeling and Life Cycle Assessment to Analyze Trade-offs in Resilient Energy Transitions [11.897157516218057]
  Transitioning to sustainable and resilient energy systems requires navigating complex and interdependent trade-offs across environmental, social, and resource dimensions.<n>We present an integrated modeling framework that couples agent-based modeling and Life Cycle Assessment (LCA) to simulate how energy transition pathways interact with regional resource competition, ecological constraints, and community-level burdens.<n>Results demonstrate how integrated and multiscale decision making can shape energy pathway deployment and reveal spatially explicit trade-offs under scenario-driven constraints.
  arXiv  Detail & Related papers  (2025-11-10T07:28:03Z)
• Emergence from Emergence: Financial Market Simulation via Learning with Heterogeneous Preferences [3.722808691920657]
  We develop a multi-agent reinforcement learning framework in which agents endowed with heterogeneous risk aversion, time discounting, and information access collectively learn trading strategies.<n>The experiment reveals that (i) learning with heterogeneous preferences drives agents to develop strategies aligned with their individual traits, fostering behavioral differentiation and niche specialization within the market, and (ii) the interactions by the differentiated agents are essential for the emergence of realistic market dynamics.
  arXiv  Detail & Related papers  (2025-11-07T12:54:27Z)
• Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets [74.91125572848439]
  We study two-sided agentic marketplaces where Assistant agents represent consumers and Service agents represent competing businesses.<n>This environment enables us to study key market dynamics: the utility agents achieve, behavioral biases, vulnerability to manipulation, and how search mechanisms shape market outcomes.<n>Our experiments show that frontier models can approach optimal welfare-- but only under ideal search conditions. Performance degrades sharply with scale, and all models exhibit severe first-proposal bias, creating 10-30x advantages for response speed over quality.
  arXiv  Detail & Related papers  (2025-10-27T18:35:59Z)
• Reinforcement Learning-Based Market Making as a Stochastic Control on Non-Stationary Limit Order Book Dynamics [0.0]
  Reinforcement Learning has emerged as a promising framework for developing adaptive and data-driven strategies.<n>This paper explores the integration of a reinforcement learning agent in a market-making context.
  arXiv  Detail & Related papers  (2025-09-15T21:08:13Z)
• From Individual Learning to Market Equilibrium: Correcting Structural and Parametric Biases in RL Simulations of Economic Models [3.036596192442501]
  The application of Reinforcement Learning to economic modeling reveals a fundamental conflict between the assumptions of equilibrium theory and the emergent behavior of learning agents.<n>This paper first demonstrates this discrepancy within a search-and-matching model with concave production, showing that a standard RL agent learns a non-equilibrium, monopsonistic policy.<n>We propose a calibrated Mean-Field Reinforcement Learning framework that embeds a representative agent in a fixed macroeconomic field and adjusts the cost function to reflect economic opportunity costs.
  arXiv  Detail & Related papers  (2025-07-24T09:21:02Z)
• From Bias to Behavior: Learning Bull-Bear Market Dynamics with Contrastive Modeling [13.039189005779534]
  This paper explores the potential of bull and bear regimes in investor-driven market dynamics.<n>We propose the Bias to Behavior from Bull-Bear Dynamics model (B4), a unified framework that embeds temporal price sequences and external contextual signals into a shared latent space.<n>Our model achieves superior performance in predicting market trends and provides interpretable insights into the interplay of biases, investor behaviors, and market dynamics.
  arXiv  Detail & Related papers  (2025-07-12T11:36:26Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning [151.03738099494765]
  We study a heterogeneous agent macroeconomic model with an infinite number of households and firms competing in a labor market. We propose a data-driven reinforcement learning framework that finds the regularized competitive equilibrium of the model.
  arXiv  Detail & Related papers  (2023-02-24T17:16:27Z)
• Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations [16.48389671789281]
  We study a game between liquidity provider and liquidity taker agents interacting in an over-the-counter market. By playing against each other, our deep-reinforcement-learning-driven agents learn emergent behaviors. We show convergence rates for our multi-agent policy gradient algorithm under a transitivity assumption.
  arXiv  Detail & Related papers  (2022-10-13T17:06:08Z)
• Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation [93.52573037053449]
  H-MARL (Hallucinated Multi-Agent Reinforcement Learning) learns successful equilibrium policies after a few interactions with the environment. We demonstrate our approach experimentally on an autonomous driving simulation benchmark.
  arXiv  Detail & Related papers  (2022-03-14T17:24:03Z)
• Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning [72.23843557783533]
  We show that deep reinforcement learning can discover stable solutions that are epsilon-Nash equilibria for a meta-game over agent types. Our approach is more flexible and does not need unrealistic assumptions, e.g., market clearing. We demonstrate our approach in real-business-cycle models, a representative family of DGE models, with 100 worker-consumers, 10 firms, and a government who taxes and redistributes.
  arXiv  Detail & Related papers  (2022-01-03T17:00:17Z)
• Multi-Asset Spot and Option Market Simulation [52.77024349608834]
  We construct realistic spot and equity option market simulators for a single underlying on the basis of normalizing flows. We leverage the conditional invertibility property of normalizing flows and introduce a scalable method to calibrate the joint distribution of a set of independent simulators.
  arXiv  Detail & Related papers  (2021-12-13T17:34:28Z)
• Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [80.49176924360499]
  We establish a framework for directing a society of simple, specialized, self-interested agents to solve sequential decision problems. We derive a class of decentralized reinforcement learning algorithms. We demonstrate the potential advantages of a society's inherent modular structure for more efficient transfer learning.
  arXiv  Detail & Related papers  (2020-07-05T16:41:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.