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

arXiv: https://arxiv.org/abs/2512.17979 · HTML: https://mlsanity.com/paper/2512.17979/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 67 references that are in the map.
- First posted on arXiv 19 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits](https://mlsanity.com/paper/2411.18704/) (2024)
- [Reinforcement Learning for Market Making in a Multi-agent Dealer Market](https://mlsanity.com/paper/1911.05892/) (2019)
- [Variational Bayesian Reinforcement Learning with Regret Bounds](https://mlsanity.com/paper/1807.09647/) (2018)
- [SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes](https://mlsanity.com/paper/2305.13998/) (2023)
- [Online Optimization Algorithms in Repeated Price Competition: Equilibrium Learning and Algorithmic Collusion](https://mlsanity.com/paper/2412.15707/) (2024)
- [SMT-EX: An Explainable Surrogate Modeling Toolbox for Mixed-Variables Design Exploration](https://mlsanity.com/paper/2503.19496/) (2025)
- [A spatial agent based model for simulating and optimizing networked eco-industrial systems](https://mlsanity.com/paper/2003.14133/) (2020)
- [Smart Containers With Bidding Capacity: A Policy Gradient Algorithm for Semi-Cooperative Learning](https://mlsanity.com/paper/2005.00565/) (2020)
- [Evaluation of a Supervised Learning Approach for Stock Market Operations](https://mlsanity.com/paper/1301.4944/) (2013)

## Built on by (most cited first, 1 of 1)

- [From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models](https://mlsanity.com/paper/2604.03350/) (2026)

## Abstract (arXiv)

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.