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.
Deep Dive into Adaptive Agents in Spatial Double-Auction Markets: Modeling the Emergence of Industrial Symbiosis.
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
Adaptive Agents in Spatial Double-Auction Markets: Modeling
the Emergence of Industrial Symbiosis
Matthieu Mastio
IRIT, UMR 5505 CNRS, Université Toulouse Capitole
Toulouse, France
matthieu.mastio@irit.fr
Paul Saves
IRIT, UMR 5505 CNRS, Université Toulouse Capitole
Toulouse, France
paul.saves@irit.fr
Benoit Gaudou
IRIT, UMR 5505 CNRS, Université Toulouse Capitole
Toulouse, France
benoit.gaudou@irit.fr
Nicolas Verstaevel
IRIT, UMR 5505 CNRS, Université Toulouse Capitole
Toulouse, France
nicolas.verstaevel@irit.fr
ABSTRACT
Industrial symbiosis fosters circularity by enabling firms to re-
purpose 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 behav-
ior, 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 in-
teractions. Leveraging reinforcement learning, firms adapt their
bidding strategies to maximize profit while accounting for transport
costs, disposal penalties, and resource scarcity. Simulation exper-
iments reveal the economic and spatial conditions under which
decentralized exchanges converge toward stable and efficient out-
comes. Counterfactual regret analysis shows that sellers’ strategies
approach a near Nash equilibrium, while sensitivity analysis high-
lights how spatial structures and market parameters jointly govern
circularity. Our model provides a basis for exploring policy inter-
ventions 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.
KEYWORDS
Agent Based Simulation, Industrial Symbiosis, Circular Economy,
Market Analysis
ACM Reference Format:
Matthieu Mastio, Paul Saves, Benoit Gaudou, and Nicolas Verstaevel. 2026.
Adaptive Agents in Spatial Double-Auction Markets: Modeling the Emer-
gence of Industrial Symbiosis. In Proc. of the 25th International Conference on
Autonomous Agents and Multiagent Systems (AAMAS 2026), Paphos, Cyprus,
May 25 – 29, 2026, IFAAMAS, 10 pages.
1
INTRODUCTION
Industrial Symbiosis (IS) refers to the exchange of byproducts be-
tween firms, turning one company’s waste into valuable inputs for
Proc. of the 25th International Conference on Autonomous Agents and Multiagent Systems
(AAMAS 2026), C. Amato, L. Dennis, V. Mascardi, J. Thangarajah (eds.), May 25 – 29,
2026, Paphos, Cyprus. © 2026 International Foundation for Autonomous Agents and
Multiagent Systems (www.ifaamas.org). This work is licenced under the Creative
Commons Attribution 4.0 International (CC-BY 4.0) licence.
another one. By reusing materials such as heat, rubble, or chem-
ical residues, IS reduces environmental impact while generating
economic benefits, embodying the circular economy principle of
closing resource loops. For instance, the RETDA eco-industrial park
in China reported a 33% increase in input productivity, a 3650% rise
in water use efficiency, and a 30.91% reduction in emissions [12].
Despite its potential, IS rarely emerges spontaneously. Exchanges
depend on local supply-demand compatibilities, economic incen-
tives, and logistical constraints [29]. Poor coordination often results
in wasted resources and missed opportunities, highlighting the
gap between individual strategies and collective circularity goals.
Identifying the conditions under which decentralized firms can
coordinate effectively is therefore a key research challenge.
Analytical solutions are often intractable due to the combinato-
rial complexity of interactions and the emergent nature of prices
and traded quantities. Simulation-based approaches, by contrast,
are particularly suited to study such nonlinear, decentralized, and
heterogeneous systems [28]. By systematically varying system con-
figurations, simulations can indeed reveal market behaviors and
highlight factors that support efficient and resilient local circularity.
Agent-based models, in particular, are able to capture heterogene-
ity among actors and test how different market designs, decision
rules, and learning mechanisms affect the emergence of exchanges
under realistic economic constraints [19]. Their ability to explic-
itly represent geosituated agents makes them especially suitable
for studying spatially dependent interactions, such as proximity
based collaborations, transportation costs, and localized resource
flows. Unlike theoretical game-theoretic models, which often rely
on a small number of representative players to ensure tractability,
agent-based models can accommodate many heterogeneous actors
and interactions.
However, their effectiveness is often constrained by the simpli-
fications made in representing agent beha
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