At its core, my research grapples with a pivotal question: How can independent AI learners coevolve—and ultimately cooperate—within shared systems or environments? In an era increasingly shaped by AI, this inquiry transcends mere engineering; it sits at the heart of our collective trajectory as a civilization.
Technically, this pursuit lives at the intersection of generative AI, multi-agent reinforcement learning, and algorithmic game theory. My favorite testing ground? Economics—a microcosm of societal structures, offering both a mirror to human behavior and a sandbox for probing emergent collaboration.
List of Research Papers
Implementing a Hierarchical Deep Learning Approach for Simulating Multilevel Auction Data
| Authors | Igor Sadoune, Marcelin Joanis, Andrea Lodi |
| Year | 2024 |
| Publisher | Computational Economics |
| Access | arXiv |
| Keywords | Deep Generative Modeling, Multilevel Discrete Data, Auction Data Simulation |
| Abstract | We present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. The complexities encountered in this type of auction data include high-cardinality discrete feature spaces and a multilevel structure arising from multiple bids associated with a single auction instance. Our methodology combines deep generative modeling (DGM) with an artificial learner that predicts the conditional bid distribution based on auction characteristics, contributing to advancements in simulation-based research. This approach lays the groundwork for creating realistic auction environments suitable for agent-based learning and modeling applications. Our contribution is twofold: we introduce a comprehensive methodology for simulating multilevel discrete auction data, and we underscore the potential of DGM as a powerful instrument for refining simulation techniques and fostering the development of economic models grounded in generative AI. |
@article{Sadoune2024BidNet,
title={Deep Generative Simulation of Multilevel Discrete Auction Data},
author={Igor Sadoune and Marcelin Joanis and Andrea Lodi},
journal={Computational Economics},
year={2024},
note={arXiv preprint arXiv:2207.12255},
url={https://arxiv.org/abs/2207.12255}
}
Algorithmic Collusion and the Minimum Price Markov Game
| Authors | Igor Sadoune, Marcelin Joanis, Andrea Lodi |
| Year | 2025 |
| Publisher | submitted |
| Access | arXiv |
| Keywords | Algorithmic Game Theory, Multiagent Reinforcement Learning, Algorithmic Coordination |
| Abstract | This paper introduces the Minimum Price Markov Game (MPMG), a theoretical model that reasonably approximates real-world first-price markets following the minimum price rule, such as public auctions. The goal is to provide researchers and practitioners with a framework to study market fairness and regulation in both digitized and non-digitized public procurement processes, amid growing concerns about algorithmic collusion in online markets. Using multi-agent reinforcement learning-driven artificial agents, we demonstrate that (i) the MPMG is a reliable model for first-price market dynamics, (ii) the minimum price rule is generally resilient to non-engineered tacit coordination among rational actors, and (iii) when tacit coordination occurs, it relies heavily on self-reinforcing trends. These findings contribute to the ongoing debate about algorithmic pricing and its implications. |
@article{Sadoune2025MPMG,
author = {Sadoune, Igor and Joanis, Marcelin and Lodi, Andrea},
title = {Algorithmic Collusion and the Minimum Price Markov Game},
journal = {Games and Economic Behavior},
year = {2025},
note = {submitted, \url{https://arxiv.org/abs/2407.03521}}
}
Strategic Equilibrium Policy Gradient: On Learning Tacit Coordination
| Authors | Igor Sadoune, Marcelin Joanis, Andrea Lodi |
| Year | 2025 |
| Publisher | submitted |
| Access | upcoming |
| Keywords | Multi-agent Reinforcement Learning, Tacit Coordination, Strategic Equilibrium Policy Gradient |
| Abstract | This paper introduces the Strategic Equilibrium Policy Gradient (SEPG), a multi-agent reinforcement learning approach designed to foster tacit coordination in coordination games, namely, competitive social dilemmas for which individual interests align with group welfare. The SEPG is implemented and tested on the Minimum Price Markov Game (MPMG), a dynamic coordination game that models first-price auctions governed by the minimum price rule. Unlike methods that rely on explicit mechanisms for cooperation such as centralized learning or communication, SEPG leverages a combination of pre-game planning and online adaptation to guide agents toward the Pareto equilibrium through implicit, or tacit, coordination. This study demonstrates that SEPG agents achieve robust tacit coordination in both homogeneous and heterogeneous scenarios, challenging existing MARL methods and highlighting the potential for tacit collusion in AI-driven markets. Our experiments reveal that SEPG encourages coordination among malicious actors while also promoting rational behavior in competitive settings. |
@unpublished{Sadoune2025SEPG,
author = {Sadoune, Igor and Joanis, Marcelin and Lodi, Andrea},
title = {Strategic Equilibrium Policy Gradient: On Learning Tacit Coordination},
year = {2025},
note = {working paper, forthcoming}
}
Spatiotemporal Reinforcement Learning and Graph Attention Network for Dynamic Vessel Routing
| Authors | Igor Sadoune, Thierry Warin, Nathalie De Marcellis, Martin Trepanier |
| Year | 2025 |
| Publisher | working paper |
| Access | upcoming |
| Keywords | Geo-Spatial Data, Multi-agent Systems, Graph Attention Network, Supply-Chain Optimization |
| Abstract | This paper introduces the Strategic Maritime shipping networks face dynamic challenges, including fluctuating water levels, vessel congestion, and trade wars. Ships must adapt routes in real time to balance safety, efficiency, and operational constraints. Traditional routing algorithms struggle with spatiotemporal complexity and inter-agent dependencies, especially in uncertain environment caused by climatic change and political instabilities. We propose a multi-agent reinforcement learning (MARL) framework that leverages graph attention networks (GATs) to: (1) Model dynamic maritime networks as a graph with time-varying edge weights; (2) Learn decentralized policies for ships to optimize routes while coordinating with other agents; (3) Generalize to unseen scenarios using attention-based spatial reasoning. |
@unpublished{Sadoune2025SEPG,
author = {Sadoune, Igor and Warin, Thierry and De Marcellis, Nathalie and Trepanier, Martin},
title = {Spatiotemporal Reinforcement Learning and Graph Attention Network for Dynamic Vessel Routing},
year = {2025},
note = {working paper, forthcoming}
}
A Foundation Model for the North-American Economy
| Authors | Igor Sadoune, Thierry Warin, Adam Touré, Lucien Chaffa |
| Year | 2025 |
| Publisher | working paper |
| Access | upcoming |
| Keywords | Transformers, Foundation Models, Counterfactual Predictions |
| Abstract | This paper explores the application of foundation models in economic transactions, focusing on counterfactual predictions and their implications for economic modeling and decision-making. |
@unpublished{Sadoune2025SEPG,
author = {Sadoune, Igor and Warin, Thierry and Touré, Adam and Chaffa, Lucien},
title = {A Foundation Model for the North-American Economy},
year = {2025},
note = {working paper, forthcoming}
}