About the position
About the research centre or Inria department Created in 2008, the Inria center at the University of Lille employs 360 people, including 305 scientists in 16 research teams. Recognized for its strong involvement in the socio-economic development of the Hauts-De-France region, the Inria center at the University of Lille maintains a close relationship with large companies and SMEs. By fostering synergies between researchers and industry, Inria contributes to the transfer of skills and expertise in the field of digital technologies, and provides access to the best of European and international research for the benefit of innovation and businesses, particularly in the region. For over 10 years, the Inria center at the University of Lille has been at the heart of Lille's university and scientific ecosystem, as well as at the heart of Frenchtech, with a technology showroom based on avenue de Bretagne in Lille, on the EuraTechnologies site of economic excellence dedicated to information and communication technologies (ICT). Context The growing development of digital services and artificial intelligence relies on increasingly large volumes of data. Data markets provide a promising framework for enabling decentralized actors to share, exchange, and exploit data while creating economic incentives for data production and sharing. However, current data-driven systems often rely on intensive data collection, communication, storage, and computation, raising important questions about their environmental footprint. Designing frugal data markets therefore requires going beyond the classical objectives of efficiency and individual utility. Agents should be able to strategically decide what data to collect, share, acquire, or process, while taking into account the associated economic and environmental costs. These decisions are inherently interdependent: the value of information depends on what other agents know and do, while individual decisions affect both the information available to the system and its overall resource consumption. This PhD thesis will investigate the interplay between strategic information, learning, equilibrium formation, and frugality in decentralized data markets . Building on game theory and multi-agent learning, the objective is to develop models and methods that explain and control how strategic agents learn and interact in data markets, and how their collective behavior can be steered towards equilibria that achieve desirable trade-offs between economic value, information quality, and resource consumption. This PhD thesis is part of the PEPR NumEco research program, dedicated to the development of a more frugal and sustainable digital ecosystem. Assignment The first objective is to develop game-theoretic models of decentralized data markets in which agents strategically decide how much information to acquire, share, or exploit. The models will explicitly account for the costs associated with data collection, communication, storage, and processing, as well as the value generated by the resulting information. The second objective is to study learning and equilibrium formation in these markets. Agents may have incomplete or asymmetric information about other participants, the value of data, or the state of the system. The thesis will investigate how learning dynamics and strategic information exchange affect the equilibria that emerge, and under which conditions decentralized learning leads to efficient or frugal outcomes. The third objective is to develop equilibrium-engineering mechanisms for frugal data markets. Rather than taking the resulting equilibrium as given, the thesis will explore how incentives, information structures, pricing mechanisms, and learning objectives can be designed to steer strategic agents towards equilibria that jointly balance data value and resource consumption. This will include studying the trade-offs between the benefits of additional information and the environmental cost of acquiring and processing it. Finally, the thesis will investigate how these mechanisms can be implemented through multi-agent learning algorithms , combining game-theoretic equilibrium concepts with reinforcement learning, no-regret learning, and equilibrium-seeking methods. Particular attention will be paid to settings where agents learn from limited information and where the learning process itself contributes to the overall computational and communication footprint. Main activities The thesis is expected to contribute: new game-theoretic models for decentralized and strategic data markets incorporating economic and environmental costs; theoretical results on equilibrium formation and selection under incomplete information and learning; learning and equilibrium-seeking algorithms for frugal multi-agent systems; incentive, pricing, and information-design mechanisms for engineering equilibria that balance data value and resource consumption; quantitative measures and trade-offs between information value, strategic performance, and digital resource consumption; experimental validation on representative data-market scenarios. The overarching objective is to establish game-theoretic and learning-based foundations for frugal data markets , where agents do not simply optimize the value generated from data, but collectively learn to determine which information is worth acquiring, sharing, and processing given its economic and environmental cost . Skills Required skills: game theory, optimization, and multi-agent learning; a strong background in applied mathematics and an interest in frugal digital systems and data markets.
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