Synthesizing Network Economics and Algorithmic Game Theory: Dynamic Pricing and Resource Allocation in Decentralized Multi-Agent Networks

Recent works like Scatá et al. (2024) and Rahman et al. (2023) apply network economics and evolutionary game theory to network function placement and cache allocation, but typically treat these domains in isolation. This research would develop a generalizable framework—applicable to federated learning, resource allocation in blockchains, or decentralized wireless networks—that integrates the economic incentives, strategic behaviors, and dynamic topology of modern multi-agent systems. By connecting disparate strands from algorithmic game theory, network economics, and distributed platform design, this synthesis could yield new algorithms for efficient, fair, and robust resource management in next-gen platforms. The impact: a principled, cross-disciplinary toolkit for tackling complex resource and incentive problems in the platforms of the future.

References:

1. A Complex Network and Evolutionary Game Theory Framework for 6G Function Placement. Marialisa Scatá, Aurelio La Corte, A. Marotta, Fabio Graziosi, D. Cassioli (2024). IEEE Open Journal of the Communications Society.
2. Information-Centric Networking Cache Memory Allocation: A Network Economics Approach. Aisha B. Rahman, P. Charatsaris, E. Tsiropoulou, S. Papavassiliou (2023). Global Communications Conference.