Competition among seaports through Mean Field Games and real-world data
Charles-Albert Lehalle (X), Giulia Livieri (LSE)

TL;DR
This paper develops a Mean Field Game model for maritime traffic, integrating real-world data to optimize seaport navigation, and provides explicit solutions and parameter inference methods for practical application.
Contribution
It introduces a mesoscopic MFG model for maritime traffic, derives explicit solutions, and proposes a statistical approach to infer model parameters from real-world data.
Findings
Explicit stationary MFG solutions under certain conditions
Conditions for uniqueness of the Mean Field Equilibrium
Methodology for parameter inference from real-world data
Abstract
This paper presents a Mean Field Game (MFG) model for maritime traffic flow, treating the navigation of ships between seaports as a large-scale stochastic control problem. The MFG framework enables the modeling of agents at a microscopic level as rational decision-makers who seek to optimize their utility, thereby translating complex microscopic behaviors into macroscopic models. We build upon this MFG framework to develop a mesoscopic-scale MFG model that defines the payoff and cost functions for a coordinator at each seaport considered in our study. The coordinator determines the routes taken by ships transporting goods between ports by evaluating several key factors: transportation costs, expected profit margins from loading specific goods at the seaports and unloading them at various destinations, and a congestion term that reflects the costs associated with accessing the…
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Taxonomy
TopicsTraffic control and management · Maritime Ports and Logistics · Vehicle Routing Optimization Methods
