Mean Field Analysis of Mutual Insurance Market

Battle Count: 53

The paper is primarily focused on mutual insurance market analysis and actuarial science rather than quantitative trading. However, the extended mean field game framework and deep BSDE methodology have potential transferability to large-population trading problems, market microstructure with many agents, and risk management in insurance-linked securities. The surplus-sharing mechanism is analogous to risk-sharing in portfolio management contexts.

High complexity due to: (1) solving coupled MF-FBSDEs with extended mean field structure requiring additional fixed-point on control; (2) modified deep BSDE algorithm with penalty terms for mean field equilibrium; (3) handling insurance constraints via projection maps; (4) continuation method for global existence; (5) multiple neural networks (6 networks for 2-class case) with specific architectures; (6) Monte Carlo simulation of coupled SDEs; (7) validation against Riccati equation solutions. Requires expertise in stochastic analysis, mean field games, and deep learning.

Code is publicly available on GitHub. The paper provides detailed parameter settings, neural network architecture (4-32-32-1), training hyperparameters (learning rate 5e-4, 1000 iterations, 10000 sample paths, M=100 time steps), and complete mathematical formulations. However, the deep BSDE training involves stochastic elements that may introduce minor variability. The unconstrained case has a closed-form ODE benchmark for validation.