[2506.13113] Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning

Computer Science > Artificial Intelligence arXiv:2506.13113 (cs) This paper has been withdrawn by Stella Dong [Submitted on 16 Jun 2025 (v1), last revised 23 Mar 2026 (this version, v2)] Title:Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning Authors:Stella C. Dong, James R. Finlay View a PDF of the paper titled Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning, by Stella C. Dong and James R. Finlay No PDF available, click to view other formats Abstract:This paper develops a novel multi-agent reinforcement learning (MARL) framework for reinsurance treaty bidding, addressing long-standing inefficiencies in traditional broker-mediated placement processes. We pose the core research question: Can autonomous, learning-based bidding systems improve risk transfer efficiency and outperform conventional pricing approaches in reinsurance markets? In our model, each reinsurer is represented by an adaptive agent that iteratively refines its bidding strategy within a competitive, partially observable environment. The simulation explicitly incorporates institutional frictions including broker intermediation, incumbent advantages, last-look privileges, and asymmetric access to underwriting information. Empirical analysis demonstrates that MARL agents achieve up to 15% higher underwriting profit, 20% lower tail risk (CVaR), and over 25% improvement in Sharpe ratios relative to actuarial and heuristic baselines. Sensitivity tests confirm robu...