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Computational identification of the transition point under DRLP-MG’s high complexity

Develop computational methods to identify the phase-transition point in the Dual Reinforcement Learning Policies Minority Game (DRLP-MG) despite the model’s high computational complexity that prevents extensive brute-force simulation sweeps.

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Background

While the paper empirically demonstrates a first-order phase transition, locating the transition point precisely via large-scale simulations is computationally demanding.

The authors identify computational complexity as a barrier to exhaustive simulations, suggesting the need for more efficient computational strategies to determine the transition point.

References

Our research has shown that reinforcement policies with different learning granularities can form synergistic effects in resource allocation through the momentum strategy, yet several open questions remain. Lastly, the computational complexity of DRLP-MG impedes the identification of the transition point via numerous simulations.

— Dual Reinforcement Learning Synergy in Resource Allocation: Emergence of Self-Organized Momentum Strategy (2509.11161 - Zhang et al., 14 Sep 2025) in Section 5 (Discussion and Conclusion)