The threat of algorithmic collusion, and whether it merits regulatory intervention, remains debated, as existing evaluations of its emergence often rely on long learning horizons, assumptions about counterparty rationality in adopting collusive strategies, and symmetry in hyperparameters and economic settings among players. To study collusion risk, we introduce a meta-game design for analyzing algorithmic behavior under test-time constraints. We model agents as possessing pretrained policies with distinct strategic characteristics (e.g., competitive, naively cooperative, or robustly collusive), and formulate the problem as selecting a meta-strategy that combines a pretrained, initial policy with an in-game adaptation rule. We seek to examine whether collusion can emerge under rational choices and how agents co-adapt toward cooperation or competition. To this end, we sample normal-form empirical games over meta-strategy profiles, compute relevant game statistics (e.g., payoffs against individuals and regret against an equilibrium mixture of opponents), and construct empirical best-response graphs to uncover strategic relationships. We evaluate reinforcement-learning, UCB, and LLM-based strategies in repeated pricing games under symmetric and asymmetric cost settings, and present findings on the feasibility of algorithmic collusion and the effectiveness of pricing strategies in practical ``test-time'' environments. The source code is available at: https://github.com/chailab-rutgers/CollusionMetagame.

翻译：算法合谋的威胁及其是否值得监管干预仍存争议，因为现有对其涌现性的评估通常依赖于长期学习过程、对交易对手采用合谋策略之理性的假设，以及参与者间超参数与经济环境的对称性。为研究合谋风险，我们提出一种用于分析测试时间约束下算法行为的元博弈设计。我们将智能体建模为具有不同策略特征（如竞争性、朴素合作性或强合谋性）的预训练策略，并将问题形式化为选择一种元策略——该策略结合了预训练的初始策略与游戏内适应规则。我们旨在考察合谋是否能在理性选择下涌现，以及智能体如何协同适应走向合作或竞争。为此，我们在元策略配置上采样正规型经验博弈，计算相关博弈统计量（如对抗个体的收益与对抗对手均衡混合策略的遗憾），并构建经验最佳响应图以揭示策略关系。我们在对称与非对称成本设置下的重复定价博弈中评估了基于强化学习、UCB和大语言模型的策略，并就算法合谋的可行性及定价策略在实际“测试时间”环境中的有效性提出了研究发现。源代码发布于：https://github.com/chailab-rutgers/CollusionMetagame。