In repeated games, such as auctions, players rely on autonomous learning agents to choose their actions. We study settings in which players have their agents make monetary transfers to other agents during play at their own expense, in order to influence learning dynamics in their favor. Our goal is to understand when players have incentives to use such payments, how payments between agents affect learning outcomes, and what the resulting implications are for welfare and its distribution. We propose a simple game-theoretic model to capture the incentive structure of such scenarios. We find that, quite generally, abstaining from payments is not robust to strategic deviations by users of learning agents: self-interested players benefit from having their agents make payments to other learners. In a broad class of games, such endogenous payments between learning agents lead to higher welfare for all players. In first- and second-price auctions, equilibria of the induced "payment-policy game" lead to highly collusive learning outcomes, with low or vanishing revenue for the auctioneer. These results highlight a fundamental challenge for mechanism design, as well as for regulatory policies, in environments where learning agents may interact in the digital ecosystem beyond a mechanism's boundaries.

翻译：在重复博弈（如拍卖）中，参与者依赖自主学习的智能体来选择其行动。我们研究这样一种场景：参与者在博弈过程中，让自身智能体以承担成本为代价，向其他智能体进行货币转移，以期使学习动态朝有利于自身的方向发展。我们的目标是理解参与者在何种情况下有动机使用此类支付、智能体间的支付如何影响学习结果，以及这对社会福利及其分配产生何种最终影响。我们提出了一个简单的博弈论模型来刻画此类情境中的激励结构。研究发现，在相当普遍的情况下，放弃支付行为无法抵御学习智能体使用者的策略性偏离：自利的参与者会通过让其智能体向其他学习方支付而获益。在广泛的博弈类别中，学习智能体间的这种内生性支付能为所有参与者带来更高的福利。在第一价格与第二价格拍卖中，由此引发的“支付策略博弈”均衡会导致高度共谋的学习结果，使拍卖者获得极低甚至趋于零的收益。这些结果凸显了机制设计以及监管政策面临的根本性挑战——当学习智能体可能在数字生态中突破机制边界进行交互时，此类问题将尤为突出。