Social dilemmas exist in various fields and give rise to the so-called free-riding problem, leading to collective fiascos. The difficulty of tracking individual behaviors makes egoistic incentives in large-scale systems a challenging task. However, the state-of-the-art mechanisms are either individual-based or state-dependent, resulting in low efficiency in large-scale networks. In this paper, we propose an egoistic incentive mechanism from a connected (network) perspective rather than an isolated (individual) perspective by taking advantage of the social nature of people. We make use of a zero-determinant (ZD) strategy for rewarding cooperation and sanctioning defection. After proving cooperation is the dominant strategy for ZD players, we optimize their deployment to facilitate cooperation over the whole system. To further speed up cooperation, we derive a ZD alliance strategy for sequential multiple-player repeated games to empower ZD players with higher controllable leverage, which undoubtedly enriches the theoretical system of ZD strategies and broadens their application domain. Our approach is stateless and stable, which contributes to its scalability. Extensive simulations based on a real world trace data as well as synthetic data demonstrate the effectiveness of our proposed egoistic incentive approach under different networking scenarios.

中文翻译：

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基于零决定性联盟的大规模系统利己主义激励

社会困境存在于各个领域，导致所谓的搭便车问题，导致集体惨败。跟踪个人行为的困难使得大规模系统中的利己激励成为一项具有挑战性的任务。然而，最先进的机制要么基于个体，要么依赖于状态，导致大规模网络效率低下。在本文中，我们利用人的社会性，从连接（网络）的角度而不是孤立（个人）的角度提出了一种利己主义的激励机制。我们利用零决定因素 (ZD) 策略来奖励合作和制裁叛逃。在证明合作是ZD玩家的优势策略后，我们优化了他们的部署，以促进整个系统的合作。为进一步加快合作，我们推导出一种针对连续多人重复博弈的ZD联盟策略，赋予ZD玩家更高的可控杠杆，这无疑丰富了ZD策略的理论体系，拓宽了其应用领域。我们的方法是无状态且稳定的，这有助于其可扩展性。基于真实世界跟踪数据和合成数据的大量模拟证明了我们提出的利己激励方法在不同网络场景下的有效性。