External incentive mechanisms have been studied as a method to promote cooperation in sequential social dilemmas involving multiple autonomous agents. Mutual Acknowledgment Token Exchange (MATE) is one such approach: by enabling agents to exchange acknowledgment tokens, it induces cooperation without additional training. However, MATE’s use of fixed, manually tuned token values limits adaptability to nonstationary environments and can constrain performance. To enable a dynamically adapted token, we introduce Social Influence-based MATE (SI-MATE), which allows agents to share their individual improvement signals and to self-punishment in response to inequality. Experiments in a four-agent environment show that SI-MATE outperforms MATE across multiple metrics, including learning speed.