DECISION-MAKING METHOD IN INTERDEPENDENT COMPUTING SYSTEMS

In this work, a decision-making method for interdependent computational systems has been developed. The proposed approach integrates Bayesian reputation updates, log-linear strategy selection, and reinforcement learning mechanisms to enable autonomous agents to make context-aware and reliable decisions. The method effectively balances strategic adaptability, system stability, and robustness to unreliable or malicious agents. A distinctive feature of the method is its ability to dynamically adjust agent strategies based on reputation scores and prior interaction outcomes, thus facilitating convergence toward Bayesian-Nash equilibrium. The implementation includes a mechanism for iterative reputation correction and probabilistic strategy optimization, which ensures that the system achieves stable coordination in a decentralized environment. Simulation results demonstrate that the proposed method significantly improves the convergence rate, reduces the impact of low-reputation agents, and enhances system-wide cooperation. Increasing the number of agents leads to moderate growth in system complexity, but the reputation-aware mechanism effectively mitigates instability and delays in strategy synchronization. Conversely, adding more interaction rounds improves reliability and accelerates equilibrium attainment. Future research directions include adapting the model for various types of interdependent systems, such as edge computing environments, IoT infrastructures, and mobile multi-agent platforms. It will also be necessary to explore multi-objective optimization formulations that incorporate not only performance and stability, but also energy efficiency, communication overhead, and quality of service (QoS) constraints.