Decision-making method in interdependent computing systems

Abstract

In this paper, we develop a decision-making method for interdependent computing systems that combines Bayesian reputation updating, log-linear strategy learning, and reinforcement learning mechanisms. The peculiarity of the proposed method is its ability to adapt to changes in the environment and effectively detect unscrupulous agents by dynamically adjusting reputations. The algorithmic implementation of the model allows achieving the Bayesian-Nash equilibrium, which indicates the stability of the system even in complex interaction scenarios. The results of experimental modeling have demonstrated that the proposed method strikes a balance between adaptability, reliability, and efficiency of interactions. The system demonstrates the ability to self-organize, stabilizes in fewer iterations compared to classical approaches, and effectively prevents the influence of sabotaging behavior of individual agents.