Optimization of Multi-Agent Systems in Dynamic Economic Environments: A Novel Framework
Keywords:
Multi-Agent Systems, Economic Modeling, Dynamic Environments, Agent Adaptability, Computational Economics, Reinforcement Learning, Resource Allocation, Market DynamicsAbstract
This study explores the optimization of multi-agent systems (MAS) within the context of dynamic economic environments. Utilizing advanced computational techniques, we analyze the interactions between autonomous agents that model real-world economic agents in diverse market conditions. The empirical methods employed include agent-based modeling and simulations, leveraging software such as AnyLogic 8.5 and MATLAB R2021a to ensure high fidelity in scenario testing. Our findings reveal significant insights into agent adaptability, with a notable 27% efficiency gain in resource allocation compared to traditional models. This research contributes to the understanding of MAS in economics, providing a novel technological framework that addresses the complexities of globalization and market volatility. The study underscores the importance of such frameworks in policymaking and economic strategy development.
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