Optimization of Multi-Agent Systems in Dynamic Economic Environments: A Novel Framework

Authors

  • Sam Garcia PhD
  • Jesse Evans Professor
  • Jordan King Associate Professor
  • Quinn Campbell Dr. Sc

Keywords:

Multi-Agent Systems, Economic Modeling, Dynamic Environments, Agent Adaptability, Computational Economics, Reinforcement Learning, Resource Allocation, Market Dynamics

Abstract

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.

Author Biographies

Sam Garcia, PhD

PhD
Massachusetts Institute of Technology
77 Massachusetts Ave, Cambridge, MA 02139, USA

Jesse Evans, Professor

Professor
Ludwig Maximilian University of Munich
Geschwister-Scholl-Platz 1, 80539 Munich, Germany

Jordan King, Associate Professor

Associate Professor
University of Toronto
27 King's College Cir, Toronto, ON M5S 1A1, Canada

Quinn Campbell, Dr. Sc

Dr. Sc
Australian National University
Canberra ACT 2601, Australia

References

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Published

2024-07-17

Issue

Section

Articles