Emergent Cooperation and Strategy Adaptation in Multi-Agent Systems: An Extended Coevolutionary Theory with LLMs

Abstract

The increasing complexity of Multi-Agent Systems (MASs), coupled with the emergence of Artificial Intelligence (AI) and Large Language Models (LLMs), have highlighted significant gaps in our understanding of the behavior and interactions of diverse entities within dynamic environments. Traditional game theory approaches have often been employed in this context, but their utility is limited by the static and homogenous nature of their models. With the transformative influence of AI and LLMs on business and society, a more dynamic and nuanced theoretical framework is necessary to guide the design and management of MASs. In response to this pressing need, we propose an Extended Coevolutionary (EC) Theory in this paper. This alternative framework incorporates key aspects of coevolutionary dynamics, adaptive learning, and LLM-based strategy recommendations to model and analyze the strategic interactions among heterogeneous agents in MASs. It goes beyond game theory by acknowledging and addressing the diverse interactions (economic transactions, social relationships, information exchange) and the variability in risk aversion, social preferences, and learning capabilities among entities. To validate the effectiveness of the EC framework, we developed a simulation environment that enabled us to explore the emergence of cooperation and defection patterns in MASs. The results demonstrated the potential of our framework to promote cooperative behavior and maintain robustness in the face of disruptions. The dynamics and evolution of the Multi-Agent System over time were also visualized using advanced techniques. Our findings underscore the potential of harnessing LLMs to facilitate cooperation, enhance social welfare, and promote resilient strategies in multi-agent environments. Moreover, the proposed EC framework offers valuable insights into the interplay between strategic decision making, adaptive learning, and LLM-informed guidance in complex, evolving systems. This research not only responds to the current challenges faced in modeling MASs, but also paves the way for future research in this rapidly developing field.

Optimized Financial Planning: Integrating Individual and Cooperative Budgeting Models with LLM Recommendations

“…The provided pipeline exemplifies practical applications of the EC theory [27], demonstrating a marriage between adaptive learning mechanisms and LLMs to generate financial advisories, as an instantiation of a new economic theory where human decisions can be greatly influenced by AI agents. Through iterative refinements influenced by LLM recommendations, the system iteratively approaches optimal financial allocations.…”

“…Cooperative budgeting for households represents a tangible instantiation of the extended coevolutionary (EC) framework's principles first introduced in [27]. By analyzing this in the context of the EC theory, we illuminate the synergistic confluence of gametheoretic models, LLMs, and adaptive learning in practical MAS scenarios.…”

Guidelines For Rigorous Evaluation of Clinical LLMs For Conversational Reasoning

“…In conclusion, our study introduces CRAFT-MD, a novel framework that represents a significant departure from traditional methods of evaluating large language models (LLMs) in clinical contexts. Through the use of multiple AI agents 34 , our approach uncovers new dimensions of understanding, revealing both the potential and the limitations of LLMs like GPT-4 and GPT-3.5. While the findings highlight areas for improvement, they also pave the way for a more nuanced and iterative evaluation process that resonates with the complexities of real-world clinical settings.…”

“…It also provides a controlled, safe environment that mitigates the ethical and safety concerns associated with premature real patient engagement. By using AI agents 37 to emulate the patient role, we can conduct a broad and diverse range of interaction scenarios without exposing real patients to unverified LLMs.…”

Pursuit Problem of Unmanned Aerial Vehicles

“…We also note that some related studies have applied MAS (multi-agent systems) [33][34][35], large language models (LLMs) [36][37][38], and visual language models (VLMs) [39] to robot navigation and guidance. They utilize sensors like cameras, laser scanners, or radar to gather detailed environmental data.…”