CompeteAI: An AI framework for understanding the competitive dynamics of large-scale language model-based agents

Machine Learning


https://arxiv.org/abs/2310.17512

Competition profoundly shapes human society, influencing economies, social structures, and technology. Traditional competition studies that rely on empirical studies suffer from limited access to data and lack micro-level insights. Agent-Based Modeling (ABM) emerged to overcome these limitations and has evolved from rule-based to machine learning-based agents. However, these approaches still struggle to accurately simulate complex human behavior. The advent of large language models (LLMs) has enabled the creation of autonomous agents for social simulation. Recent studies have explored LLM-based agents in a variety of environments, but studies that specifically investigate competitive dynamics remain sparse. This gap prevents a comprehensive understanding of competition across various domains.

Empirical studies of competition have revealed valuable insights, such as inter-team competition encouraging intra-team cooperation and the “Matthew effect” in academia. However, these studies are limited in controlling variables and collecting comprehensive data. Recent advances in ABM, powered by LLM, have revolutionized social simulation. Notable projects include Generative Agent, which established a foundational framework for agent design, and studies investigating information transfer, recommender systems, and macroeconomic environments. Significant advances have also been made in collaborative simulation.

Despite these advances, research into competitive mechanisms using LLM-based agents remains limited. Existing studies have investigated auction scenarios and competition between firms, but have yet to simulate complex competitive environments and thoroughly analyze competitive behavior and system evolution. This research gap presents an opportunity for more comprehensive research into competitive dynamics using LLM-based agent simulations, which may overcome the limitations of traditional empirical studies and provide deeper insights into competitive phenomena.

Researchers from the University of Science and Technology of China, Microsoft Research, the College of William and Mary, Georgia Institute of Technology, and Carnegie Mellon University Competitive AIis a comprehensive framework for studying competitive dynamics among LLM-based agents. The framework consists of selecting an environment, setting up, running a simulation, and analyzing. Using GPT-4, the researchers developed a virtual town simulation that includes restaurant agents and customer agents. The restaurant agents compete to attract customers, driving continuous evolution and innovation. The customer agents, with diverse characteristics, act as judges by selecting restaurants and providing feedback. This setup allows for a detailed look into the competitive behavior and evolution of the system. The framework starts with selecting an appropriate competitive context, then setting up the environment, running experiments to capture the agents' interactions, and finally analyzing the behavior to derive insights into the competitive dynamics. And a core component of the framework is creating a competitive environment with meticulously designed competitors, judges, and interactions. Constraints such as resource and service limitations for competitors and financial limitations for judges are crucial for success. The design is inspired by resource dependence theory, which states that competition for resources affects organizational behavior and strategies.

The CompeteAI framework implements a simulated small-town environment with two competing restaurants and 50 diverse customers. The simulation runs for 15 days or until one restaurant closes. Both restaurants and customers are driven by GPT-4 (0613) LLM-based agents. The restaurant agent manages the restaurant through predefined actions such as modifying the menu, managing chefs, and creating advertisements. The customer agents (individuals or groups) select a restaurant every day based on the information provided and leave feedback after dining.

To overcome the practical challenges, the researchers developed a comprehensive restaurant management system with APIs to enable text-based LLM agents to effectively interact with the simulated environment. The system incorporates diverse customer characteristics and relationships to trigger more realistic competitive behavior. Restaurant agents analyze daily information, design strategies, interact with the management system, and store summaries for future planning. Customer agents with different characteristics and group dynamics make decisions based on restaurant information, individual preferences, and group discussions. The framework also includes a food quality evaluation mechanism that considers factors such as chef skill level, food cost, and sales price. This empirical approach ensures a realistic representation of service quality in a competitive environment.

The researchers conducted nine experiments with individual customers and six with group customers. The analysis covers both micro- and macro-level perspectives.

Micro-level results revealed sophisticated behavior of LLM-based agents in the CompeteAI framework. The agents exhibited context awareness and analyzed scenarios from “shallow to deep” – that is, they examined customer flow trends, dish feedback, and rivals' behavior before conducting deeper strategic analysis. The agents employed traditional market strategies such as differentiation, imitation, customer orientation, and social learning. Although customers' decisions were influenced by multiple factors, “needs satisfaction” was important for all customers. In particular, individual customers placed more importance on the restaurant's reputation and groups were more proactive in exploring new options, demonstrating that the framework can simulate diverse consumer behavior.

The macro-level analysis revealed several important phenomena in the simulated competitive environment. Strategic dynamics showed a complex interplay of differentiation and mimetic behavior among competing restaurants. The Matthew effect was observed, with an initial advantage leading to the continued success of one restaurant through a positive feedback loop. Interestingly, customer grouping reduced the “winner-take-all” phenomenon, occurring less frequently among group customers (16.7%) compared to individual customers (66.7%). Perhaps most importantly, competition consistently improved the overall product quality. In 86.67% of cases, the average food score of at least one restaurant improved over time, with Restaurant 1 increasing its average food score by 0.26 from day 1 to day 15, and Restaurant 2 increasing its average food score by 0.22.

These findings demonstrate the complex dynamics of competition among LLM-based agents and provide insights into market behavior, customer decision-making, and the impact of competition on service quality in the simulated environment.

The CompeteAI framework introduces an innovative approach to study competitive dynamics using LLM-based agents. By simulating a virtual town with competing restaurants and diverse customers, the study reveals sophisticated agent behavior that is aligned with classical economic and social theories. Key findings include the emergence of complex strategic dynamics, Matthew effects, and the impact of customer grouping on market outcomes. The study demonstrates that LLM-based agents can effectively simulate competitive environments and consistently improve product quality over time. This innovative framework provides valuable insights for future research in sociology, economics, and human behavior, providing a promising platform for interdisciplinary research in controlled and realistic settings.


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Asjad is an Intern Consultant at Marktechpost. He is pursuing a B.Tech in Mechanical Engineering from Indian Institute of Technology Kharagpur. Asjad is an avid advocate of Machine Learning and Deep Learning and is constantly exploring the application of Machine Learning in Healthcare.

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