LLM-driven Multi-Agent Simulation: Simulating Product Word-of-Mouth Diffusion with Go+Python Dual Architecture

This article introduces the llm-abm-wom-diffusion project, which combines Go's high performance with Python's AI ecosystem and integrates Large Language Models (LLM) into multi-agent simulation, providing an innovative framework for new product diffusion research. The core is to enable agents to think, communicate, and make decisions like real humans, breaking through the limitations of hard-coded rules in traditional Agent-Based Modeling (ABM). The following discussion will cover background, architecture, agent behavior, network dynamics, application value, technical challenges, and future outlook.

In the field of marketing, understanding the diffusion of new products in social networks is a classic topic. While traditional ABM can capture individual interactions, the hard-coded rules for agent behavior make it difficult to reflect the complexity and diversity of real human decision-making. The innovation of the llm-abm-wom-diffusion project lies in introducing LLM to empower agent decision-making, addressing this pain point.

The project adopts a dual-language architecture:

• Go Language: Responsible for the core of the simulation engine, using goroutine mechanisms to handle high-density interactions among thousands of agents, ensuring efficient operation.
• Python: Undertakes AI and data analysis tasks, integrates mainstream LLM APIs, and generates personalized decision logic for agents. This division of labor fully leverages Go's performance advantages and Python's AI ecosystem advantages.

In traditional ABM, agent behavior is determined by simple rules or probabilities. In this framework, each agent has a "personality profile" that includes demographics, interests, social tendencies, etc. When making decisions, agents construct prompts based on their own profiles to call LLM for reasoning, capturing the context dependence of human behavior (such as differences in product evaluations among different agents, decision changes under social pressure) without pre-defining all rule combinations.

The core of new product diffusion is Word-of-Mouth (WOM). In the project, the network evolves dynamically with the simulation: satisfied users expand their influence to form new connections, while disappointed customers may cut ties or spread negative reviews. LLM determines how agents "narrate" their usage experiences, influencing the listeners' perceptions and subsequent behaviors. Researchers can adjust parameters such as product quality, initial seed user characteristics, and network structure to explore different market scenarios.

For enterprises: Provides a virtual testing environment before product launch to evaluate the effects of different strategies (such as selection of early adopter groups, WOM critical points, impact of negative reviews), reducing marketing budget waste. For academia: Breaks through the data and experimental control limitations of traditional diffusion theory research, supports systematic testing of theoretical hypotheses, explores model behaviors in extreme scenarios, and promotes theoretical development.

Integrating LLM into ABM faces three major challenges:

1. Latency: LLM calls are time-consuming; mitigated via intelligent caching, batch requests, and asynchronous processing.
2. Cost: High costs from large numbers of API calls; supported by local deployment of open-source models as alternatives.
3. Reproducibility: Randomness of LLM leads to unstable results; balanced between behavioral diversity and statistical stability through prompt engineering and temperature parameter control.

With the improvement of LLM capabilities and the development of multimodal technologies, the potential of the simulation framework will be further unleashed: In the future, agents can process multimodal information beyond text, such as images and tones, making decisions closer to reality; combined with reinforcement learning, agents can evolve from simulation experiences to form more complex social dynamics. This project represents a new direction in computational social science, using AI to enhance traditional simulations and make virtual "humans" more realistic.