From Agent-Based Models to LLM Agents: How Artificial Societies Are Changing

Large language models can enrich agent-based social simulations by allowing agents to interpret natural-language messages, retain memories, maintain relationships, and respond contextually, overcoming some limits of traditional models that reduce communication to numerical variables. However, this added realism makes causal mechanisms harder to inspect because agent behavior may reflect prompts, personas, retrieval systems, sampling choices, and learned model biases alongside explicit social rules. Evidence that generative agents can approximate individual responses does not establish that networks of such agents reproduce real collective dynamics, which also depend on topology, exposure order, recommendation systems, feedback loops, and amplification effects. The article argues that persuasive artificial individuals are not necessarily evidence of faithful artificial societies and emphasizes the need to validate simulations at individual, interaction, collective, and especially intervention levels. It advocates hybrid systems in which LLMs handle semantic interpretation, memory retrieval, and communication while explicit, manipulable variables represent beliefs, trust, uncertainty, exposure, and network structure. Such designs could support stronger testing through controlled changes to language, models, networks, and social mechanisms, helping distinguish genuine causal explanations from implementation-specific outcomes.

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