Emotion-Regulated Internal States for Human-Aligned Social Agents

Large language models (LLMs) are increasingly used as agents in social simulations to study human behavior across repeated interactions. However, many LLM-based simulations exhibit unstable or unrealistic behavior over time, limiting their ability to model long-term social dynamics. A key reason is that social decisions are often treated as direct responses to situational inputs, overlooking the role of emotion in human decision making. We propose EmoSim, an emotion-regulated framework for LLM-based social simulation. EmoSim models emotion as an explicit internal state represented by low-dimensional emotion vectors that guide agent decisions. Emotional responses are formed through structured social memory, including summaries of past interactions, impressions of other agents, and weak normative regularities, and are grounded in a human dataset of 500 participants reporting emotional reactions across repeated social decisions. Experiments on a third-party punishment task show that EmoSim produces more stable emotional predictions and decision patterns that better align with human behavior.