Social Agents: Collective Intelligence Improves LLM Predictions

In human society, collective decision making has often outperformed the judgment of individuals. Classic examples range from estimating livestock weights to predicting elections and financial markets, where averaging many independent guesses often yields results more accurate than experts. These successes arise because groups bring together diverse perspectives, independent voices, and distributed knowledge, combining them in ways that cancel individual biases. This principle, known as the Wisdom of Crowds, underpins practices in forecasting, marketing, and preference modeling. Large Language Models (LLMs), however, typically produce a single definitive answer. While effective in many settings, this uniformity overlooks the diversity of human judgments shaping responses to ads, videos, and webpages. Inspired by how societies benefit from diverse opinions, we ask whether LLM predictions can be improved by simulating not one answer but many. We introduce Social Agents, a multi-agent framework that instantiates a synthetic society of human-like personas with diverse demographic (e.g., age, gender) and psychographic (e.g., values, interests) attributes. Each persona independently appraises a stimulus such as an advertisement, video, or webpage, offering both a quantitative score (e.g., click-through likelihood, recall score, likability) and a qualitative rationale. Aggregating these opinions produces a distribution of preferences that more closely mirrors real human crowds. Across eleven behavioral prediction tasks, Social Agents outperforms single-LLM baselines by up to 67.45% on simple judgments (e.g. webpage likability) and 9.88% on complex interpretive reasoning (e.g. video memorability). Social Agents’ individual persona predictions also align with human judgments, reaching Pearson correlations up to 0.71. These results position computational crowd simulation as a scalable, interpretable tool for improving behavioral prediction and supporting societal decision making.