Multi-Agent Artificial Intelligence (MAAI) is Justin Lane's signature method for modeling human societies: instead of training one monolithic model, you build a simulation out of many interacting individual agents, each carrying its own psychology, beliefs, and rules of engagement. The premise is structural — groups are nothing if not collections of individuals — so the right unit of analysis is the agent, not the aggregate. From the bottom-up interaction of those agents, macro-level patterns emerge: cooperation, polarization, ritual, conflict. This makes culture, belief, and social behavior quantifiable, empirical, and testable rather than merely narrated, producing psychologically realistic simulations you can interrogate and run forward. MAAI is the engine behind the 📝digital twins at 📝CulturePulse, the company Justin founded to turn this approach into operational forecasting. It is also the subject of his book 📝Understanding Religion Through Artificial Intelligence: Bonding and Belief (Bloomsbury, 2021), the first book written on MAAI. Where single-model and pure-LLM approaches compress a population into one statistical surface, MAAI preserves the heterogeneity that actually drives social outcomes.
I bet on many agents over one model because the alternative collapses under its own assumptions. Humans did not machine-learn their way to intelligence — we became intelligent in groups, through bonding, imitation, and belief. A single model trained on text can mimic the output of a society; it cannot reproduce the mechanism that generates one. If you want to know why a population fractures or holds, you have to build the individuals and let them interact. That is the unglamorous, structural work, and it is the only thing that has ever held up under testing.