Application of Large Language Models in Stochastic Sampling Algorithms for Predictive Modeling of Population Behavior

Agent-based modeling of human behavior is often challenging due to restrictions associated with parametric models. Large language models (LLM) play a pivotal role in modeling human-based systems because of their capability to simulate a multitude of human behavior in contextualized environments; this makes them effective as a mappable natural language representation of human behavior. This paper proposes a Monte Carlo type stochastic simulation algorithm that leverages large language model agents in a population survey simulation (Monte-Carlo based LLM agent population simulation, MCLAPS). The proposed architecture is composed of a LLM-based demographic profile data generation model and an agent simulation model which theoretically enables complex modelling of a range of different complex social scenarios. An experiment is conducted with the algorithm in modeling quantitative pricing data, where 9 synthetic Van Westendorp Price Sensitivity Meter datasets are simulated across groups corresponding to pairings of 3 different demographics and 3 different product types. The 9 sub-experiments show the effectiveness of the architecture in capturing key expected behavior within a simulation scenario, while reflecting expected pricing values.