MetaSociety: A Simulation-Driven AI Policy Advisor for Data-Grounded Smart Governance

Abstract: Policy decisions in local governance are frequently taken with limited empirical grounding, relying on precedent, intuition, or small-scale pilots that are slow and costly to run. This paper presents Meta Society, a smart- governance platform that lets a policymaker state a goal in natural language, receive structured policy recommendations, and evaluate candidate policies against a virtual model of a town before committing to a real-world deployment. The platform combines three components. First, an AI Policy Advisor executes a multi-stage large-language-model (LLM) reasoning pipeline-orchestrated with LangGraph and served through a FastAPI backend-that turns a natural-language goal and multi-domain town statistics into structured candidate policies. Second, a Simulation Lab reports its outcomes from a deterministic engine rather than from an LLM: one agent per citizen is generated for a real town (Pandharpur, Solapur district, Maharashtra) so that published Census 2011 totals are reproduced exactly; a 35-node Bayesian network- thirty core nodes plus five exogenous shock roots-is rolled forward one period at a time with conditional-probability tables counted from the population wherever the data can speak; a historical-grounding stage transfers characteristics from real predecessor programmes to the proposed policy and derives a grounded effect scale that the causal network consumes; an external-shock layer stress-tests the policy under hypothetical events; and a Differential Evolution search with NSGA-II non-dominated selection, reported alongside an equal-budget random-search control, explores the policy- parameter space. Third, a data and dashboard layer renders the same population together with a field-by-field provenance ledger that separates verified Census fields from modelled ones. Every headline metric is a stated summation over the agent population, and each run re-checks the accounting identities, the network’s documented response directions, and reproducibility, reporting the outcome as an evidence pack. We describe the architecture, the modules, the workflow, and the verification the engine performs, report a seed-ensemble uncertainty protocol and a draw-count sensitivity analysis, and state the system’s boundaries plainly: response directions are validated but magnitudes are not calibrated against an evaluated real programme, income and sector are modelled rather than measured, and the platform is a counterfactual comparison tool rather than a forecasting service.

Keywords: smart governance, policy simulation, large language models, LangGraph, Bayesian networks, differential evolution, NSGA-II, agent-based modeling, microsimulation, synthetic population, external shocks, historical grounding.