Government affairs teams operate where the stakes are highest and the audiences hardest to reach: lawmakers, regulators, civil servants, and the political stakeholders who shape whether a policy position lands or stalls. Artificial Societies lets these teams pressure-test a position, an engagement strategy, or a public narrative before it goes live — by constructing networks of 200 to 3,500 interconnected AI personas that represent each political audience and measuring how they respond. Because the work is simulation, sensitive positions can be tested without anything entering the field, and results arrive within 24 hours rather than the weeks conventional research would take.
Policymakers, regulators, and their advisors are among the most difficult audiences to research directly — they are hard to recruit, expensive to reach, and often impossible to survey on a sensitive question. Yet a misjudged regulatory narrative or a poorly framed policy position carries outsized consequences. Simulation lets government affairs teams anticipate how each constituency will interpret a position, where opposition will form, and which framing holds up under scrutiny, using personas grounded in real-world observations validated against human panels rather than assumptions.
Artificial Societies constructs purpose-built networks of AI personas for each relevant audience — legislators and their staff, regulators, civil servants, trade bodies, journalists, and affected publics — defined by demographics, organisations and roles, or a client's own first-party data and primary research. Baseline positions are established, competing framings or engagement approaches are introduced, and the resulting shifts are measured across the network. Every number opens down to segments, drivers, and individual reasoning, and researchers can interview any persona directly to understand why a position resonated or backfired.
Typical applications include stress-testing a regulatory submission or consultation response, sequencing a policy announcement, preparing for legislative hearings, anticipating opposition to a proposed rule, and shaping coalition and stakeholder-engagement strategies. In each case the value lies in seeing the full range of reactions across a fragmented political landscape before committing — not a single directional read, but comprehensive, defensible evidence that a recommendation can be built on.
Traditional polling reaches the general public but rarely the specific, hard-to-recruit audiences that matter most in government affairs — sitting regulators, senior civil servants, or the staff around a legislator. Artificial Societies constructs networks of AI personas representing those precise audiences and models how opinion forms and spreads among them, returning both distributions and individual-level reasoning.
Yes. Because the audience is simulated, a confidential policy position or regulatory narrative can be tested without exposing it to any real participant, eliminating leakage risk during the pre-decisional window.
Artificial Societies' opinion distributions reach 95% of the human self-replication level, with 89% internal coherence, as set out in the method and evaluation. Personas are strongest for audiences where diverse observations exist, whether public or from client-provided data.