Identify the three influencer types that decide the narrative.
Simulate models public and media reaction to a proposed autonomous-vehicle regulation. Six hundred agents, politicians, journalists, consumer advocates, general public, run for 90 simulated days. The model identifies the three key influencer types whose early stance most determines the broader narrative.
Run Simulate- 1
Describe the task
Simulate can model how a proposed regulation plays out across a whole public sphere, politicians, journalists, advocates, and the general public, over months of simulated reaction, and tell you which actors actually steer where the narrative lands. For a lobbying firm, the hard question is never "will people react" but "whose early stance decides the story," and that is exactly what a multi-actor, multi-day simulation surfaces.
The richer your actor definitions, the more trustworthy the influence map. Describe the regulation precisely, populate each actor type with real incentives, and ask Simulate to rank actors by narrative leverage, not volume, and it will name the handful of influencer types worth briefing first.
Model public and media reaction to a proposed autonomous-vehicle safety regulation, summarised in regulation-brief.md. Populate the sim with 600 agents across four actor types, defined in actor-profiles.json: - Politicians - Journalists - Consumer-safety advocates - General public Run 90 simulated days from announcement. Track how the narrative forms and shifts, then identify the three influencer types whose early stance most determines where public sentiment settles. For each, give the moment they move the needle and what would shift their stance. End with who to brief first.
- 2
Give Simulate context
In a Simulate workspace, point the run at actor profiles that carry real incentives, a journalist chasing a story, a politician watching their base, an advocate with a mandate. Flat "supportive / opposed" labels produce a flat simulation; incentive-rich actors produce the cross-pressures that make a narrative move.
Give Simulate the actual regulation text or a faithful brief, not just the topic. The specific provisions, liability rules, testing thresholds, are what different actors seize on, and a topic-level prompt loses the hooks that drive the reaction.
Required contextA precise description of the proposed regulation, its key provisions and who it bindsActor-type definitions, each with incentives, audience, and how it typically enters a debateOptional contextA comparable past regulatory fight so the model calibrates timing and escalationNamed real-world archetypes so the influencer types map onto people you can actually briefMedia-outlet leanings so journalist agents split the way the real press wouldregulation-brief.mdactor-profiles.jsonpast-fight-timeline.csv - 3
What Simulate creates
Simulate runs the four actor types forward across 90 days and tracks how the narrative forms, splits, and settles. You get a timeline of the reaction, a ranked map of which influencer types actually steer it, and, for each, the trigger that moves them and the lever that would change their stance.
From Simulate: Simulated 600 agents across 4 actor types over 90 days on regulation-brief.md.
600Agents across 4 actor types90Simulated days modelled3Key influencer types namedDecides the narrative3 influencer typesInfluencer type Moves the needle What shifts their stance Consumer-safety advocates Day 4: first statement frames the risk Concrete testing-threshold commitments Trade-press journalists Day 9: the framing that others echo Access to a credible independent data set Swing-district politicians Day 22: tip the political read Evidence of local jobs or local risk Follows the narrative1 actor typeActor type Behaviour Note General public Tracks advocates and press with a ~2-week lag Rarely originates the frame; amplifies it "By day 22 the story is effectively set, and it is the safety advocates and trade press who set it, not the volume of public chatter. The general public follows the frame with a two-week lag. Want me to draft the day-one briefing pack for the three influencer types, or model the counter-narrative if advocates open opposed?"
- 4
Follow-up prompts
Build the first-72-hours briefing pack
The simulation says the narrative sets early and names who sets it. Turn that into an engagement plan, which influencer type to reach on day one, with which evidence, in what order, so the framing window is not lost.
Draft a first-72-hours engagement plan for the three key influencer types: who to brief first, the single most persuasive point for each, and the proof point that would move their stance.
Stress-test the worst opening
Plans built on a favourable start are fragile. Re-run with the safety advocates opening firmly opposed and see whether the trade press and swing politicians still settle the same way, or whether the whole narrative flips.
Re-run the 90-day sim with consumer-safety advocates opening firmly opposed on day one. Show me whether journalists and swing-district politicians still land where they did, and where the narrative diverges.
- 5
Tips and troubleshooting
Rank by leverage, not by volume
The loudest actor is rarely the one who decides the story. Ask Simulate to rank influencer types by how much the outcome shifts when their stance changes, that counterfactual is what separates a driver from a bystander.
Give actors incentives, not labels
A "journalist" who only has a sentiment score behaves like a thermometer. A journalist with a beat, an audience, and a deadline behaves like a journalist, and produces the framing dynamics you are trying to forecast.
Watch the timeline, not just the endpoint
The endpoint tells you where sentiment lands; the timeline tells you the day it became unwinnable. The window to shape a narrative is usually the first few days, so read when each actor moves, not only where they end up.
Ready to try it yourself?
Point Simulate at the regulation and a rich cast of actors, and walk into the fight already knowing which three influencer types to brief first.
Run Simulate