2nd Place
Sim Francisco
Sim Francisco: a living digital twin of San Francisco's population: poll a representative, Census-seeded synthetic city in seconds, and run what-if futures before they happen.
Core trick
Census-sampled agents each carry a generated life history, and one poll asks all of them the same question to yield population-level probabilities.
Description
Sim Francisco: a living digital twin of San Francisco's population: poll a representative, Census-seeded synthetic city in seconds, and run what-if futures before they happen.
Sim Francisco is a live simulated digital twin of the entire population of San Francisco. We seeded thousands of agents sampled from the US Census Data to represent the population of SF. These agents each represent one human, complete with demographic data (like age, gender, religion, occupation, etc), and personal history (where they grew up, schools attended, hobbies, formative experiences) that gives them a coherent worldview. They don't just hold opinions; they reason from a life. They live on a map of SF, going about their day and reacting to the news in real time.
Ask the city anything and you poll the whole synthetic electorate at once, watching verdicts accumulate neighborhood by neighborhood. That same panel forecasts prediction markets: point it at a market question — "Will this measure pass?", "Who wins this race?" — and it returns a probability you can hold up against the real result.
Below are some historically predictive examples:
2024 Presidency
Question: In November 2024 do you vote for the Democratic presidential ticket (Harris) over the Republican (Trump)?
Actual: 83.8% Dem
Predicted: 81.3% Dem
March 2024 Prop A
Question: There's a measure on your March ballot, Proposition A, that would let the City borrow up to $300 million through general obligation bonds to build and preserve affordable housing, for low-income families, seniors, and working residents. It's backed by Mayor Breed and would need a two-thirds supermajority to pass. Are you voting yes or no?
Actual: 70.38%
Predicted: 70% yes
README excerpt
# sim francisco **simulate and predict our golden city and beyond** - **Live app:** https://simfrancisco.org - **Live API:** https://sf-digital-twin-tp.fly.dev (`GET /health`) A *distributionally accurate* synthetic population of San Francisco — sampled from real US Census microdata — that can be polled and perturbed with events to produce population-level predictions: elections, ballot measures, issue/approval polling, prediction-market probabilities, and counterfactual opinion shifts. Open the app, click the **?** for the project brief, and ask the city anything. --- ## The brief ### The pitch What if you could predict how your customers responded to releases before they went out into the real world? Or if you could get a signal on who will win an election? We simulated the population of San Francisco distributed across real US Census data and achieved near-parity with these types of real-world events. ### Historically predictive How do we know if our simulation of SF is accurate? The honest test needs a model whose knowledge cutoff *predates* the event, so it can't have just memorized the outcome. The older Claude models with a 2023 cutoff have since been **retired** — so for this leakage-free backtest we ran **GPT-4o** (knowledge cutoff **October 2023**) and asked the twin city to predict results it had never seen. (The live app runs the current **Claude Sonnet 4.6**.) **2024 Presidency** — *In November 2024 do you vote for the Democratic presidential ticket (Har