Funding
Funding not disclosed
Founders
Product
Problem
Policymakers often lack tools to explore complex policy options, predict how different population groups will respond, and balance trade‑offs such as efficacy, equity, cost, and feasibility. Traditional analysis relies on coarse demographic data or costly bespoke surveys, leading to decisions that may not reflect real‑world behavioral outcomes.
Solution
Priorfoundry offers a computational platform that maps the decision space for public‑sector challenges, then simulates policy impacts on synthetic populations derived from large‑scale survey data. Users can import their own survey results to create customized agent profiles, enabling scenario analysis that reflects cross‑cultural behavioral patterns. The platform runs built‑in robustness checks, permutation tests, and sensitivity analyses to validate outcomes against human respondents. Results are presented as decision‑ready outputs that quantify trade‑offs across efficacy, equity, cost, and feasibility, helping officials select evidence‑grounded policies.
Target Audience
Primary users are government agencies, public‑sector analysts, and international organizations that design and evaluate social, economic, or health policies.
Features
- Synthetic population generation from extensive survey datasets with optional integration of an organization’s proprietary survey data
- Decision‑space mapping tools to define policy options, evidence sources, and key constraints
- Simulation engine grounded in peer‑reviewed computational behavioral science, including robustness testing and sensitivity analysis
- Comparative outcome dashboards that break down impacts by demographics and validate against real respondent data
- Multi‑objective optimization that balances efficacy, equity, cost, and feasibility to produce actionable policy recommendations