Experiment
AI Policy Experiment Designer
Turn policy assumptions into the smallest useful experiments that can generate real evidence quickly.
01
Problem to address
Teams can spend months debating uncertain propositions or jump from policy intent to full service implementation without testing the assumptions that matter most.
02
What the evidence tells us
There are open questions that could be tested before scaling, such as whether site-specific information helps people make better choices. This idea comes from those gaps, not from a direct request.
03
The proposition to explore
The idea
Turn a policy idea into explicit, prioritised hypotheses and design the smallest meaningful experiments that can reduce the most important uncertainties.
04
Hypothesis
If teams decompose a policy proposition into testable assumptions and start with the cheapest high-risk uncertainty, they can learn faster and avoid expensive commitments built on weak evidence.
05
How it could work
Input the desired outcome and proposed mechanism. AI turns it into behavioural, operational and evidence hypotheses, ranks them by impact/uncertainty and suggests a staged sequence of experiments with measurable signals.
06
Example: bring it to life
Before building personalised environmental recommendations nationally, test 50 farmers: current guidance vs contextual recommendations. Measure comprehension, trust, selection and intention to participate.
07
AI opportunity
Hypothesis decomposition, risk/uncertainty prioritisation, experiment-pattern suggestions and measurement planning. Human researchers and policy experts own methodological quality.
08
Potential value
- Faster learning
- Lower cost of being wrong
- Better evidence before scaling
- A repeatable experimentation discipline for policy
09
Reduce uncertainty without overcommitting
Smallest meaningful experiment
Use it on one live policy question with a service designer, researcher, policy lead and analyst. Assess whether the AI-generated experiment plan improves the team’s own plan.
10
Open questions and risks
What we still need to learn
- AI can suggest weak or unethical experiments
- Not a substitute for research expertise
- Need governance for experiments affecting real users