Scenario. A national government's policy machinery, now run largely by AI systems, is acting fast across many domains at once. The public can see the resulting posture no longer tracks any party's platform, and the complaints are mounting. Policymakers want to get a real read on what people want, but the next election is three years away, a referendum is too binary, a citizens' assembly would take a year, and polls are too gameable to bind anyone. No one knows what institution would actually fit.

Challenge: Design a preference-aggregation institution that can produce legitimate, binding-or-near-binding output on policy-relevant timescales (weeks, not years), and that can keep pace with AI policy systems without devolving into rolling plebiscite.

Evaluation. A better proposal sits inside the existing constitutional order rather than replacing it, and is robust to the manipulation pressures that come with any fast cheap aggregator.

Scenario. A regional government faces a contested decision on water rights. A traditional citizens' assembly would cost millions and delay the decision by a year, so a vendor offers three faster alternatives. The first uses AI as a facilitator and summarizer of a compressed human deliberation. The second lets each citizen send a personal AI agent, interviewed at length by its principal, to participate on their behalf in a multi-agent deliberation. The third replaces the citizens entirely with calibrated language-model proxies. The minister wants to use one; opponents call all three laundering. The legislature has to decide which, if any, can carry democratic standing.

Challenge: Design a regime that decides when, if ever, AI-mediated deliberation — at each of these levels of mediation — can carry democratic standing, and what evidence and procedure must be in place for its outputs to count.

Evaluation. A better proposal distinguishes accuracy (the procedure predicts what real citizens would conclude) from authorization (real citizens have empowered this output to bind them), and is specific about what each requires at each tier of mediation.

Scenario. Polling on a contested AI-regulation bill swings double digits week to week as agent-driven campaigns reach different demographics with different framings, and no one in government takes the numbers seriously anymore. Underneath the surface, on values like safety, autonomy, economic security, and fairness across regions, the public's commitments appear more stable and more shared than the polling suggests. A research consortium proposes a national elicitation: in-depth interviews with a representative sample, designed to surface the values people actually hold and the conditions under which they would endorse one as wiser than another. Six months, tens of millions of dollars.

Challenge: Design an elicitation institution that produces a high-resolution picture of the public's values on a contested issue — beneath surface preferences, surfacing shared commitments and bridges across apparently opposed positions — and that stays robust to agent-scale manipulation even as it remains slow and expensive.

Evaluation. A better proposal is clear about when the cost is worth paying, what authority the output carries, and how the elicitation itself is defended from the manipulation pressures that broke polling.

Scenario. A country is about to hold a national referendum on overhauling its long-term-care policy — who pays, who provides, what happens when someone can no longer stay at home. Polling will capture yes and no. What it won't capture is what Yelena, a home-health aide, has learned over fifteen years: that the families she works with care about different things than either side of the campaign is talking about. They want to be sure their parent is still known by name; they want a single aide who isn't always changing; they want to know that their own turn, when it comes, won't reduce them to a bed-flip statistic. A pilot moral-graph process could capture what the referendum cannot. The minister's office has a month to decide whether to run it.

Challenge: Design a pilot moral-graph-elicitation process that supplements a country's legislative or referendum process for a single policy domain you choose (education, drug scheduling, immigration quotas, land use — whatever best tests the method), specifying selection, elicitation, aggregation, and its interface with existing authority, plus the protocol the minister's office would actually run.

Evaluation. A strong pilot survives the three strongest objections a democratic theorist would raise about substituting an elicited value-structure for the vote, with sketched responses.