PolicyAide
Adversarial multi-agent policy research for municipal governance
PolicyAide isn't just a tool—it's an applied synthesis of ideas from AI safety, game theory, and democratic theory, designed for a domain where getting it right actually matters.
The core insight: Municipal policy decisions are non-verifiable (no “correct answer” to train against), but the quality of policy research can be verified through adversarial testing. PolicyAide creates verification mechanisms for research quality while preserving human authority over value-laden decisions.
How It Works
Multiple specialized agents attack policy questions from different angles. Proposals compete head-to-head, weak arguments get eliminated, and stronger positions emerge through adversarial iteration. An ELO rating system—borrowed from chess—tracks which proposals survive rigorous competition.
Generation Agents (6)
- Precedent Researcher — What have other jurisdictions done?
- Stakeholder Mapper — Who's affected and how?
- Implementation Analyst — What would execution require?
- Fiscal Modeler — Costs and economic effects
- Legal Reviewer — Regulatory and constitutional issues
- Counterargument Generator — Strongest objections
Evaluation Agents (2)
- Tournament Judge — Evaluates head-to-head matchups, assigns ELO updates, identifies preservable elements
- Synthesis Coordinator — Combines successful elements across proposals, manages evolution between rounds
The result: policy research that's been attacked from multiple angles before it reaches the decision-maker.
Theoretical Foundations
PolicyAide synthesizes ideas from multiple disciplines. Each element serves a purpose.
AI Safety via Debate
Irving, Amodei et al.A single AI giving you an answer has no incentive to reveal weaknesses in its own reasoning. But an adversary does. PolicyAide's tournament architecture is debate-as-alignment applied to policy research—it's a safety mechanism, not just a quality mechanism.
Reduces confidently wrong outputs. Creates interpretable audit trails. Keeps humans as final judges.
Mechanism Design
Hurwicz, Maskin, Myerson (Nobel 2007)How do you design rules so that participants—each pursuing their own objectives—produce desired system outcomes? It's “reverse game theory.” No single agent tries to produce “the best policy”—each optimizes for its perspective. But the mechanism produces strong proposals as an emergent outcome.
Deliberative Democracy
Habermas, FishkinDemocratic legitimacy and decision quality both improve when diverse perspectives engage in structured dialogue. PolicyAide is automated deliberative democracy applied to policy research—we're not replacing democratic deliberation with AI; we're using AI to enhance the deliberative quality of inputs.
PolicyAide does the research deliberation; elected officials and constituents do the value deliberation.
Constitutional AI
Anthropic (2022)Train AI systems to critique and revise outputs against explicitly defined principles. PolicyAide's Judge agent implements constitutional evaluation—the judging criteria are a constitution for policy research quality: internal consistency, evidence quality, counterargument anticipation, implementation feasibility.
The architecture is values-transparent, not values-hidden. Users can adjust the constitution to reflect their priorities.
Cognitive Diversity Research
Scott Page, MichiganDiverse groups outperform homogeneous expert groups on complex problems—not for fairness reasons, but for accuracy reasons. Different perspectives catch different errors. PolicyAide's perspective agents implement cognitive diversity by design: fiscal conservative, progressive equity, libertarian, communitarian, technocratic, rights-based lenses.
You're not hoping for diversity; you're architecturally guaranteeing it.
Red Teaming
Military/Security, AI Safety standardHave a dedicated adversary try to break your system before real adversaries do. PolicyAide implements automated red teaming for policy proposals—the Counterargument Generator explicitly attacks proposals, and the tournament structure forces proposals to survive adversarial challenge.
“Every proposal gets attacked before it reaches you. If it can't survive our tournament, you never see it.”
Dialectical Reasoning
Socrates, HegelThesis → antithesis → synthesis. Ideas strengthen through confrontation with their oppositions. PolicyAide's evolution mechanism is dialectical synthesis: winning proposals incorporate strong elements from eliminated alternatives. Good ideas survive regardless of which agent generated them.
The Synthesis
This isn't a gimmick—it's a principled architecture where each element serves a purpose.
| Discipline | Contribution | PolicyAide Element |
|---|---|---|
| AI Safety | Debate as alignment | Tournament architecture |
| Game Theory | Mechanism design | Competition rules, incentives |
| Political Science | Deliberative democracy | Multi-perspective dialogue |
| Decision Science | ELO rating systems | Objective quality ranking |
| Cognitive Science | Diversity prediction | Heterogeneous perspective agents |
| Security | Red teaming | Adversarial stress-testing |
| Philosophy | Dialectical reasoning | Synthesis through opposition |
| Anthropic Research | Constitutional AI | Values-transparent judging |
Why This Matters for Municipal Governance
The stakes are real. Budget decisions affect services. Policy choices affect lives. Immigration enforcement affects families. These aren't academic exercises.
The decisions are non-verifiable. There's no “correct” budget, no “optimal” policy. Only tradeoffs between competing values that require democratic deliberation and human accountability.
The pressure to automate is growing. Vendors promise transformation. Budgets are tight. The temptation to hand decisions to algorithms is real.
PolicyAide offers a third path: use sophisticated AI architecture to improve the quality of inputs to human decision-making, while preserving human authority over value-laden choices. Automate the research stress-testing; keep the decisions human.
Connection to the Verifiability Framework
PolicyAide embodies the Verifiability Framework insight: policy questions are non-verifiable at the decision level, but we can verify research quality through adversarial testing. The tournament doesn't tell you which policy is “right”—it tells you which proposals have survived rigorous competition and why.
Council makes the decision; PolicyAide makes sure we've stress-tested the decision and considered the strongest counterarguments first.
Read the Verifiability FrameworkInterested in PolicyAide or civic AI consulting?
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