Executive summary: This exploratory proposal outlines a system that combines causal reasoning, economic knowledge graphs, and retrieval-augmented generation to help policymakers, analysts, and the public understand the ripple effects of economic policies—prioritizing transparent, structured explanations over predictive certainty—and invites feedback and collaboration to shape its development.
Key points:
Problem diagnosis: Current tools for assessing economic policy impacts are fragmented, opaque, and inaccessible to non-experts, making it hard to trace causal effects and undermining public trust and policy design.
Proposed solution: The author proposes a domain-specific LLM system that simulates the step-by-step effects of policy changes across interconnected economic actors using a dynamic knowledge graph and historical/contextual retrieval (RAG), emphasizing explanation rather than prediction.
System architecture: The model integrates four modules—(1) a historical text database, (2) an economic knowledge graph, (3) a reasoning-focused LLM, and (4) a numerical prediction layer—designed to trace and visualize how policy affects sectors, stakeholders, and outcomes over time.
Use cases and benefits: This system aims to support clearer communication among policymakers, researchers, and the public by making assumptions explicit, surfacing tradeoffs, and enabling structured, multi-perspective dialogue on economic consequences.
Challenges and design considerations: Key hurdles include building a comprehensive yet ideologically neutral knowledge graph, simulating historical events for causal validation, and designing interfaces that clearly convey uncertainty and avoid false confidence in results.
Call to action: The project is in an early stage and seeks input from policy experts, economists, and generalist users to refine the design and ensure it serves real-world needs.
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Executive summary: This exploratory proposal outlines a system that combines causal reasoning, economic knowledge graphs, and retrieval-augmented generation to help policymakers, analysts, and the public understand the ripple effects of economic policies—prioritizing transparent, structured explanations over predictive certainty—and invites feedback and collaboration to shape its development.
Key points:
Problem diagnosis: Current tools for assessing economic policy impacts are fragmented, opaque, and inaccessible to non-experts, making it hard to trace causal effects and undermining public trust and policy design.
Proposed solution: The author proposes a domain-specific LLM system that simulates the step-by-step effects of policy changes across interconnected economic actors using a dynamic knowledge graph and historical/contextual retrieval (RAG), emphasizing explanation rather than prediction.
System architecture: The model integrates four modules—(1) a historical text database, (2) an economic knowledge graph, (3) a reasoning-focused LLM, and (4) a numerical prediction layer—designed to trace and visualize how policy affects sectors, stakeholders, and outcomes over time.
Use cases and benefits: This system aims to support clearer communication among policymakers, researchers, and the public by making assumptions explicit, surfacing tradeoffs, and enabling structured, multi-perspective dialogue on economic consequences.
Challenges and design considerations: Key hurdles include building a comprehensive yet ideologically neutral knowledge graph, simulating historical events for causal validation, and designing interfaces that clearly convey uncertainty and avoid false confidence in results.
Call to action: The project is in an early stage and seeks input from policy experts, economists, and generalist users to refine the design and ensure it serves real-world needs.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.