PPCR-IM: A System for Multi-layer DAG-based Public Policy Consequence Reasoning and Social Indicator Mapping

2026-02-25

AI Summary

Public policy decisions often rely on limited headline indicators, making it difficult to comprehensively assess their broad societal impacts. This work proposes a multilayer directed acyclic graph (DAG)-based framework that leverages large language models to hierarchically generate intermediate consequence nodes from policy texts and aligns them—via a dedicated mapping module—to a standardized set of social indicators, annotated with directional effects. The approach represents the first automated method to map unstructured policy documents to structured societal impact representations, supporting multiple parent nodes to capture joint effects. The system outputs a policy impact DAG, indicator mappings, and three evaluation metrics: expected coverage, overlooked indicator discovery rate, and relative focus ratio, enabling both online demonstration and batch analysis.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Preferences

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Public policy decisions are typically justified using a narrow set of headline indicators, leaving many downstream social impacts unstructured and difficult to compare across policies. We propose PPCR-IM, a system for multi-layer DAG-based consequence reasoning and social indicator mapping that addresses this gap. Given a policy description and its context, PPCR-IM uses an LLM-driven, layer-wise generator to construct a directed acyclic graph of intermediate consequences, allowing child nodes to have multiple parents to capture joint influences. A mapping module then aligns these nodes to a fixed indicator set and assigns one of three qualitative impact directions: increase, decrease, or ambiguous change. For each policy episode, the system outputs a structured record containing the DAG, indicator mappings, and three evaluation measures: an expected-indicator coverage score, a discovery rate for overlooked but relevant indicators, and a relative focus ratio comparing the systems coverage to that of the government. PPCR-IM is available both as an online demo and as a configurable XLSX-to-JSON batch pipeline.

Problem

• public policy
• social impact
• consequence reasoning
• indicator mapping
• policy evaluation

Innovation

• DAG-based reasoning
• LLM-driven policy analysis
• social indicator mapping
• multi-parent consequence modeling
• policy impact evaluation