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

TL;DR

PPCR-IM is a system that uses layered DAGs and large language models to reason about policy consequences and map social indicators, providing structured, comparable impact assessments.

Contribution

It introduces a multi-layer DAG-based framework with LLM-driven consequence reasoning and indicator mapping for comprehensive policy impact analysis.

Findings

Constructs DAGs capturing joint influences of policy consequences

Maps consequences to fixed social indicators with impact directions

Provides quantitative evaluation metrics for policy impact coverage

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.