CGE-ABM Framework

Version: 2.0

Status: Conceptual Framework — Under Development

Validation Target: Q4 2026 - Q1 2027

Introduction

Problem

Policy analysis faces a persistent structural gap. Macro models provide economy-wide consistency but flatten populations into “representative agents”—averaging away the distributional effects that matter most for equity. Micro models capture heterogeneity but lack the aggregate constraints that keep results economically coherent.

The tools available today force a choice: rigor at the macro level, or realism at the micro level. For research programs concerned with how policies actually affect different communities, this is an unacceptable trade-off.

Objective

This framework bridges macro and micro scales through bi-directional coupling between a Computable General Equilibrium (CGE) model and an Agent-Based Model (ABM). The goal is structured scenario exploration: examining how policies interact with social dynamics to produce equity outcomes, without collapsing into false precision or losing aggregate consistency.

Methods

System Architecture

The framework specifies a modular architecture with five primary components:

Component	Description	Status
Economic (CGE)	General equilibrium solver with SAM calibration, nested production functions, and market-clearing conditions	Core: Complete
Social (ABM)	10,000+ heterogeneous agents with demographic attributes, cultural profiles, and social network positions	70% Complete
Equity Assessment	Gini coefficient, Theil index, and Atkinson index with decomposition capabilities	Validated
Media Influence	Broadcast signal model for information campaigns affecting cultural traits	60% Complete
Integration	Bi-directional coupling orchestrating CGE-ABM feedback loops	One-way: Complete

Data Sources

Economic Data: BEA Input-Output tables, IMPLAN databases, state-level SAMs
Social Data: ACS PUMS, CPS, Consumer Expenditure Survey, PSID
Cultural Proxies: World Values Survey, General Social Survey
Network Structures: Calibrated canonical models (small-world, scale-free, spatial lattice)

Key Assumptions

Competitive markets with flexible prices (no quantity rationing)
Annual time-stepping with within-period equilibrium
Stylized social network topologies
Simplified media effects (broadcast without algorithmic targeting)
Exogenous technology and productivity

Results

Scenario Templates

The framework demonstrates four scenario templates illustrating analytical capabilities:

1. Progressive Tax Reform

Policy: +10% marginal rate on top quintile, +$2,000 transfers to bottom quintiles
Media: Tax fairness campaign (60% reach)
Expected: ~14% Gini reduction, ~-0.8% GDP impact

2. Environmental Policy

Policy: $50/ton carbon tax with lump-sum rebate
Media: Climate awareness campaign (70% reach)
Expected: 20-30% emissions reduction, sectoral reallocation

3. Education Equity Investment

Policy: +2% GDP education spending to bottom quintiles
Media: Education opportunity campaign
Expected: ~10% Gini reduction over 50-year horizon

4. Social Cohesion Shock

Scenario: Polarizing media event causing trust distribution bifurcation
Expected: Network segregation, ~-1.5% GDP, persistent polarization

These are conceptual templates—NOT empirical results or predictions.

Discussion

Validation Status

Phase	Status
Component-level testing	Complete
One-way integration	Validated
Two-way coupling stability	60% Complete
Empirical calibration	Not Started
Historical backcasting	Not Started
Peer review	Not Started

Limitations

CGE equilibrium assumptions may overstate market efficiency
Cultural parameters have weak empirical grounding
Media model excludes algorithmic targeting and echo chambers
Within-group heterogeneity underrepresented
Time horizon limited to 5-20 years

Appropriate Use Cases

Academic research and methodological development
Scenario exploration and comparative policy analysis
Stakeholder deliberation and policy discussion
Educational demonstrations of multi-scale modeling

Not Approved For

Regulatory compliance determinations
Automated decision-making or policy optimization
Predictive forecasting of specific outcomes
Sole basis for irreversible resource allocation

Access

The framework is positioned as a decision-support tool for deliberation, not a predictive engine. Reference implementation available under Apache 2.0 license.

Download Full Whitepaper (PDF)

Framework developed by Khipu Research Labs. For inquiries: info@krlabs.dev