Bridging Economy and Culture: The CGE-ABM Framework for Equity Analysis
Combining Macroeconomic Equilibrium Modeling with Agent-Based Cultural Simulation, Enriched by Equity Metrics and Media Influence
The Modeling Gap Nobody Talks About
Economic policy models are everywhere. They shape tax reform, climate regulation, trade agreements, carbon pricing. Governments spend billions on decisions informed by these models. And yet the architecture beneath them is stuck in a paradigm from the 1970s.
Computable General Equilibrium models—CGE, the workhorse of policy analysis—compute economy-wide resource allocation under assumptions of market clearing, representative agents, and perfect information. They produce internally consistent equilibria. They handle multisector, multi-country scenarios with theoretical elegance. What they cannot do is tell you who gets hurt.
That is not a trivial gap. It is a structural failure that reverberates through every policy decision downstream.
The Distributional Blind Spot
CGE models assume one representative agent. A fiction, obviously—but a useful one for computing aggregate welfare. The problem emerges when policymakers need to understand not just the total effect of a carbon tax, but which communities absorb the cost. Not just whether trade liberalization improves GDP, but whether the gains concentrate in coastal metros while hollowing out rural manufacturing towns.
Agent-based models (ABMs) solve half of this problem. They simulate heterogeneous, interacting populations—individuals with distinct incomes, cultural attributes, network positions, behavioral rules. Classic ABMs like Schelling’s segregation model and Epstein-Axtell’s Sugarscape demonstrated decades ago that simple local rules generate large-scale inequality patterns. Cultural ABMs following Axelrod showed how social influence sustains diversity rather than collapsing into uniformity. The micro-level resolution is there. What’s missing is the macroeconomic discipline.
CGE without ABM gives you an economy without people. ABM without CGE gives you people without an economy. Neither alone answers the question that matters: how do economic shocks and cultural dynamics jointly shape who wins and who loses?
What the CGE-ABM Framework Actually Does
Khipu Research Labs’ CGE-ABM framework couples these two paradigms into a single scenario exploration platform, supplemented by explicit equity metrics and a media influence module. The architecture is modular: four distinct components with documented interfaces, coordinated by a central simulation controller.
The economic solver (CGE module) implements a standard CGE algorithm using Social Accounting Matrices and nonlinear solvers. Given policies—tax changes, subsidies, carbon prices—it computes market-clearing prices, outputs, and incomes. The social simulation (ABM module) places individual agents in a networked environment, each carrying income levels, demographic and cultural attributes, and behavioral rules governing consumption, work, and social interaction.
After each policy shock, equity indices—Gini, Theil, Atkinson—track distributional outcomes across population subgroups. A media influence module can inject information campaigns that shift agent preferences, modeling indirect policy effects like public education drives or narrative interventions.
The coupling is bidirectional. Macro outcomes flow down to agents as price signals and income constraints. Aggregated agent behavior flows back up as shifted demand and supply. In each scenario step: introduce a shock, solve the CGE equilibrium, run the ABM, compute equity metrics, update media campaigns, repeat. The framework operates in discrete scenario slices—comparative statics and what-if exploration, not high-frequency forecasting.
The Innovation: Equity as a First-Class Output
Most multi-model integration efforts treat distributional effects as an afterthought—a post-processing step bolted onto aggregate results. The CGE-ABM framework treats equity indices as primary model outputs, computed at every time step, decomposed by subgroup. This is not a cosmetic difference. It means inequality is visible throughout the simulation.
The framework explicitly does not encode what “fair” means. It calculates distributional metrics but leaves normative judgments to the users. As the white paper states: users must supply their own fairness criteria when interpreting results. This non-prescriptive stance is deliberate—and, for institutional adoption, essential. The tool provides measurement, not morality.
Governance Infrastructure: What Sets This Apart
The institutional credibility of any modeling framework lives or dies on governance. The CGE-ABM white paper implements governance practices that exceed most academic standards and approach regulatory documentation quality.
Every claim in the white paper carries an explicit epistemic classification: Design Intent, Formal Model, Assumption, Non-Goal, Authoritative Source, Engineering Judgment (bounded), or Open Research Gap. This taxonomy prevents the single most common failure mode in computational research: inflating conceptual designs into empirical claims.
A full-page validation status disclosure opens the document—listing what has been completed, what remains in progress, and what the framework is explicitly not approved for. A governance attestation appendix maps every citation to its corresponding claims, classified by epistemic status, with a meta-governance note preventing citation gaming.
This is not decorative transparency. Federal agencies reviewing this framework will find the exact documentation structure they require under SR 11-7 model risk management standards. Academic reviewers will find assumption documentation that exceeds typical journal submissions.
Current Status: Honest Capability Disclosure
The framework is under active development. Conceptual design is complete. Core component implementation and module integration are in progress. Synthetic data testing is partial. Estimated validation completion: Q3 2026 through Q1 2027.
This is not a production system. Most frameworks at this stage either oversell their readiness or under-document their architecture. The CGE-ABM white paper does neither.
What has been validated: The CGE solver reproduces known benchmark outcomes. The ABM replicates the classic Axelrod diversity result under face validity checks. Equity index calculations have been cross-checked against analytical formulae. The scenario engine passes internal consistency tests for mass balance and demographic aggregation.
What remains: Full empirical calibration to real-world data. Validation against historical policy outcomes and Peer review.
Market Position: Why This Matters Now
The Evidence Act of 2018 mandates federal agencies to build evidence-based policymaking capacity. Executive Order 13985 requires equity assessments across federal programs. State and local governments face similar legislative pressures with fewer analytical resources. The demand for tools that combine economic analysis with distributional equity tracking is structural.
No existing platform integrates macroeconomic equilibrium modeling, agent-based cultural simulation, and equity measurement in a single transparent framework. CGE tools exist. ABM platforms exist. Equity dashboards exist. They don’t talk to each other. The CGE-ABM framework occupies uncontested analytical space.
Our open-source notebook repository KASS (https://github.com/KhipuResearch/KASS), currently available on GitHub, provides the educational and community engagement infrastructure surrounding this framework. Mathematical specifications, reproducible code, and validation benchmarks are being consolidated through KASS as the framework matures.
Use Cases and Scenario Architecture
The framework supports four illustrative scenario templates, each demonstrating different coupling dynamics between economy, culture, and equity:
Progressive Tax and Redistribution. A stylized tax reform explores effects on income distribution and equity indices, with ABM responses varying by cultural group. Reveals how identical fiscal policies produce different equity outcomes depending on community structure.
Environmental Policy with Media Campaign. Combines a carbon tax with a media-driven preference shift toward green behavior. Studies qualitative interactions between price signals, cultural adoption patterns, and distributional equity.
Education Equity Initiative. Links public spending, human capital assumptions, and long-run inequality pathways. Tests whether education investment reduces inequality faster through direct economic effects or through cultural norm shifts.
Shock to Social Cohesion. A polarizing media shock illustrates how information disruptions ripple through social networks and affect distributional outcomes. Maps the interaction between narrative fragmentation and economic resilience.
Structural Limitations: What We’re Not Hiding
The CGE component inherits every standard weakness of equilibrium modeling: black-box complexity, strong assumptions about perfect information and fixed technology, inability to capture market failures or innovation dynamics without extensions. The ABM component requires detailed specification of agent rules, utility functions, and network topology—computational cost scales with population size, and behavioral parameters may not be precisely known.
Data availability constrains realism. Consistent demographic data and cultural parameters are unevenly available across jurisdictions. Equity indices capture distributional justice but abstract away procedural and recognition dimensions. The media module is a high-level simplification that does not reflect algorithmic social media dynamics.
These limitations are documented in the white paper’s architecture section.
What Comes Next
The development roadmap targets three milestones: publication of the mathematical specification appendix enabling independent reproduction, release of the prototype code repository through the KASS GitHub infrastructure, and Monte Carlo sensitivity analysis on key parameters to establish robustness bounds. Peer review submission to computational economics journals is planned for mid-2026.
Collaboration is open. Researchers and students can engage through the KASS repository. Institutional partners interested in scenario development or validation against specific policy domains can contact Khipu Research Labs directly.
The framework’s core ethos remains constant: illuminate possibilities, not dictate them.
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This article is based on the CGE-ABM Framework White Paper (Version 2.0, January 2026) published by Khipu Research Labs™. The framework is under active development and has not yet completed full empirical validation or peer review. All claims reflect their documented epistemic classification.
Download our CGE-ABM Framework White Paper (Version 2.0, January 2026) published by Khipu Research Labs™:
For technical details, visit https://khipuresearch.github.io/Khipu-Research-Methods/.
Also explore the KASS (Khipu Analytics for Social Science) open-source notebook repository
https://github.com/KhipuResearch/KASS
