The Computational Transmission Gap
Monetary Policy, Inflation Persistence, and Sovereignty Risks in AI-Mediated Markets
Abstract
This working paper introduces the Computational Transmission Gap (CTG), a framework for analyzing how AI-mediated markets may affect monetary-policy transmission. The paper examines whether monetary policy can remain financially operative while becoming computationally incomplete when AI-mediated discovery, eligibility, ranking, verification, and actionability influence how policy-induced demand reaches firms, assets, households, sectors, and jurisdictions.
We introduce a five-stage accounting framework: G_potential (potential monetary-policy response), G_dir (direct computational response), U_pool (unassigned computational demand pool), Q_domestic (domestic recovered demand), R_external (external computational leakage), and L (genuinely unrealised activity). The Computational Monetary Transmission Index (CMTI) and Computational Transmission Gap Index (CTGI) provide measurement frameworks.
The paper introduces Computationally Augmented Inflation as a regime where prices remain flexible while participation becomes sticky, External Computational Leakage as a sovereignty exposure, and the Domestic Computational Retention Rate (DCRR) as a policy effectiveness metric.
Published: July 11, 2026 (Working Paper v1.4)
This working paper has been published on Zenodo with DOI 10.5281/zenodo.21307163. The proposed metrics and simulation results are illustrative and require empirical calibration. This research is theoretical and does not present HomeSelf as a monetary-policy tool.
Research Question
The central question this paper addresses
Can monetary policy remain financially operative while becoming computationally incomplete?
When AI systems mediate discovery, eligibility, ranking, verification, and actionability, policy-induced demand may not reach intended targets. The question is whether monetary policy can achieve financial transmission (changing nominal conditions) while experiencing computational incompleteness (demand not reaching firms, assets, households, sectors, or jurisdictions as intended).
The Core Distinction
Financial operability means central banks can change policy rates, balance sheets, and forward guidance. Computational completeness means those changes reach intended economic targets through AI-mediated allocation systems.
Financially Operative
Policy transmission mechanisms function: rates change, balance sheets adjust, guidance signals.
Computationally Complete
Policy-induced demand reaches intended targets through AI-mediated allocation.
Contributions
Four theoretical contributions of this paper
Computational Transmission Gap Framework
A five-stage accounting framework for monetary-policy transmission in AI-mediated markets, distinguishing potential response, direct response, unassigned pool, domestic recovery, external leakage, and unrealised activity.
Computationally Augmented Inflation
A regime where prices remain flexible while participation becomes sticky, creating asymmetric inflation dynamics where computationally admissible actors transmit policy while others remain responsive to demand but excluded from consideration.
External Computational Leakage
A sovereignty exposure where domestic monetary stimulus leaks to foreign AI-mediated consideration sets, reducing domestic policy effectiveness while benefiting computationally liquid foreign assets and jurisdictions.
Computational Sovereignty Exposure
A framework for measuring how AI-mediated market infrastructure affects monetary sovereignty, introducing DCRR, CMTI, CTGI, and policy effectiveness metrics for central banks in AI-mediated economies.
Accounting Framework
Five-stage decomposition of monetary-policy transmission
The Computational Transmission Gap Framework
The framework decomposes monetary-policy transmission into five stages, distinguishing between policy intent, computational response, unassigned demand, and reallocation outcomes.
Potential Monetary-Policy Response
The intended aggregate demand response from monetary policy under full transmission.
Direct Computational Response
The portion of policy-induced demand that AI systems can directly route to consideration sets.
Unassigned Computational Demand Pool
Policy-induced demand not initially routed by AI systems, awaiting reallocation or becoming unrealised.
Domestic Recovered Demand
Unassigned demand successfully recovered through domestic rerouting mechanisms.
External Computational Leakage
Unassigned demand that leaks to foreign consideration sets—sovereignty exposure.
Genuinely Unrealised Activity
Demand that never reaches any consideration set—permanent loss.
Aggregate Identity
G_realised = G_dir + Q_domestic = G_potential - R_external - LRealised demand equals direct response plus domestic recovery. Equivalently, it equals potential response minus external leakage and unrealised activity. Direct CTG does not automatically mean aggregate demand loss—reallocation through domestic recovery can recover part of the unassigned pool.
Important Clarification
Direct Computational Transmission Gap (CTG_dir = 1 - kappa_dir) is not automatically aggregate demand loss. The U_pool can be partially recovered through domestic rerouting (Q_domestic). The net aggregate effect depends on recovery efficiency, not the initial gap.
Propositions
Four propositions derived from the framework
Monetary Policy Can Remain Financially Operative While Computationally Incomplete
Central banks can change policy rates, balance sheets, and forward guidance (financial operability) while AI-mediated allocation systems prevent policy-induced demand from reaching intended targets (computational incompleteness).
Implication
Traditional transmission metrics may appear functional while actual demand allocation is computationally diverted.
Direct CTG Is Not Automatically Aggregate Demand Loss
The gap between potential response and direct computational response (CTG_dir = 1 - kappa_dir) measures initial computational incompleteness, not final demand loss. The unassigned pool can be partially recovered through domestic rerouting.
Implication
Policy effectiveness depends on recovery infrastructure, not just initial computational admissibility.
External Computational Leakage Creates Sovereignty Exposure
Domestic monetary stimulus may leak to foreign AI-mediated consideration sets when foreign assets or jurisdictions have higher computational liquidity, reducing domestic policy effectiveness while benefiting foreign economic actors.
Implication
Computational representation quality becomes a sovereignty-relevant variable for monetary policy.
Flexible Prices, Sticky Participation Creates Computationally Augmented Inflation
When prices remain flexible while participation becomes sticky due to computational constraints, inflation dynamics become asymmetric: computationally admissible actors transmit policy while others remain price-responsive but consideration-excluded.
Implication
Inflation may appear contained in measured aggregates while reflecting demand from a shrinking set of computationally included actors.
Simulation Summary
Illustrative and non-empirical
Simulation Disclaimer
The following simulation results are illustrative and non-empirical. They demonstrate the framework's mechanics under parameterized assumptions. Real-world calibration requires empirical measurement of computational transmission coefficients, rerouting constraints, and jurisdictional leakage rates.
Simulation Parameters
Potential Response
G_potential = 100
Direct Response
G_dir = 52
Direct CTG
CTG_dir = 48%
DCRR (Restricted)
≈ 47.7%
Unrestricted Rerouting
AI systems can reroute unassigned demand without computational or jurisdictional constraints.
G_potential
100
G_realised
100
Unrealised
0%
Full recovery—no net loss.
Restricted Rerouting
AI systems face computational and jurisdictional constraints on rerouting.
G_potential
100
G_realised
85.55
Unrealised
14.45%
Partial recovery—14.45% loss.
No Rerouting
AI systems cannot recover unassigned demand.
G_potential
100
G_realised
81.06
Unrealised
18.94%
Direct transmission only—18.94% loss.
Key Insight
The Domestic Computational Retention Rate (DCRR) determines policy effectiveness. Under unrestricted rerouting, full recovery is possible (DCRR ≈ 100%). Under realistic constraints, DCRR determines how much of the unassigned pool can be recovered. The framework shows that computational representation quality affects monetary sovereignty even when financial transmission mechanisms remain operational.
Metrics Framework
Measurement indices for computational monetary transmission
Direct Computational Transmission Coefficient
kappa_dirkappa_dir = G_dir / G_potentialThe proportion of potential monetary-policy response that AI systems can directly route to consideration sets.
kappa_dir → 1: Direct transmission complete. kappa_dir → 0: High direct CTG.
Direct Computational Transmission Gap
CTG_dirCTG_dir = 1 - kappa_dirThe gap between potential response and direct computational response.
CTG_dir → 0: No direct gap. CTG_dir → 1: Complete direct incompleteness.
Domestic Computational Retention Rate
DCRRDCRR = Q_domestic / U_poolThe proportion of unassigned demand recovered through domestic rerouting.
DCRR → 1: Full domestic recovery. DCRR → 0: No domestic recovery.
Computational Monetary Transmission Index
CMTICMTI = G_realised / G_potentialThe proportion of potential monetary-policy response that reaches domestic economic targets.
CMTI → 1: Full transmission. CMTI → 0: Complete transmission failure.
Computational Transmission Gap Index
CTGICTGI = 1 - CMTIThe aggregate gap between potential and realised monetary-policy transmission.
CTGI → 0: No aggregate gap. CTGI → 1: Complete transmission failure.
External Leakage Rate
ELRELR = R_external / U_poolThe proportion of unassigned demand that leaks to foreign consideration sets.
ELR → 1: Full external leakage. ELR → 0: No external leakage.
Core Concepts
Key concepts introduced in this paper
Computational Transmission Gap
The gap between potential monetary-policy response and the portion AI systems can directly route to consideration sets, which may be partially recovered through domestic rerouting.
Computationally Augmented Inflation
A regime where prices remain flexible while participation becomes sticky, creating asymmetric inflation dynamics between computationally included and excluded actors.
External Computational Leakage
Domestic monetary stimulus leaking to foreign AI-mediated consideration sets, reducing domestic policy effectiveness while benefiting foreign economic actors.
Domestic Computational Retention Rate
The proportion of unassigned demand that can be recovered through domestic rerouting mechanisms.
Computational Sovereignty Exposure
The vulnerability of monetary policy to computational incompleteness and external leakage due to AI-mediated allocation systems.
Flexible Prices, Sticky Participation
A phenomenon where prices adjust freely while computational constraints prevent some economic actors from participating in consideration sets.
Computational Monetary Observability
The ability of central banks to observe and measure how AI-mediated allocation systems affect monetary-policy transmission.
Internal Net Reallocation
Domestic rerouting of unassigned demand across consideration sets, which is zero-sum in aggregate (sum NR_i = 0).
Genuinely Unrealised Activity
Demand that never reaches any consideration set—permanent loss from both domestic and foreign allocation.
Concepts in Detail
Detailed definitions of these concepts are available in the HomeSelf Research Concepts glossary. Each concept includes extended definitions, evidence status, related research, and DOI references.
Related Research
How this paper connects to the Representation Economy program
Computational Monetary Theory
Settlement mechanisms and computational credits as theoretical accounting units for allocative access in AI-mediated markets.
Computational Sovereignty
Structural economic risks, Representation Capital, Law of Computational Visibility, and computational market infrastructure for AI-mediated markets.
Agent-Ready Market Infrastructure
Agent-readiness, Computational Eligibility, Global Agent-Readiness Index, and cross-jurisdictional market access.
Agent Action Infrastructure
Permissioned action, verified mandates, Action Boundary Objects, Agent Actionability Index, and transaction-ready economic objects.
The AI Allocability Discount
Computational liquidity, AI allocability risk, GARI, Inference Burden Score, and representation-driven valuation/liquidity discount.
Position in the Sequence
This paper (Volume XI, Research Layer 20) extends the Representation Economy framework from micro-level allocation to macroeconomic transmission. It builds on Computational Monetary Theory (Volume VI), Computational Sovereignty (Volume VII), Agent-Ready Market Infrastructure (Volume VIII), Agent Action Infrastructure (Volume IX), and The AI Allocability Discount (Volume X) to analyze how AI-mediated markets affect monetary policy, inflation dynamics, and economic sovereignty.
Recommended Citation
How to cite this working paper
DOI: 10.5281/zenodo.21307163
Zenodo Record: zenodo.org/records/21307163APA Style
Patrone, M. (2026). The Computational Transmission Gap: Monetary Policy, Inflation Persistence, and Sovereignty Risks in AI-Mediated Markets. HomeSelf Research Working Paper v1.4. DOI: 10.5281/zenodo.21307163BibTeX
@workingpaper{patrone2026computational_transmission,
title={The Computational Transmission Gap: Monetary Policy, Inflation Persistence, and Sovereignty Risks in AI-Mediated Markets},
author={Patrone, Marco},
year={2026},
institution={HomeSelf Research},
series={Representation Economy Research Program},
volume={XI},
research_layer={20},
version={1.4},
doi={10.5281/zenodo.21307163},
url={https://homeself.ai/research/representation-economy/computational-transmission-gap}
}License
Contact: protocol@homeself.ai
Research Caveats
This is a theoretical working paper. The proposed metrics, accounting framework, and simulation results are illustrative and require empirical calibration.
Important Disclaimers
- •This paper does not present HomeSelf as a monetary-policy tool or claim any capability to influence central bank decisions.
- •Nothing in this paper should be interpreted as endorsement or policy guidance by the European Central Bank, Bank for International Settlements, or any central bank institution.
- •This paper is not peer-reviewed. It represents a theoretical exploration requiring validation through empirical study and academic review.
- •The findings should not be used for investment decisions, trading strategies, or financial planning without independent verification.