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Research PublicationJune 21, 2026•DOI: 10.5281/zenodo.20784780•Part of: Representation Economy Research Program

Computational Monetary Theory

Credits, Settlement, and Unit-of-Account Formation in AI-Mediated Markets

Abstract

This paper examines how monetary mechanisms may emerge in AI-mediated markets where computational admissibility determines allocative access. We argue that when representation quality determines consideration set inclusion, a new scarcity class emerges: computational scarcity.

Unlike traditional monetary scarcity (limited supply of money) or physical scarcity (limited supply of goods), computational scarcity arises from bounded inference—the constraint that AI systems can evaluate only a subset of available options. We introduce computational credits as a theoretical construct representing claims on evaluation, verification, priority, or inclusion capacity.

Epistemic Status: Theoretical / Non-Empirical

This paper presents a theoretical framework for computational monetary mechanisms. No empirical claims about credit adoption are advanced. All proposed mechanisms require validation.

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Core Concepts

Theoretical primitives introduced in this paper

Computational Scarcity

K < n

The binding constraint that AI systems can evaluate only K options from n available due to bounded inference.

Computational Credits

Cᵢ = φ(allocation, verification, priority)

Theoretical units representing claims on evaluation, verification, or inclusion capacity.

Allocative Access

P(access) = f(credits, representation)

Probability of being considered in AI-mediated allocation as a function of credits and representation.

Settlement by Verification

V → S

Representation verification as settlement mechanism for computational credit transactions.

Computational Inflation

π = dC/dt → 0

Devaluation of admissibility advantage through universal representation investment.

What This Does NOT Claim

Important scope limitations

Theory Safety Check

This paper develops theoretical mechanisms. It does not replace monetary theory or predict specific market outcomes.

Does NOT replace monetary theory

This paper does not claim that computational credits replace or obsolete traditional monetary theory, credit theory, or supply and demand. It explores how allocative infrastructure dependencies may interact with existing monetary systems.

Does NOT predict cryptocurrency adoption

Computational credits are not cryptocurrency tokens, blockchain-based currencies, or DeFi instruments. The framework is about allocative access in AI-mediated markets, not speculative digital assets.

Does NOT guarantee credit emergence

We do not claim that computational credits will definitively emerge or become widely adopted. The framework describes what might happen under specified conditions, not what will happen.

Does NOT claim AI controls money

The paper explores how allocative access may become a constraint that interacts with monetary systems. It does not claim that AI systems will control money supply or replace central banks.

Plain Language Explanation

What this paper explores in simpler terms

This paper asks: If AI systems become the gatekeepers of market participation, what kind of "money" might emerge to manage access to those systems?

The Core Idea

Traditional markets use money as the medium of exchange—you need money to buy things. But what if the primary constraint isn't having money, but being considered at all? What if AI systems can only evaluate a small subset of available options?

When that happens, the scarce resource shifts from "money to buy" to "being considered." Computational credits are a theoretical construct for managing that new scarcity—the right to be evaluated, verified, and included in AI consideration sets.

Key Concepts

Computational Scarcity: AI systems can only evaluate K options from n available. This creates a new scarcity class.
Computational Credits: Theoretical tokens representing claims on evaluation, verification, or priority capacity.
Settlement by Verification: Unlike traditional money settling through payment, computational transactions settle through representation verification.
Computational Inflation: If everyone achieves good representation, the allocative advantage erodes—similar to how money printing causes inflation.

Why This Matters

If AI-mediated markets become widespread, the bottleneck may shift from "can I afford this?" to "can I be considered for this?" Understanding how allocative access might be priced, settled, and governed is important for anticipating market structure changes.

What This Does NOT Mean

This is theory, not prediction. We do not claim that computational credits will emerge, that they will replace money, or that AI will control monetary systems. The framework explores potential structural changes, not guaranteed outcomes. Supply and demand remain valid; this framework addresses pre-price allocative access.

Paper Structure

Six-part framework covering credit theory, settlement, and governance

Part I

Computational Scarcity

K < n constraintInference budgetRepresentation costVerification cost

Part II

Computational Credits

DefinitionForms: Evaluation, Verification, Priority, ActionWhat credits representCredit vs money

Part III

Settlement Infrastructure

Verification as settlementVPR exampleFinality and revocationTrust models

Part IV

Unit-of-Account Formation

Quantifiability conditionsPricing accessComputational liquidityDenomination problems

Part V

Computational Inflation

Representation saturationVerification scarcityInflationary racesPost-inflation transitions

Part VI

Governance Implications

Credit issuanceCredit consumptionValue captureConcentration risks

Key Insights

Structural implications of computational monetary theory

Computational Scarcity Creates New Money

When AI systems can evaluate only K options from n, the right to be evaluated becomes a scarce resource that may require a monetary primitive.

Credits vs. Store of Value

Computational credits are allocative access tokens, not store-of-value instruments. They represent claims on computation, not purchasing power.

Verification as Settlement

In computational markets, representation verification may serve as the settlement mechanism—proof of representation validity closes the transaction loop.

Computational Inflation

Universal representation investment may devaluate the allocative advantage of credits, creating inflationary pressure in the computational economy.

Relationship to Research Program

How this paper extends the Representation Economy framework

Program Development Flow

Representation Economy
→ Computational Market Access
→ Computational Market Economics
→ Network-Dependent Allocation
→ Computational Pricing Theory
→ Computational Monetary Theory (this paper)
→ Representation Capital (Volume I)
→ Representation Capital Dynamics
→ Computational Creditworthiness (Volume III)
→ Agent-Readable Property Markets

Computational Pricing Theory

Price formation mechanisms

Computational Creditworthiness (Volume III)

Trust assessment theory

Citation

How to cite this research publication

APA Style

Patrone, M. (2026). Computational Monetary Theory: Credits, Settlement, and Unit-of-Account Formation in AI-Mediated Markets. HomeSelf Research Publication Series. https://doi.org/10.5281/zenodo.20784780

BibTeX

@article{patrone2026computational_monetary,
  title={Computational Monetary Theory: Credits, Settlement, and Unit-of-Account Formation in AI-Mediated Markets},
  author={Patrone, Marco},
  year={2026},
  month={6},
  day={21},
  version={1.0},
  institution={HomeSelf Research},
  series={HomeSelf Research Publication Series},
  doi={10.5281/zenodo.20784780},
	  url={https://doi.org/10.5281/zenodo.20784780}
}

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