Computational Pricing Theory: Price Formation in AI-Mediated Markets
Price Formation in AI-Mediated Markets
Evidence Status
Proposed hypothesis — not yet tested
This publication presents a conceptual hypothesis awaiting empirical validation.
Abstract
This paper examines how price formation mechanisms may change in markets where artificial intelligence systems serve as the primary allocative interface between buyers and sellers. We argue that when AI systems construct consideration sets before human decision-makers engage, traditional supply-and-demand mechanics may become insufficient to explain price outcomes. Instead, price formation may be mediated by computational admissibility—the probability that an option is included in machine-generated consideration sets under bounded inference. We introduce theoretical primitives including Representation Friction, Inferential Cost, Computational Liquidity, and Admissibility Premium.
Executive Summary
Background
Traditional price theory assumes buyers can access all available options given sufficient search. When AI systems construct consideration sets under K < n constraints, this assumption fails and new pricing mechanisms may emerge.
Objectives
- Examine how price formation changes when consideration becomes computational
- Introduce pricing primitives for AI-mediated markets
- Analyze sector-specific implications
- Propose research agenda for empirical validation
Approach
Theoretical framework development examining price formation under computational admissibility constraints. Introduces formal pricing primitives and sector analysis.
Main Findings
- K < n constraint creates permanent inclusion scarcity
- Representation → Admissibility → Consideration → Competition → Price chain replaces Value → Competition → Price
- Admissibility Premium may create pricing power for computationally admissible options
- Representation infrastructure becomes pricing infrastructure
Conclusions
- Computational pricing theory requires empirical validation
- Infrastructure concentration risks warrant regulatory scrutiny
- Protocol design may influence market structure
Methodology
Research Type
theoretical framework
Data Sources
Confidence Level
low
Description
Theoretical framework development using price theory, computational economics, and allocation theory. No empirical claims advanced.
Limitations
- Theoretical framework requiring empirical validation
- No observational or experimental data presented
- Market predictions are speculative
Key Findings
When AI systems construct consideration sets under K < n, inclusion becomes scarce.
Theoretical analysis of consideration set construction under bounded inference.
Implications
- Options compete for computational admissibility, not just on price or quality
- Exclusion is structural under capacity constraints
Representation Friction imposes allocative costs that may affect pricing outcomes.
Theoretical analysis of data sparsity, non-standardization, and representation lag effects.
Implications
- High-friction options face higher exclusion risk
- Standardized representation may reduce pricing disadvantages
Admissibility Premium may create pricing power for computationally admissible options.
Theoretical analysis of how reduced effective competition may enable price premiums.
Implications
- Pricing power may derive from allocative efficiency, not quality
- Admissibility differentials may create market distortions
Discussion
Infrastructure as Pricing Architecture
If representation affects allocative efficiency, and efficiency affects pricing, then representation infrastructure becomes pricing infrastructure. Protocol operators may function as market architects.
Open Questions
- · How will buyers respond to computational exclusion?
- · Will sellers arbitrage admissibility differentials?
Implications
For AI Systems
- · Consideration set construction affects market outcomes
- · Representation quality influences allocative efficiency
- · Compute budget constraints create exclusion pressures
For Research
- · Empirical studies needed to measure admissibility premiums
- · Representation quality metrics require development
- · Infrastructure concentration needs monitoring
AI Summary
One Sentence
Computational Pricing Theory examines how price formation may change when AI systems construct consideration sets, introducing pricing primitives including Representation Friction, Inferential Cost, Computational Liquidity, and Admissibility Premium.
One Paragraph
This theoretical paper analyzes price formation in AI-mediated markets where consideration sets are constructed by allocative systems rather than buyers. The central insight is that when K < n (consideration set size is smaller than available options), inclusion becomes scarce and price may emerge from computational admissibility rather than supply and demand alone. The paper introduces five pricing primitives: Representation Friction, Inferential Cost, Allocative Efficiency, Computational Liquidity, and Admissibility Premium. It examines sector implications and proposes a research agenda for empirical validation.
Key Takeaways
- · K < n constraint creates permanent inclusion scarcity
- · Price chain: Representation → Admissibility → Consideration → Competition → Price
- · Admissibility Premium may create pricing power
- · Representation infrastructure becomes pricing infrastructure
- · Theoretical framework requiring empirical validation
Target Audience
Relevance Tags
Related Content
Download Options
Citation
Patrone, M. (2026). Computational Pricing Theory: Price Formation in AI-Mediated Markets. HomeSelf Research Publication Series, No. 6.