AI Allocability Discount (AAD)
AADPotential Valuation, Liquidity, or Demand-Access Penalty from Poor Computational Representation
Proposed hypothesis — not yet testedpublished
AAD measures valuation discount from poor computational representation.
By Marco Patrone
aadvaluation_discountallocability_riskbridge_metric
Definition
AAD captures the potential reduction in asset value, liquidity, or demand access from expensive computational representation. AAD bridges allocability and valuation risk.
AAD estimates the potential reduction in asset value, liquidity, or demand access arising from poor computational representation in AI-mediated markets.
Conceptual Formula
AAD(a) = 1 - V_a^AI-allocable / V_a^theoretical, where V represents value under AI-mediated vs theoretical allocability.Methodology
Type
index construction
Data Sources
synthetic
Confidence Level
medium
Description
AAD(a) = 1 - V_a^AI-allocable / V_a^theoretical, where V represents value under AI-mediated vs theoretical allocability.
Limitations
- Theoretical value is unobservable
- Calibration requires market data
Key Takeaways
Key Points
- AAD scales 0-1
- Bridge between allocability and valuation
- Higher AAD indicates greater discount
Target Audience
investorsasset managersfirms
Relevance Tags
aadvaluation_discountallocability_riskbridge_metric
Source Paper
Citation
For the AI Allocability Discount (AAD), see HomeSelf Research (2026), The AI Allocability Discount.