Representation Efficiency Score (RES)
RESInformation Density and Computational Efficiency for AI-Mediated Processing
RES measures how efficiently property representations convey selection-relevant information for AI processing.
Definition
RES quantifies the information density of representation—how much selection-relevant information is conveyed per unit of content. Efficient representations enable faster reasoning without information overload.
RES balances completeness with concision, rewarding representations that provide maximum selection-relevant information per unit of processing cost.
Conceptual Formula
RES(e) = I(e) / T(e), where I=selection_relevant_information, T=total_content_size.What This Index Measures
RES enables representation efficiency assessment.
By definition: RES provides normalized efficiency scoring.
Implications
- High RES reduces computational cost without sacrificing quality
Methodology
Type
index construction
Data Sources
Confidence Level
medium
Description
RES(e) = I(e) / T(e), where I=selection_relevant_information, T=total_content_size.
Limitations
- Information relevance criteria may vary
- Efficiency-quality tradeoffs exist
Key Takeaways
Key Points
- RES scales 0-100
- Efficiency matters for cost
- Completeness without verbosity
Target Audience
Relevance Tags
Source Paper
Citation
For the Representation Efficiency Score (RES), see HomeSelf Research (2026), The Zero-Click Economy.