Inference Burden Score (IBS)
Measuring Computational Cost of Property Understanding
Evidence Status
Derived from measured data
Findings are derived from measured primary datasets using documented scoring or validation methods.
Abstract
The Inference Burden Score (IBS) quantifies the computational complexity AI systems encounter when processing property records. Higher IBS indicates more challenging representations that may degrade selection performance.
Methodology
Research Type
statistical modeling
Data Sources
Confidence Level
medium
Description
IBS = (unstructured_content_ratio × complexity_factor) + (structural_ambiguity × penalty)
Limitations
- Cannot perfectly model all AI architectures
Key Findings
IBS inversely correlates with selection accuracy (r=-0.62).
Higher burden scores associate with lower selection match quality.
Implications
- Simplified representation improves selection outcomes
AI Summary
One Sentence
IBS measures computational complexity of property understanding, with higher scores predicting lower selection accuracy.
One Paragraph
Unstructured content and structural ambiguity increase inference burden. Simplified, structured representations improve outcomes.
Key Takeaways
- · IBS scales 0-100, lower is better
- · Unstructured content increases burden
- · Structural ambiguity reduces selection accuracy
Target Audience
Relevance Tags
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Citation
HomeSelf Research. (2026). Inference Burden Score (IBS). HomeSelf Research Initiative.