Preference Selection Index
PSI (Preference Selection Index) is a ranking multi-criteria decision-making (MCDM) method introduced by Maniya, K., Bhatt, M. G. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
Key highlights
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
Intuition
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How it works
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When to use it
I_i ∈ [0,1] (approximately). Higher I means better. PSI is unique among MCDM methods in that it derives criterion weights objectively from the data (via Preference Variation Value) rather than requiring user-specified weights. Criteria with higher variance (greater discrimination between alternatives) receive higher weights.
Strengths & limitations
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
- Assumes full compensation — a strong score on one criterion can offset a weak score on another.
Common pitfalls
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Sources
- 1.Maniya, K., Bhatt, M. G. (2010). A selection of material using a novel type decision-making method: Preference selection index method. Materials & Design
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Cite this page
ScholarGate. (2026, June 2). PSI. ScholarGate. https://scholargate.app/decision-making/psi