Persona Matching Score (PMS)

Quantifying fit

At the center of the Economic Engine is a metric that makes Personas actionable: the Persona Matching Score (PMS). It is a way to measure how well a person fits the profile that a project defines.

The PMS is calculated as a weighted sum across criteria. Each criterion represents a feature, for instance DeFi activity, governance participation, or NFT engagement. Projects assign a weight to each criterion, reflecting its relative importance. The user's data is then evaluated against those criteria through a value function:

$$PMS=\sum^{n}_{i=1}​w_i​⋅V(C_i​)$$

• n is the number of criteria defined by the project.

• wᵢ is the weight assigned to criterion i.

• V(Cᵢ) is the value function that evaluates the user’s data for that criterion.

Value functions can take different shapes:

• A criterion like “did the user vote in governance?” may be binary.

• A criterion like “how much liquidity did they provide?” can be scaled.

• A criterion like “social reach” may use a logarithmic function so that an account with 1,000 followers and one with 100,000 followers are both recognized, without letting one metric dominate.

This flexibility makes PMS powerful. A gaming project can weigh user interests higher, while a DeFi protocol can weigh asset stability and governance experience. Every project defines its own scoring profile, and the engine computes PMS in real time across the Persona Graph.

For users, PMS is invisible. They see relevant opportunities and tasks that fit their profile, not a score. For projects, PMS provides precision targeting and measurable efficiency. Instead of chasing addresses broadly, they engage the people most likely to contribute, and stay.

PMS quantifies alignment between what projects need and what people can offer, making the Value Graph useful at scale.