💡 In simple words: Some people always give 1 star or always give 5 stars. WikiDeal gently balances those extremes, and invites many different people to give their opinion, so reviews stay fair and honest.

🎯 In 20 seconds (expert summary): The Rating Balancing Policy (previously presented as "Judgment Balance Indicator") aims at proactively stimulating balance in marketplace ratings, not just measuring it. The goal is feedback that is honest, calibrated and diversified: coming from many different people, not always the same ones, supported by motivation and incentive tools close to the civic incentive approach. It concerns the marketplaces and pairs with Incentivized Feedback.

Rating Balancing Policy

Innovation, WikiDeal R&D. Previously presented as "Judgment Balance Indicator": renamed, because this is about rating, not judgment.

Origin	🟢 WikiDeal concept
Status	Under study (July 2026, proposal)

What is it?

The Rating Balancing Policy aims at keeping marketplace ratings honest, calibrated and diversified. It was previously presented as the "Judgment Balance Indicator"; the name changed for two reasons:

what is balanced is rating, not judgment;
it is not just an indicator: the policy aims at proactively stimulating balance, not only measuring it.

Honest, calibrated, diversified

Honest: ratings should reflect real experiences, without gaming.
Calibrated: extreme patterns (such as systematic 1-star or 5-star scores) are weighted with reference to each rater's track record, so consistently extreme behaviour carries less distorting weight.
Diversified: feedback should come from many different people, not always the same ones commenting (and not always the same ones commenting negatively). Motivation and incentive tools, close to the civic incentive approach, aim at widening the circle of people who give feedback.

How it works on WikiDeal

The policy concerns the marketplaces and belongs to the governance family of innovations. It pairs with Incentivized Feedback, which stimulates detailed feedback, while the Rating Balancing Policy keeps the resulting ratings fair. The exact weighting model and stimulation tools are documented progressively as the platform evolves.