Gov/en/Portal:R&D/Innovations:Rating Balance

💡 In simple words: On WikiDeal, feedback should be fair and honest. If you leave detailed, useful comments, you earn thank-you rewards like Karma tokens. But if someone always gives 5 or 1 star without thinking, the system balances it fairly.

🎯 In 20 seconds (scientific summary): Rating Balance groups the proactive policies proposed to stimulate honest, calibrated, diversified marketplace ratings. Still under construction via upcoming open calls, it would integrate incentivized feedback (rewarding constructive reviews with Karma rather than money) and mechanisms linking to the Maturity Score and Civic Incentive, including a corrective on systematic ignorance or artificial positivity.

Summary

• The Policy in Brief
• Incentivized Feedback
• Mechanisms and Connections
• State of the art

Rating Balance

Innovation, WikiDeal R&D. (Previously known under the concepts of "Judgment Balance Indicator", "Rating Balancing Policy", and "Incentivized Feedback", which are now unified here).

Origin	🟢 WikiDeal concept
Status	Under study (proposal), to be built via Open Calls

The Policy in Brief

The Rating Balance framework aims at keeping marketplace ratings honest, calibrated and diversified. It is not just an indicator: it is a policy that proactively stimulates balance rather than merely measuring it.

Because this system is highly complex, the specific algorithms and rules are not yet finalized. They are intended to be built and refined through dedicated open calls (e.g., Open Call: Rating Balance Processes).

The envisioned policy rests on three pillars:

• Honest: ratings should reflect real experiences.
• Calibrated: extreme patterns (such as systematic 1-star or 5-star scores) are weighted relative to a rater's track record, minimizing distortion.
• Diversified: feedback should come from a wide variety of users, supported by motivation and incentive tools close to the civic incentive approach.

Incentivized Feedback

A core paragraph of the Rating Balance policy is Incentivized Feedback. Feedback on WikiDeal is meant to be detailed and useful, covering soft skills, communication, and problem-solving. To stimulate this, users receive advantages, never money (as paying for reviews compromises honesty).

These incentives include:

• Karma tokens (see the Time and Resource Bank).
• User and admin rights, or the right to run for certain functions.
• Direct credits from providers: e.g., an authorized late check-in, an extra night in a booking, or a local cultural experience.

Mechanisms and Connections

The policy connects directly with the Civic Incentive and the Maturity Score systems through three core mechanisms:

1. User Education: Users are educated on how to leave constructive, precise comments without exaggerating or wasting time (moderation, precision, and contextual use cases).
2. Early Reviewer Advantages: The very first users who comment on new services or persons receive proactive advantages (notably in Karma tokens, for example earning the right to an extra free night in a 7-night accommodation booking). This jumpstarts the reputation and maturity of the offering.
3. Feedback "Tax" and Penalties: To prevent skewed distributions and artificial positivity, a "tax" or penalty is applied to users who too frequently ignore feedback requests or exclusively leave positive comments without nuance.

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State of the art

Proactive balancing of marketplace ratings starts from a rich empirical literature on online reviews, their biases and their manipulation. The studies below document the problems this innovation aims to address.

• Steven Tadelis (2016), Reputation and Feedback Systems in Online Platform Markets, Annual Review of Economics, 8: doi:10.1146/annurev-economics-080315-015325.
• Dina Mayzlin, Yaniv Dover and Judith Chevalier (2014), Promotional Reviews: An Empirical Investigation of Online Review Manipulation, American Economic Review, 104(8): doi:10.1257/aer.104.8.2421.
• Gary Bolton, Ben Greiner and Axel Ockenfels (2013), Engineering Trust: Reciprocity in the Production of Reputation Information, Management Science, 59(2): doi:10.1287/mnsc.1120.1609.
• Nan Hu, Jie Zhang and Paul A. Pavlou (2009), Overcoming the J-shaped distribution of product reviews, Communications of the ACM, 52(10): doi:10.1145/1562764.1562800.
• Judith A. Chevalier and Dina Mayzlin (2006), The Effect of Word of Mouth on Sales: Online Book Reviews, Journal of Marketing Research, 43(3): doi:10.1509/jmkr.43.3.345 · reputation system on Wikipedia.

See also: Open Call: Rating Balance Processes · Civic incentive · Maturity Score · All innovations