DARSAN: A Decentralized Review System Suitable for NFT Marketplaces

TL;DR

DARSAN addresses trust challenges in NFT marketplaces where few verifiable buyers create bias in reviews. It proposes a blockchain-backed, two-phase review system that iteratively assembles an expert pool to predict objective popularity and assess subjective asset quality, with post-sale incentive distributions and a checks-and-balances mechanism to deter misbehavior. The architecture integrates off-chain pre-deployment configuration with on-chain per-asset rounds, featuring endorsement- and prediction-based expertise gains, and uses ground-truth sales signals to align reviewer incentives with market outcomes. Simulations demonstrate that, when bootstrapped with capable initial experts, DARSAN self-corrects toward high-quality experts, resists certain adversarial actions, and remains scalable as new participants join, supporting practical deployment in NFT markets and adaptable applicability to other scarce-asset ecosystems.

Abstract

We introduce DARSAN, a decentralized review system designed for Non-Fungible Token (NFT) marketplaces, to address the challenge of verifying the quality of highly resalable products with few verified buyers by incentivizing unbiased reviews. DARSAN works by iteratively selecting a group of reviewers (called ``experts'') who are likely to both accurately predict the objective popularity and assess some subjective quality of the assets uniquely associated with NFTs. The system consists of a two-phased review process: a ``pre-listing'' phase where only experts can review the product, and a ``pre-sale'' phase where any reviewer on the system can review the product. Upon completion of the sale, DARSAN distributes incentives to the participants and selects the next generation of experts based on the performance of both experts and non-expert reviewers. We evaluate DARSAN through simulation and show that, once bootstrapped with an initial set of appropriately chosen experts, DARSAN favors honest reviewers and improves the quality of the expert pool over time without any external intervention even in the presence of potentially malicious participants.

Figures (8)

• Figure 1: Illustration of the integration of DARSAN within an NFT marketplace. The red ovals symbolize different roles, i.e, an individual may simultaneously act as a customer and a reviewer.
• Figure 2: Steps involved in the pre-listing period.
• Figure 3: Steps involved in the pre-sale period.
• Figure 4: Steps involved in the post-sale period.
• Figure 5: A randomly drawn initial population of 500 reviewers, with red dots denoting the initial set of experts. The blue, magenta, and orange lines represent slope parameters of $0$, $-\infty$, and $-1$ respectively. The ideal final expert set comprises the dots positioned above and/or to the right of the selected slope line.