  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.
