[Paper Review] Fairness in ERC token markets: A Case Study of CryptoKitties

Kentaro Sako, Shin’ichiro Matsuo+1 more|arXiv (Cornell University)|Feb 7, 2021

Blockchain Technology Applications and Security2 references4 citations

TL;DR

This paper investigates fairness in decentralized markets using CryptoKitties as a case study, revealing that its gene algorithm lacks true randomness, enabling users who understand its bias to predict and profit from high-value token creation. The study identifies systemic unfairness due to information asymmetry, hidden collusion in auctions, and regressive fees, proposing cryptographic and design improvements to enhance market equity in ERC-721 token markets.

ABSTRACT

Fairness is an important trait of open, free markets. Ethereum is a platform meant to enable digital, decentralized markets. Though many researchers debate the market's fairness, there are few discussions around the fairness of automated markets, such as those hosted on Ethereum. In this paper, using pilot studies, we consider unfair factors caused by adding the program. Because CryptoKitties is one of the major blockchain-based games and has been in operation for an extended period of time, we focus on its market to examine fairness. As a result, we concluded that a gene determination algorithm in this game has little randomness, and a significant advantage to gain profit is given to players who know its bias over those who do not. We state incompleteness and impact of the algorithm and other factors. Besides, we suppose countermeasures to reduce CryptoKitties' unfairness as a market.

Motivation & Objective

• To investigate whether decentralized markets built on smart contracts, such as CryptoKitties, uphold fairness principles comparable to traditional financial markets.
• To identify specific technical and design flaws in CryptoKitties that create information asymmetry and unfair advantages for knowledgeable users.
• To evaluate how factors like breeding fees, auction mechanics, and algorithmic randomness impact market equity.
• To propose actionable countermeasures—such as user-controlled inputs and delayed auction starts—to reduce unfairness in blockchain-based token markets.
• To establish foundational criteria for assessing fairness in smart contract-driven financial systems.

Proposed method

• Reverse-engineered the CryptoKitties gene algorithm to analyze its randomness and predictability using Solidity source code and on-chain transaction data.
• Evaluated the impact of the breeding fee (0.008 ETH) and gas fees on user participation, especially for low-wealth players.
• Analyzed auction mechanics to detect collusion risks, including early-bid advantages when sellers and bidders coordinate.
• Identified suspicious off-auction transactions via blockchain analysis, suggesting potential secret trades.
• Proposed cryptographic improvements, such as allowing users to contribute inputs to hash functions, to eliminate predictability in token generation.
• Suggested design changes like pre-announcing auctions and delaying bid starts to reduce information asymmetry.

Experimental results

Research questions

• RQ1 To what extent does the gene determination algorithm in CryptoKitties exhibit predictable patterns that create information asymmetry?
• RQ2 How do breeding and transaction fees affect market access and profitability for low-wealth users?
• RQ3 In what ways do auction mechanics and collusion between sellers and bidders undermine market fairness?
• RQ4 What role does blockchain anonymity play in enabling insider trading and obscuring unfair market behavior?
• RQ5 What technical and design improvements can enhance fairness in smart contract-based token markets?

Key findings

• The gene algorithm in CryptoKitties is not cryptographically random; its predictability allows users who understand its bias to strategically breed high-value tokens.
• Players with knowledge of the algorithm’s input mechanism can reliably produce valuable kitties, creating a significant information asymmetry.
• The breeding fee of 0.008 ETH acts as a regressive barrier, limiting participation and profit potential for low-wealth users.
• Auction mechanics allow colluding parties to win bids immediately upon listing, excluding other participants and undermining fair competition.
• Suspicious transactions without auction participation suggest the existence of off-chain or secret trades, violating transparency principles.
• The lack of user control over hash inputs in token generation enables predictability and reduces fairness in token value distribution.