The CoinAlg Bind: Profitability-Fairness Tradeoffs in Collective Investment Algorithms

2026-01-02

arXiv.org

AI Summary

This work addresses a fundamental trade-off in collective investment algorithms (CoinAlgs) between profit maximization and economic fairness: transparency invites arbitrage that erodes returns, while privacy enables unfair insider arbitrage. The paper introduces and formally defines the notion of “CoinAlg Bind,” establishing theoretical limits on the simultaneous achievability of high returns and fairness. Through formal modeling, game-theoretic analysis, empirical evaluation of on-chain Uniswap transactions, and simulations of covert channels, the study demonstrates that privacy is a prerequisite for insider attacks and that even minimal-bandwidth information leakage can yield significant unfair profits. These findings rigorously connect privacy, fairness, and arbitrage in decentralized finance protocols.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Machine Learning: Privacy
• Multiagent Systems: Mechanism Design

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Collective Investment Algorithms (CoinAlgs) are increasingly popular systems that deploy shared trading strategies for investor communities. Their goal is to democratize sophisticated -- often AI-based -- investing tools. We identify and demonstrate a fundamental profitability-fairness tradeoff in CoinAlgs that we call the CoinAlg Bind: CoinAlgs cannot ensure economic fairness without losing profit to arbitrage. We present a formal model of CoinAlgs, with definitions of privacy (incomplete algorithm disclosure) and economic fairness (value extraction by an adversarial insider). We prove two complementary results that together demonstrate the CoinAlg Bind. First, privacy in a CoinAlg is a precondition for insider attacks on economic fairness. Conversely, in a game-theoretic model, lack of privacy, i.e., transparency, enables arbitrageurs to erode the profitability of a CoinAlg. Using data from Uniswap, a decentralized exchange, we empirically study both sides of the CoinAlg Bind. We quantify the impact of arbitrage against transparent CoinAlgs. We show the risks posed by a private CoinAlg: Even low-bandwidth covert-channel information leakage enables unfair value extraction.

Problem

• Collective Investment Algorithms
• profitability-fairness tradeoff
• economic fairness
• arbitrage
• privacy

Innovation

• Collective Investment Algorithms
• Profitability-Fairness Tradeoff
• Economic Fairness
• Algorithmic Privacy
• Arbitrage