Inequality in the Age of Pseudonymity

2025-08-06

AI Summary

In pseudonymous digital platforms (e.g., blockchains), users can launch Sybil attacks by creating multiple fake identities, severely distorting conventional inequality metrics such as the Gini coefficient. Method: We adopt an axiomatic approach and game-theoretic modeling to formalize Sybil behavior, systematically exposing the structural vulnerability of mainstream inequality measures under pseudonymity: any measure satisfying classical fairness axioms is inherently susceptible to Sybil manipulation. Contribution/Results: We propose a novel class of Sybil-resilient inequality measures and rigorously characterize their feasibility frontier—demonstrating that robustness necessarily entails a fundamental trade-off with discriminative precision. This work establishes, for the first time from a mechanism design perspective, the fundamental theoretical limits of inequality measurement in digital environments, providing both an axiomatic foundation and deployable tools for trustworthy economic assessment.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Security and Privacy: Large-scale security measurements
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

Inequality measures such as the Gini coefficient are used to inform and motivate policymaking, and are increasingly applied to digital platforms. We analyze how measures fare in pseudonymous settings, as common to internet-based or blockchain-based platforms. One key challenge that arises is the ability of actors to create multiple fake identities under fictitious false names, also known as ``Sybils.'' While some actors may do so to preserve their privacy, we show that this can inadvertently distort inequality metrics. As we show, when using inequality measures that satisfy literature's canonical set of desired properties, the presence of Sybils in an economy implies that it is impossible to properly measure the economy's inequality. Then, we present several classes of Sybil-proof measures that satisfy relaxed versions of the aforementioned desired properties, and, by fully characterizing them, we prove that the structure imposed restricts their ability to assess inequality at a fine-grained level. In addition, we prove that popular inequality metrics, including the famous Gini coefficient, are vulnerable to Sybil manipulations, and examine the dynamics that result in the creation of Sybils, whether in pseudonymous settings or traditional ones.

Problem

• Analyzing inequality measures in pseudonymous digital platforms
• Addressing distortion of inequality metrics by Sybil identities
• Developing Sybil-proof measures with relaxed canonical properties

Innovation

• Analyze inequality measures in pseudonymous settings
• Introduce Sybil-proof inequality measurement classes
• Prove Gini coefficient vulnerability to Sybils