Paper: arXiv 2609.02992

Authors: Federico Gatta, Manuel Naviglio, Francesco Tarantelli

Abstract

Reputation is a fundamental mechanism through which markets sustain trust when service quality cannot be perfectly assessed ex ante, constituting a form of intertemporal economic capital by attracting future demand. Its effectiveness as a disciplinary mechanism depends not only on past interactions but also on the persistence of the identity to which reputation is attached. When identities can be abandoned and recreated cheaply, reputational capital may itself become an object of opportunistic exploitation. This paper develops a dynamic economic framework to study when reputation is sufficient to discipline autonomous agents. We model reputation as capital attracting future economic activity. At each point, an agent chooses between operating honestly, investing in quality to preserve future gains, or executing a one-shot deviation to extract its reputation’s value and restart from a penalized identity. Our analysis relates the temptation to opportunistic behavior to identity-reset costs, reputation persistence, demand sensitivity, and enforcement design, deriving comparative statics on optimal quality provision. Autonomous AI-agent operating on the blockchain are a relevant application: infrastructures such as ERC-8004, ERC-8183, and x402 combine reputation, identity, and payments in permissionless markets. Nonetheless, our framework applies to any environment where reputation generates future business and identities are replaceable.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: This paper presents a highly novel theoretical framework for reputation in AI agent markets, demonstrating strong mathematical modeling. However, it lacks empirical validation, focusing purely on theoretical derivations and comparative statics without real-world data or backtesting.

Research Flowchart

  flowchart TD
    A[Research Goal: Understand Reputation's Role in AI Agent Markets with Abandonable Identity] --> B{Dynamic Economic Framework Development};
    B --> C[Model Components: Agent Choices (Honest vs. Opportunistic), Reputation as Capital, Identity-Reset Costs];
    C --> D[Inputs: Identity-Reset Costs, Reputation Persistence, Demand Sensitivity, Enforcement Design];
    D --> E{Comparative Statics & Analysis of Optimal Quality Provision};
    E --> F[Outcomes: Relationship between Opportunism, Identity-Reset Costs, Reputation Persistence, Demand Sensitivity, Enforcement Design];
    F --> G[Application: AI Agents on Blockchain (ERC-8004, ERC-8183, x402) & General Markets with Replaceable Identities];