Tempting the Agent: The Economics of Reputation without Persistent Identity in AI Agent Markets

Battle Count: 86

The paper is primarily about mechanism design and reputation economics in AI agent markets, not about trading strategies or financial market microstructure. However, it has tangential relevance to quantitative trading in the context of: (1) decentralized finance (DeFi) protocol design where agents execute trades, (2) MEV extraction and adversarial behavior in blockchain markets, (3) the economics of trust in automated trading systems, and (4) staking/slashing mechanisms that affect capital allocation in DeFi. The dynamic programming framework and incentive analysis could inform the design of trading agent protocols, but the paper does not directly address price prediction, portfolio construction, or execution optimization.

The theoretical model requires understanding of dynamic programming, Bellman equations, Lipschitz continuity, supermodularity/submodularity (Topkis theorem), and comparative statics. The numerical implementation involves value iteration on a 2D discretized state space (F, M) with an additional optimization over effort q, requiring careful handling of convergence (tolerance 10^-12) and grid resolution (up to 1000x1000x1000). The analytical results in the small-update regime are more accessible but require comfort with uniform continuity arguments and limit analysis. No code is provided for reproduction.

The paper provides complete mathematical formulations, all assumptions (A.1-A.6), detailed proofs in the appendix, and specific numerical parameters for simulations (θ=0.7, f=0.06, γ=0.25, κ_R=0.05, Cobb-Douglas volume, grid sizes, convergence tolerance 10^-12). However, no code repository is provided, and the numerical implementation details (while described) would need to be independently coded. The theoretical results are fully self-contained with proofs.