Intent-based decentralized exchanges delegate execution to a competitive class of agents -- solvers -- whose behavior is shaped by protocol-designed reward rules. We measure how a change to those rules reshapes who captures value, using a governance-dated natural experiment: CoW Protocol CIP-74 (effective 8 December 2025), which replaced a fixed solver-reward cap with one tied to protocol revenue and introduced an ad-valorem volume fee. Using daily solver shares over 395 days, we find the reform reallocated trading value by order size. The robust signature is a monotone size gradient: concentration fell in small orders and rose in large ones across four order-value buckets (Spearman rho=1.00, exact permutation p=0.042) -- a pattern that survives dropping the single largest solver. Aggregate concentration also rose (volume-weighted HHI 0.176->0.241), substantially carried by the incumbent top solver. By trade count the market de-concentrated (count-HHI -0.060). A simple solver-economics model rationalizes the pattern: an ad-valorem fee is competitively neutral, while a revenue-linked reward cap raises the marginal payoff to inventory-rich solvers on large orders -- consistent with restricted-entry predictions (Chitra et al. 2024). A control venue (UniswapX) shows no matching break. We detect no change in average execution quality (~7 bps bound). A triple-difference exploiting a February 2026 fee cut is directionally consistent but underpowered. Reward design measurably reallocates who captures value in intent markets, without moving the average price users receive.
We present a system that organizes investment knowledge into four knowledge bases—Graham, Buffett, dividend investing, and general market heuristics—each grounded in citable, published thresholds. The system employs Holland’s bucket brigade algorithm to learn rule reliability from backtested outcomes. Multiple rules re...
Jae C. Oh· IEEE Conference on Computati...· 0 citations
We study repeated contract design when a principal observes outcomes but not the actions that generate them. The principal may use any bounded outcome-contingent payment vector, and the agent's best response can make expected profit discontinuous in those payments. For every fixed number $m\ge2$ of outcomes, the minima...
The proposed dynamic bipartite matching framework supports the advantages of decentralized matching with LLM-based behavioral modeling and online learning under incomplete information for economic simulation and computational social science research.
Wang-Xuan Fan, Xiao-Yu Nie, Zhou-Tian Shi et al.· 0 citations
Long-horizon competition tests agents'ability to coordinate business decisions under uncertainty and adapt to changing rival strategies. We introduce CEO Arena, a benchmark that uses matched replacement evaluation to assess operating returns alongside an agent's effects on rivals and the market. Each CEO agent is compa...
An Yan, Yu-Jia Huo, Zhi-Wei Shang et al.· 0 citations
We extend the standard Principal-Agent framework to scenarios where the Agent selects from a suite of technologies, each characterized by a distinct cost-capability profile. This framework is increasingly critical in the era of Large Language Models (LLMs), where Agents choose both a model and an associated effort leve...
Nanda Kishore Sreenivas, Kate Larson· 2 citations
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