零和竞争让多个模型公平打分，避免误导决策者。
Joint Scoring Rules: Zero-Sum Competition Avoids Performative Prediction
- 让多个模型互相竞争打分，形成零和博弈
- 实验证明预测准确率与决策者收益双提升
- 适合需要公平评估的智能系统设计
在决策场景中，决策方常依赖专家模型的条件预测来辅助判断。但此方式会引发根本性利益冲突：追求预测精度的模型有动机诱导决策方采取更易预测的行为，从而掩盖其真实偏好。本文证明，通过联合评估多个模型可克服这一困境。当模型间进行零和竞争时，其影响决策行为的动机被消除，决策方得以识别并执行自身最偏好的行动。进一步证明该零和机制具有唯一性、可高效实现，并适用于随机选择情形。在模拟环境中实验表明，基于零和目标训练显著提升预测准确率与决策者效用，且能消除原有操纵行为。
原文摘要 · Abstract (English)
In a decision-making scenario, a principal could use conditional predictions from an expert agent to inform their choice. However, this approach would introduce a fundamental conflict of interest. An agent optimizing for predictive accuracy is incentivized to manipulate their principal towards more predictable actions, which prevents that principal from being able to deterministically select their true preference. We demonstrate that this impossibility result can be overcome through the joint evaluation of multiple agents. When agents are made to engage in zero-sum competition, their incentive to influence the action taken is eliminated, and the principal can identify and take the action they most prefer. We further prove that this zero-sum setup is unique, efficiently implementable, and applicable under stochastic choice. Experiments in a toy environment demonstrate that training on a zero-sum objective significantly enhances both predictive accuracy and principal utility, and can eliminate previously learned manipulative behavior.
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