竞争环境下理性个体序贯决策中的集体准确性优化
Optimising collective accuracy among rational individuals in sequential decision-making with competition
AI总结：
本文针对序贯决策中理性个体因观察他人选择而产生相关性、损害集体准确性的问题，提出一种竞争性奖励方案，通过奖励正确选择少数人已选选项来诱导独立决策，恢复并提升群体智慧，且该方案在多种竞争强度下均稳健有效。
摘要：
支撑“群体智慧”的理论结果，如孔多塞陪审团定理，表明通过聚合决策或意见可以显著提高准确性，但该定理的基础在个体能够根据观察其他代理人的选择来调整自身选择的情况下经常被削弱。在序贯决策中，理性代理人将他人的选择视为关于正确决策的信息来源，从而在不同代理人的选择之间产生强相关性，这违反了孔多塞陪审团定理所依赖的独立性假设。在本文中，我展示了当代理人仅根据其个人准确性获得奖励时，这种相关性如何产生，以及这对集体准确性的影响。随后，我证明了一种简单的竞争性奖励方案——即如果代理人正确选择了少数人已选择的选项，则其奖励更高——能够诱导理性代理人做出独立选择，使群体恢复到最优的集体准确性水平。我进一步表明，该奖励方案具有稳健性，在广泛的竞争强度范围内都能改善集体准确性，这表明此类方案可以在现实世界中有效实施，以提升集体智慧。
英文摘要：
Theoretical results underpinning the Wisdom of Crowds, such as the Condorcet Jury Theorem, point to substantial accuracy gains through aggregation of decisions or opinions, but the foundations of this theorem are routinely undermined in circumstances where individuals are able to adapt their own choices based after observing what other agents have chosen. In sequential decision-making, rational agents use the choices of others as a source of information about the correct decision, creating powerful correlations between different agents' choices that violate the assumptions of independence on which the Condorcet Jury Theorem depends. In this paper I show how such correlations emerge when agents are rewarded solely based on their individual accuracy, and the impact of this on collective accuracy. I then demonstrate how a simple competitive reward scheme, where agents' rewards are greater if they correctly choose options that few have already chosen, can induce rational agents to make independent choices, returning the group to optimal levels of collective accuracy. I further show that this reward scheme is robust, offering improvements to collective accuracy across of wide range of competition strengths, suggesting that such schemes could be effectively implemented in real-world contexts to improve collective wisdom.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
