是幸运还是优秀？结果噪声、有效样本量与技能归因
Lucky or Good? Outcome Noise, Effective Sample Size, and the Attribution of Skill
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中文总结 AI 辅助
该研究针对结果记录信号不足时的技能归因问题，提出用噪声与有效结果数量表征决策领域的框架，并建议采用医学领域的群体层面经验验证方法作为替代评估方式。
中文摘要 AI 辅助
当结果记录包含足够信号以支持对技能的可靠推断时，以及当信号不足时，评估者应采用何种替代方式？回答第一个问题的框架将任何决策领域用两个参数表征：每个结果中反映的噪声，以及在观测窗口内可用的独立结果的有效数量。当将这些领域置于噪声与结果数量构成的二维空间中时，那些常规根据已实现结果分配资本、声望和政治权力的领域（例如共同基金管理、风险投资、高管绩效）处于结果记录包含信号过少、无法支持可靠个体层面推断的区域。当结果记录不足时，评估者可采用医学领域长期使用的群体层面经验验证方法：该行为者是否采用了群体层面与更好结果相关联的实践？
英文摘要
When do outcome records carry enough signal to support reliable inferences about skill? When they do not, what should evaluators substitute? The framework answering the first question characterizes any decision domain with two parameters: the noise reflected in each outcome and the effective number of independent outcomes that are available over an observation window. When domains are positioned in a two-dimensional space of noise versus number of outcomes, those in which capital, prestige, and political power are routinely allocated on the basis of realized outcomes (e.g., mutual fund management, venture capital, executive performance) fall in the region where outcome records contain too little signal to support reliable individual-level inferences. Evaluating actors when outcome records are insufficient can be done by adopting the populationlevel empirical validation methods long used in medicine: has the actor adopted the practices that, at the population level, are associated with better outcomes?
