英文标题：
《On the Statistical Differences between Binary Forecasts and Real World
Payoffs》
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作者：
Nassim Nicholas Taleb
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最新提交年份：
2019
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英文摘要：
What do binary (or probabilistic) forecasting abilities have to do with overall performance? We map the difference between (univariate) binary predictions, bets and \"beliefs\" (expressed as a specific \"event\" will happen/will not happen) and real-world continuous payoffs (numerical benefits or harm from an event) and show the effect of their conflation and mischaracterization in the decision-science literature. We also examine the differences under thin and fat tails. The effects are: A- Spuriousness of many psychological results particularly those documenting that humans overestimate tail probabilities and rare events, or that they overreact to fears of market crashes, ecological calamities, etc. Many perceived \"biases\" are just mischaracterizations by psychologists. There is also a misuse of Hayekian arguments in promoting prediction markets. We quantify such conflations with a metric for \"pseudo-overestimation\". B- Being a \"good forecaster\" in binary space doesn\'t lead to having a good actual performance}, and vice versa, especially under nonlinearities. A binary forecasting record is likely to be a reverse indicator under some classes of distributions. Deeper uncertainty or more complicated and realistic probability distribution worsen the conflation . C- Machine Learning: Some nonlinear payoff functions, while not lending themselves to verbalistic expressions and \"forecasts\", are well captured by ML or expressed in option contracts. D- Fattailedness: The difference is exacerbated in the power law classes of probability distributions.
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中文摘要：
二元（或概率）预测能力与总体性能有什么关系？我们绘制了（单变量）二元预测、下注和“信念”（表示为特定的“事件”将发生/不会发生）与现实世界连续收益（事件带来的数字利益或伤害）之间的差异，并在决策科学文献中显示了其合并和错误描述的影响。我们还研究了瘦尾巴和胖尾巴下的差异。其影响是：许多心理结果都是虚假的，尤其是那些证明人类高估尾部概率和罕见事件，或者对市场崩溃、生态灾难等恐惧反应过度的结果。许多被感知的“偏见”只是心理学家的错误描述。在推动预测市场方面，哈耶克的观点也被滥用。我们用一个“伪高估”指标来量化这种融合。B-在二进制空间中做一个“好的预报员”并不会带来好的实际性能}，反之亦然，尤其是在非线性情况下。在某些类别的分布下，二元预测记录可能是一个反向指标。更深层次的不确定性或更复杂和现实的概率分布加剧了这种融合。机器学习：一些非线性支付函数虽然不适合口头表达和“预测”，但很好地被ML捕获或在期权合约中表达。D-肥胖：这种差异在概率分布的幂律类中加剧。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：General Finance 一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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一级分类：Physics 物理学
二级分类：Physics and Society 物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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