英文标题：
《Economics of disagreement -- financial intuition for the R\\\'enyi
  divergence》
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作者：
Andrei N. Soklakov
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最新提交年份：
2020
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英文摘要：
  Disagreement is an essential element of science and life in general. The language of probabilities and statistics is often used to describe disagreements quantitatively. In practice, however, we want much more than that. We want disagreements to be resolved. This leaves us with a substantial knowledge gap which is often perceived as a lack of practical intuition regarding probabilistic and statistical concepts.   Take for instance the R\\\'enyi divergence which is a well-known statistical quantity specifically designed as a measure of disagreement between probabilistic models. Despite its widespread use in science and engineering, the R\\\'enyi divergence remains a highly abstract axiomatically-motivated measure. Certainly, it offers no practical insight as to how disagreements can be resolved.   Here we propose to address disagreements using the methods of financial economics. In particular, we show how a large class of disagreements can be transformed into investment opportunities. The expected financial performance of such investments quantifies the amount of disagreement in a tangible way. This provides intuition for statistical concepts such as the R\\\'enyi divergence which becomes connected to the financial performance of optimized investments. Investment optimization takes into account individual opinions as well as attitudes towards risk. The result is a market-like social mechanism by which funds flow naturally to support a more accurate view. Such social mechanisms can help us with difficult disagreements (e.g., financial arguments concerning the future climate).   In terms of scientific validation, we used the findings of independent neurophysiological experiments as well as our own research on the equity premium.
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中文摘要：
分歧是科学和生活的基本要素。概率和统计的语言通常用于定量描述分歧。然而，实际上，我们想要的远不止这些。我们希望分歧得到解决。这给我们留下了一个巨大的知识缺口，这通常被认为是缺乏关于概率和统计概念的实际直觉。例如，R趵enyi散度是一个众所周知的统计量，专门设计用于衡量概率模型之间的不一致性。尽管在科学和工程中得到了广泛的应用，但雷尼分歧仍然是一个高度抽象的公理化度量。当然，它没有提供如何解决分歧的实际见解。在这里，我们建议使用金融经济学的方法来解决分歧。特别是，我们展示了如何将一大类分歧转化为投资机会。这些投资的预期财务业绩以有形的方式量化了分歧的数量。这为统计概念提供了直觉，例如与优化投资的财务绩效相关的R趵yi分歧。投资优化考虑个人意见以及对风险的态度。其结果是形成了一种类似市场的社会机制，通过这种机制，资金可以自然流动，以支持更准确的观点。这种社会机制可以帮助我们解决棘手的分歧（例如，关于未来气候的财务争论）。在科学验证方面，我们使用了独立神经生理学实验的结果以及我们自己对股票溢价的研究。
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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：Computer Science        计算机科学
二级分类：Computer Science and Game Theory        计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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一级分类：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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