未知概率下的赌博作为不确定性下现实世界决策的代理模型
Gambling under unknown probabilities as a proxy for real world decisions under uncertainty
AI总结：
该研究提出一个以赌博为场景的不确定性决策框架，用均方误差衡量概率估计准确度，可用于分析估计误差的代价，并形式化“概率估计更准的智能体在竞争中更易成功”的直观结论。
摘要：
我们在一个研究不确定性下决策的框架内给出了基础示例，该框架中的概率仅能被粗略知晓。用赌博术语来说，该框架的设定是：赌注金额与赌徒基于其感知概率得出的感知优势成正比，而他们估计真实概率的准确度用均方误差来衡量。在该框架下，人们可以研究估计误差的代价，并试图将一个“显而易见”的概念形式化：在行动依赖于自身感知概率的智能体之间的竞争互动中，估计概率更准确的个体通常会不如准确的个体更成功。
英文摘要：
We give elementary examples within a framework for studying decisions under uncertainty where probabilities are only roughly known. The framework, in gambling terms, is that the size of a bet is proportional to the gambler's perceived advantage based on their perceived probability, and their accuracy in estimating true probabilities is measured by mean squared-error. Within this framework one can study the cost of estimation errors, and seek to formalize the ``obvious" notion that in competitive interactions between agents whose actions depend on their perceived probabilities, those who are more accurate at estimating probabilities will generally be more successful than those who are less accurate.
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。
