对风险的掌控是面对不确定事件时最坚实的底气。正如巴菲特对风险本质的洞察："风险来自于你不知道自己在 做什么。"所以，面对风险，巴菲特会选择"将盈利概率乘上可能盈利的数量，减去亏损的概率乘上可能亏损的 数量"，让"不知道"变得可计算、可优化，再去做决策。 所幸，我们有一个威力庞大的数学工具——贝叶斯定理。这个诞生于18世纪的概率学工具，正是帮助我们"知 道"自己在做什么的钥匙。通过了解这个公式背后的逻辑，我们将学会不断修正对事件概率的判断，从而把不确 定性转化为可管理的风险。 本文摘编自《贝叶斯定理》。荐读之。 《贝叶斯定理》 作 者 ： 汤 姆 · 奇 弗 斯 译者： 韩潇潇 出版时间：2 0 2 5年5月 出版社：中信出版集团 首先，让我们来认识一下大名鼎鼎的贝叶斯定理： $$P(A\,|\,B\,)={\frac{P(B\,|\,A\,)\!\cdot\!P(A)}{P(B)}}$$ 先验概率 P（A）：在观测到新证据B之前，事件A的初始概率。 似然度 P（B∣A）：在事件A发生的条件下，观察到证据B的概率。 边际概率 P（B）：证据B在所有可能情况下的总概率（通常通过全概率公式计算）。 后验概率P（A∣ ...

Group 1 - OpenAI has launched a new feature called "Study and Learn" mode in ChatGPT, designed specifically for university students to help them grasp complex subjects through guided interaction [1][8][50] - The new learning mode utilizes a Socratic method, providing interactive prompts and step-by-step guidance to enhance understanding rather than just delivering answers [8][12][66] - This feature is available to all versions of ChatGPT, including free, Plus, professional, and team versions, with an Edu version set to launch soon [1][3][8] Group 2 - The learning mode includes personalized support based on the user's knowledge level and previous interactions, allowing for tailored educational experiences [8][12][66] - Feedback from students indicates that the learning mode effectively breaks down complex materials into clear, manageable explanations, making it feel like a supportive tutoring session [50][58] - The implementation of this feature reflects a shift in educational models, suggesting that traditional methods may soon be disrupted by AI-driven learning tools [3][8][66] Group 3 - The system prompts behind the learning mode have been discovered and shared, indicating a structured approach to guiding users through their studies without doing the work for them [51][52][53] - ChatGPT's learning mode emphasizes understanding over rote memorization, encouraging users to engage with the material actively [8][12][66] - Other companies, including Google and Anthropic, are also exploring similar Socratic-style questioning features, indicating a broader trend in AI education tools [66]

Group 1 - OpenAI has launched a new "learning mode" for ChatGPT, aimed primarily at university students, which utilizes Socratic questioning to guide users in building their knowledge systems step by step [2][7][10] - The learning mode is designed to assist with homework, exam preparation, and learning new concepts, allowing users to simply input their questions after selecting "Research and Learning" in the ChatGPT tool [7][9] - This feature is available to all users, including free, Plus, Pro, and Team versions, with Edu users set to gain access in the coming weeks [9] Group 2 - The learning mode is driven by system prompts rather than a specially trained AI model, allowing for rapid iteration and flexible adjustments [10] - OpenAI plans to integrate this interactive mode into its core model over time, making teaching logic a fundamental capability of ChatGPT [10][33] - Future enhancements will include visual representations of complex concepts, goal setting, progress tracking, and deeper personalization, with the long-term goal of making ChatGPT a "personal tutor" for every student [33] Group 3 - The article discusses the increasing role of AI in education, highlighting a rise in reported AI cheating cases in UK universities, with approximately 7,000 cases reported for the 2023-2024 academic year [35] - OpenAI's CEO, Sam Altman, expresses optimism about AI's role in education, comparing it to the initial fears surrounding Google, suggesting that AI can enhance thinking rather than replace it [38][39] - The industry is collectively focusing on educational applications, with competitors like Anthropic and Google also developing AI tools that emphasize Socratic questioning and personalized learning [40][41]

Core Viewpoint - The article emphasizes the importance of reading and how it contributes to personal growth and decision-making, particularly through the lens of Bayesian thinking [2][3][7]. Group 1: Importance of Reading - The article highlights that reading is a crucial path for growth, as echoed by notable figures like Charlie Munger and Warren Buffett, who advocate for extensive reading as a means to gain wisdom [2][12]. - The initiative encourages readers to engage in discussions about books, fostering a community of learning and interaction [4][5][6]. Group 2: Bayesian Thinking - The article introduces Bayesian thinking as a powerful decision-making framework that helps individuals navigate uncertainty by updating beliefs based on new evidence [12][16][36]. - It explains the essence of Bayesian decision-making, which involves quantifying prior beliefs and continuously adjusting them with new data to optimize decisions [18][21][38]. Group 3: Three Realms of Bayesian Thinking - The first realm focuses on how to scientifically quantify prior beliefs, transforming intuition into measurable probabilities [18][20]. - The second realm emphasizes the importance of dynamic adjustment, where decision-makers should continuously update their strategies based on new information [21][22]. - The third realm discusses the concept of probabilistic thinking, where experts avoid absolute conclusions and instead consider multiple scenarios and their associated probabilities [23][26][29]. Group 4: Practical Applications - The article provides examples of how Bayesian thinking can be applied in various contexts, such as Netflix's recommendation algorithm and decision-making in the electric vehicle market [22][24]. - It also illustrates how top hedge funds utilize probabilistic scenarios to inform their strategies, demonstrating the practical utility of Bayesian methods in finance [29][36]. Group 5: Conclusion - The article concludes by asserting that Bayesian thinking is an essential tool for making informed decisions in an uncertain world, encouraging readers to adopt this mindset for better outcomes [34][37].

Core Insights - The "AI + Healthcare" forum highlighted the transformative impact of AI in the healthcare sector, emphasizing the integration of cloud computing, big data, and AI technologies to enhance medical services and patient experiences [1][17] - The forum featured contributions from various experts, indicating a collaborative effort in advancing AI applications in healthcare, particularly in areas like disease prevention, diagnosis, and drug design [3][10] Group 1: AI Applications in Healthcare - AI is expected to address the increasing demands of life sciences and medicine due to rising life expectancy, with a focus on developing new AI technologies tailored for healthcare [3][10] - The collaboration between Volcano Engine and researchers has led to the development of Bio-OS-Co-Pilot, which significantly reduces research timelines from years to hours, enhancing efficiency in modeling and analysis [4] - Companies like Tianjin Pharmaceutical Group have reported a 14.3% increase in digital maturity through strategic digital transformation initiatives, showcasing the effectiveness of AI in optimizing workflows [6][8] Group 2: Future Directions and Challenges - The healthcare industry faces challenges such as high complexity and strict requirements for data governance, necessitating a shift towards sustainable iterative mechanisms for AI applications [12] - AI is positioned to enhance pre-consultation processes, patient education, and overall efficiency in healthcare delivery, while maintaining a supportive role rather than replacing human decision-making in high-risk scenarios [15] - Future efforts will focus on low-risk, high-value areas for AI implementation, such as research data analysis and logistics support, to ensure effective integration into healthcare systems [14]

Core Insights - The article emphasizes the importance of a corrective feedback loop in decision-making, particularly in uncertain environments, as exemplified by Elon Musk's approach to business and innovation [1][2]. Group 1: Elon Musk's Approach - Musk operates with a strong belief in his mission, distinguishing between faith in achieving goals and belief in the probability of success [3]. - He applies first principles thinking and probability optimization, notably in SpaceX, focusing on user satisfaction and product excellence [4]. - Musk's success stems from understanding how to iterate and improve products in an uncertain world while recognizing the limits of probability and physical laws [5]. Group 2: Bayesian Thinking - Bayesian theory serves as a powerful mathematical tool for understanding and managing uncertainty, allowing for cognitive calibration through updated beliefs based on new evidence [6][7]. - The article outlines the components of Bayesian reasoning, including prior probability, likelihood, marginal probability, and posterior probability, which help in updating beliefs [8][9][10][12]. Group 3: Decision-Making Framework - The article presents a three-tiered framework for applying Bayesian thinking in decision-making: establishing prior beliefs, dynamically adjusting based on new data, and adopting probabilistic thinking [20][28][35]. - The first tier involves quantifying prior beliefs to avoid rigid decision-making based on intuition [21][24]. - The second tier emphasizes the need for dynamic adjustments in decision models as market conditions change, illustrated by examples from Netflix and the electric vehicle market [29][31][32]. - The third tier highlights the importance of probabilistic thinking, where decision-makers consider multiple scenarios and their associated probabilities rather than making absolute claims [36][38][41]. Group 4: Implications of Bayesian Thinking - The article argues that Bayesian thinking is essential for navigating uncertainty in today's complex environment, providing a framework for continuous evolution in decision-making [60][67]. - It suggests that the ability to manage uncertainty through Bayesian principles will be crucial for future success in various fields [68].

Core Insights - The article emphasizes the significance of Bayesian theorem as a framework for updating knowledge and making decisions under uncertainty, complementing Stoic philosophy [2][5][10] Application of Bayesian Thinking - In business, Bayesian theorem can be utilized as a "possibility dashboard" to enhance customer engagement and increase sales conversion rates through real-time probability assessments [3] - In project management, it serves as an intelligent warning system, allowing for proactive risk management by adjusting probabilities based on new evidence [4] - In medical decision-making, it provides a rational filter to assess true probabilities of conditions, countering misleading surface data [4] Complementary Thinking Systems - Bayesian theorem acts as a dynamic guide for updating cognition and quantifying uncertainty, while Stoic philosophy offers psychological resilience by distinguishing between what can and cannot be changed [5][6] - The combination of both approaches is particularly effective in various life scenarios, such as healthcare, investment, and skill acquisition [6] Practical Guidelines - Before taking action, evaluate possibilities using Bayesian theorem [8] - During execution, maintain a Stoic mindset to accept outcomes [9] - After actions, establish a feedback loop for cognitive improvement [10] Conclusion - The integration of Bayesian reasoning and Stoic philosophy is presented as an ultimate survival strategy in an uncertain world, promoting both dynamic decision-making and internal stability [10][11]

大家好，我是银行螺丝钉，欢迎来到这期的螺丝钉带你读书。 「螺丝钉带你读书」也陪伴大家度过了三百多期，为大家讲解了很多有趣、经典的书籍和故 事，比如《三十几岁，财务自由》、《如何读一本书》、《战胜拖延症》等等。 还为大家详细介绍了几位投资大师：股神巴菲特、他的好搭档查理芒格和指数基金之父约翰博 格。分享了他们的人生经历、投资生涯和投资的理念。 大家可以点击下面链接查看部分螺丝钉带你读书合集： 《 世界读书日，螺丝钉送你103本私藏经典好书 》 人人都爱预测 为什么人人都爱预测？ 因为我们都想抓住未来的答案。 最近看了一本挺有意思的新书，《科学预测》。 在算命的时候，最经典的就是"彩虹话术"。 意思是，用两组或者两组以上，彼此对立的语句，来描述事情。 例如： "你表面上坚强独立，但内心渴望被理解。" "看似果断的行动背后，其实经历过痛苦的纠结。" 这种对立话术，几乎能让所有人找到共鸣点。 投资领域也经常充斥这种话术。 例如，1923年的《华尔街日报》，有一篇文章叫《市场建议》。 其中有一段，非常经典的市场涨跌描述。 "正如预期的那样，道琼斯指数，接近140点的时候会有阻力， 但经过一天的下跌后，成交量减少，目前市 ...

Quantitative Models and Construction Methods - **Model Name**: Black-Litterman (BL) Model **Model Construction Idea**: The BL model combines market equilibrium portfolio weights with subjective investor views using Bayesian theorem, aiming to improve stability and flexibility in asset allocation[2][3][8] **Model Construction Process**: 1. **Market Equilibrium Portfolio**: The starting point is the CAPM-based market equilibrium portfolio, where asset weights are determined by market capitalization. The equilibrium returns are calculated using the utility function: $U = w^{T}\Pi - \frac{\delta}{2}w^{T}\Sigma w$ Here, $\Pi$ represents equilibrium returns, $\Sigma$ is the covariance matrix, and $\delta$ is the risk aversion coefficient[12][13][14]. Alternatively, equilibrium returns can be derived as: $\Pi = \delta\Sigma_{eq}$[14][15]. 2. **Bayesian Integration**: Bayesian theorem is applied to combine prior information (market equilibrium returns) with subjective investor views. The posterior mean and covariance matrix are calculated as: $\mu_{p} = [(\tau\Sigma)^{-1} + P^{T}\Omega^{-1}P]^{-1}[(\tau\Sigma)^{-1}\Pi + P^{T}\Omega^{-1}Q]$ $\Sigma_{p} = [(\tau\Sigma)^{-1} + P^{T}\Omega^{-1}P]^{-1}$ Here, $\tau$ represents the uncertainty of prior returns, $P$ is the matrix indicating assets involved in subjective views, $Q$ is the vector of expected returns for subjective views, and $\Omega$ is the confidence matrix for subjective views[9][10][29]. 3. **Final Asset Weights**: Using the posterior mean and covariance matrix, asset weights are optimized via mean-variance optimization: $\mathbf{w} = (\delta\Sigma_{p}^{*})^{-1}\mu_{p}$ $\Sigma_{p}^{*}$ can be calculated using two methods: - $\Sigma_{p}^{*} = \Sigma_{p} + \Sigma$ (recommended for practical use)[30][31] - $\Sigma_{p}^{*} = \Sigma_{p}$ (used in specific cases)[31][32]. - **Model Evaluation**: The BL model improves stability by starting with market equilibrium weights and allows flexible incorporation of subjective views. It avoids the sensitivity issues of traditional mean-variance models and provides intuitive results[2][8][9]. Model Backtesting Results - **BL Model**: No specific numerical backtesting results are provided in the report. Quantitative Factors and Construction Methods - **Factor Name**: None explicitly mentioned in the report. Factor Backtesting Results - **Factor Results**: None explicitly mentioned in the report.