Clearer Thinking with Spencer Greenberg 2024年07月17日
Beyond cognitive biases: improving judgment by reducing noise (with Daniel Kahneman)
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文章探讨了如何将测量准确性理论应用于人类判断,分析了认知偏见如何影响测量误差的偏差项和噪声项,并讨论了在各类判断中预期的噪声量。同时,文章还探讨了机器是否会在所有领域超越人类决策,以及机器决策与人类决策的差异。此外,文章还涉及了应在哪些领域减少决策方差,以及机器学习使用人类决策作为训练数据时,人类偏见如何“融入”机器决策,并探讨这些偏见是否可以得到补偿。

🧠 文章首先提出了一个核心问题:如何将测量准确性理论应用于人类判断。作者分析了人类判断中的偏差和噪声,并探讨了如何量化这些误差。

📊 对于认知偏见,文章指出它不仅影响测量误差的偏差项,还影响噪声项。作者通过实例说明了在不同类型的判断中,偏见是如何产生和加重的。

🤖 文章接着探讨了机器决策与人类决策的差异,以及机器是否有可能在所有领域都做出比人类更好的决策。作者提出了几个关键点,比较了两种决策方式的优劣。

🎯 在讨论决策方差时,文章提出了一些领域,在这些领域中减少方差是至关重要的。作者解释了为什么在某些情况下,减少决策的不确定性比其他任何事情都重要。

🔄 最后,文章讨论了当机器学习使用人类决策作为训练数据时,人类偏见如何被“融入”机器决策,以及我们是否能够补偿这些偏见。作者还提出了人类判断可能在哪些领域始终优于机器判断的问题。

Read the full transcript here.

How can we apply the theory of measurement accuracy to human judgments? How can cognitive biases affect both the bias term and the noise term in measurement error? How much noise should we expect in judgments of various kinds? Is there reason to think that machines will eventually make better decisions than humans in all domains? How does machine decision-making differ (if at all) from human decision-making? In what domains should we work to reduce variance in decision-making? If machines learn use human decisions as training data, then to what extent will human biases become "baked into" machine decisions? And can such biases be compensated for? Are there any domains where human judgment will always be preferable to machine judgment? What does the "fragile families" study tell us about the limits of predicting life outcomes? What does good decision "hygiene" look like? Why do people focus more on bias than noise when trying to reduce error? To what extent can people improve their decision-making abilities? How can we recognize good ideas when we have them? Humans aren't fully rational, but are they irrational?

Daniel Kahneman is Professor of Psychology and Public Affairs Emeritus at the Princeton School of Public and International Affairs, the Eugene Higgins Professor of Psychology Emeritus at Princeton University, and a fellow of the Center for Rationality at the Hebrew University in Jerusalem. Dr. Kahneman has held the position of professor of psychology at the Hebrew University in Jerusalem (1970-1978), the University of British Columbia (1978-1986), and the University of California, Berkeley (1986-1994). He is a member of the National Academy of Science, the Philosophical Society, the American Academy of Arts and Sciences, and is a fellow of the American Psychological Association, the American Psychological Society, the Society of Experimental Psychologists, and the Econometric Society. He has been the recipient of many awards, among them the Distinguished Scientific Contribution Award of the American Psychological Association (1982) and the Grawemeyer Prize (2002), both jointly with Amos Tversky, the Warren Medal of the Society of Experimental Psychologists (1995), the Hilgard Award for Career Contributions to General Psychology (1995), the Nobel Prize in Economic Sciences (2002), the Lifetime Contribution Award of the American Psychological Association (2007), and the Presidential Medal of Freedom (2013). He holds honorary degrees from numerous universities. Find out more about him here.

Here's the link to the Thought Saver deck that accompanies this episode: https://app.thoughtsaver.com/embed/JGXcbe19e1?start=1&end=17

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测量准确性 认知偏见 机器决策 人类判断 决策方差
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