1. 大討論與超預測
Dr. Chia-Yen Lee (李家岩 博士)
(2016/07/22)
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Institute of Manufacturing Information and Systems (製造資訊與系統研究所)
Institute of Engineering Management (工程管理碩士在職專班)
Department of Computer Science and Information Engineering (資訊工程學系)
National Cheng Kung University (國立成功大學)
5. Productivity Optimization Lab Dr. Chia-Yen Lee大討論與超預測
謙卑、謙卑、再謙卑
• In May 2009, the child, Jacob Philadelphia, was visiting the
White House with his father…
"I want to know if my hair is just like yours," he told Mr. Obama, so
quietly that the president asked him to speak again.
Jacob did, and Mr. Obama replied, "Why don't you touch it and see for
yourself?" He lowered his head, level with Jacob, who hesitated.
"Touch it, dude!" Mr. Obama said.
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51. Productivity Optimization Lab Dr. Chia-Yen Lee大討論與超預測
超預測:遇見未來的藝術和科學
• 到底要作刺蝟還是作狐狸? 問題沒有絕對的答案!
• 如果真的有..答案就是…It Depends…
短期 vs. 長期
• 交男女朋友 vs. 結婚對象
• 台灣向左靠(!?) vs. 南進政策
• 英國脫歐 vs. 歐洲經濟體
• 讀書工作 vs. 自我實現
景氣好 vs. 景氣差
• Good to Great《從A到A+》(2001) vs. Superforecasting《超預測》(2015)
• Target vs. Walmart
• Idea (America) vs. Realization (China)
Condition非常重要!! 方法使用上的”條件” (必要條件與充分條件)
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56. Productivity Optimization Lab Dr. Chia-Yen Lee大討論與超預測 56
• Bayes’ Theorem
Some natural status and possible outcome H1,H2,…,Hn,satisfies the
assumption of MECE (Mutually Exclusive Collectively Exhaustive).
Let P(Hi) be the prior probability (驗前機率) of event Hi,that is, the
subjective probability (主觀機率) of Hi which decision-maker conjectures
without any other information.
Let P(E|Hi) be the likelihood function (概似函數機率) of Hi. It shows the
probability of evidence E we can observe in the sample when event Hi
occurs.
After decision-maker obtains the evidence (E) from sample, he could
correct the probability of Hi given event E using Bayes’ theorem, called
posterior probability (驗後機率) of Hi, i.e., P(Hi |E).
Bayes’ Theorem
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57. Productivity Optimization Lab Dr. Chia-Yen Lee大討論與超預測 57
• Application of Bayes’ Theorem
In NBA game, when the Lakers joins the final game, the
fans of Lakers estimate
• The probability of the Lakers wins (H1) the champion: P(H1)=0.7
• The probability of the Lakers loses (H2) the champion: P(H2)=0.3
Now, the Lakers loses the first game (the evidence you
observed), what is the probability to win the champion?
Based on the historical records, in the NBA final game…
• The probability of losing-first-game (E) given it wins the champion
is P(E|H1)=0.4
• The probability of losing-first-game (E) given it loses the champion
is P(E|H2)=0.5
Bayes’ Theorem
58. Productivity Optimization Lab Dr. Chia-Yen Lee大討論與超預測 58
• Probability Correction
What is the posterior probability P(H1|E)
P(wins champion | losing-first-game)
Thus, after you observe the “evidence”, the probability is
corrected from 0.7 to 0.65.
Bayes’ Theorem