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  • Imagine a police lineup where ten witnesses are asked to identify a bank robber they glimpsed fleeing the crime scene.


  • If six of them pick out the same person, there's a good chance that's the real culprit, and if all ten make the same choice, you might think the case is rock-solid, but you'd be wrong.

    目撃者のうち 6 人が同じ人物を選べば、その人物が真犯人である可能性は高いし、もし 10 人全員が同じ人物を選べば、もう間違いないと思うかもしれません。でも、そうではないでしょう。

  • For most of us, this sounds pretty strange.


  • After all, much of our society relies on majority vote and consensus, whether it's politics, business, or entertainment.


  • So it's natural to think that more consensus is a good thing.


  • And up until a certain point, it usually is.


  • But sometimes, the closer you start to get to total agreement, the less reliable the result becomes.


  • This is called the paradox of unanimity.


  • The key to understanding this apparent paradox is in considering the overall level of uncertainty involved in the type of situation you're dealing with.


  • If we asked witnesses to identify the apple in this lineup, for example, we shouldn't be surprised by a unanimous verdict.


  • But in cases where we have reason to expect some natural variance, we should also expect varied distribution.


  • If you toss a coin one hundred times, you would expect to get heads somewhere around 50% of the time.

    コイントスを 100 回した場合、表が出る可能性は 50% あたりだと予測するでしょう。

  • But if your results started to approach 100% heads, you'd suspect that something was wrong, not with your individual flips, but with the coin itself.

    でも、表の出る確率が 100% に近づいていったら、何かおかしいと思うはず。自分の投げ方に対してではなく、コインそのものにです。

  • Of course, suspect identifications aren't as random as coin tosses, but they're not as clear-cut as telling apples from bananas, either.


  • In fact, a 1994 study found that up to 48% of witnesses tend to pick the wrong person out of a lineup, even when many are confident in their choice.

    実際に、1994 年の調査では目撃者の最大 48% が違う人を選ぶ傾向があったことが分かっています。目撃者の多くが自分の選択に自信があった場合ですらです。

  • Memory based on short glimpses can be unreliable, and we often overestimate our own accuracy.


  • Knowing all this, a unanimous identification starts to seem less like certain guilt, and more like a systemic error, or bias in the lineup.


  • And systemic errors don't just appear in matters of human judgement.


  • From 1993-2008, the same female DNA was found in multiple crime scenes around Europe, incriminating an elusive killer dubbed the Phantom of Heilbronn.

    1993 年から 2008 年にかけて、同じ女性の DNA がヨーロッパ各地の複数の犯罪現場で見つかり、神出鬼没の殺人鬼の仕業であるとされました。通称ハイルブロンの怪人です。

  • But the DNA evidence was so consistent precisely because it was wrong.

    ですが、その証拠 DNA はあまりにも矛盾がなさ過ぎました。なぜなら、それは間違っていたからです。

  • It turned out that the cotton swabs used to collect the DNA samples had all been accidentally contaminated by a woman working in the swab factory.

    実は、DNA の検体を集めるために使われた綿棒は、綿棒工場で働いていた女性がうっかり汚染して(素手で触れて)いたことが分かったのです。

  • In other cases, systematic errors arise through deliberate fraud, like the presidential referendum held by Saddam Hussein in 2002, which claimed a turnout of 100% of voters with all 100% supposedly voting in favor of another seven-year term.

    別のケースで言うと、計画的詐欺で系統誤差が生じています。例えば 2002 年にサダム・フセインが行った大統領信任投票では、投票率が有権者の 100% であったことと、投票の 100% が引き続き 7 年の信任を支持しただろうとされました。

  • When you look at it this way, the paradox of unanimity isn't actually all that paradoxical.


  • Unanimous agreement is still theoretically ideal, especially in cases when you'd expect very low odds of variability and uncertainty, but in practice, achieving it in situations where perfect agreement is highly unlikely, should tell us that there's probably some hidden factor affecting the system.


  • Although we may strive for harmony and consensus, in many situations, error and disagreement should be naturally expected.


  • And if a perfect result seems too good to be true, it probably is.


Imagine a police lineup where ten witnesses are asked to identify a bank robber they glimpsed fleeing the crime scene.


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