unit 1 TextA
Love and logic: The story of a fallacy
1 I had my first date with Polly after I made the trade with my roommate Rob. That year every guy on campus had a leather jacket, and Rob couldn't stand the idea of being the only football player who didn't, so he made a pact that he'd give me his girl in exchange for my jacket. He wasn't the brightest guy. Polly wasn't too shrewd, either.
2 But she was pretty, well-off, didn't dye her hair strange colors or wear too much makeup. She had the right background to be the girlfriend of a dogged, brilliant lawyer. If I could show the elite law firms I applied to that I had a radiant, well-spoken counterpart by my side, I just might edge past the competition.
3 "Radiant" she was already. I could dispense her enough pearls of wisdom to make her "well-spoken".
4 After a banner day out, I drove until we were situated under a big old oak tree on a hill off the expressway. What I had in mind was a little eccentric. I thought the venue with a perfect view of the luminous city would lighten the mood. We stayed in the car, and I turned down the stereo and took my foot off the brake pedal. "What are we going to talk about?" she asked.
6 "Cool," she said over her gum.
7 "The doctrine of logic," I said, "is a staple of clear thinking. Failures in logic distort the truth, and some of them are well known. First let's look at the fallacy Dicto Simpliciter."
8 "Great," she agreed.
9 "Dicto Simpliciter means an unqualified generalization. For example: Exercise is good. Therefore, everybody should exercise."
10 She nodded in agreement.
11 I could see she was stumped. "Polly," I explained, "it's too simple a generalization. If you have, say, heart disease or extreme obesity, exercise is bad, not good. Therefore, you must say exercise is good for most people."
12 "Next is Hasty Generalization. Self-explanatory, right? Listen carefully: You can't speak French. Rob can't speak French. Looks like nobody at this school can speak French."
13 "Really?" said Polly, amazed. "Nobody?"
14 "This is also a fallacy," I said. "The generalization is reached too hastily. Too few instances support such a conclusion."
15 She seemed to have a good time. I could safely say my plan was underway. I took her home and set a date for another conversation.
16 Seated under the oak the next evening I said, "Our first fallacy tonight is called Ad Misericordiam."
17 She nodded with delight.
18 "Listen closely," I said. "A man applies for a job. When the boss asks him what his
qualifications are, he says he has six children to feed."
19 "Oh, this is awful, awful," she whispered in a choked voice.
20 "Yes, it's awful," I agreed, "but it's no argument. The man never answered the boss's question. Instead he appealed to the boss's sympathy —Ad Misericordiam."
21 She blinked, still trying hard to keep back her tears.
22 "Next," I said carefully, "we will discuss False Analogy. An example, students should be allowed to look at their textbooks during exams, because surgeons have X-rays to guide them during surgery."
23 "I like that idea," she said.
24 "Polly," I groaned, "don't derail the discussion. The inference is wrong. Doctors aren't taking a test to see how much they have learned, but students are. The situations are altogether different. You can't make an analogy between them."
25 "I still think it's a good idea," said Polly.
26 With five nights of diligent work, I actually made a logician out of Polly. She was an analytical thinker at last. The time had come for the conversion of our relationship from academic to romantic.
27 "Polly," I said when next we sat under our oak, "tonight we won't discuss fallacies."
28 "Oh?" she said, a little disappointed.
29 Favoring her with a grin, I said, "We have now spent five evenings together. We get along pretty well. We make a pretty good couple."
30 "Hasty Generalization," said Polly brightly. "Or as a normal person might say, that's a little premature, don't you think?"
31 I laughed with amusement. She'd learned her lessons well, far surpassing my expectations. "Sweetheart," I said, patting her hand in a tolerant manner, "five dates is plenty. After all, you don't have to eat a whole cake to know it's good."
32 "False Analogy," said Polly promptly. "Your premise is that dating is like eating. But you're not a cake. You're a boy."
33 I laughed with somewhat less amusement, hiding my dread that she'd learned her lessons too well. A few more false steps would be my doom. I decided to change tactics and try flattery instead.
34 "Polly, I love you. Please say you'll go out with me. I'm nothing without you."
35 "Ad Misericordiam," she said.
36 "You certainly can discern a fallacy when you see it," I said, my hopes starting to crumble. "But don't take them so literally. I mean this is all academic. You know the things you learn in school don't have anything to do with real life."
37 "Dicto Simpliciter," she said. "Besides, you really should practice what you preach."
38 I leaped to my feet, my temper flaring up. "Will you or will you not go out with me?"
39 "No to your proposition," she replied.
40 "Why?" I demanded.
41 "I'm more interested in a different petitioner —Rob and I are back together."
42 With great effort, I said calmly, "How could you give me the axe over Rob? Look at me, an ingenious student, a tremendous intellectual, a man with an assured future. Look at Rob, a muscular idiot, a guy who'll never know where his next meal is coming from. Can you give me one good reason why you should be with him?"
43 "Wow, what presumption! I'll put it in a way someone as brilliant as you can understand," retorted Polly, her voice dripping with sarcasm. "Full disclosure —I like Rob in leather. I told him to say yes to you so he could have your jacket!"
Why do smart people do dumb things?
1 Orthodox views prize intelligence and intellectual rigor highly in the modern realm of universities and tech industry jobs. One of the underlying assumptions of this value system is that smart people, by virtue of what they've learned, will formulate better decisions. Often this is true. Yet psychologists who study human decision-making processes have uncovered cognitive biases common to all people, regardless of intelligence, that can lead to poor decisions in experts and laymen alike.
2 Thankfully these biases can be avoided. Understanding how and in what situations they occur can give you an awareness of your own limitations and allow you to factor them into your decision-making.
3 One of the most common biases is what is known as the fundamental attribution error. Through this people attribute the failures of others to character flaws and their own to mere circumstance, subconsciously considering their own characters to be stainless. "Jenkins lost his job because of his incompetence; I lost mine because of the recession." It also leads us to attribute our own success to our qualifications, discounting luck, while seeing others' success as the product of mere luck.
4 In other words, we typically demand more accountability from others than we do from ourselves. Not only does this lead to petty judgments about other people, it also leads to faulty risk assessment when you assume that certain bad things only happen to others. For example, you might assume, without evidence, that the price of your house will go up even though 90 percent of them have dropped in price, because you yourself are more competent.
5 Confirmation bias is sometimes found together with fundamental attribution error. This one has two parts. First, we tend to gather and rely upon information that only confirms our existing views. Second, we avoid or veto things that refute our preexisting hypotheses.
6 For example, imagine that you suspect your computer has been hacked. Every time it stalls or has a little error, you assume that it was triggered by a hacker and that your suspicions are valid. This bias plays an especially big role in rivalries between two opposing views. Each side partitions their own beliefs in a logic-proof loop, and claims their opponent is failing to recognize valid points. Outwitting confirmation bias therefore requires exploring both sides of an argument with
7 Similar to confirmation bias is the overconfidence bias. In an ideal world, we could be correct 100 percent of the time we were 100 percent sure about something, correct 80 percent of the time we were 80 percent sure about something, and so on. In reality, people's confidence vastly exceeds the accuracy of those judgments. This bias most frequently comes into play in areas where someone has no direct evidence and must make a guess —estimating how many people are in a crowded plaza, for example, or how likely it will rain. To make matters worse, even when people are aware of overconfidence bias, they will still tend to overstate the chances that they are correct. Confidence is no prophet and is best used together with available evidence. When witnesses are called to testify in a court trial, the confidence in their testimony is measured along with and against the evidence at hand.
8 The availability bias is also related to errors in estimation, in that we tend to estimate what outcome is more likely by how easily we can recount an example from memory. Since the retention and retrieval of memories is biased toward vivid, sensational, or emotionally charged examples, decisions based on them can often lead to strange, inaccurate conclusions.
9 In action this bias might lead someone to cancel a trip to, for example, the Canary Islands because of a report that the biggest plane crash in history happened there. Likewise some people might stop going out at night for fear of assault or rape.
10 Repelling the availability bias calls for an empirical approach to a particular decision, one not based on the obscured reality of vivid memory. If there is a low incidence of disaster, like only one
out of 100,000 plane landings results in a crash, it is safe to fly to the Canary Islands. If one out of one million people who go out is assaulted, it is safe to go out at night.
11 The sunk cost fallacy has a periodic application and was first identified by economists. A good example of how it works is the casino slot machine. Gamblers with a high threshold for risk put money into a slot machine hoping for a big return, but with each pull of the lever they lose some money playing the odds. If they have been pulling the lever many times in a row without success, they might decide that they had better keep spending money at the machine, or they will have wasted everything they already put in.
12 The truth is that every pull of the lever has the same winning probability of nearly one in a trillion, regardless of how much money has been put in before —the previous plays were sunk costs.
13 In everyday life this can lead people to stay in damaging situations because of how much they have already put in, stuck on the erroneous belief that the value of that time or energy they have invested will decay or disappear if they leave. The wisest course is to recognize the effects of the sunk cost fallacy and to leave a bad situation regardless of how much you have already invested. 在日常生活中，这种谬误会导致人们由于顾及之前所投入的成本，而持续停留在损失的状态中，同时困顿于一种错误的观念，即他们害怕自己一旦离开，之前所投入的时间和精力就会贬值或付诸东流。而最明智的办法则是，要充分认识沉没成本谬误导致的结果，离开糟糕的境况，不论之前已投入了多少。
14 While there are still more biases, the key to avoiding them remains the same: When a decision matters, it is best to rely on watertight logic and a careful examination of the evidence and to remain aware that what seems like good intuition is always subject to errors of judgment.