When people are behaving independently of their social learning, it is likely that they have independent information and that they believe in that information enough to fight the effects of social influence. Find as many of these “wise guys” as possible and learn from them. Such contrarians sometimes have the best ideas, but sometimes they are just oddballs. How can you know which is which? If you can find many such independent thinkers and discover that there is a consensus among a large subset of them, then a really, really good trading strategy is to follow the contrarian consensus. For instance, in the eToro network the consensus of these independent strategies is reliably more than twice as good as the best human trader.
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to answer the question of how habits form, my research group studied the spread of health behaviors in a tightly knit undergraduate dorm for one year. In the Social Evolution Study, led by PhD student Anmol Madan and myself, with Professor David Lazer helping with design of the experiment and data analysis, we gave all the participating students smartphones with special software so that we could track their social interactions with both close friends and acquaintances. In total, this study produced more than five hundred thousand hours of data and included face-to-face interactions, phone calls, and texting, as well as extensive surveys and weight measurements. These hundreds of gigabytes of data allowed us to examine what goes into the creation of habits.
One particular health behavior that we focused on was weight change and on whether this was more influenced by the behavior of friends or by peers in the surrounding community...
exposure to the behavior examples that surrounded each individual dominated everything else we examined in this study. It was more important than personal factors, such as weight gain by friends, gender, age, or stress/happiness, and even more than all these other factors combined. Put another way, the effect of exposure to the surrounding set of behavior examples was about as powerful as the effect of IQ on standardized test scores.
It might be asked how we can know that exposure to the surrounding behaviors actually caused the idea flow; perhaps it is merely a correlation. The answer is in this experiment we could make quantitative, time-synchronized predictions, which make other noncausal explanations fairly implausible. Perhaps even more persuasively, we have also been able to use the connection between exposure and behavior to predict outcomes in several different situations, and even to manipulate exposure in order to cause behavior changes. Finally, there also have been careful quantitative laboratory experiments that show similar effects and in which the causality is certain.
Therefore, people seem to pick up at least some habits from exposure to those of peers (and not just friends). When everyone else takes that second slice of pizza, we probably will also. The fact that exposure turned out to be more important for driving idea flow than all the other factors combined highlights the overarching importance of automatic social learning in shaping our lives.
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How do we choose who to vote for? Do our preferences also come from exposure to those around us? We tackled this question in the Social Evolution experiment by analyzing students’ political views during the 2008 presidential election.9 The question we asked was: Do political views reflect the behaviors that people are exposed to or are they formed more by individual reasoning? By giving these students specially equipped smartphones, we monitored their patterns of social interaction by tracking who spent time with whom, who called whom, who spent time at the same places, and so forth.
We also asked the students a wide range of questions about their interest in politics, involvement in politics, political leanings, and finally (after the election), we inquired which candidate had received their vote. In total, this produced more than five hundred thousand hours of automatically generated data about their interaction patterns, which we then combined with survey data about their beliefs, attitudes, personality, and more.
When sifting through these hundreds of gigabytes of data, we found that the amount of exposure to people possessing similar opinions accurately predicted both the students’ level of interest in the presidential race and their liberal-conservative balance. This collective opinion effect was very clear: More exposure to similar views made the students more extreme in their own views.
Most important, though, this meant that the amount of exposure to people with similar views also predicted the students’ eventual voting behavior. For first-year students, the size of this social exposure effect was similar to the weight gain ones I described in the previous section, while for older students, who presumably had more fixed attitudes, the size of the effect was less but still quite significant.
But what did not predict their voting behavior? The views of the people they talked politics with, and the views of their friends. Just as with weight gain, it was the behavior of the surrounding peer group—the set of behavior examples that they were immersed in—that was the most powerful force in driving idea flow and shaping opinion.
More (#1) from Social Physics:
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