史考特．貝里納托（Scott Berinato）《哈佛商業評論》英文版資深編輯，解釋在社會環境中做決策的機會和陷阱，內容取材自山迪．潘特蘭（Sandy Pentland）的文章〈超越決策一言堂〉（Beyond the Echo Chamber）。
Hi! I'm Scott Berinato, a senior editor at Harvard Business Review. I was one of the editors who worked with Professor Sandy Pentland to develop his article in the November issue of HBR called “Beyond the Echo Chamber,” which is about decision making in a social context. I urge you to read his article in the print magazine or on hbr.org. What makes it great is the fact that it takes a normally soft topic like decision making and applies hard math to it, in the form of this. This is not a bad photocopy. It's a scatter plot that shows you investment decisions -- millions of them -- on a social trading platform called eToro.
On eToro, users can make trades based on other users' investment strategies. Users whose strategies are copied earn a small commission each time. And every trader's ultimate performance is available for all others to see. EToro gave Pentland, post-doctoral student Yaniv Altshuler, and graduate student Wei pan a real-world lab to explore social decision making. The decision of who to emulate trading, along with the resulting performance, would show them what decision making strategies in a social context led to good returns and which ones led to bad returns.
In fact, the beauty of this visualization that it shows you the sweet spot of investment strategies and those who perform the best. To really understand this graph, though, let's hypothetically zoom in to see how Pentland and his team created it. The chart maps the same investors on both the X- and Y- axis. At full size, there are 1.6 million users on each axis.
Here we're looking at five hypothetical users – Allison, Dan, Susan, Amy, and Allen. On the x-axis, these users are traders. They are the ones making investment decisions with their money. On the y-axis, they are the traders' strategies being copied by other traders. So when Allison trades based on Dan's portfolio, a point is created at the intersection of Allison on the x-axis and Dan on the y-axis.
Each trader's investments are plotted against the person whose trade they emulated. Dan trades based on Allison's and Susan's trades. Susan's trades copy Allison, Dan, and Allen. Amy and Allen copy everybody else here. Do this 1.6 x million times over. And you arrive at this -- 10 million trades. Let's zoom in again, though. In analyzing the data, Pentland and his team found a phenomenon that's hard to ignore.
They saw decision making bubbles. In social contexts, users may think they're getting new information and making good decisions based on that, when in fact, they are simply getting the information back that they put out there in the first place. It happens like this. Allen invested based on Amy's strategy. Amy's trade was based on Dan's. Dan's was based on Susan's. And Susan's investments, it turns out, were based on Allen's strategy. In fact, Allen's strategy is based on following users who are actually following him.
But he can't see that because he's been filtered through three other users. Zoom out again. When mapped on a large scale, circular strategies like Allen's show up as clearly define dense fields of trades, which you can see here. Many of these tens of thousands of traders think they're diversifying their strategies, when they're simply following the same ideas that they themselves and those close to them are putting out there. It's the same as Allen's problem, only in a far more complex array of trades.
So it's even harder to discern the circularity. On the other end of the spectrum are the Allisons. If you remember, she only made one trade based on other people's decisions. She's part of a group that don't copy many strategies at all, as seen by the sparse population of dots here in the lower left. If those traders in the dense field in the upper right are in an echo chamber, Allison and her cohorts are in an isolation chamber. Their decisions are simply not taking into account other people's ideas.
The middle traders, who shows some density but across a far wider swath, are following lots of different strategies, but not so many so close that they've entered into an echo chamber. They're seeking many ideas and making decisions that aren't really just reflections of their own ideas. They are in the sweet spot. How do we know they're in the sweet spot? Well, this is the beauty of the fact that this is a trading platform. We know the traders' returns. And those in the sweet spot perform 30% better than those in the echo chamber and those who were isolated.
To further confirm the pattern of decision making that produces the best returns, Pentland and Altshuler intervened. They suggested strategies to the isolated traders that would move them up into the sweet spot. They told the Allisons to follow more different traders. And they also offered advice to those in the echo chamber, telling the Allens of the world to diversify and follow the strategies of more and different people. Move them down into the sweet spot.
In both cases, the researchers improved the investment performance of the people they moved into the sweet spot. In a vastly interconnected world, social decision making is becoming the norm. A trading platform gave Pentland and his team hard math to test his hypotheses. But he believes that the same idea applies to all decision making settings, even the ones that are less easily measured -- setting corporate strategy, making innovation bets, hiring. Any management decision is, in part, a social decision. And the deciders need to avoid both the isolation chamber and the echo chamber and find that sweet spot.