Over on the Cheap Talk blog (@CheapTalkBlog), Jeff Ely (@jeffely) has an interesting post about the “Ignorance of Crowds.” The basic idea is that when there are lots of connections among people, each individual has less incentive to seek out costly information — e.g. subscribe to the newspaper — on their own, because instead they can just get that information (“free ride”) from others. More connections means more free riding and fewer informed individuals.
I take a much more complicated route to the same conclusion in “Network Games with Local Correlation and Clustering.” Besides being sufficiently mathematically intractable to, hopefully, be published, the paper does show a few other things too. In particular, I look at how network clustering affects “public goods provision,” which is the fancy term for what Jeff Ely calls subscribing to the newspaper. Lots of real social networks are highly clustered. This means that if I’m friends with Jack and Jill, there is a good chance that Jack and Jill are friends with each other. What I find in the paper is that clustering increases public goods provision. In other words, when people are members of tight knit communities, more people should subscribe to the newspaper (and volunteer, and pick up trash, and …)
It’s pretty clear that the Internet, social media etc… are increasing the number of contacts that we have, but an interesting question that I haven’t seen any research on is How are these technologies affecting clustering (if at all)?
On Friday, August 5, Standard & Poor’s downgraded the credit rating of the U.S. long-term debt to AA+. On Monday, the first day the markets opened since the downgrade, the Dow Jones Industrial average dropped 5.6 percent and the S&P 500 fell 6.7 percent — the biggest single day drops since the crisis in 2008. A lot of people might be confused about this turmoil in the markets, since US debt is still considered one of the safest investments there is. Jay Forrester, founder of the field of System Dynamics, calls puzzles like this the “counterintuitive behavior of social systems.”
Undoubtedly, the world economy is incredibly complex, and no individual or organization has a complete picture of how it works or where it’s headed. Through pricing, the market is supposed to aggregate all of the pieces of partial information that we each hold and then converge to the “truth” — that is prices should reflect true underlying value. In some situations this can actually work. Prediction markets have been shown to be valuable tools for businesses to harvest the “wisdom of the crowds” and assess the probabilities that future events occur. But, this mechanism works best when individuals place their trades independently based on their own private information. In the real world, market dynamics are fundamentally social dynamics and as such they are subject to cascades of panic and the accumulation of overconfidence (what Alan Greenspan famously referred to as “irrational exuberance” (see also Robert Shiller)).
The current panic illustrates how even when there is no fundamental basis for a panic, social dynamics can amplify the signal of a panic to the point where an actual crisis ensues. The gas shortages of 1979 are a classic example of this phenomenon. The Iranian revolution sharply cut oil imports to the US from Iran. Nervous consumers rushed to top off their tanks and even to hoard gasoline at home. This drained the supply of gasoline at filling stations leading to an actual gasoline shortage. Word-of-mouth and media coverage reinforced consumer fears of shortages, leading to even more topping off and hoarding, as well as government policies such as odd/even day purchase rules that actually further incentivized consumers to top off frequently and store gasoline at home. Surprisingly, despite the very real shortage of gasoline at filling stations, US oil imports for the year actually increased in 1979 compared 1978. The crisis was caused by social dynamics, not an actual drop in supply. (See Sterman, Business Dynamicsp. 212).
A similar but more comical crisis occurred in 1973 when Johnny Carson made a joke saying, “You know what’s disappearing from the supermarket shelves? Toilet paper. There’s an acute shortage of toilet paper in the United States.” Consumers rushed out to stock up on toilet paper, leading to a real toilet paper shortage in the US that lasted several days. Even though Carson tried to correct the joke a few days later, by that time toilet paper was in fact in short supply because people were hoarding it at home.
In today’s New York Times, Robert Shiller argues that the recent crisis should precipitate a movement to collect better data that could help in predicting similar events in the future. He likens the problem to predicting hurricanes; something that was impossible at one time, but which we can now do accurately because of better data. Shiller’s point is a sharp contrast to the perspective of Duncan Watts espoused in his recent book, Everything is Obvious (Once You know the Answer). Watts argues that no matter how good the data, predicting many things is just impossible, in part because its impossible to even know what it is you should be predicting. Shiller’s opinion on this matter has an extra sense of authority of course, because he is one of the few economists who saw the housing bubble/credit crunch/financial crisis coming and said something about it. He also forecasted the stock market collapse of 2001.
So, who’s right? Is Shiller just another example of luck and large numbers? After all, a lot of people are making a lot of predictions out there, so we shouldn’t be surprised that someone got it right. And Watts’ argument that in many cases we can’t know what we should have been predicting until we put the outcome in a context has merit. Both are right to an extent. I do believe the crisis was predictable, and that Shiller wasn’t just lucky in foreseeing it, but I also agree with Watts that more data isn’t the full solution. We also need to know where to look — how to put that data together. We can forecast hurricanes today not just because we have better data, but because we have better models and a better understanding of how the weather works. It’s a little surprising that Shiller doesn’t point this out, because he is also well-known for arguing that economists should in part be looking in a different direction than they have in the past. Namely, our economic models need to incorporate more social and psychological effects (see his recent book with George Akerlof, Animal Spirits).