Stephen Downes’ new column on e-Learn does a great job of showing that solving the informational cascade problem is more challenging than I had presented it to be in my own article on the topic. In fact, his own analysis reveals that the problem may be harder to solve than even he himself suggests. The problem, as is often the case, is largely due to those pesky externalities, i.e., reality stubbornly refuses to conform to an elegant theory (and alas, Stephen is even more of a sucker for an elegant theory than I am). (more…)
Tag: informational-cascades
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Jeremy Wagstaff's LOOSE wire: Blogs And The Suppression Of Dissent
Jeremy Wagstaff, after giving a fairly in-depth summary of my informational cascades article, responds with the following counterpoint:
On the other hand, there’s also plenty of evidence to suggest blogs foster a healthy discussion and if someone says something controversial, it’s likely to be challenged. It’s not always easy to see your words criticised on the Internet but once you get used to it, there’s a lot that can be learned. And while rudeness and personal attacks masquerading as debate abound, in general my experience of blogs and the comments system has been that few feel inhibited about pointing out errors or making well-reasoned counter-arguments. Or am I completely wrong?
Nope. Not wrong at all. As I noted in this earlier post, it is possible to believe that weblogs, on average, do a lot more good than harm and still worry that informational cascades within weblog discourse can do quite a bit of harm under the right circumstances. Think of vulnerability to informational cascades as a weak point in social networks. Just because a network has a vulnerability doesn’t make it inherently harmful. We just need to be aware of the vulnerability and do what we can to compensate for it.
As a side note, I want to emphasize that informational cascades are not primarily about peer pressure. That’s a whole different phenomenon known as a “reputational cascade”. Informational cascades happen not (primarily) because I’m afraid to look like a fool if I argue with you but because I believe you know more than I do. I defer to what I perceive to be the superior collective knowledge of the group. In many cases, far from accepting the group’s opinion while rejecting my dissenting opinion, I may not even bother to try to form an opinion of my own because I perceive the group’s collective judgment to provide overwhelming evidence of the correct answer. It’s a work-saving instinct. Unfortunately, we can be fooled into thinking that the group all agrees on something when the don’t because…well…it’s kind of a long explanation. Read the article if you want the full scoop.
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Correction on the Origins of Informational Cascade Research
I was mistaken in an earlier post when I claimed that informational cascades research comes from the “heuristics and biases approach” in psychology. It definitely comes from behavioral economics.
Both behavioral economics and the heuristics and biases approach share common ancestry from the work of Herbert Simon. A genuine polymath, Simon won a Nobel Prize in economics for his theory of bounded rationality while also helping to give birth to modern cognitive psychology and making major contributions to the field of artificial intelligence. In the case of bounded rationality, Simon’s insight is that we cannot realistically expect that the human mind works perfectly rationally without regard to computational cost. Some rational calculations just take too much time and attention to be useful in real-world decision-making situations. Simon suggested that humans use mental short cuts that are good enough to get the job done most of the time and take far less time and energy than fully rational calculations.
Both behavioral economics and the heuristics and biases approach draw heavily on this insight. However, the specific work on informational cascades definitely came from the economists. The earliest references to them were in the following papers:
- Abhijit V. Banerjee. August 1992. A Simple Model of Herd Behavior. Quarterly Journal of Economics 107:3, 797-818
- Sushil Bikhchandani, David Hirshleifer, Ivo Welch. October 1992. A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades. Journal of Political Economy 100:5, 992-1026
- Ivo Welch. June 1992. Sequential Sales, Learning, and Cascades. Journal of Finance 47:2, 695-732
For more info on the informational cascades literature, see this bibliography. For more info on the heuristics and biases approach, consider reading Heuristics and Biases: The Psychology of Intuitive Judgment
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Informational Cascades, Network Theory, and Behavioral Economics
Stephen Downes’ mention of my article on informational cascades (thanks for the plug, Stephen) led me to his post in the trdev discussion group. He writes:
In network theory, ‘groupthink’ is an instance of what is known as a cascade phenomenon. A cascade occurs (all other things being equal) when the propogation of a property (an idea, a software acquisition, a disease) exceeds 1 – that is, each instance of the phenomenon replicates on average at least one time. It is important to note that the political organization of the group is irrelevant – that’s why mixing ideology with the study of groups can be dangerous.
Classic examples of this principle include stock market bubbles, ranging from the tulip mania that swept through Holland in the 1630s to the Tech Bubble we just experienced to the widespread adoption of email to the influenza epidemic of 1919.
While Stephen is correct to point out the interest that network researchers have in cascades, and while some network researchers are interested in informational cascades in particular (for example, Duncan Watts has a small section on them in Six Degrees), it may be worthwhile to point out that informational cascades come from a different intellectual lineage.
For many decades now, economics has been dominated by the “rational choice” model of human behavior. This basically says that, when making a decision, humans look at how likely it is that that a given option will produce the desired outcome and the expected utility of that outcome. People (according to the model) choose the option that is most likely to produce the best outcome.
Except there are lots of cases where people clearly don’t perform these calculations as the rules of logic and statistics would require. They are doing some kind of calculations, but not exactly the ones that we would expect from the rational choice model. So a school of thought has developed within economics and psychonology that suggests that humans use mental shortcuts to make approximations of those utility calculations. Often those shortcuts will lead to a “good enough” answer with much less time and mental effort, but occasionally they lead people astray. This model, called the “bounded rationality” model, is the heritage from which informational cascade theory comes. Listening to the advice of trusted friends and colleagues is one of those short cuts, and it is the essential mechanism that leads to informational cascades. (A whole list of these shortcuts is being researched in the field of psychology called “heuristics and biases.” There’s also a rich literature on it within behavioral economics.)
At any rate, what’s interesting is that informational cascades happen to map so well to network theory despite the fact that they come from a totally different research thread. In my view, one of the more fertile and interesting ways to start figuring out what kinds of wild and whacky things can happen in online knowledge sharing is to overlay heuristics and biases theory on top of network theory.
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Book Recommendation: Why Societies Need Dissent
If you liked my article on informational cascades then you will probably want to read Cass Sunstein’s Why Societies Need Dissent. Sunstein, a law professor at the University of Chicago, writes in detail about the impact of informational cascades on democratic dialogue, the rulings of panels of judges, and other critical areas related to civil discourse. He also writes about their counterpart, reputational cascades (where people go along to get along) and about polarization effects (where people talking to like-minded people tend to come out more extreme in their positions than when they started the conversation). As a bonus, the end notes are just chock full of great references. Its the kind of book where you spend half your time jumping back and forth between the main text and the notes because you just want to know where he’s coming up with this stuff.
This is a very important book, in my view. Oddly, it doesn’t read quite as compellingly as it ought to (possibly because it is taken from a series of guest lectures at a law school) but it’s not a terrible read by any stretch of the imagination. It just needs a few good stories to spice it up a bit. At any rate, the content is incredibly informative, and even if it doesn’t quite read like The da Vinci Code, it’s a page turner in its own way.
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New Article on Informational Cascades
My new article just came out in eLearn. It started out wanting to be about “emergent learning” but, once I realized that I still have no idea what emergent learning actually is, I removed all references to it in the article. At any rate, I feel pretty good about the piece.
The question will arise whether the arguments I make contradict optimistic pieces like this Cialdini article in HBS Working Knowledge that has been making the rounds on elearning blogs lately.
Nope. Not at all.
You can believe that properly functioning groups can arrive at better judgments than their smartest individual participants and still believe that problems such as informational cascades can derail performance. That’s why studying these problems is so important; we can learn the weaknesses of group processes and shore them up to produce more reliably positive results. Ain’t science grand?
Please feel free to use the comments section on this post to add your thoughts and feedback regarding the article.
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Idea Viruses as Informational Cascades
In a previous post, I suggested that so-called “idea viruses” might be thought of as either causes of or manifestations of informational cascades. I now think manifestation is the right characterization rather than cause. I am persuaded by this fascinating and frightening piece of research [PDF] showing that legislators tend to try to garner support for their positions by creating informational cascades within the legislative body. It’s not quite a classic idea virus in the sense that it isn’t a new idea that each recipient “catches,” but it does reflect a contagion of an opinion or position.