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.