Mediapost reported recently on the results of a survey done by research firm Pollara, and Marshall Kirkpatrick over at ReadWriteWeb commented on it in a post titled “There is No Tipping Point”.
A number of thinkers… and now the Pollara study have been arguing that large numbers of people do not make decisions based on the advice of a small number of powerful influencers…
The marketing world was really hoping they could just win over some existing social media power users and have everyone else fall like dominoes. This study says that’s not how it works.
The study says that while 80% of respondents were “very or somewhat more likely to consider buying products recommended by real-world friends and family” only 23% were similarly influenced by “well-known bloggers”. I do not disagree with this data, what I disagree with is the understanding of the mechanisms of social influence. A concept from economics called “Informational Cascades” shows us why the domino-effect/tipping point idea is valid, even in light of this study.
To understand this mechanism an example is helpful. Suppose there are two restaurants and a group of people on the street outside deciding which one to eat at. The most well-informed individuals (those with higher precision in making these types of decisions) will decide first and everyone will see some people start to line up outside of one restaurant. If the others know this person is of higher precision (and even if they don’t) a few people will follow their lead and join the line. Each new person who lines up outside of the restaurant sends a signal to the rest of the group (and in particular their friends and family) that this is the restaurant to pick. The more people who follow the signal, the stronger it gets and you have an Informational Cascade.
The idea is presented in rigorous detail in a paper called “Theory of Fads, Fashion, Custom and Cultural Change as Informational Cascades” written by Sushil Bikhchandani, David Hirshleifer and Ivo Welch and published in 1992. To paraphrase:
An informational cascade occurs when it is optimal for an individual, having observed the actions of those ahead of him to follow the behavior of the preceding individual without regard to his own information…
The prediction that a low-precision individual imitates a higher-precision predecessor is consistent with the evidence of numerous psychological experiments demonstrating that a subject’s previous failure in a task raises the probability that in further trials he will imitate a model performing the task (see Thelen, Dollinger, and Kirkland 1979, p. 146)…
an individual who wishes to bring about a social change, for example, introduce a desirable innovation such as an improved sanitary method in a peasant community, must focus his efforts on persuading early community leaders.
This phenomenon will happen even if the early deciders are not experts (although it is certainly enhanced if they are), or influential people normally, by the act of making a choice before others, they become influential to the rest of the group.
Real-world examples of Informational Cascades can be found everywhere, from the stock market and voting patterns to the fashion industry, but we may ask the question, why do humans put so much stock into choices made by others before them? For an answer to this we look to evolution.
For ancient (and modern) humans, learning by trial and error is energy and time expensive (not to mention dangerous), it is safer and more efficient to let someone else figure out what plants are safe to eat and then just imitate them. A 1980 paper by John Conlisk titled “Costly Optimizers and Cheap Imitators” showed that “imitators may have as high a long-run ‘fitness’ as optimizers“, and in 1992 Peter J. Richerson and Robert Boyd published a report called “Cultural Inheritance and Evolutionary Ecology” where they showed that in many instances social learning (like imitating your peers) is preferred by natural selection.
If we can agree that the choices and actions of others influence our own decisions then the mathematics of Information Cascades prove that as the size of the participating population increases, “Tipping Points” become inevitable.