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Influx interview- dr. alex bentley- random copying and culture

May 15, 2007

Dr. Alex Bentley is a lecturer at Durham University’s Department of Anthropology. Alex’s recent research has explored what drives change in children’s names and in the Top 100 music charts. His research shows that people “randomly copying” one another is one the key drivers for change.

1. Can you tell us your background?

I majored in physics at Bowdoin College, and later got my Ph.D. in Anthropology at the University of Wisconsin, after an M.Sc. at Cornell.  What led me into this study was attending the Santa Fe Institute’s Complex Systems Summer School in 2002, where I met Dr. Matt Hahn (Biology/Informatics at Indiana University).  Matt showed me how population genetics provides an ideal set of tools for studying culture change.

2. What were the main findings of your research into change in popular culture?

Our first studies, published in 2003-2004 in Proceedings of the Royal Society B, showed how a simple model of random copying, with a small amount (i.e., < 2%) of innovation, can explain many patterns of popular culture change at the national level, including “long-tail” distributions of popularity, discussed by Chris Anderson’s book (‘The Long Tail’, Random House 2006). Our studies suggested that popular success does not necessarily require any inherent qualities — it can happen just by luck through the process of people randomly copying each other. What was amazing is that such a simple model could work so well — that at a population level, a model of random copying with occasional innovation can explain the data as well as anything else.

In the random-copying model, predicting which particular innovation will become the next big hit is fundamentally impossible, because we model the copying as random.  However, what is very predictable is the fact that new successes will replace old ones at the top of the charts, and they do so at a remarkably consistent rate.  To show this, we looked at the Billboard Top 200 chart and found that it turned over at a constant average rate for 30 years, from the 1950s to the 1980s. The number of albums entering and exiting the chart varied from day to day and month to month, but overall the turnover averaged about 6% per month for the full 30-year period. In collaboration with collaboration with Dr Hahn, Dr. Carl Lipo (California State University, Long Beach) and Prof Harold Herzog (Western Carolina University), we discovered a similar consistency in turnover for the top baby names and dog breeds. We showed how this same consistency of turnover resulted from a computer simulation of the random copying model, in which we kept track of the Top 40, Top 100 etc. most popular ‘fashions’ and monitored their turnover.

Particularly surprising was that the rate a list changes depends on the size of the list – a Top 100 changes proportionally faster than a Top 40 – but not the size of the population. So while a larger population means more new ideas, it also means more competition to reach the top, and the two balance each other out: the turnover on bestseller lists remains steady as population size changes.

3. Experts talk about culture speeding up- with new ideas coming at us faster, did you find any evidence of this?

Absolutely – by the 1990s we see an increase in innovation rate in many areas, particularly baby names.  A century ago, it was common for girls and especially boys to be named after their parents, whereas nowadays much fewer kids in Western cultures are named after their parents, and many parents strive to come up with unique names. However, even as novelty in and of itself becomes more popular, the result still fits a model of random copying with only a small degree of innovation — it’s very hard to be truly original!

4. You found “innovators” have an important role to play. What are the characteristics of these innovators?

Innovators introduce something new, and that’s it.  The explanatory insight in our model lies in its simplicity – just a population of copiers with occasional innovation.  The innovations are represented by random numbers, i.e., with no inherent “superiority” or desirability over what is already circulating in the population.  Nevertheless, every new innovation has a small, but still finite chance of becoming the next big hit, just through the process of random copying.  In the model it is absolutely inevitable that what is currently popular now will eventually be replaced by something that began as an obscure innovation.

5. How might your research help those looking to induce positive change amongst consumers? – energy saving for example

For fashions like baby names and pop music, copying is fine, as the inherent content of a baby name or pop song is not what’s important.  However, for behaviors whose content does matter in society (saving energy, voting, buying responsibly), copying is bad.  Copying is what underlies random drift in the market, leading the market to become detached from rational needs. If people think independently about their consumer choices, then it is very likely that the average of all those decisions will converge on something intelligent, and something that does track the situation in the real world. A great book on this phenomenon is James Surowiecki’s book, The Wisdom of Crowds (Anchor, 2005).

6. What does it mean for brands and their new product development efforts?

There should be certain aspects of products that are just subject to random drift in terms of popular choice.  There is little value in expending R&D into these aspects, since their popularity is inherently unpredictable.  In contrast, other aspects should be subject to independent decisions, i.e., selection, and will be predictable because they track real consumer needs.   This may sound obvious — e.g., consumer preferences for food packaging may drift more than their preferences for the food itself — but the model provides the potential to resolve this much more finely: what parts that we thought were important actually drift in popularity?  What things we thought were just style, are actually important?  Instead of surveying customers to find out, you can address these questions more directly by looking at the market data themselves.


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