The flow of ideas

By Wayne Brown


Assuming everything is observable in principle at least, we have two sets of interest:

  • Meme space available to the individual (roughly their "vocabulary" of ideas
  • Meme space available to the group (what ideas can be communicated)
Any model is made exponentially more useful if it can be made dynamic.
  • We can make the model dynamic by observing what ideas are communicated at any time. We can spot trends.

"Big Data" techniques allow is to scoop up vast amounts of data from the Internet, providing good maps of how "memes" turn up down to the individual level. We can also see what ideas are being shared between individuals and groups.  Along the way of using the model, we are bound to discover its limitations. One obvious one is defining the slippery concept of "meme." The common meaning of meme these days refers to anything that gets spread on the internet, especially some catchy picture. We need to pay attention to this concept since "ideas" are not spread by words alone but by visual images.

I'd attempt to get closer to the original idea proposed by Dawkins. Much can be learned by tracking individual words and phrases, such as "fake news", "democracy" and "climate". What you trace depends on what you are interested in. This type of study can actually reveal groups such as "echo chambers" and clusters of influencers, such as Fox News.

An important type of study would watch the evolution of memes - how they pop up, die and replace each other in an "evolutionary" way. An example would be how "anti-abortion" competes with "pro-life".

A very wide range of related projects is ongoing under the heading of network visualization. A slightly older idea, requiring less fine-grained data, is mapping of information access - following links. Such information is fundamental to how Google works. "Meta" information can identify patterns of information flow with relatively coarse "content". 

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