Now THAT'S the way to stop a bandwagon!

Claude Shannon invented information theory in 1948, and it quickly became a bandwagon; a summary of the relevant history is here. A big bandwagon is probably impossible to stop single-handed–but the editorial by Elias (1958) came pretty close (or so I understand; I’m not an expert on the history of information theory). And just to top it off, Elias then goes on to skewer a second, entirely different bandwagon (well, really a certain style of science more than a bandwagon). I don’t want to spoil it for you by previewing it; just click through and read it (it’s only one page). A masterpiece of scientific rhetoric.

10 thoughts on “Now THAT'S the way to stop a bandwagon!

    • William Poundstone’s Fortune’s Formula, about the Kelly criterion (the optimal way to decide bet size in a compounding series of bets), involves Shannon and is also very good.

  1. The idea of information theory as a bandwagon points out that bandwagons usually start with a really novel important idea and that there is some core (10-20%) of the wagon that is lasting important work. I am largely skeptical that it is possible to prevent the remaining 80-90% from happening. But I would have to become a social scientist to figure out exactly why that is. Public shaming of those who should know better (i.e. not graduate students) a la the Elias paper is probably one of the few ways to do it. Which brings us back to your earlier discussions about whether ecologists are too nice. Physicists by and large don’t have this problem.

  2. As a then-physics grad student who lived through the “Power laws!! Let’s all calculate critical exponents for *everything*!!” phase of the 1990s (and earlier, I suspect), I can assure you that physicists have their bandwagons. I think many bandwagons start because people see a cool idea and say, “Hey! I’ll measure/calculate that for my system/model and call it a paper!” What’s missing is often a discussion of why we care about that system/model and what the implications of the measurement/calculation are. I see this happening with networks in ecology now. It’s great that people are measuring nestedness, centrality, etc. for Their Favorite Network, but I’d like to see more discussion of what those measurements tell us and what new questions they raise.

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