I wrote a lengthy post last year on how the networked information economy offered potential challenges to the market as the primary form of economic organization. In that post I noted the market’s efficiency at aggregating information through individual preferences and used Netflix of an example of how networks are making information aggregation easier in a way that could potentially challenge markets. I want to expand on that. Here’s what I wrote last year:
Just as the market’s claim to dominance in motivating us is starting to be challenged, some are revisiting its dominance in aggregating information. Sunstein explores the subject in Infotopia and highlights increasing efforts to aggregate human preferences online, including Amazon and Netflix. If it’s obvious that we are doing better and better at aggregating information thanks to the Net, it’s less obvious how this might challenge the role of the market.
Imagine that Netflix has a small, set number of a rare movie to rent, and that it’s in high demand. Who should get it first? Auction the privilege off to the highest bidder, responds the free market advocate. And, particularly in a scenario where customers have equal wealth at their disposal, this method has a lot to recommend it. The market is incredibly efficient at allocating resources under ideal settings. Tremendous gains in human welfare have been predicated on this fact. But Netflix is developing sophisticated algorithms to use your preferences for movies you’ve seen to predict what movies you’ll like. Is it so hard to believe that some day in the future an algorithm could – given the aim of maximizing viewer enjoyment – “beat the market” in determining how to distribute the movie?
I want to articulate this challenge in slightly greater detail. Let’s start small and simple…
There are 100 video customers, each of whom has a token for one free movie. Each customer browses the library of videos and picks out the one they want. They watch it and fill out a survey rating how much they enjoyed it. That’s scenario 1.
Scenario 2: Still 100 customers. This time Netflix’s algorithm, looking at their past ratings, gives them the movie to watch. After watching, they rate their satisfaction.
Which group will be more satisfied? What does it mean if Netflix gets to a point where its algorithm wins out? And how about if we change the example by adding scarcity, as was implied in the quote above? Now there’s only one of each movie and we’re comparing Netflix doling out movies via its algorithm to customers bidding on movies in an auction of some kind.
I’d like to ask for some help in thinking about this.
To my tech friends: obviously a ton of people are writing smart stuff about recommendation algorithms. What should I be reading on this?
To my economist and wonk friends: what do you think of the comparison of these two distribution mechanisms? What am I missing? I’d love help thinking about how you’d actually design the specifics of a challenge. While the comparison makes broad sense to me at the macro level I’d appreciate hearing from someone familiar with the economics of auctions, etc. It’s been a while since I’ve thought too much about basic micro.
beyond the times 