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Tag Archives: algorithms

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During an interview at the Nantucket Film Festival last summer, film writer/director Nancy Meyers (It’s Complicated and Something’s Gotta Give) said she was having a tougher time getting scripts approved. It was apparent that she preferred the intuition of one eccentric movie mogul who ran the company. Instead, now she deals with committees that have practical concerns. They focus on possible male (not female) stars more than her story line.

For her newest Sony Pictures movie, Meyers might also be coping with a “script analyst.”

Vinny Bruzzese is a “script analyst” whom the NY Times recently described. A former statistics professor, Bruzzese decomposes successful scripts and movie goer preferences. The stats give him the answers for the 20 or so page reports he gives to film makers. His suggestions include deleting bowling scenes because they won’t grab an audience and adding “guardian” rather than “cursed” superheroes.

Script analysis has become only a part of the quantitative picture. One movie investor said, “You have to think of it as math.” For him, the “math” is a risk assessment algorithm. What is the best weekend for the release? The rating? The star? The genre? He uses more than 10,000 variables. Asked if a movie should include an extra fight scene that will cost $1 million to shoot, he said he replies, “yes” only if box office receipts will go up by more than $1 million. The results? No home run and no big risks. No Matrix and no Waterworld.

As economists, we could say that the math of the movies is all about marginal analysis…all about what we add or subtract. Which actor will make the difference? Do we include a certain scene or delete it? Many of the questions are timeless. The answers though might no longer be intuitive. Instead, they can be reduced to a list of statistics that form a move making algorithm.

Sources and Resources: For some fascinating insight on the math behind the movies, I recommend the NY Times article on script analysis, this indiewire blog that takes it a step further, and Esquire’s piece, “Mathematics of Movie-making.”

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Dynamic Pricing for Tickets Displays the Power of Prices

For $75 you can see all 9 Big Ten Conference basketball games…Maybe.

Here’s the story.

If you buy a Golden Ticket online from the Minnesota Golden Gophers, you can attend up to 9 Big Ten games but only if Minnesota wins every time you attend. If the team loses when you are there, your ticket is deactivated. At $8.33 a game, you could be saving huge money.

What is going on?

The 250 Golden Tickets are being sold in order to build interest in the less competitive matches. As the thinking goes, worried that his ticket will deactivate, a fan will attend at least one or two games at which he expects Minnesota to win. In that way, by changing the incentive, owners can fill seats that otherwise might have been empty.

Called dynamic pricing, algorithms that maximize revenue are being used by ticket sellers. On a plane, the business person probably paid more than the vacationer sitting nearby; online, prices could change from one hour to the next; at certain theaters, based on sales, ticket prices could spike unexpectedly for orchestra seats. The Golden Ticket is a new take on dynamic pricing but it still involves charging different prices for different people.

Our bottom line? I continue thinking about how miraculous a price is. Conveying information and creating incentives, unregulated prices enable us, as buyers and as sellers, to make decisions that suit us individually.

More specifically, last year at econlife, we explained it this way:

Dynamic pricing is all about price elasticity of demand. If price changes a lot and the quantity we buy remains almost the same, as with medication, then our demand is inelastic. By contrast, if price swings have a big impact on buying, then our response is elastic. With Broadway shows and airline seats, certain consumers have an elastic response to higher prices; when price ascends they say, “No.” Others, the inelastic group, will buy no matter what.

Sources and Resources: This article from ESPN tells the basic story about the Golden Ticket while here is more about dynamic prices and algorithms from marketplace.org. And then, looking for more about dynamic pricing I came across this article on Chicago theater tickets that was fascinating. And here is more about dynamic pricing from econlife.

Hat Tip: Cheap Talk

Note: The title was minimally edited after it appeared.

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