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Their area of specialization is $50 million+ blockbusters, for which they claim to have found a formula that can accurately predict profits before the films are made-I O Why was Appeasing Created? Nobody, nobody-?not now, not ever-?knows the least goddamn thing about what is or what isn’t going to work at the box office. . Every time out it’s a guess. ” 1 1 Or maybe not. The primary reason Appeasing came into being was because Nick Meany had a long-held desire to bring the principles of risk management to the movie business. 12 He and Cowpoke also shared a fascination with artificial neural networks and decided that it might now be possible to bring neural networks to Hollywood. They could treat screenplays as mathematical propositions using the work already begun by Mr..

Pink and Mr.. Brown, 1 3 (who have potentially been identified by columnist David Poland of online blob The Hot Button as the former Michael Adam & Associates). 14 How did Appeasing Develop its System? The Appeasing method is based on the notion that the best way to calculate he commercial potential of a film is through its script and plot development, rather than by its stars or directors. CEO Dick Meany says: “If you give us a script, there are hundreds of elements in that copy that have an outcome.

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Each element is given a number and those numbers are introduced to our personalized data prediction system. ” 15 Based on the results generated by this system, Appeasing will then ask studio executives whether they believe they are making the best film possible and will offer their expertise regarding key narrative elements that could be adjusted to make the project more financially viable. To reach this point, Cowpoke and Meany started with a “training set” of screenplays Mr.. Pink and Mr.. Brown had already graded. Those scores, along with U. S. Ox-office receipts for each of the films made from those screenplays, were fed into a neural network built by a computer scientist of Meanness acquaintance, whom Caldwell identifies in his article as “Mr.. Bootstraps. ” 1 6 (Caldwell tells us that Cowpoke and Meany won’t disclose how many scripts were in the training set – he throws out the random figure of two hundred for example purposes – but Rachel Gnocchi claims that Meany told her they used “hundreds of scripts. “1 8) Caldwell continues: Mr.. Bootstraps then went to work, trying to use Mr.. Pink and Mr..

Brown’s scoring data to predict the box-office receipts of every movie in the training set. He started with the first film and had the neural network make a guess: maybe it said that the hero’s moral crisis in act one, which rated a 7 on the 10- point moral crisis scale, was worth $7 million, and having a gorgeous red- headed eighteen-year-old female lead whose characterization came in at 6. 5 was worth $3 million and a 9-point bonding moment between the male lead and a four-year-old boy in act three was worth $2 million, and so on, putting a lard figure on every grade on Mr..

Pink and Mr.. Brown’s report card until the system came up with a prediction. Then it compared its guess with how the movie actually did. Was it close? Of course not. The neural network then went back and tried again. If it had guessed $20 million and the movie actually made $1 10 million, it would reweighs the movie’s Pinewood scores and run the numbers a second time. And then it would take the formula that worked best on Movie One and apply it to Movie Two, and deviate that until it had a formula that worked on Movies One and Two, and take that formula to Movie

Three, and then to four and five, and on through all two hundred movies, whereupon it would go back through all the movies again, through hundreds of thousands of iterations, until it had worked out a formula that did the best possible job of predicting the financial success of every one of the movies in its database. 19 Based on the above, Caldwell comments: “The way the neural network thinks is not that different from the way a Holly. Voodoo executive thinks. ” 20 If you pitch a movie to a studio, the executive uses an ad-hoc algorithm – perfected through years of trial and error – to put a value on all the monuments in the story.

Neural networks, though, can handle problems that have a great many variables, and they never play favorites – which means (at least in theory) that as long as you can give the neural network the same range of information that a human decision-maker has, it ought to come out ahead. 21 As we read in Turban et al (2011), common uses of neural networks can be categorized into four overarching classes, corresponding to the range of general tasks addressed by data mining, I. . , classification, regression, clustering, and association. Given Membranes claim that Appeasing uses a personalized data prediction system,” 22 I’m going to venture that its ANN would be best classified as “clustering,” because it deals with a very complicated dataset for which there is no obvious way to classify the data into different categories. This technique is also useful for identifying natural groupings of data for commercial and scientific problems. 3 When I asked my correspondent at Appeasing to which of the above categories he would assign their neural network, he replied: Our neural network expert says that we use elements of most of those neural network processes, but that broadly speaking our main neural network process clusters” – working out a three-dimensional “curve of best fit. ” He points out that unlike neural networks, which are used in, say, the commodities markets which use “hard data” (weather, prices, etc. ), our neural network is built from “human data” provided by our human expert film analysts. 24 What Results Has Appeasing Achieved?

Appeasing is proud of the results it has achieved, although, at this point, they are largely anecdotal and based on a couple of stories that have become lore in the promotional literature about the company. In the first, Dick Cowpoke asked Mr.. Bootstraps, Mr.. Pink, and Mr.. Brown to run sixteen television pilots wrought the neural network and predict the size of each shows eventual audience. He then approached Josh Berger, a senior executive at Warner Brows. In Europe and said, “Stick this in a drawer, and I’ll come back at the end of the season and we can check to see how we did. 25 In January 2004, Cowpoke tabulated the results and found that in six cases, Appeasing guessed the number of American homes that would tune in to a show to within . 06 percent. In thirteen of the sixteen cases, its predictions were within two percent. The Warner Brows. Executive was surprised beyond belief. “It was incredible,” he recalls. It was like someone saying to you, ‘W?re going to show you how to count cards in Lass Vegas. ” It had that sort of quality. ” 26 Caldwell then tells us that Cowpoke approached another Hollywood studio, where he was given nine unreleased movies to analyze: Mr.. Pink, Mr.. Brown, and Mr..

Bootstraps worked only from the script-?without reference to the stars or the director or the marketing budget or the producer. On three of the films-?two of which were low-budget-?the Appeasing estimates were way off. On the remaining six-?including two of the studio’s biggest-budget productions-?they correctly identified whether the elm would make or lose money. On one film, the studio thought it had a picture that would make a good deal more than $100 million. Appeasing said $49 million. The movie made less than $40 million. On another, a big-budget picture, the team’s estimate came within $1. 2 million of the final gross.

On a number of films, they were surprisingly close. “They were basically within a few million,” a senior executive at the studio said. “It was shocking. It was kind of weird. ” Had the studio used Appeasing on those nine scripts before filming started, it could have saved tens of millions of dollars. 27 Six out of nine isn’t perfect, but studios are generally accurate on only one third of their predictions of gross revenues. Cowpoke comments that if the larger studios had both “the benefit of our advice and the discipline to adhere to it, they could probably net about a billion dollars or more per studio per year. 28 This kind of success has not gone unnoticed by Wall Street. Meany tells of a New York hedge fund that wished to gauge Passage’s accuracy before establishing a film fund based on Passage’s insights. The fund looks to invest in “non-market-correlated asset classes,” such as film projects identified by Appeasing as good investments. Meany recounts: One of the test films, “Lucky You,” starring Drew Barrymore, had a budget of $50 million. Appeasing assessed the screenplay prior to filming “and we said Lucky You would be lucky to make 12. 5 million dollars in the LISA and Canada.

If the studio was our client, we would have told them not to make it. It ended up making only 6 million dollars. You can say we were out by 6 million, but the studio was out by 44 million. 29 Cowpoke attributes Appeasing”s success to the ruthless objectivity of its system. He adds: “It doesn’t care about maintaining relationships with stars or agents r getting invited to someone’s party. It doesn’t care about climbing the corporate ladder. It has one master and one master only: how do you get to bigger box office? Nobody else in Hollywood is like that. “30 Another executive who worked at the studio referenced above agrees.

S/he States: “l was impressed by the things they thought mattered to a movie. They weren’t the things that we typically give credit to. They cared about the venue, and whether it was a love story, and very specific things about the plot that they were convinced determined the outcome more than anything else. It felt ere objective. And they could care less about whether the lead was Tom Cruise or Tom Jones. ” 31 Appeasing claims that the formula it has designed can be applied successfully to new scripts, as well, allowing studios to pull the plug on projects that won’t fulfill expectations.

This skill set has caused The Sun to dub Appeasing the “Blockbusters. ” 32 In addition, prominent agents and agencies have discussed using Passage’s tools to help their actor-clients evaluate roles and decide whether they should get their money up front or be paid as a percentage of box-office receipts. 33, 34 Film by Formula? Not surprisingly, a system like the one designed by Appeasing is not without detractors. There are some who feel that it represents “the death of art” or “film by formula” and will “lead to … Grinding uniformity’ in a future where “the artist is handcuffed by the Gerhard. 35 But, as Ian Ares astutely points out, “the centralization train left the station long, long ago” and “the biggest problem isn’t that studios have been interfering,” but rather that “they’ve been doing it badly. ” 36 Indeed, as Thomas H. Davenport and Jeanne G. Harris (2009) remind us, many studios already “use regression analysis to reject the success of a film before its release. ” 37 They write: Some studio executives … Have thus far been less than enthusiastic about turning their decision-making art into a science. The primary obstacles appear to be cultural rather than analytical or technological.

One executive suggested to Appeasing executives that he would be ostracizes by the Holly. Voodoo community-?and not invited to the good parties! -?if word got out that he was producing movies based on analytical prediction models. 38 Another studio executive said: There are many people who have come forward saying they have a way of redacting box-office success, but so far nobody has been able to do it. Think we know something. We just don’t know enough. I still believe in something called that magical thing-?talent, the unexpected. The movie god has to shine on you. 9 As Ares observes: There will always be legitimate and ultimately irresolvable tensions between artistic and commercial goals. However, there should be no disagreement that it’s a tragedy to mistakenly interfere. If a studio is going to change a writers vision in the name of profitability, it should be confident that it’s right. Appeasing is moving us toward evidence-based interference. 40 Caldwell has astutely observed that the formula developed by Appeasing doesn’t necessarily make filming any easier. It can, in fact, make it harder: So long as nobody knows anything, you’ve got license to do whatever you want.

You can start a movie in Africa. You can have male and female leads not go off together-?all in the name of making something new. Once you came to think that you knew something, though, you had to decide just how much money you were willing to risk for your vision. 41 And this is a question that Appeasing cannot answer. It is not in the imagination business; it is a company of technicians with tools, I. E. , computer orgasm, analytical systems, and proprietary software, that calculate mathematical relationships among a laundry list of structural variables.

The dreaming will still have to come from the studios, but the balance between art and science is shifting. As Davenport and Harris (2009) write: Today companies have unprecedented access to data and sophisticated technology that allows even the best-known experts to weigh factors and consider evidence that was unobtainable just a few years ago. 42 As the cost of making films grows ever more expensive, the science-based predictive methods available to studios will become increasingly attractive. Conclusion: This was a fascinating subject to me.

I’ve been a die-hard movie fan for decades and I have always thrilled to the magic of a darkened theatre, the electric energy of an audience, and the heightened anticipation of waiting for the screen to blaze to life. Movies have been an important part of my life and a way of expanding my emotional and psychological horizons. They’ve also had a tremendous impact on my world view. I am extremely excited about the convergence between art and artificial intelligence and what this powerful interaction will tell us about how our brains are wired and thus influence our perceptions and choices.


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