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Blockbusters, Bombs & Sleepers The Income Distribution of Movies Sitabhra Sinha The Institute of Mathematical Sciences Chennai (Madras), India Slide 2 A Pareto Law for Movies Why look at Movie Income ? Movie income is a well-defined quantity; Income distribution can be empirically determined Asset exchange models for explaining Pareto Law in wealth/income distribution cannot be applied ! Movies dont exchange anything between themselves !! Theres no business like show business Pareto exponent for Movie Income : 2 But Slide 3 Popularity of Products/Ideas Movies: S Sinha & S Raghavendra (2004) Eur Phys J B, 42, 293 Scientific Papers: S Redner (1998) Eur Phys J B, 4, 131 Books: D Sornette et al (2004) Phys Rev Lett, 93, 228701 Movies popularity distribution a prominent member of the class of popularity distributions Slide 4 1+ = 3 The Popularity of Scientific Papers 1/ 0.48 Measure of popularity : citation distribution Relation between exponents for : Cumulative probability (Pareto Law) 1+ : Probability distrn (Power law) 1/ : Rank distribution (Zipfs Law) Pareto exponent 2 ISI Phys Rev D Slide 5 The Popularity of Books Measure of popularity : Book sales at amazon.com Pareto exponent 2 Slide 6 A Hit is Born: The Dynamics of Popularity Conjecture: Universality Pareto exponent for popularity distributions 2 Slide 7 Outline of the Talk Empirical : Distributions SS & S Raghavendra (2004) Eur Phys J B, 42: 293-296 Empirical : Time evolution SS & R K Pan, in preparation Model SS & S Raghavendra (2004) SFI Working Paper 04-09-028 SS & S Raghavendra (2005) to appear in Practical Fruits of Econophysics, Proc 3 rd Nikkei Econophysics Symposium, Springer-Tokyo Slide 8 Outline of the Talk Empirical : Distributions SS & S Raghavendra (2004) Eur Phys J B, 42: 293-296 Empirical : Time evolution SS & R K Pan, in preparation Model SS & S Raghavendra (2004) SFI Working Paper 04-09-028 SS & S Raghavendra (2005) to appear in Practical Fruits of Econophysics, Proc 3 rd Nikkei Econophysics Symposium, Springer-Tokyo Slide 9 Measuring Popularity However, these are for movies released long ago: lot of information available for people to decide What about newly released movies still running in theatres ? Popularity of a movie can be estimated in various ways: e.g., Number of votes received from registered users in IMDB database Or, DVD/Video rentals from Blockbuster Stores Whats the income, dude ? Slide 10 Income Distribution Snapshot Too few data points, too much scatter Each week, about 100-150 movies running in theatres across USA Hard to make a call on the nature of the distribution ! Slide 11 The Movie Year: Seasonal Fluctuations in Movie Income over a Year Makes sense to look at income distribution over a year: we can ignore seasonal variations Slide 12 Popularity Distribution of movies released in USA during 1999-2003 acc to weeks in Top 60 Gaussian distribution Long tail: the most popular movies do not fit a Gaussian! Rank distribution of movies: explores the tail of the distribution containing the most popular movies Data for all years fall on the same curve after normalizing !! slope - 0.25 Slide 13 Gross Income Distrn of movies released in USA during 1997-2003 Opening Gross Kink indicating bimodality Bimodal distribution of opening gross Movies either do very badly or very well on opening ! Distribution scaled by average gross to correct for inflation Slide 14 Gross Income Distrn of movies released in USA during 1997-2003 Opening Gross Total Gross Unimodal 1/ 0.5 Pareto exponent 2 at opening week and remains so through the entire theatre lifespan The only contribution of movies which perform well long after opening (sleepers) Distribution scaled by average gross to correct for inflation Slide 15 Relation between longevity at Top 60 & Total Gross IMAX movies Slope ~ 2.14 G Total ~ T 2 Slide 16 Outline of the Talk Empirical : Distributions SS & S Raghavendra (2004) Eur Phys J B, 42: 293-296 Empirical : Time evolution SS & R K Pan, in preparation Model SS & S Raghavendra (2004) SFI Working Paper 04-09-028 SS & S Raghavendra (2005) to appear in Practical Fruits of Econophysics, Proc 3 rd Nikkei Econophysics Symposium, Springer-Tokyo Slide 17 A Movie Bestiary Classifying Movies according to the time evolution of their income Blockbusters: High Opening Gross, High Total Gross Intermediate to long theatre lifespan Bombs: Low Opening Gross, Low Total Gross Short theatre lifespan Sleepers: Low Opening Gross, High Total Gross Long theatre lifespan Slide 18 Spiderman (2002) A classic blockbuster Peaks on weekends Daily earnings Weekend earnings Exponential decay Slide 19 Spiderman 2 (2004) A blockbuster but like most sequels, earned less & ran fewer weeks than the original ! Slide 20 The Blockbuster Strategy If it doesnt open, you are dead ! - Robert Evans, Hollywood producer The opening is the most critical event in a films commercial life FACT: > 80 % of all movies earn maximum box-office revenue in the first week after release Jaws (1975) : the first movie to be released using the (now classic) blockbuster strategy : Heavy pre-release advertising Presence of star/stars with name recognition Wide release Underlying assumption : Herding effect among movie audience A large opening will induce others to see the movie ! Slide 21 BLOCKBUSTERS: Examples Very high opening gross Exponential decay in subsequent earnings Slide 22 Lord of the Rings 3: Return of the King (2003) Top grosser of the year ! Slide 23 Harry Potter and the Sorcerers Stone (2001) Slide 24 The Sixth Sense ( 1999) Blockbuster. but behaved like a sleeper very late in its theatre lifespan ! (longest time at top 60 for non-IMAX movie - 40 weeks) Slide 25 BOMBS: Examples Very low opening gross Exponential decay in subsequent earnings Earns significantly less than budget Slide 26 Bulletproof Monk (2003) Spectacular flop ! Production budget: $ 50 Million Advertising budget: $ 25 Million Slide 27 American Psycho (2000) Slide 28 SLEEPERS: Examples Very low opening gross Sudden rise in subsequent earnings before eventual exponential decay before eventual exponential decay Slide 29 My Big Fat Greek Wedding (2002) A classic sleeper ! Produced outside Hollywood Extremely long theatre lifespan Gradual rise in income Subsequent exponential decay Slide 30 The Blair Witch Project (1999) Another Hollywood outsider sleeper Slide 31 Mystic River (2003) Publicity Buildup to Oscar Awards A Hollywood insider sleeper ! Unusual: Multiple rises in income during theatre lifespan Slide 32 To compare 2004 Spiderman 2 2003 Lord of the Rings 3: Return of the King Mystic River Bulletproof Monk 2002Spiderman My Big Fat Greek Wedding 2001 Harry Potter and the Sorcerers' Stone 2000 American Psycho 1999 The Sixth Sense Blair Witch Project Color code: BlockbusterSleeperBomb Slide 33 Scaled by opening gross Income of most movies decay exponentially with the same decay rate < 5 weeks Comparing the Income Growth / Decay of Movies Slide 34 Outline of the Talk Empirical : Distributions SS & S Raghavendra (2004) Eur Phys J B, 42: 293-296 Empirical : Time evolution SS & R K Pan, in preparation Model SS & S Raghavendra (2004) SFI Working Paper 04-09-028 SS & S Raghavendra (2005) to appear in Practical Fruits of Econophysics, Proc 3 rd Nikkei Econophysics Symposium, Springer-Tokyo Slide 35 Puzzle The Pareto tail appears at the opening week itself Asset exchange models dont apply Cant be explained by information exchange about a movie through interaction between people Need a different approach The Pareto tail appears at the opening week itself Asset exchange models dont apply Cant be explained by information exchange about a movie through interaction between people Need a different approach Slide 36 Popularity = Collective Choice Process of emergence of collective decision in a society of agents free to choose in a society of agents free to choose constrained by limited information constrained by limited information having heterogeneous beliefs. having heterogeneous beliefs. Example: Example: Movie popularity. Movie popularity. Slide 37 Collective Choice: A Naive Approach Each agent chooses randomly independent of all other agents. Each agent chooses randomly independent of all other agents. Collective decision: sum of all individual choices. Collective decision: sum of all individual choices. Example: YES/NO voting on an issue Example: YES/NO voting on an issue For binary choice For binary choice Individual agent: S = 0 or 1 Individual agent: S = 0 or 1 Collective choice: M = S Collective choice: M = S Result: Normal distribution. Result: Normal distribution. NOYES 0 % Collective Decision M 100% Slide 38 Modeling emergence of collective choice Agents choice depends on Personal belief (expectation from a particular choice)Personal belief (expectation from a particular choice) Herding (through interaction with neighbors)Herding (through interaction with neighbors) 2 factors affect the evolution of an agents belief Adaptation (to previous choice):Adaptation (to previous choice): Belief changes with time to make subsequent choice of the same alternative less likely Belief changes with time to make subsequent choice of the same alternative less likely Learning (by global feedback through media):Learning (by global feedback through media): The agent will be affected by how her previous choice accorded with the collective choice (M). The agent will be affected by how her previous choice accorded with the collective choice (M). Slide 39 The Model: Adaptive Field Ising Model Binary choice :2 possible choice states (S = 1). Binary choice :2 possible choice states (S = 1). Belief dynamics of the i th agent at time t: Belief dynamics of the i th agent at time t: where is the collective decision : Adaptation timescale : Adaptation timescale : Learning timescale : Learning timescale Choice dynamics of the ith agent at time t: Choice dynamics of the ith agent at time t: for square lattice Slide 40 Results Long-range order for > 0Long-range order for > 0 Slide 41 Initial state of the S field: 1000 1000 agents Slide 42 = 0: No long-range order =0.1 N = 1000, T = 10000 itrns Square Lattice (4 neighbors) Slide 43 =0.1 > 0: clustering = 0.05 N = 1000, T = 200 itrns Square Lattice (4 neighbors) Slide 44 Results Long-range order for > 0Long-range order for > 0 Self-organized pattern formationSelf-organized pattern formation Slide 45 =0.1 = 0.05 Ordered patterns emerge asymptotically Slide 46 Results Long-range order for > 0Long-range order for > 0 Self-organized pattern formationSelf-organized pattern formation Multiple ordered domains Behavior of agents belonging to each such domain is highly correlated Distinct cultural groups (Axelrod). Slide 47 Results Long-range order for > 0Long-range order for > 0 Self-organized pattern formationSelf-organized pattern formation Multiple ordered domains Behavior of agents belonging to each such domain is highly correlated Distinct cultural groups (Axelrod). Phase transitionPhase transition Unimodal to bimodal distribution as increases. Slide 48 Bimodality with increasing Slide 49 Results Long-range order for > 0Long-range order for > 0 Self-organized pattern formationSelf-organized pattern formation Multiple ordered domains Behavior of agents belonging to each such domain is highly correlated Distinct cultural groups (Axelrod). Phase transitionPhase transition Unimodal to bimodal distribution as increases. Similar results for agents on scale-free networkSimilar results for agents on scale-free network Slide 50 OK but does it explain reality ? Rank distribution: Compare real data with model US Movie Opening Gross Model Model: randomly distributed Slide 51 Rank Distribution according to Ratings A DeVany & W D Walls (2002) J Business 75:425 Rank distrn of G-rated movies similar to that for = 0 Rank distrn of PG, PG-13 and esp R-rated movies similar to that for > 0 Slide 52 Conclusion Movie income distribution is Gaussian but with a power law tail having Pareto exponent ~ 2 Possibly universal for popularity distributions ! True for opening gross income as well as total gross income distribution Pareto tail cannot be explained by information exchange through interaction among agents Bimodality in opening gross distribution can be explained by a collective choice model Movie income distribution is Gaussian but with a power law tail having Pareto exponent ~ 2 Possibly universal for popularity distributions ! True for opening gross income as well as total gross income distribution Pareto tail cannot be explained by information exchange through interaction among agents Bimodality in opening gross distribution can be explained by a collective choice model