Gotta Catch ‘Em All: Surveillance Capitalism in Augmented Reality

I still play Pokemon GO. Yes, people are still playing it after its monumental presence in summer 2016 seemingly dissipated. Pokemon GO is still the highest grossing mobile game , yet the app’s value arguably lies in the sheer amount of geographic data it is constantly mining from players. The mechanics of the game force you to check it every day, keep the app turned on with location services enabled from point A to point B, interact with neighborhood spots, and finally keep responding to the constant vibrations that indicate a new (potentially rare, but almost never) Pokemon has spawned. Thus, players happily give up their daily cycles of activity in real time, and can be physically programmed to visit ‘local hotspots’ with the promise of a rare Pokemon.

I am interested in modelling the way Pokemon GO has designed its functionality of constant interaction and stimuli to keep track of player movements and begin influencing their detours and destinations. My crude model follows the player objects as they wander through a neighborhood, interacting with Pokestops, constantly bombarded by weak monsters until a rare enemy spawns- drawing all the players to the same physical location. My code is currently very messy, and the model and graphics are not nearly where they should be. But, the basic logic of each interaction can be useful in understanding how in AR games the roles get reversed and the human body becomes the game object and the AI becomes the controller.

Overall simulation with multiple players

Simulation with single player

Code for immediately swarming toward the ‘rare’ spawn

Code for interacting with a Pokestop if it is within a certain radius

Code for spawning Pokemon near player’s location

Gauri Bahuguna