Generative Movement

Algorithm

For our movement algorithm, we each started in a different corner of the third floor Skibo activities room. Then, a two minute timer was started and we began walking in a random direction with our eyes closed. If a person bumps into something (another person, wall, mat), they yell out "bump." At that moment, the other people start walking towards the sound while the person who "bumped" moves in a random direction. This is continued until the two minute timer went off.

TRIAL 1

This trial turned out to be relatively balanced. All participants moved throughout the whole space and collided with each other several times. However, the video does show some patterns that we predicted. Near the middle of the trial, two participants separate off from the third, following each other's bumps. The third person is separated on the other side of the room by the mat. 

TRIAL 2

Like the first trial, the participants start off relatively dispersed but quickly start to bunch up. Unlike the first trial, one member does not get separated from the rest, and the group becomes concentrated in the corner, bumping into each other and the corner walls many times in succession. This forces them to stay in this area for a majority of the trial. 

TRIAL 3

After adding several mats to the space, our movement became much more constricted. However, the group was not confined to a single area after bunching up. We were able to slowly traverse the space together, while bumping into walls and each other many times. We also noticed smaller versions of patterns from the first two trials. The group separated briefly into the 2-1 pattern from the first trial several times. 

Outcome

We decided to "perform the action in a different space", but instead of actually relocating, we modified the space. This choice was made partially because the location was fairly ideal for the physical constraints of our project (being able to hear each other, closed space where collisions might actually occur), and had more control and flexibility over the location of obstacles.

We chose to omit the use of sight from this movement as it helped eliminate some of the biases that the actors would automatically put in certain movements to intentionally avoid or create collisions. In this way we were able to make the actions more authentically generative, as most of the movement was a product of the algorithm instead of a more natural response to our surroundings.

Moving in a random direction helped create entropy in the movement, while the reactionary aspect of moving towards where collisions occurred created a nice balance of order and entropy. While the movement was very predictable immediately after a collision, it guaranteed that another collision would take place later creating more entropy. Overall, nice patterns emerged from the seemingly random movement. Although, it seems this would have been better captured with a camera directly above the scene.