Fig 1.
The basic mechanisms of Place.
a) Reddit users could select a single pixel from a set of 16 colours, place it anywhere on the 1000x1000 canvas, and then wait for a fixed period until they could place another pixel. This represents the introduction of variation to a homogeneous space on an individual level. b) Through collaboration, simple artworks can form, with variation being organised into structured, compressible patterns. c) Some groups pursued goals internal to Place, like dominating as much of the canvas as possible, which highlights how compressible patterns can arise via the removal of variation.
Fig 2.
Two proposals for artworks in PlaceDE subreddit.
Left: Proposal for artwork which successfully made it onto the canvas with 225 votes. Right: An unsuccessful proposal with 0 votes. Subreddits allows for selection of artworks based on up-voting or down-voting as well as via comments below the proposals.
Fig 3.
The development of the place canvas.
Top: State of the canvas in 10-hour-intervals. Bottom: The final image after 72 hours, frozen in its state.
Fig 4.
Compression measure applied to an image.
Top: How a .png format can be used to assess the compressibility of an image: The pixels on the image to the right are identical to the left image in colour value, but shuffled randomly in their position. Still, the file sizes indicate that the left image is more compressed. Bottom: Randomisation can still create compressible patterns, as demonstrated by the image in the middle, which is more compressible than the original. Note, however, that a strongly structured image like the one on the right will always be more compressible than the average randomised image.
Fig 5.
Some real examples from the canvas and their corresponding entropy values.
As can be seen, a completely non-uniform distribution of colours (i.e., a single colour) results in an entropy of 0 bit, whereas a strongly uniform distribution of colours (such as a rainbow pattern) results in entropy values close to the maximum of 4 bit.
Fig 6.
Local entropy of the final canvas.
Blue regions correspond to 10x10 neighbourhoods with low entropy and orange regions correspond to 10x10 neighbourhoods with high entropy.
Fig 7.
How the stability measure is applied.
To compare two states of the canvas, the difference between the values of the pixels is computed. On the resulting difference image, stable pixels appear in black, since all their values are zero.
Fig 8.
The time course of compression.
File sizes in bytes are plotted in black. The quadratic and logarithmic model are plotted in orange and blue, respectively. The quadratic model showed the best fit to the data.
Fig 9.
User activity and number of used pixels.
Top: the amount of used space (i.e., pixels) for the duration of Place. Bottom: the amount of user activity for the duration of Place (total number of changes per hour).
Table 1.
Results of regression model with activity, used pixels, and time (plus interactions) as predictors of compression (file sizes).
Fig 10.
Frequency distribution of the 16 colours in Place over time.
White pixels are represented by the dotted line and the remaining 15 colours are depicted in their original hue. Note that the canvas started with all of the one million pixels in white (a completely non-uniform distribution). Over time, each colour approaches its own asymptotic level and stabilises at approximately the 50-hour mark.
Fig 11.
The time course of global entropy.
Entropy values in bit are plotted in black. The quadratic and logarithmic model are plotted in orange and blue, respectively. The logarithmic model showed the best fit to the data.
Fig 12.
Comparison of compression between the actual data and the simulation.
The black line represents the original file sizes and the red line the mean values for simulated images at each time step. For Place, the actual images are more compressible than randomly shuffled versions for a period during the first half and during the entire second half of the run-time.
Fig 13.
Comparison of summary entropy values both globally and locally.
Whilst both types of entropy follow similar trajectories through time, global entropy (black line) remains much higher than local entropy (green line).
Fig 14.
Heatmap of local entropy for nine time-stamps.
Several heatmaps showing the local entropy for the following time-stamps (from top-left to bottom-right): 1-hour, 9-hours, 17-hours, 25-hours, 33-hours, 41-hours, 49-hours, 57-hours, 72-hours. The axis on the right shows the colour-coded entropy values (0 bit: dark blue and 4 bit: dark red). At 1-hour, the canvas is highly predictable at a local level, as the dark blue values indicate, and gradually becomes less predictable as time progresses. Between 25- and 41-hours Place reaches its highest levels of entropy locally (as highlighted by the large regions of orange and green), before gradually decreasing the entropy again (as the growing number of blue regions shows).
Fig 15.
The development of stability over time.
The black lines represent the number of stable pixels between two time slices, plotted for three different resolutions. Purple and green lines show intercept-only and linear models fitted to the data, respectively. (a) Number of stable pixels over time for 1-hour-intervals. (b) Number of stable pixels over time for 2-hour-intervals. (c) Number of stable pixels over time for 4-hour-intervals. (d) All three levels of granularity in one figure. There is a positive slope in the linear models for each, but the slope is steeper at coarser time intervals (as indicated by the lower numbers of stable pixels, more change can occur in these within a single time step).
Fig 16.
Selected examples in Place and their development over time.
(a) Some isolated, simple and rather independent artworks in an early phase of the event. Note the chaotic single pixels and white space still dominating most of the space. (b) The development of the German-French (and later European) region. Germany naturally expanded its (horizontal) flag to the right, taking over French territory. The French (vertical) flag expanded to the top and bottom, and after diplomatic talks, a European alliance was formed; the two artworks became interdependent. Following the formation of this agreement, there was still constant modification within the two flags, adding national icons and sights. (c) The rise and fall of the Blue Corner. As one of the earliest groups, a subreddit formed that was determined to cover as much ground as possible in blue pixels, starting from the bottom right corner. This expansionary strategy was very successful early on, but later the group had to incorporate small artworks into their territory to be able to maintain it. Only a small portion of the Blue Corner remained upon completion of Place. (d) Destruction and innovation caused by the actions of the Void. Determined to destroy other creations, members of the Void group strategically attacked pieces of the canvas with black pixels. Ultimately, however, most of their efforts were overwritten over the time course of the event. Note that some, but not all of the original art destroyed by the Void resurfaces at the the end; some creations are entirely novel.