My previous blog post on the mobile/casual game Citalis, focused on the role of algorithms creating the game environment and limiting the game experience with their simplicity. In this post I will explore how these same algorithms make assumptions about the world around us and the implications of these assumptions.
In The Algorithmic Experience, Burden describes in reference to Portal, how “The ability of algorithms to perform sufficiently better in the regulation of human affairs leaves us without the confidence of our own identity – those who can see beyond the system’s assumptions can only scrawl the truth on the confined walls outside the official chamber.” I believe he is saying that there is power in recognizing the underlying assumptions of an algorithm and what it says about us and our culture, because only when we recognize these assumptions can we break away from those that we believe are morally or inherently wrong.
In Citalis, the happiness formula utilizes a ratio of parks to businesses and homes. There is the assumption that all happiness derives from natural beauty (parks) and when paired with the assumption that crime is tied to not everyone being happy, the assumption that a lack of plentiful (enough parks) natural beauty will lead to crime. While these connections sounds ridiculous when paired together, during gameplay, this assumption flows naturally as you concentrate on producing profits and maintaining happiness. It only when you investigate the assumptions that the reality falls apart.
An important cultural assumption the game makes is the simulation of capitalist dominant money focused objective. The machine behind the city is money and money comes from building more and more until we reach an end goal of having more money than before. I imagine the process as a snowball rolling down a hill collecting more and more snow as it heads towards an abyss. There’s nothing interesting in the end, just the accumulation of wealth and overall emptiness.
Recognizing the assumption of processes around us can be a catalyst for change. As Burden also states: “Algorithms are unable to adapt to change, and we are limited by the parameters of the machine and the way it is designed to process those parameters.” It’s when change renders an algorithm false and ineffective, that its important for people to learn about the underlying assumptions that make these algorithms invalid, and make the adjustments necessary to accommodate the changing times.