It is not uncommon to hear the phrase “gaming the system” when someone fools an algorithm, but game as a metaphor can go beyond this perspective. Besides calculation, games are also about meanings and design.
We recently published an article called “Gaming Algorithmic Hate-Speech Detection: Stakes, Parties, and Moves” with Salla-Maaria Laaksonen and Airi Lampinen in the Social Media+ Society journal. In it, building on Erving Goffman’s work, we use the game metaphor to study the implementation of and subsequent resistance to an automatic hate-speech detection system. This blogpost describes my perspective on how games can be used as a metaphor to approach algorithmic systems from different angles. It is not meant as a comprehensive list, but more as an example of some of my thoughts on the topic during my Ph.D. journey so far.
I leave the concept of an algorithmic system intentionally vague here. You may consider recommender systems that suggest content to you in Netflix or Youtube as one type of an example, and social network services such as Facebook as another one. Generally, many of the information systems we interact with on a daily basis can be thought of as algorithmic systems. Thus, a tongue in cheek way of describing algorithmic systems in the context of this blog post could be “any information system that uses algorithms in a way that interests the author of this blog post”.
A game as a general metaphor
This is a category where I would group approaches such as the one which we took in the aforementioned article: if everyday life is (sometimes) a game, what can we learn if we approach it analytically as such? At least two different ways of understanding this type of approach can be identified: ideas about gaming the system, which often refer to behavior where someone is seen to “cheat” an algorithm and, on the other hand, following the rules, where individuals maximize their gains by acting in line with how they consider the designers of the system(s) wish them to act. Cotter’s article “Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram” wonderfully illuminates and also problematizes this distinction when discussing simulated and relational influence in terms of how Instagram influencers attempt to increase their visibility. Additionally, in her recent article “Algorithmic Experts: Selling Algorithmic Lore on YouTube”, Bishop notes that algorithms may be treated as ‘games’ by those attempting to figure out how they could be used to one’s advantage.
This line of consideration also raises questions about who or what are players in a particular game playing for. Companies specializing in search engine optimization are obviously trying to make their clients’ pages generate more traffic, and fans of musical groups might be trying to give the target of their fandom more visibility. We certainly do not play only for ourselves.
Algorithmic systems as world creating
For me, this second way of looking at algorithmic systems – algorithmic systems as world creating – entails looking at what kind of a ‘micro-cosmos’ of meanings the encounter with the system holds. In a game of chess, pieces have different values for the player based on their shape, but the relationship between the shape and the value only makes sense in the context of chess. Goffman points out that other encounters share this element of encounter-specific meanings: “It is only around a small table that one can show coolness in poker or the capacity to be bluffed out of a pair of aces; but, similarly, it is only on a road that the roles of motorist and pedestrian takes on full meaning”. Encounters, then, are world-creating events, and encounters with algorithmic systems are no exception.
Algorithmic systems have different kinds of transformation rules. The concept originates from Goffman, but I find Di Filippo’s use of it in his book chapter “MMORPG as Locally Realized Worlds of Action” easier to grasp than the original definition. Di Filippo states that these transformation rules refer to “the fact that individuals adapt resources to match the relevance of the situation”. In the aforementioned chapter, Di Filippo uses the concept to analyze how the world created in fantasy books is transformed to serve as a backdrop for a video game. When considering recommender systems, transformations occur on how behavior should be understood: clicks or other forms of behavior such as decisions to buy something are transformed into recommendations. If a couple of strangers in front of us in a café buy the same kind of coffees, we most likely do not consider it as a recommendation: however, it could very well be transformed into one based on the data collected from the transaction that happens between the clients and the café. We have followed this line of inquiry with Airi Lampinen in a study that drew from Goffman’s Frame Analysis by analyzing interviews of users and the head designer of a recommender system that used reading time to generate its recommendations.
Game-likeness from a design perspective
This third category – game-likeness in the context of design – focuses on the design perspective of algorithmic systems, or more specifically, what algorithmic systems that are not intended as games may share with the design of games. Algorithmic systems may incorporate elements that make them “game-like” or gamified. Instagram and Facebook quantify “likes” other users can give to one’s content, making these systems potentially more enthralling for their users. From a perhaps more serious perspective, Chan (2019) has pointed out in his article “The rating game: The discipline of Uber’s user-generated ratings” that the on-demand taxi service Uber’s customer reviews make drivers attempt to maximize positive ratings as their livelihoods may be on the line: get enough negative reviews and you won’t be getting customers anymore.
Game design can also be a method that ties together some of the elements from the first two categories. In the 2nd NOS-HS workshop for Nordic Perspectives on Algorithmic Systems, Michael Hockenhull and Mace Ojala organized a session where the participants designed tabletop-games from empirical research cases Bastian Jørgensen, Cæcilie Laursen, Silja Vase and Rikke Torenholt were working on and were kind enough to let us use as starting points for the games. This activity was inspired by Dumit’s article “Game Design as STS Research”. The process of designing a game based on an algorithmic system forced one to consider both the calculative nature of interactions individuals may have with such systems and the ways these interactions could be transformed into a playable format.
As pointed above, games can be used as a metaphor to illuminate different kinds of things about algorithmic systems. The focus can be placed on the strategic nature of everyday dealings with them, the set of meanings interactions with them contain, or the design aspects that may mimic those we encounter in actual games. One could probably discover further perspectives that the concept of a game might afford, but these are the three that I have identified in the extant literature and found productive for my own research.
Jesse Haapoja is a Ph.D. student in Social Psychology at the University of Helsinki who has the privilege of working on topics such as the one presented here in the Kone Foundation funded project “Algorithmic Systems, Power, and Interaction”.
Thanks to Airi Lampinen for comments on a draft of this blog post