The Gamification of Games

Despite its name, gamification has never really been about making experiences more game-like. If there were a common characteristic that defined all games, it would certainly not be the use of badges, achievements, and points as incentives for engagement. Games, if anything, share an embodiment of the spirit of play — a temporary suspension of the rules of life to make space for intensities of experience: levity, rivalry, concentration, joy. If historian Johan Huizinga — whose 1938 book Homo Ludens is one of the pivotal works of game studies — had the opportunity to define gamification according to his theory of play, he might have reserved the term for a “temporary abolition of the ordinary world” where “inside the circle of the game the laws and customs of ordinary life no longer count.”

Now gamification evangelists like Jane McGonigal advocate for games to be understood as fundamentally productive, offering a set of tactics to make life under neoliberalism appear more fun and addictive — a “magic circle” we should never step out from, even if we had the choice. The concept first gained traction at the end of the 2000s within game development and marketing communities, which saw an opportunity to use aspects of games to monetize the web. In 2008, before the word had a standardized spelling, a blog explained “gameification”  as “taking game mechanics and applying [them] to other web properties to increase engagement.” In the Wharton School of Business’s popular online course titled Gamification, the instructor professes that “there are some game elements that are more common than others and that are more influential than others in shaping typical examples of gamification.” These elements are “points, badges, and leaderboards.” These offer scores that constitute “a universal currency, if you will, that allows us to create a system where doing one sort of action, going off on a quest with your friends, is somehow equivalent or comparable to doing some other sort of action, sitting and watching a video on the site.”

For the my full article on the gamification of games see:

Platform Monopoly

Even when it looks similar to past iterations of monopoly, the rules of platform monopoly are slightly different. Instead of rental property, the object is to collect intellectual property . Instead of houses and hotels, there will be servers and data centers. A less random mechanic will let users feel like this gamified experience requires more strategy and skill. It is possible to win by buying up every startup and all the infrastructure but it is also possible to win by teaming together with other players to stop one person from owning the board.

Feature Extraction: Aesthetics and Politics of Algorithms — Navel Assembly

The Feature Extraction Assembly included a series of talks, demos, workshops, and discussion groups that brought together artists and researchers engaged with machine-learning. Under the banner of “artificial intelligence,” machine learning has become central to interlocking domains of political-economic control, including: predictive policing, financialization, ad-tech, social media, and logistics. Unlike the proprietary algorithms used in these applications, artistic uses of machine learning allow people to experience and engage with algorithms directly. In this series, we paired artistic uses of machine learning with scholarly research to explore the social repercussions of algorithmic governance under algorithmic capitalism.

Documentation and Syllabus hosted on


NASDAQ IN THE NEWS [1969-1989]

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Google image Results for “Global Finance”

Why is global finance blue? When you Google “global finance” there is no mistaking that a certain repertoire of networks, numbers, translucent images, and glowing blue lights — all overlaid atop the earth — have come to stand for ‘the global’ in finance. In these images, the world has been subsumed by finance. Global finance leverages the power of the digital to render the whole earth in the image of capitalist rationality. This is troubling because there is no doubt that financialization has had global repercussions. Ever since the first modern stock market was constructed in Amsterdam to raise capital for the Dutch East India Company, the stock market industry has been “global” — or at least entangled with particular planetary webs of colonial expansion.1 But the images that advertise finance’s domination as ‘global’ say nothing about the felt effects of finance. Nowhere in these abstract images overlaid with low opacity strings of numbers and symbols can we see the flow of capital in and out of developing nations or the profiting from the worsening climate crisis. As a visualization of data, these images do more to occlude the relation of data to the earth, than to represent it.

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Feature Extraction

feature extraction

Feature Extraction
A weekend program exploring the interconnection between art and machine learning.
November 15, 2019 (night-time only) – Navel – Downtown LA
November 16-17, 2019 – EDA (Broad Art Center 1250), UCLA
Feature Extraction is a series of talks and workshops centered around machine learning, abstraction, and algorithmic subjectivity organized by Blaine O’Neill and Ulysses Pascal, grad students in the Design Media Arts and Information Studies departments. Participants in the first Feature Extraction weekend will learn how certain machine learning models work, play with them in creative/critical ways, and contextualize them in social, cultural, and political frameworks. We will kick off the weekend Friday night at NAVEL in downtown LA with an evening panel discussion and social, followed by 1.5 days of workshops at the EDA led by Gene Kogan and Lou Cantor.