My book, GAMES: AGENCY IS ART is out! It's about:
How game designers sculpt agency.
How games let us record, transmit, and explore new forms of agency.
How real games make us more free.
How gamification undermines our freedom.
One of my favorite podcasts ever is a 2022 conversation between @add_hawk and @ezraklein. I'm thrilled to continue the conversation in the interviewer seat today.
@DialecticPod Ep. 36: C Thi. Nguyen - Measurement, Meaning, and Play
C. Thi Nguyen (@add_hawk) is a professor of philosophy focused on games, values, data, and metrics.
I visited him to discuss his new book, The Score: How to Stop Playing Someone Else's Game.
Thi is focused on a dilemma with scoring systems:
1) in games, they're are great: they allow game designers to sculpt the player's agency. By shaping goals, abilities, and obstacles, action becomes easy, even harmonious. The player can try on different roles, explore different values, and move "lightly between worlds"
2) in the real world, they flatten us: We've quantified our lives, and easily countable metrics produce what Thi calls "value capture." By obsessively measuring what matters, we only value what we can easily measure.
We went deep on the personal and societal implications of this:
- What are different types of agency? What is the shape of good values? What is the difference between recognition and perception?
- How can we be more playful, in and out of games? How can we find more beauty in process, not outcomes?
- How can we trust each other, scale progress, keep bad actors in check--all while not extracting nuance out of complex fields by relying too much on legible data?
- Are objectivity and truth the same thing? Is technology really value-neutral?
Available on all platforms below and here on X.
Timestamps:
0:00 - Opening Highlights
1:39 - Introduction to C. Thi Nguyen
5:13 - Thanks to Notion
6:31 - Start: What Does it Mean to Be Playful?
13:41 - Starting Local: Agency, Scoring Systems, and Games
23:36 - Value Capture: Incentives, Values, and the Collapse of Meaning
36:28 - What is the Shape of Good Values?
49:45 - Attention, Recognition vs. Perception, and Aesthetic Openness
58:46 - Process vs. Outcome, Striving Play vs. Achievement Play, Recipe vs. Dish
1:10:00 - Aesthetic Value & Autotelic Pursuits in Life
1:16:59 - Metrics, "Measure What Matters," and What We Miss
1:24:16 - Quantitative vs. Qualitative Ways of Knowing and Different Conceptions of Rules
1:38:01 - Scaling Trust, Data, Experts, and Legibility
1:54:37 - Objectivity & Truth, Value-Laden Technology & Decisions, and "Objectivity Laundering"
2:07:57 - Advice for Technologists: Ethics, Maps, Value-Neutrality, and Playfulness
2:18:52 - Closing Thanks to Notion
A five-star review for @add_hawk's book, THE SCORE, from @washingtonpost: "Brilliant and wildly original . . . THE SCORE is socially attentive, historically literate and imbued with sensual glee. It is exuberantly eclectic." On sale tomorrow!
https://t.co/yMBl3HiWEc
An incredible review by @FT for THE SCORE by @add_hawk:
“Like a latter-day Socrates ... [Nguyen] advocat[es] a kind of playful rebellion against rules and metrics. . . . I give this excellent book five stars.” https://t.co/rpNk6MHYAn
🕹️ Does gamification lead us to prioritise what can be measured and monetised, over what is truly meaningful?
Philosopher and gamer @add_hawk explores this question in his forthcoming book, reviewed here in @guardian.
https://t.co/QEO9LHjXWC
Congratulations to @add_hawk, one of the coolest philosophers around today, who's on the @voxdotcom
Future Perfect 50 list!!!! Read about his ideas on "value capture," optimization, & the limits of data. Then read about our other 49 brilliant honorees!
https://t.co/sYmeF1XBYn
Brat summer is out, attic wife autumn is in. We’re hissing at people. We’re withdrawing from society. We’re growing our hair below our waist. We’re setting fire to his curtains. We’re gaslighting his new side piece.
Interesting paper on the mental health of people doing qualitative research:
Historically, those doing qualitative work—either by itself or in mixed method research—have not necessarily felt readily understood or as respected by funders, mainstream journals, or even colleagues.
A key point missing in a lot of post debate analysis is that Trump’s claim about immigrants eating pets almost perfectly syncs up to the piano in the Peanuts theme song.
Infographics of this dataset have been kicking around on the internet for years. It is an insult to real scientists everywhere. For every 10 likes, I will post a new ridiculous fact about how fake and ridiculous this "data" is.
Beloved Apple blog TUAW was shut down in 2015, sold to private equity, then sold to a company in Hong Kong. It recently relaunched as an AI content farm using the stolen identities and bylines of its former human staff. A nightmare:
https://t.co/NKUXy1Y2YT
Thank you @add_hawk for this lovely thread! If people want to read more:
- name artifacts paper by @VeredShwartz, @rachelrudinger, & Tafjord: https://t.co/FPNaCYMyM2
- our papers:
https://t.co/QalXhLSbwy
https://t.co/9CS8yUGGev https://t.co/xWFLKgv6DT https://t.co/YC0EvZ1cou
And, she says, this is a tough problem to get out of, because it's hard to train different models on different data sets - because there's only one Internet.
PS @KathleenACreel is a trained philosopher and software engineer who’s appointed as a prof in both phil and CS, and she has proposals for technical solutions to partially ameliorate these problems which are above my pay grade.
And she says there's a bunch of distinctive new LLM biases, like that naming artifact. Which is because LLMs are trained on the internet, and the internet is, like, 60% by weight Reddit. So resume algorithms are all systematically biased against anybody named Donald or Hillary.
This creates a distinctive new social harm: the possibility that you'll get shut out of *every* job search because your resume doesn't fit that one model.
@KathleenACreel says: there are a bunch of different corporate products, but they're usually adaptations of one original model. Or even if they aren't, they're different models trained on the same base data set.