Why do the same scores that delight us in games drain the joy from our work? That is the puzzle at the heart of The Score: How to Stop Playing Somebody Else's Game, the recent book by philosopher C. Thi Nguyen.
FCAT's VP of Research John Dalton connected with Nguyen to discuss why we start chasing the number, what we lose when we do, and how to keep the metrics we need without missing what matters.
JOHN DALTON: Your book, The Score: How to Stop Playing Somebody Else’s Game, has been getting a lot of attention. What motivated you to write it?
C. THI NGUYEN: I realized that the two different threads of my research had come into conflict. One is about how games are amazing, and how play is often not just random, but carefully structured. Games include points, goals, and motivations that shape our actions and decision structures. And in my earlier work, I ended up saying that play is beautiful and games are beautiful precisely because they shape us.
At the same time, I've been deeply worried about the impact of oversimplified metrics on institutions. And I ended up with a puzzle: point systems and scoring systems in games are so delightful and wonderful, and then when we encounter them in our work, they can drain the life and the joy out of what we’re doing. So that's the thing that I've been trying to figure out.
JOHN DALTON: What strikes me is just how far scoring and metrics have reached into our lives—not just games, education, and work environments, but also in sports, dating, dining, vacation planning, you name it.
C. THI NGUYEN: I was invited to an early conference on art and AI. Everyone there was a machine learning practitioner or a cognitive scientist, and they were all talking about optimizing the machines they were building to make great art. About halfway through, I raised my hand: “I'm the only philosopher in the room, this is a dumb question, but how are you operationalizing good art?”
And they said, “We're just using the data from online streaming platforms on increased engagement hours.”
I lost my mind. I was like, “Look, that is not going to create good art. You're building an addiction machine. You're not building a machine for good art.” They then challenged me to find a better large-scale database that operationalizes good art. And I thought, “There’s not going to be one, and it's not just an accident that there's not going to be one.”
A lot of what I've been thinking about is “why.” Why that gut instinct? So, the book focuses on something called value capture. Value capture is any case where an agent—and by agent, I mean an individual or a team or an institution—has rich or subtle values—or is in the process of developing rich and subtle values—and they enter a social environment that presents them with a simplified, typically quantified version of those values. What happens is that those simplified metrics dominate their practical reasoning in the domain.
JOHN DALTON: Yes, you start chasing the number and before you know it, the external measure matters more than the activity itself. You focus on the number of steps rather than how you’re feeling, how your body’s doing. But you are not against metrics. That’s not the point.
C. THI NGUYEN: No. I’m not saying that all measurements are bad and that science doesn’t work. But for those who think it’s just a matter of finding better metrics, that’s not always possible. Some areas of life are inherently resistant to metrification, especially when it comes to large-scale quantification. That tension between what’s easy to count at an institutional scale and what’s actually important is really interesting to me. There’s a trade-off between engineered communicability and domain-specific expertise.
That’s the real tension. We accept scoring systems because they’re simple and transfer easily. They produce singular verdicts. The social power of those numbers is no accident: they’ve been engineered to remove evaluative criteria that are high nuance, high expertise, or require high background knowledge. Behind those metrics is the desire for stability across fungible workers. In order to do that, you have to engineer out the kind of adaptability of expert judgment. I’m not saying that’s necessarily bad. What I am saying is that it’s easy to forget about this trade-off and assume that mechanically objective rules are the only value we should target and hit.
JOHN DALTON: So what is a manager—one who’s on the hook for hitting a basket of metrics, eager for promotion (or at least positive recognition)—to do?
C. THI NGUYEN: It’s really hard. I work at a university—believe me, I know this is really hard. One of the ways to think about this is to remember that aggregative tools have an essential baked-in blind spot. And what you should be thinking about is counterbalancing that with qualitative information from local people on the ground. Listen for any case where the local expert says that a certain metric doesn’t capture what they care about.
I’m a faculty member, in a fairly privileged position, and my life consists of measuring student success, but those metrics don’t include being more ethical or reflective. So, I made up an experiment where I let the students use generative AI to define their grading policy and assignment structure. And guess what? It went great. They understood that various policies would undercut their ability to learn, and they built really ingenious solutions that required a lot more work than you might think.
References & Disclaimers
The opinions provided are those of the author and not necessarily those of Fidelity Investments or its affiliates. Fidelity does not assume any duty to update any of the information. Fidelity and any other third parties mentioned are independent entities and not affiliated. Mentioning them does not suggest a recommendation or endorsement by Fidelity.
© 2026 FMR LLC. All Rights Reserved. 1288882.1.0
