Rationality Cardinality is a card game which takes memes and concepts from the rationality/Less Wrong sphere, and mixes them with jokes to make a game. After nearly two years of card-creation, playtesting and development, today, I'm taking the "beta" label off the web-based version of Rationality Cardinality. Go to the website and, if at least two other people visit at the same time, you can play against them.

I've put a lot of thought and a lot of work into the cards, and they're not just about humor; I also went systematically through blog posts and glossaries collecting terms and concepts that I think people should know about and be reminded of, and wrote concise explanations for them. It provides an easy way for everyone to quickly learn the jargon that's floating around, in a fun way; and it provides spaced repetition for concepts that might not otherwise have sunk in.

Rationality Cardinality will also soon have a print version. The catch is that in order to mass-produce it, I need to be sure there's enough demand. So, here's the deal: once enough people have played the online version, I'll launch a Kickstarter to sell print copies. You can speed this up by inviting people who might not otherwise see it to play.

Rationality Cardinality is somewhat inspired by Cards Against Rationality. Software for the web-based implementation is based on Cards for Humanity, with modifications.

Among my friends interested in rationality, effective altruism, and existential risk reduction, I often hear: "If you want to have a real positive impact on the world, grad school is a waste of time. It's better to use deliberate practice to learn whatever you need instead of working within the confines of an institution."

While I'd agree that grad school will not make you do good for the world, if you're a self-driven person who can spend time in a PhD program deliberately acquiring skills and connections for making a positive difference, I think you can make grad school a highly productive path, perhaps more so than many alternatives. In this post, I want to share some advice that I've been repeating a lot lately for how to do this:

1. Find a flexible program. PhD programs in mathematics, statistics, philosophy, and theoretical computer science tend to give you a great deal of free time and flexibility, provided you can pass the various qualifying exams without too much studying. By contrast, sciences like biology and chemistry can require time-consuming laboratory work that you can't always speed through by being clever.

    

2. Choose high-impact topics to learn about. AI safety and existential risk reduction are my favorite examples, but there are others, and I won't spend more time here arguing their case. If you can't make your thesis directly about such a topic, choosing a related more popular topic can give you valuable personal connections, and you can still learn whatever you want during the spare time a flexible program will afford you.

    

3. Teach classes. Grad programs that let you teach undergraduate tutorial classes provide a rare opportunity to practice engaging a non-captive audience. If you just want to work on general presentation skills, maybe you practice on your friends... but your friends already like you. If you want to learn to win over a crowd that isn't particularly interested in you, try teaching calculus! I've found this skill particularly useful when presenting AI safety research that isn't yet mainstream, which requires carefully stepping through arguments that are unfamiliar to the audience.

    

4. Use your freedom to accomplish things. I used my spare time during my PhD program to cofound CFAR, the Center for Applied Rationality. Alumni of our workshops have gone on to do such awesome things as creating the Future of Life Institute and sourcing a $10MM donation from Elon Musk to fund AI safety research. I never would have had the flexibility to volunteer for weeks at a time if I'd been working at a typical 9-to-5 or a startup.

    

5. Organize a graduate seminar. Organizing conferences is critical to getting the word out on important new research, and in fact, running a conference on AI safety in Puerto Rico is how FLI was able to bring so many researchers together on its Open Letter on AI Safety. It's also where Elon Musk made his donation. During grad school, you can get lots of practice organizing research events by running seminars for your fellow grad students. In fact, several of the organizers of the FLI conference were grad students.

    

6. Get exposure to experts. A top 10 US school will have professors around that are world-experts on myriad topics, and you can attend departmental colloquia to expose yourself to the cutting edge of research in fields you're curious about. I regularly attended cognitive science and neuroscience colloquia during my PhD in mathematics, which gave me many perspectives that I found useful working at CFAR.

    

7. Learn how productive researchers get their work done. Grad school surrounds you with researchers, and by getting exposed to how a variety of researchers do their thing, you can pick and choose from their methods and find what works best for you. For example, I learned from my advisor Bernd Sturmfels that, for me, quickly passing a draft back and forth with a coauthor can get a paper written much more quickly than agonizing about each revision before I share it.

    

8. Remember you don't have to stay in academia. If you limit yourself to only doing research that will get you good post-doc offers, you might find you aren't able to focus on what seems highest impact (because often what makes a topic high impact is that it's important and neglected, and if a topic is neglected, it might not be trendy enough land you good post-doc). But since grad school is run by professors, becoming a professor is usually the most salient path forward for most grad students, and you might end up pressuring yourself to follow that standards of that path. When I graduated, I got my top choice of post-doc, but then I decided not to take it and to instead try earning to give as an algorithmic stock trader, and now I'm a research fellow at MIRI. In retrospect, I might have done more valuable work during my PhD itself if I'd decided in advance not to do a typical post-doc.

That's all I have for now. The main sentiment behind most of this, I think, is that you have to be deliberate to get the most out of a PhD program, rather than passively expecting it to make you into anything in particular. Grad school still isn't for everyone, and far from it. But if you were seriously considering it at some point, and "do something more useful" felt like a compelling reason not to go, be sure to first consider the most useful version of grad that you could reliably make for yourself... and then decide whether or not to do it.

Please email me (lastname@thisdomain.com) if you have more ideas for getting the most out of grad school!

Summary: the problem with Pascal's Mugging arguments is that, intuitively, some probabilities are just too small to care about. There might be a principled reason for ignoring some probabilities, namely that they violate an implicit assumption behind expected utility theory. This suggests a possible approach for formally defining a "probability small enough to ignore", though there's still a bit of arbitrariness in it.

Over and over again, someones says that living for a very long time would be a bad thing, and then some futurist tries to persuade them that their reasoning is faulty.  They tell them that they think that way now, but they'll change their minds when they're older.

The thing is, I don't see that happening.  I live in a small town full of retirees, and those few I've asked about it are waiting for death peacefully.  When I ask them about their ambitions, or things they still want to accomplish, they have none.

Suppose that people mean what they say.  Why do they want to die?

As an exercise on programming Android, I've made an app to log predictions you make and keep score of your results. Like PredictionBook, but taking more of a personal daily exercise feel, in line with this post.

The "statistics" right now are only a score I copied from the old Credence calibration game, and a calibration bar chart.

I'm hoping for suggestionss for features and criticism on the app design.

Here's the link for the apk (v0.4), and here's the source code repository. You can download it at Google Play Store.

Pending/Possible/Requested Features:

• Set check-in dates for predictions
• Tags (and stats by tag)
• Stats by timeframe
• Beeminder integration
• Trivia questions you can answer if you don't have any personal prediction to make
• Ring pie chart to choose probability

Edit:

2015-08-26 - Fixed bug that broke on Android 5.0.2 (thanks Bobertron)

2015-08-28 - Change layout for landscape mode, and add a better icon

2015-08-31 -

• Daily notifications
• Buttons at the expanded-item-layout (ht dutchie)
• Show points won/lost in the snackbar when a prediction is answered
• Translation to portuguese

There is dispute about what exactly a “mental model” is and the concepts related to it are often aren't clarified well. One feature of them that is generally accepted is that “the structure of mental models ‘mirrors’ the perceived structure of the external system being modelled.” (Doyle & Ford, 1998, p. 17) So, as a starting definition we can say that mental models, in general, are representations in the mind of real or imaginary situations. A full definition won’t be attempted because there is too much contention about what features mental models do and do not have. The features that are accepted will be described in detail which will hopefully lead you to gain a intuitive understanding of what mental models are probably like.