Thanks for posting this. It inspired me to write a more general roommate coordination thread. I'm interested in the living situation you describe, but my housing situation is set until I finish my computer science degree in May. I also don't have a steady source of income right now.

When considering my prospects about where to live post-graduation, I'm torn between Silicon Valley and places that might have a higher quality/cost ratio. Can you share some of your rationale for choosing Silicon Valley over your other options? How would not having a steady source of income change your thinking about where to live?

Anyone want to live in a historic mansion in the sunny part of San Francisco? http://sfbay.craigslist.org/sfc/apa/3160458077.html

Discuss the concept of this thread here. For example, how could it be more useful? What would you do differently?

I attended the Center for Applied Rationality's June rationality camp in Berkeley, and would very much like to have a full-time living environment similar to the environment at camp. I'm very interested in joining or working to create a living environment that values open communication and epistemic hygiene, facilitates house-wide life-hacking experimentation, provides a collaborative, fulfilling environment to live and work in, and those sorts of things.

I'll finish my computer science degree in May, and I plan to make changes to my living situation at that time. I plan to apply a portion of my time over the next ten months to identifying and assessing potential living environments, and I am interested in collaborating with others throughout the process. Contact me if you think collaboration could be mutually beneficial (I would rather you err on the side of contacting me.)

I started a software development company last summer under which I have been developing a web application that assesses tasks' utility in order to suggest high-utility tasks to users. I have not publicly released the application, but I use it daily to manage my own tasks. Contingent on my startup remaining a high-utility prospect in my mind, I'd like to work on it full-time after I graduate. I am very interested in live-work arrangements (e.g. working and living on the same premises), or in living close to a coworking space or an affordable office space.

My finances are limited right now. That would change if I got a full-time software engineering job once I graduate, but I'd rather work for my startup and finance things through part-time or contract work if necessary (if you're interested in hiring me, please contact me.) I'm especially interested in collaborating with other programmers, working in Python or Go, working on data visualizations in D3, programming rationality exercises, or working on something that qualifies as "data science".

I live in Kansas, and it's alright here. I preferred the weather in Berkeley when I visited there last month. I think I would enjoy living in the San Francisco bay area, but the cost of living is high there. I'm interested in identifying affordable places to live that are competitive with the amenities of the bay area. I'm also very interested in meeting and networking with potential roommates.

In terms of resources, I have found Sperling's BestPlaces to have a lot of good information about U.S. cities.

I was considering writing some more discussion posts, but am not sure if people would find them valuable. Possible ideas:

1) In light of the relatively recent discussion of the value of history in social engineering, a summary of Politics of the Prussian Army 1640-1945 in order to have a proper case study for whether learning history is a reasonable effort improvement for those trying to raise the sanity line.

2) A post on hyperlexis - the idea that modern society has too much law.

3) Law: Real World Hidden Complexity of Wishes. This post would be useful for showing skeptics why hidden complexity of wishes is an intractable problem. Also, it might help to bring a different discipline's perspective on the problem.

4) A followup to Please Don't Fight the Hypothetical called "When to Fight the Hypothetical"

Thoughts? Suggestions?

There are many different ways we could represent a personality (to varying degrees of accuracy.) I have not found a widely-accepted format, but I think we can each make our own for now. Whenever you wonder why someone acted a certain way, think about what the relevant parameters might have been and write them down. If several people work on this and share their results, perhaps one or more standardized personality representation formats will emerge.

The parameters collected by online user profiles such as those maintained by Facebook, Google Plus, or OkCupid might provide some inspiration.

If we had a good dataset of people and their personality attributes along with some performance measures, we could use machine learning to do neat things like predict relationship compatibility between two people. Imagine a rationalist dating service that used personality data to suggest matches!

Schema.org defines a "Person" model but it focuses primarily on circumstantial attributes rather than mental state.

I like "AI Risk Reduction Institute". It's direct, informative, and gives an accurate intuition about the organization's activities. I think "AI Risk Reduction" is the most intuitive phrase I've heard so far with respect to the organization.

• "AI Safety" is too vague. If I heard it mentioned, I don't think I'd have a good intuition about what it meant. Also, it gives me a bad impression because I visualize things like parents ordering their children to fasten their seatbelts.
• "Beneficial Architectures" is too vague. It's not clear it's AI-related.
• "AI Impacts Research" is too vague and non-prescriptive. Unlike "AI Risk Reduction", it's ambiguous in its intentions.

I'm writing a forward planner to help me figure out whether to attend university for another year to finish my computer science degree, or do something else such as working for my startup full-time. I have a working prototype of the planner but still need to input most of the possible actions and their effects.

I chose this project because I think my software will do a better job assessing the utility of alternatives than my intuition, and because I implemented a forward planner for an artificial intelligence class I'm taking and wanted to apply something similar to my own life to help me plan my future.

1) Yes, the solution should be an agent program. It can't be something as simple as "return 1", because when I talk about solving the LPP, there's an implicit desire to have a single agent that solves all problems similar enough to the LPP, for example the version where the agent's actions 1 and 2 are switched, or where the agent's source code has some extra whitespace and comments compared to its own quined representation, etc.

2) We imagine the world to be a program with no arguments that returns a utility value, and the agent to be a subprogram within the world program. Even though the return value of an argumentless program is just a constant, the agent can still try to "maximize that constant", if the agent is ignorant about it in just the right way. For example, if the world program calls the agent program and then returns 0 or 1 depending on whether the agent's return value was even or odd, and the agent can prove a theorem to that effect by looking at the world program's source code, then it makes sense for the agent to return an odd value.

Newcomb's Problem can be formalized as a world program that makes two calls to the agent (or maybe one call to the agent and another call to something provably equivalent). The first call's return value is used to set the contents of the boxes, and the second one represents the agent's actual decision. If a smart enough agent receives the world's source code as an argument (which includes possibly mangled versions of the agent's source code inside), and the agent knows its own source code by quining, then the agent can prove a theorem saying that one-boxing would logically imply higher utility than two-boxing. That setting is explored in a little more detail here.

Before you ask: no, we don't know any rigorous definition of what it means to "maximize" the return value of an argumentless program in general. We're still fumbling with isolated cases, hoping to find more understanding. I'm only marginally less confused than you about the whole field.

3) You can think about an agent as a program that receives the world's source code as an argument, so that one agent can solve many possible world programs. I usually talk about agents as if they were argumentless functions that had access to the world's source code via quining, but that's just to avoid cluttering up the proofs. The results are the same either way.

4) Usually you can assume that S is just Peano arithmetic. You can represent programs by their Gödel numbers, and write a PA predicate saying "program X returns integer Y". You can also represent statements and proofs in PA by their Gödel numbers, and write a PA predicate saying "proof X is a valid proof of statement Y". You can implement both these predicates in your favorite programming language, as functions that accept two integers and return a boolean. You can have statements referring to these predicates by their Gödel numbers. The diagonal lemma gives you a generalized way to make statements refer to themselves, and quining allows you to have programs that refer to their own source code. You can have proofs that talk about programs, programs that enumerate and check proofs, and generally go wild.

For example, you can write a program P that enumerates all possible proofs trying to find a valid proof that P itself returns 1, and returns 1 if such a proof is found. To prove that P will in fact return 1 and not loop forever, note that it's just a restatement of Löb's theorem. That setting is explored in a little more detail here.

Please let me know if the above makes sense to you!