I'm getting serious about decision theory. I have some foundational observations, upon which I'm building a mathematical framework. I have some partial drafts, and plan to write papers on it.

Most problems in decision theory are the result of insufficient rigor. "Rigor" to me means formalizing problems fully and proving things about the formalization, without use of fuzzy language. Full formalization means writing out the problem statement as two type-checkable functions, a world function which maps strategies to outcomes and a preference function which defines an ordering over outcomes. To my knowledge, none of the decision theory in the literature is done at this level of rigor. I have chosen a subset of SML/NJ, extended to include limiting expressions, as the representation for formalized problems, because it has solid mathematical foundations with which I am already familiar, and also a rigorous model of partial evaluation.

There is a pre-existing body of work on how to prove things about programs and transform and simplify them in provably correct ways in computer science: compiler theory. One of the major themes I've picked up from studying compilers is that different representations enable us to prove different things and perform different operations. In practice every compiler takes programs through a long series of intermediate representations. One of these representations, Static Single-Assignment (SSA) form, is strongly analogous to Pearl's causal models. Another representation, continuation-passing style (CPS), enables a natural and precise statement of one interpretation of causal decision theory, along with a precise description of when it does and doesn't work, such that you could prove that CDT is valid for a particular problem as a lemma and then apply CDT to it.

I haven't proved it yet, but I think I can prove within the framework I've defined that Eliezer's answer to the Prisoner's Dilemma, "cooperate iff your opponent cooperates iff you cooperate", is uncomputable, and that there is an infinite series of successively better approximations such that the Prisoner's Dilemma has no optimal answer.

• Data analysis for a really great experiment on calibration by one of my lecturers. He got about 20000 people to do a calibration test and fill in forms about all their demographic data, and now we're going to see what factors affect how well-calibrated people are. Also involved is some attempts to get them to predict political events in the year 2010 and see how ridiculously wrong they were. Currently bumbling along with SPSS to make it yield results and inventing new scoring rules. Timescale: Finish before summer vacation.

• I've started a FAQ on why everyone should be consequentialists similar to my Non-Libertarian FAQ. People say they're not consequentialists all the time, and it really annoys me, and I need a document to link them to. This has proven harder than I expected, mostly for organizational reasons. I have a feeling a lot of it is going to be gathering some of Eliezer's arguments in one place and making them explicit. Timescale: Sometime in 2011?

• About halfway through first draft of a sequence on the role of reinforcement in psychology. This may require endless revisions if it's going to be done right. Timescale: Hofstadteresque.

• Studying for my second step of the United States Medical Licensing Exam this summer.

• On the just-for-fun side, slowly learning some more ins and outs of Bryce, Sketchup, and other 3D graphics programs. My latest interest is mapping fictional cities.

• Occasional short stories when the inspiration strikes.

• And I'm finished with my Dungeons and Discourse roleplaying system, now working on a campaign for it. I might ask around for players sometime.

• Working on the tactical RPG I quit my job to work on. It's progressing reasonably well, though at the moment it looks like it will take at least 3 months longer than my original estimation. Should be finished this year, unless my savings run out and I need to find employment or another source of funding to pay for artwork.
• Slowly working through Project Euler. Was making good progress till I got a nasty cold last week, need to get back into it.
• Trying to attain a procedural knowledge of how style works, good enough that I can pick out clothing that will work well for me and know why it does. I've found it quite hard to find rules for how clothing works, nearly all writing seems to be about fashion, and comes in the form of small tidbits about individual pieces or styles of clothing.
• Coding to put my blog and a space for writing fiction back up on my website (I deleted my blog about 5 years ago, and have done nothing with my website since). Should have been finished a few weeks ago, but I find it hard to finish coding on my game for the day and then code on something else.

Several other things I want to try are currently on hold until I finish my game, as I need a reasonably steady income to pursue them.

Following up on my work on Bayesian stats tools (PyMC): I have put together MCEx, a pretty nice package for messing around with MCMC package design. It's considerably more modular than PyMC, far far smaller, has fewer features and uses Theano as its computational core (compiles to C, does graph optimizations, calculates derivatives). I have run into one major problem: I currently link Free Variables (parameters to fit) to prior distributions by passing them to a distribution function; I have been convinced(look for Anand's comments near the bottom) this approach is Not Right, but the approach PyMC takes (free variables come from a prior distribution function) seems clunky and Not Right either. I suspect understanding this better requires me to learn more foundational probability theory. Probability Theory textbook recommendations welcome.

Learning Type Theory: Long ago, I asked about how to go about learning the foundations of math (link), I think what I was really interested in is how to do math formally, so you can drill down and understand all the component steps; math without any hand waving. After some exploration, I decided math through type theory and proof assistants seems like the most attractive option. Type Theory is attractive to me because I am a programmer and Type Theory promises a unification of math and computation.

My eventual goal is to understand type theory thoroughly enough to be able to build a proof assistant like Coq. My goal is not to build one, but just understand it well enough that I could be satisfied with my understanding when I work in Coq. I would also like to eventually write up a tutorial for how to repeat this learning process.

In the last month I've picked up this study again. I've read most of Type And Programming Languages, and I am now looking to gain more intuitive understanding by learning functional programming (Standard ML and Haskell because it seems to have better libraries). I am learning Haskell by writing interpreters for the various calculi in TaPL. My notes on learning type theory are here.

Pungent is a web-based note-taking app that I'm working on. I made this because I had a need for something to organize personal notes, but nothing I found was satisfactory. Right now it's essentially a less-featured clone of Workflowy, but I plan to develop it further once I figure out what direction to go in. Development is on hold for the moment while I spend some time using it and figuring out what I want it to do.

I'm also working on a research project to try to understand how human cognition works. I think FAI is really interesting + important, but I'm baffled by the decision theory approach that seems to be popular around here. Not that I have strong reasons to believe that this line of inquiry should not be pursued, but every time I think about intelligent entities purely in terms of decision theory (i.e. as entites with a "utility function" that assigns values to "states of the world", and then takes actions which maximize said utility), I notice that I am confused.

So I'm wading through neuroscience papers at the moment. Spatial cognition and memory seem like a well-studied phenomena that are likely to act as a foundation for other cognitive abilities, and so seems like as good a place to start as any. I don't have a website up yet for my findings, but here's what I've been looking at to start:

• Place cells, grid cells, and the brain's spatial representation system - decent, recent review article for spatial cognition.
• Tracking the Emergence of Conceptual Knowledge during Human Decision Making is a recent paper with some findings that seem relevant to understanding "concepts".
• The Medial Temporal Lobe is a good review of structures in the MTL, which includes structures important for spatial cognition and memory.
• I've also been looking at Jeff Hawkins' work on Hierarchical Temporal Memory because it is not simply another neural network model, but is actually proposed to be a model of the neocortex. Even if his views on intelligence are wrong or highly incomplete, his methodology seems sound: his work is biologically-grounded, but he doesn't get caught up in unnecessary details.

My goal for this project is to become less confused about Friendly AI. I'd like to set up a webpage to record my progress on this project, so I'll likely edit this post when I have a link for that.

I am currently building and refining an evolutionary simulator program in Python. I just finished getting the bugs out of the code, and now I'm augmenting it with more features and a better user inferface. When it has come farther and I'm more proud of it, I'll post the code on the internet somewhere.

I'm working on an LW article related to TDT together with Normal Anomaly.

I'm working on an animated short. It's mostly about the singularity, SIAI, and eutopia. Some help from Normal Anomaly so far and expecting to recruit other people for more help once it gets of the storyboarding stage. Expect an article on this if it gets past the vaporware stage.

I'm also getting an education and trying to fight of mental illness, I'm not sure if those count as projects.

I have come back to this thread to confess the creation of a monster.

http://i54.tinypic.com/20jnz1g.png

Yes. That's a My Little Pony, embodying an Element of Rationality, based of the virtues of rationality. The worst part is somehting is compelling me to make 11 more of them.

... hey! Don't look at me like that! It's not any worse then Harry Potter!

Working through Smullyan's To Mock a Mockingbird.

Why - a while ago I stumbled on one guy who'd managed to get something actually practical and useful from Smullyan's book. I could sort of figure out what it was all about but deeper understanding eluded me. I knew of Smullyan's work through of Hoftstadter but hadn't read any of his books previously. I bought TMaM a while ago but, to be frank, the puzzles had scared me away, until I picked it up again recently, determined to grok the whole thing.

Results - it has led me to revisit The Cartoon Guide to Löb's Theorem, something I wasn't quite expecting to turn up. I don't fully understand it yet, or the link between combinatorial logic and that Theorem, but it's starting to make sense.

I'm trying to break down my ugh field around learning probability and statistics by working through a few elementary level textbooks. Why? Because high-level understanding of the concepts isn't enough to let me test things myself, and the work I want to do will involve lots of statistical analysis.

I'm also working on the best way to get rid of most of my possessions, because I'll be moving overseas in several months and I intend to travel light and not leave my parents house filled with my clutter.

Suggestion: date-stamp this.

Me?

• Still pottering about with music. I played more of it to my (musician) girlfriend and she correctly diagnosed all the bits where I haven't a damn clue what the next chord is. This reassured me I was on a somewhat plausible track. She also thought as highly of one particularly good phrase as I did, and pity it's one bar repeated four times and does not as yet have all the other bits a complete piece would. I still haven't picked up a guitar. No new links to put up.
• Getting fitter. I'm down to 92kg (from 105kg - I'm 194cm tall) and my stomach still looks lardy, so it's "no really, build a six pack" time. I am including this as a project because I think about it quite a lot.
• More holistic thinking about work (I'm a sysadmin; grasping the entire system in my head, even at low resolution, is a job requirement), including starting a technical debt page on the intranet wiki. People speak of technical debt, and the analogy is actually pretty robust, but I have never heard of anyone keeping a balance sheet or using the term as a tag in a bug tracker. Just writing this stuff down actually makes it seem better, not worse, as it's difficult to list it without approaches springing to mind to clean it up far more easily than one seems to expect when it's not written down. (Which cognitive bias is that?)