Identity is mostly discussed on LW in a cautionary manner: keep your identity small, be aware of the identities you are attached to. As benlandautaylor points out, identities are very powerful, and while being rightfully cautious about them, we can also cultivate them deliberately to help us achieve our goals.

Some helpful identities that I have that seem generally applicable:

• growth mindset
• low-hanging fruit picker
• truth-seeker
• jack-of-all trades (someone who is good at a variety of skills)
• someone who tries new things
• universal curiosity
• mirror (someone who learns other people's skills)

Out of the above, the most useful is probably growth mindset, since it's effectively a meta-identity that allows the other parts of my identity to be fluid. The low-hanging fruit identity helps me be on the lookout for easy optimizations. The universal curiosity identity motivates me to try to understand various systems and fields of knowledge, besides the domains I'm already familiar with. It helps to give these playful or creative names, for example, "champion of low-hanging fruit". Some of these work well together, for example the "trying new things" identity contributes to the "jack of all trades" identity.

It's also important to identify unhelpful identities that get in your way. Negative identities can be vague like "lazy person" or specific like "someone who can't finish a project". With identities, just like with habits, the easiest way to reduce or eliminate a bad one seems to be to install a new one that is incompatible with it. For example, if you have a "shy person" identity, then going to parties or starting conversations with strangers can generate counterexamples for that identity, and help to displace it with a new one of "sociable person". Costly signaling can be used to achieve this - for example, joining a public speaking club. The old identity will not necessarily go away entirely, but the competing identity will create cognitive dissonance, which it can be useful to deliberately focus on. More specific identities require more specific counterexamples. Since the original negative identity makes it difficult to perform the actions that generate counterexamples, there needs to be some form of success spiral that starts with small steps.

Some examples of unhelpful identities I've had in the past were "person who doesn't waste things" and "person with poor intuition". The aversion to wasting money and material things predictably led to wasting time and attention instead. I found it useful to try "thinking like a trader" to counteract this "stingy person" identity, and get comfortable with the idea of trading money for time. Now I no longer obsess about recycling or buy the cheapest version of everything. Underconfidence in my intuition was likely responsible for my tendency to miss the forest for the trees when studying math or statistics, where I focused on details and missed the big picture ideas that are essential to actual understanding. My main objection to intuitions was that they feel imprecise, and I am trying to develop an identity of an "intuition wizard" who can manipulate concepts from a distance without zooming in. That is a cooler name than "someone who thinks about things without really understanding them", and brings to mind some people I know who have amazing intuition for math, which should help the identity stick.

There can also be ambiguously useful identities, for example I have a "tough person" identity, which motivates me to challenge myself and expand my comfort zone, but also increases self-criticism and self-neglect. Given the mixed effects, I'm not yet sure what to do about this one - maybe I can come up with an identity that only has the positive effects.

Which identities hold you back, and which ones propel you forward? If you managed to diminish negative identities, how did you do it and how far did you get?

New meetups (or meetups with a hiatus of more than a year) are happening in:

• Auckland Preliminary Meetup: 08 March 2014 02:00PM

Irregularly scheduled Less Wrong meetups are taking place in:

• [Atlanta] March Meetup: Body Hacking!: 09 March 2014 08:07PM
• Moscow, Meet up: 09 March 2014 04:00PM
• Munich Meetup: 08 March 2014 02:00PM
• Sydney Meetup - March: 26 March 2014 06:30PM
• Tempe, AZ (ASU): 07 March 2014 06:30PM
• Urbana-Champaign: Discussion: 09 March 2014 01:00PM
• Utrecht: Effective Altruism: 07 March 2014 07:00PM

The remaining meetups take place in cities with regular scheduling, but involve a change in time or location, special meeting content, or simply a helpful reminder about the meetup:

• Austin, TX: 08 March 2025 01:30PM
• [Berlin] Community Weekend in Berlin: 11 April 2014 04:00PM
• Brussels - Calibration and other games: 08 March 2014 01:00PM
• Canberra: Meta-meetup + meditation: 28 March 2014 06:00PM
• London Games Meetup 09/03, + Socials 02/03 and 16/02 : 09 March 2014 02:00PM
• NYC Rationality Megameetup and Unconference: April 5-6: 05 April 2014 11:00AM
• Salt Lake City UT — Open Possibilities and Improv Skills: 09 March 2014 02:00PM
• Vienna Meetup : 15 March 2014 03:00PM
• Washington DC Fun and Games Meetup: 09 March 2014 02:00PM
• West LA—Expert At Vs. Expert On: 12 March 2014 06:00PM

Locations with regularly scheduled meetups: Austin, Berkeley, Berlin, Brussels, Cambridge, MA, Cambridge UK, Canberra, Columbus, London, Madison WI, Melbourne, Mountain View, New York, Philadelphia, Research Triangle NC, Salt Lake City, Seattle, Toronto, Vienna, Washington DC, Waterloo, and West Los Angeles. There's also a 24/7 online study hall for coworking LWers.

Summary: This article is intended for those who are "earning to give" (i.e. maximize income so that it can be donated to charity). It is basically an annotated bibliography of a few recent meta-analyses of predictors of income.

Key Results

• The degree to which management “sponsors” your career development is an important predictor of your salary, as is how skilled you are politically.

• Despite the stereotype of a silver-tongued salesman preying on people’s biases, rational appeals are generally the best tactic.

• After rationality, the best tactics are types of ingratiation, including flattery and acting modest.

Ng et al. performed a metastudy of over 200 individual studies of objective and subjective career success. Here are the variables they found best correlated with salary:

(all significant at p = .05)

(For reference, the “Big 5” personality traits all have a correlation under 0.12.)

Before we go on, a few caveats: while these correlations are significant and important, none are overwhelming (the authors cite Cohen as saying the range 0.24-0.36 is “medium” and correlations over 0.37 are “large”). Also, in addition to the usual correlation/causation concerns, there is lots of cross-correlation: e.g. older people might have greater political knowledge but less education, thereby confusing things. For a discussion of moderating variables, see the paper itself.

Career Sponsorship

There are two broad models of career advancement: contest-mobility and sponsorship-mobility. They are best illustrated with an example.

Suppose Peter and Penelope are both equally talented entry-level employees. Under the contest-mobility model, they would both be equally likely to get a raise or promotion, because they are equally skilled.

Sponsorship-mobility theorists argue that even if Peter and Penelope are equally talented, it’s likely that one of them will catch the eye of senior management. Perhaps it’s due to one of them having an early success by chance, making a joke in a meeting, or simply just having a more memorable name, like Penelope. This person will be singled out for additional training and job opportunities. Because of this, they’ll have greater success in the company, which will lead to more opportunities etc. As a result, their initial small discrepancy in attention gets multiplied into a large differential.

The authors of the metastudy found that self-reported sponsorship levels (i.e. how much you feel the management of your company “sponsors” you) have a significant, although moderate, relationship to salary. Therefore, the level at which you currently feel sponsored in your job should be a factor when you consider alternate opportunities.

The Dilbert Effect

The strongest predictor of salary (tied with education level) is what the authors politely term “Political Knowledge & Skills” - less politely, how good you are at manipulating others.

Several popular books (such as Cialdini’s Influence) on the subject of influencing others exist, and the study of these “influence tactics” in business stretches back 30 years to Kipnis, Schmidt and Wilkinson. Recently, Higgins et al. reviewed 23 individual studies of these tactics and how they relate to career success. Their results:

(Only ingratiation and rationality are significant.)

This site has a lot of information on how to make rational appeals, so I will focus on the less-talked-about ingratiation techniques.

How to be Ingratiating

Gordon analyzed 69 studies of ingratiation and found the following. (Unlike the previous two sections, success here is measured in lab tests as well as in career advancement. However, similar but less comprehensive results have been found in terms of career success):

Self-presentation is split further:

Moderators

One important moderator is the direction of the appeal. If you are talking to your boss, your tactics should be different than if you’re talking to a subordinate. Other-enhancement (flattery) is always the best tactic no matter who you’re talking to, but when talking to superiors it’s by far the best. When talking to those at similar levels to you, opinion conformity comes close to flattery, and the other techniques aren't far behind.

Unsurprisingly, when the target realizes you’re being ingratiating, the tactic is less effective. (Although effectiveness doesn’t go to zero - even when people realize you’re flattering them just to suck up, they generally still appreciate it.) Also, women are better at being ingratiating than men, and men are more influenced by these ingratiating tactics than women. The most important caveat is that lab studies find much larger effect sizes than in the field, to the extent that the average field effect for the ingratiating tactics is negative. This is probably due to the fact that lab experiments can be better controlled.

Conclusion

It’s unlikely that a silver-tongued receptionist will out-earn an introverted engineer. But simple techniques like flattery and attempting to get "sponsored" can appreciably improve returns, to the extent that political skills are one of the strongest predictors of salaries.

I would like to thank Brian Tomasik and Gina Stuessy for reading early drafts of this article.

References

Cohen, Jacob. Statistical power analysis for the behavioral sciences. Psychology Press, 1988.

Gordon, Randall A. "Impact of ingratiation on judgments and evaluations: A meta-analytic investigation." Journal of Personality and Social Psychology 71.1 (1996): 54.

Higgins, Chad A., Timothy A. Judge, and Gerald R. Ferris. "Influence tactics and work outcomes: a meta‐analysis." Journal of Organizational Behavior 24.1 (2003): 89-106.

Judge, Timothy A., and Robert D. Bretz Jr. "Political influence behavior and career success." Journal of Management 20.1 (1994): 43-65.

Kipnis, David, Stuart M. Schmidt, and Ian Wilkinson. "Intraorganizational influence tactics: Explorations in getting one's way." Journal of Applied psychology 65.4 (1980): 440.

Ng, Thomas WH, et al. "Predictors of objective and subjective career success: A meta‐analysis." Personnel psychology 58.2 (2005): 367-408.

Followup to: Bridge Collapse; An Intuitive Explanation of Solomonoff Induction; Reductionism

Summary: If you want to predict arbitrary computable patterns of data, Solomonoff induction is the optimal way to go about it — provided that you're an eternal transcendent hypercomputer. A real-world AGI, however, won't be immortal and unchanging. It will need to form hypotheses about its own physical state, including predictions about possible upgrades or damage to its hardware; and it will need bridge hypotheses linking its hardware states to its software states. As such, the project of building an AGI demands that we come up with a new formalism for constructing (and allocating prior probabilities to) hypotheses. It will not involve just building increasingly good computable approximations of AIXI.

Solomonoff induction has been cited repeatedly as the theoretical gold standard for predicting computable sequences of observations.¹ As Hutter, Legg, and Vitanyi (2007) put it:

Solomonoff's inductive inference system will learn to correctly predict any computable sequence with only the absolute minimum amount of data. It would thus, in some sense, be the perfect universal prediction algorithm, if only it were computable.

Perhaps you've been handed the beginning of a sequence like 1, 2, 4, 8… and you want to predict what the next number will be. Perhaps you've paused a movie, and are trying to guess what the next frame will look like. Or perhaps you've read the first half of an article on the Algerian Civil War, and you want to know how likely it is that the second half describes a decrease in GDP. Since all of the information in these scenarios can be represented as patterns of numbers, they can all be treated as rule-governed sequences like the 1, 2, 4, 8… case. Complicated sequences, but sequences all the same.

It's been argued that in all of these cases, one unique idealization predicts what comes next better than any computable method: Solomonoff induction. No matter how limited your knowledge is, or how wide the space of computable rules that could be responsible for your observations, the ideal answer is always the same: Solomonoff induction.

Solomonoff induction has only a few components. It has one free parameter, a choice of universal Turing machine. Once we specify a Turing machine, that gives us a fixed encoding for the set of all possible programs that print a sequence of 0s and 1s. Since every program has a specification, we call the number of bits in the program's specification its "complexity"; the shorter the program's code, the simpler we say it is.

Solomonoff induction takes this infinitely large bundle of programs and assigns each one a prior probability proportional to its simplicity. Every time the program requires one more bit, its prior probability goes down by a factor of 2, since there are then twice as many possible computer programs that complicated. This ensures the sum over all programs' prior probabilities equals 1, even though the number of programs is infinite.²

Followup to: Building Phenomenological Bridges

Summary: AI theorists often use models in which agents are crisply separated from their environments. This simplifying assumption can be useful, but it leads to trouble when we build machines that presuppose it. A machine that believes it can only interact with its environment in a narrow, fixed set of ways will not understand the value, or the dangers, of self-modification. By analogy with Descartes' mind/body dualism, I refer to agent/environment dualism as Cartesianism. The open problem in Friendly AI (OPFAI) I'm calling naturalized induction is the project of replacing Cartesian approaches to scientific induction with reductive, physicalistic ones.

I'll begin with a story about a storyteller.

Once upon a time — specifically, 1976 — there was an AI named TALE-SPIN. This AI told stories by inferring how characters would respond to problems from background knowledge about the characters' traits. One day, TALE-SPIN constructed a most peculiar tale.

Henry Ant was thirsty. He walked over to the river bank where his good friend Bill Bird was sitting. Henry slipped and fell in the river. Gravity drowned.

Since Henry fell in the river near his friend Bill, TALE-SPIN concluded that Bill rescued Henry. But for Henry to fall in the river, gravity must have pulled Henry. Which means gravity must have been in the river. TALE-SPIN had never been told that gravity knows how to swim; and TALE-SPIN had never been told that gravity has any friends. So gravity drowned.

TALE-SPIN had previously been programmed to understand involuntary motion in the case of characters being pulled or carried by other characters — like Bill rescuing Henry. So it was programmed to understand 'character X fell to place Y' as 'gravity moves X to Y', as though gravity were a character in the story.¹

For us, the hypothesis 'gravity drowned' has low prior probability because we know gravity isn't the type of thing that swims or breathes or makes friends. We want agents to seriously consider whether the law of gravity pulls down rocks; we don't want agents to seriously consider whether the law of gravity pulls down the law of electromagnetism. We may not want an AI to assign zero probability to 'gravity drowned', but we at least want it to neglect the possibility as Ridiculous-By-Default.

When we introduce deep type distinctions, however, we also introduce new ways our stories can fail.

The audio version of the mega-sequence "How to Actually Change Your Mind" is now available.  It's the biggest sequence we've done yet, coming in at over 8 hours of audio.  Here at Castify we think this sequence is the most important one we've done so far.

On the wiki it says, "The most important technique that Less Wrong can offer you is 'How to Actually Change Your Mind'".  I couldn't agree more.  If you haven't read it already I'd strongly recommend it.  If you have more free "ear time" than "eye time" or you learn better from listening this audio version is for you.

Feedback is welcome: support@castify.co

[Highlights for the busy: de-bunking standard "Bayes is optimal" arguments; frequentist Solomonoff induction; and a description of the online learning framework. Note: cross-posted from my blog.]

Short summary. This essay makes many points, each of which I think is worth reading, but if you are only going to understand one point I think it should be “Myth 5″ below, which describes the online learning framework as a response to the claim that frequentist methods need to make strong modeling assumptions. Among other things, online learning allows me to perform the following remarkable feat: if I’m betting on horses, and I get to place bets after watching other people bet but before seeing which horse wins the race, then I can guarantee that after a relatively small number of races, I will do almost as well overall as the best other person, even if the number of other people is very large (say, 1 billion), and their performance is correlated in complicated ways.

If you’re only going to understand two points, then also read about the frequentist version of Solomonoff induction, which is described in “Myth 6″.

Main article. I’ve already written one essay on Bayesian vs. frequentist statistics. In that essay, I argued for a balanced, pragmatic approach in which we think of the two families of methods as a collection of tools to be used as appropriate. Since I’m currently feeling contrarian, this essay will be far less balanced and will argue explicitly against Bayesian methods and in favor of frequentist methods. I hope this will be forgiven as so much other writing goes in the opposite direction of unabashedly defending Bayes. I should note that this essay is partially inspired by some of Cosma Shalizi’s blog posts, such as this one.

This essay will start by listing a series of myths, then debunk them one-by-one. My main motivation for this is that Bayesian approaches seem to be highly popularized, to the point that one may get the impression that they are the uncontroversially superior method of doing statistics. I actually think the opposite is true: I think most statisticans would for the most part defend frequentist methods, although there are also many departments that are decidedly Bayesian (e.g. many places in England, as well as some U.S. universities like Columbia). I have a lot of respect for many of the people at these universities, such as Andrew Gelman and Philip Dawid, but I worry that many of the other proponents of Bayes (most of them non-statisticians) tend to oversell Bayesian methods or undersell alternative methodologies.

If you are like me from, say, two years ago, you are firmly convinced that Bayesian methods are superior and that you have knockdown arguments in favor of this. If this is the case, then I hope this essay will give you an experience that I myself found life-altering: the experience of having a way of thinking that seemed unquestionably true slowly dissolve into just one of many imperfect models of reality. This experience helped me gain more explicit appreciation for the skill of viewing the world from many different angles, and of distinguishing between a very successful paradigm and reality.

Here is the 2013 edition of the Best of Rationality Quotes collection. (Here is last year's.)

Best of Rationality Quotes 2013 (400kB page, 350 quotes)
and Best of Rationality Quotes 2009-2013 (1600kB page, 1490 quotes)

The page was built by a short script (source code here) from all the LW Rationality Quotes threads so far. (We had such a thread each month since April 2009.) The script collects all comments with karma score 10 or more, and sorts them by score. Replies are not collected, only top-level comments.

As is now usual, I provide various statistics and top-lists based on the data. (Source code for these is also at the above link, see the README.) I added these as comments to the post:

• Top quote contributors by total karma score collected
• Top quote contributors by karma score collected in 2013
• Top quote contributors of 2013 by statistical significance level (See this comment for a description of this metric.)
• Top original authors by number of quotes
• Top original authors by total karma score collected
• Best short quotes 2009-2013

The Berlin Meetup Group is organizing the first European community meetup. We are planning a fun weekend with a focus on bringing the LessWrong community closer together. As a treat, some participants offer rationality exercises and workshops.

If you like your local meetup we hope you will like this too. It is similar, but bigger: You will get to meet and exchange ideas with a diverse set of awesome people from all across Europe. And if you don’t have a meetup nearby or didn’t get around to participating yet, this is a great opportunity to get in touch with the rest of the community.

The community weekend will take place April 11-13, from Friday evening to Sunday early afternoon, in the Odyssee Hostel in Berlin. The cost is 70 € including accommodation and breakfast. A conference room with a projector and wifi will also be available during daytime.

Thanks to everyone who took the 2013 Less Wrong Census/Survey. Extra thanks to Ozy, who helped me out with the data processing and statistics work, and to everyone who suggested questions.

This year's results are below. Some of them may make more sense in the context of the original survey questions, which can be seen here. Please do not try to take the survey as it is over and your results will not be counted.