Filter This week

Less Wrong is a community blog devoted to refining the art of human rationality. Please visit our About page for more information.

Unpopular ideas attract poor advocates: Be charitable

27 mushroom 15 September 2014 07:30PM

Unfamiliar or unpopular ideas will tend to reach you via proponents who:

  •  ...hold extreme interpretations of these ideas.
  • ...have unpleasant social characteristics.
  • ...generally come across as cranks.

The basic idea: It's unpleasant to promote ideas that result in social sanction, and frustrating when your ideas are met with indifference. Both situations are more likely when talking to an ideological out-group. Given a range of positions on an in-group belief, who will decide to promote the belief to outsiders? On average, it will be those who believe the benefits of the idea are large relative to in-group opinion (extremists), those who view the social costs as small (disagreeable people), and those who are dispositionally drawn to promoting weird ideas (cranks).

I don't want to push this pattern too far. This isn't a refutation of any particular idea. There are reasonable people in the world, and some of them even express their opinions in public, (in spite of being reasonable). And sometimes the truth will be unavoidably unfamiliar and unpopular, etc. But there are also...

Some benefits that stem from recognizing these selection effects:

  • It's easier to be charitable to controversial ideas, when you recognize that you're interacting with people who are terribly suited to persuade you. I'm not sure "steelmanning" is the best idea (trying to present the best argument for an opponent's position). Based on the extremity effect, another technique is to construct a much diluted version of the belief, and then try to steelman the diluted belief.
  • If your group holds fringe or unpopular ideas, you can avoid these patterns when you want to influence outsiders.
  • If you want to learn about an afflicted issue, you might ignore the public representatives and speak to the non-evangelical instead (you'll probably have to start the conversation).
  • You can resist certain polarizing situations, in which the most visible camps hold extreme and opposing views. This situation worsens when those with non-extreme views judge the risk of participation as excessive, and leave the debate to the extremists (who are willing to take substantial risks for their beliefs). This leads to the perception that the current camps represent the only valid positions, which creates a polarizing loop. Because this is a sort of coordination failure among non-extremists, knowing to covertly look for other non-vocal moderates is a first step toward a solution. (Note: Sometimes there really aren't any moderates.)
  • Related to the previous point: You can avoid exaggerating the ideological unity of a group based on the group's leadership, or believing that the entire group has some obnoxious trait present in the leadership. (Note: In things like elections and war, the views of the leadership are what you care about. But you still don't want to be confused about other group members.)


I think the first benefit listed is the most useful.

To sum up: An unpopular idea will tend to get poor representation for social reasons, which will makes it seem like a worse idea than it really is, even granting that many unpopular ideas are unpopular for good reason. So when you encounter a idea that seem unpopular, you're probably hearing about it from a sub-optimal source, and you should try to be charitable towards the idea before dismissing it.

A proof of Löb's theorem in Haskell

21 cousin_it 19 September 2014 01:01PM

I'm not sure if this post is very on-topic for LW, but we have many folks who understand Haskell and many folks who are interested in Löb's theorem (see e.g. Eliezer's picture proof), so I thought why not post it here? If no one likes it, I can always just move it to my own blog.

A few days ago I stumbled across a post by Dan Piponi, claiming to show a Haskell implementation of something similar to Löb's theorem. Unfortunately his code had a couple flaws. It was circular and relied on Haskell's laziness, and it used an assumption that doesn't actually hold in logic (see the second comment by Ashley Yakeley there). So I started to wonder, what would it take to code up an actual proof? Wikipedia spells out the steps very nicely, so it seemed to be just a matter of programming.

Well, it turned out to be harder than I thought.

One problem is that Haskell has no type-level lambdas, which are the most obvious way (by Curry-Howard) to represent formulas with propositional variables. These are very useful for proving stuff in general, and Löb's theorem uses them to build fixpoints by the diagonal lemma.

The other problem is that Haskell is Turing complete, which means it can't really be used for proof checking, because a non-terminating program can be viewed as the proof of any sentence. Several people have told me that Agda or Idris might be better choices in this regard. Ultimately I decided to use Haskell after all, because that way the post will be understandable to a wider audience. It's easy enough to convince yourself by looking at the code that it is in fact total, and transliterate it into a total language if needed. (That way you can also use the nice type-level lambdas and fixpoints, instead of just postulating one particular fixpoint as I did in Haskell.)

But the biggest problem for me was that the Web didn't seem to have any good explanations for the thing I wanted to do! At first it seems like modal proofs and Haskell-like languages should be a match made in heaven, but in reality it's full of subtle issues that no one has written down, as far as I know. So I'd like this post to serve as a reference, an example approach that avoids all difficulties and just works.

LW user lmm has helped me a lot with understanding the issues involved, and wrote a candidate implementation in Scala. The good folks on /r/haskell were also very helpful, especially Samuel Gélineau who suggested a nice partial implementation in Agda, which I then converted into the Haskell version below.

To play with it online, you can copy the whole bunch of code, then go to CompileOnline and paste it in the edit box on the left, replacing what's already there. Then click "Compile & Execute" in the top left. If it compiles without errors, that means everything is right with the world, so you can change something and try again. (I hate people who write about programming and don't make it easy to try out their code!) Here we go:

main = return ()
-- Assumptions
data Theorem a
logic1 = undefined :: Theorem (a -> b) -> Theorem a -> Theorem b logic2 = undefined :: Theorem (a -> b) -> Theorem (b -> c) -> Theorem (a -> c) logic3 = undefined :: Theorem (a -> b -> c) -> Theorem (a -> b) -> Theorem (a -> c)
data Provable a
rule1 = undefined :: Theorem a -> Theorem (Provable a) rule2 = undefined :: Theorem (Provable a -> Provable (Provable a)) rule3 = undefined :: Theorem (Provable (a -> b) -> Provable a -> Provable b)
data P
premise = undefined :: Theorem (Provable P -> P)
data Psi
psi1 = undefined :: Theorem (Psi -> (Provable Psi -> P)) psi2 = undefined :: Theorem ((Provable Psi -> P) -> Psi)
-- Proof
step3 :: Theorem (Psi -> Provable Psi -> P) step3 = psi1
step4 :: Theorem (Provable (Psi -> Provable Psi -> P)) step4 = rule1 step3
step5 :: Theorem (Provable Psi -> Provable (Provable Psi -> P)) step5 = logic1 rule3 step4
step6 :: Theorem (Provable (Provable Psi -> P) -> Provable (Provable Psi) -> Provable P) step6 = rule3
step7 :: Theorem (Provable Psi -> Provable (Provable Psi) -> Provable P) step7 = logic2 step5 step6
step8 :: Theorem (Provable Psi -> Provable (Provable Psi)) step8 = rule2
step9 :: Theorem (Provable Psi -> Provable P) step9 = logic3 step7 step8
step10 :: Theorem (Provable Psi -> P) step10 = logic2 step9 premise
step11 :: Theorem ((Provable Psi -> P) -> Psi) step11 = psi2
step12 :: Theorem Psi step12 = logic1 step11 step10
step13 :: Theorem (Provable Psi) step13 = rule1 step12
step14 :: Theorem P step14 = logic1 step10 step13
-- All the steps squished together
lemma :: Theorem (Provable Psi -> P) lemma = logic2 (logic3 (logic2 (logic1 rule3 (rule1 psi1)) rule3) rule2) premise
theorem :: Theorem P theorem = logic1 lemma (rule1 (logic1 psi2 lemma))

To make sense of the code, you should interpret the type constructor Theorem as the symbol ⊢ from the Wikipedia proof, and Provable as the symbol ☐. All the assumptions have value "undefined" because we don't care about their computational content, only their types. The assumptions logic1..3 give just enough propositional logic for the proof to work, while rule1..3 are direct translations of the three rules from Wikipedia. The assumptions psi1 and psi2 describe the specific fixpoint used in the proof, because adding general fixpoint machinery would make the code much more complicated. The types P and Psi, of course, correspond to sentences P and Ψ, and "premise" is the premise of the whole theorem, that is, ⊢(☐P→P). The conclusion ⊢P can be seen in the type of step14.

As for the "squished" version, I guess I wrote it just to satisfy my refactoring urge. I don't recommend anyone to try reading that, except maybe to marvel at the complexity :-)

Superintelligence Reading Group - Section 1: Past Developments and Present Capabilities

21 KatjaGrace 16 September 2014 01:00AM

This is part of a weekly reading group on Nick Bostrom's book, Superintelligence. For more information about the group, see the announcement post. For the schedule of future topics, see MIRI's reading guide.

Welcome to the Superintelligence reading group. This week we discuss the first section in the reading guide, Past developments and present capabilities. This section considers the behavior of the economy over very long time scales, and the recent history of artificial intelligence (henceforth, 'AI'). These two areas are excellent background if you want to think about large economic transitions caused by AI.

This post summarizes the section, and offers a few relevant notes, thoughts, and ideas for further investigation. My own thoughts and questions for discussion are in the comments.

There is no need to proceed in order through this post. Feel free to jump straight to the discussion. Where applicable, page numbers indicate the rough part of the chapter that is most related (not necessarily that the chapter is being cited for the specific claim).

Reading: Foreword, and Growth modes through State of the art from Chapter 1 (p1-18)


Economic growth:

  1. Economic growth has become radically faster over the course of human history. (p1-2)
  2. This growth has been uneven rather than continuous, perhaps corresponding to the farming and industrial revolutions. (p1-2)
  3. Thus history suggests large changes in the growth rate of the economy are plausible. (p2)
  4. This makes it more plausible that human-level AI will arrive and produce unprecedented levels of economic productivity.
  5. Predictions of much faster growth rates might also suggest the arrival of machine intelligence, because it is hard to imagine humans - slow as they are - sustaining such a rapidly growing economy. (p2-3)
  6. Thus economic history suggests that rapid growth caused by AI is more plausible than you might otherwise think.

The history of AI:

  1. Human-level AI has been predicted since the 1940s. (p3-4)
  2. Early predictions were often optimistic about when human-level AI would come, but rarely considered whether it would pose a risk. (p4-5)
  3. AI research has been through several cycles of relative popularity and unpopularity. (p5-11)
  4. By around the 1990s, 'Good Old-Fashioned Artificial Intelligence' (GOFAI) techniques based on symbol manipulation gave way to new methods such as artificial neural networks and genetic algorithms. These are widely considered more promising, in part because they are less brittle and can learn from experience more usefully. Researchers have also lately developed a better understanding of the underlying mathematical relationships between various modern approaches. (p5-11)
  5. AI is very good at playing board games. (12-13)
  6. AI is used in many applications today (e.g. hearing aids, route-finders, recommender systems, medical decision support systems, machine translation, face recognition, scheduling, the financial market). (p14-16)
  7. In general, tasks we thought were intellectually demanding (e.g. board games) have turned out to be easy to do with AI, while tasks which seem easy to us (e.g. identifying objects) have turned out to be hard. (p14)
  8. An 'optimality notion' is the combination of a rule for learning, and a rule for making decisions. Bostrom describes one of these: a kind of ideal Bayesian agent. This is impossible to actually make, but provides a useful measure for judging imperfect agents against. (p10-11)

Notes on a few things

  1. What is 'superintelligence'? (p22 spoiler)
    In case you are too curious about what the topic of this book is to wait until week 3, a 'superintelligence' will soon be described as 'any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest'. Vagueness in this definition will be cleared up later. 
  2. What is 'AI'?
    In particular, how does 'AI' differ from other computer software? The line is blurry, but basically AI research seeks to replicate the useful 'cognitive' functions of human brains ('cognitive' is perhaps unclear, but for instance it doesn't have to be squishy or prevent your head from imploding). Sometimes AI research tries to copy the methods used by human brains. Other times it tries to carry out the same broad functions as a human brain, perhaps better than a human brain. Russell and Norvig (p2) divide prevailing definitions of AI into four categories: 'thinking humanly', 'thinking rationally', 'acting humanly' and 'acting rationally'. For our purposes however, the distinction is probably not too important.
  3. What is 'human-level' AI? 
    We are going to talk about 'human-level' AI a lot, so it would be good to be clear on what that is. Unfortunately the term is used in various ways, and often ambiguously. So we probably can't be that clear on it, but let us at least be clear on how the term is unclear. 

    One big ambiguity is whether you are talking about a machine that can carry out tasks as well as a human at any price, or a machine that can carry out tasks as well as a human at the price of a human. These are quite different, especially in their immediate social implications.

    Other ambiguities arise in how 'levels' are measured. If AI systems were to replace almost all humans in the economy, but only because they are so much cheaper - though they often do a lower quality job - are they human level? What exactly does the AI need to be human-level at? Anything you can be paid for? Anything a human is good for? Just mental tasks? Even mental tasks like daydreaming? Which or how many humans does the AI need to be the same level as? Note that in a sense most humans have been replaced in their jobs before (almost everyone used to work in farming), so if you use that metric for human-level AI, it was reached long ago, and perhaps farm machinery is human-level AI. This is probably not what we want to point at.

    Another thing to be aware of is the diversity of mental skills. If by 'human-level' we mean a machine that is at least as good as a human at each of these skills, then in practice the first 'human-level' machine will be much better than a human on many of those skills. It may not seem 'human-level' so much as 'very super-human'.

    We could instead think of human-level as closer to 'competitive with a human' - where the machine has some super-human talents and lacks some skills humans have. This is not usually used, I think because it is hard to define in a meaningful way. There are already machines for which a company is willing to pay more than a human: in this sense a microscope might be 'super-human'. There is no reason for a machine which is equal in value to a human to have the traits we are interested in talking about here, such as agency, superior cognitive abilities or the tendency to drive humans out of work and shape the future. Thus we talk about AI which is at least as good as a human, but you should beware that the predictions made about such an entity may apply before the entity is technically 'human-level'.

    Example of how the first 'human-level' AI may surpass humans in many ways.

    Because of these ambiguities, AI researchers are sometimes hesitant to use the term. e.g. in these interviews.
  4. Growth modes (p1) 
    Robin Hanson wrote the seminal paper on this issue. Here's a figure from it, showing the step changes in growth rates. Note that both axes are logarithmic. Note also that the changes between modes don't happen overnight. According to Robin's model, we are still transitioning into the industrial era (p10 in his paper).
  5. What causes these transitions between growth modes? (p1-2)
    One might be happier making predictions about future growth mode changes if one had a unifying explanation for the previous changes. As far as I know, we have no good idea of what was so special about those two periods. There are many suggested causes of the industrial revolution, but nothing uncontroversially stands out as 'twice in history' level of special. You might think the small number of datapoints would make this puzzle too hard. Remember however that there are quite a lot of negative datapoints - you need an explanation that didn't happen at all of the other times in history. 
  6. Growth of growth
    It is also interesting to compare world economic growth to the total size of the world economy. For the last few thousand years, the economy seems to have grown faster more or less in proportion to it's size (see figure below). Extrapolating such a trend would lead to an infinite economy in finite time. In fact for the thousand years until 1950 such extrapolation would place an infinite economy in the late 20th Century! The time since 1950 has been strange apparently. 

    (Figure from here)
  7. Early AI programs mentioned in the book (p5-6)
    You can see them in action: SHRDLU, Shakey, General Problem Solver (not quite in action), ELIZA.
  8. Later AI programs mentioned in the book (p6)
    Algorithmically generated Beethoven, algorithmic generation of patentable inventionsartificial comedy (requires download).
  9. Modern AI algorithms mentioned (p7-8, 14-15) 
    Here is a neural network doing image recognition. Here is artificial evolution of jumping and of toy cars. Here is a face detection demo that can tell you your attractiveness (apparently not reliably), happiness, age, gender, and which celebrity it mistakes you for.
  10. What is maximum likelihood estimation? (p9)
    Bostrom points out that many types of artificial neural network can be viewed as classifiers that perform 'maximum likelihood estimation'. If you haven't come across this term before, the idea is to find the situation that would make your observations most probable. For instance, suppose a person writes to you and tells you that you have won a car. The situation that would have made this scenario most probable is the one where you have won a car, since in that case you are almost guaranteed to be told about it. Note that this doesn't imply that you should think you won a car, if someone tells you that. Being the target of a spam email might only give you a low probability of being told that you have won a car (a spam email may instead advise you of products, or tell you that you have won a boat), but spam emails are so much more common than actually winning cars that most of the time if you get such an email, you will not have won a car. If you would like a better intuition for maximum likelihood estimation, Wolfram Alpha has several demonstrations (requires free download).
  11. What are hill climbing algorithms like? (p9)
    The second large class of algorithms Bostrom mentions are hill climbing algorithms. The idea here is fairly straightforward, but if you would like a better basic intuition for what hill climbing looks like, Wolfram Alpha has a demonstration to play with (requires free download).

In-depth investigations

If you are particularly interested in these topics, and want to do further research, these are a few plausible directions:

  1. How have investments into AI changed over time? Here's a start, estimating the size of the field.
  2. What does progress in AI look like in more detail? What can we infer from it? I wrote about algorithmic improvement curves before. If you are interested in plausible next steps here, ask me.
  3. What do economic models tell us about the consequences of human-level AI? Here is some such thinking; Eliezer Yudkowsky has written at length about his request for more.

How to proceed

This has been a collection of notes on the chapter. The most important part of the reading group though is discussion, which is in the comments section. I pose some questions for you there, and I invite you to add your own. Please remember that this group contains a variety of levels of expertise: if a line of discussion seems too basic or too incomprehensible, look around for one that suits you better!

Next week, we will talk about what AI researchers think about human-level AI: when it will arrive, what it will be like, and what the consequences will be. To prepare, read Opinions about the future of machine intelligence from Chapter 1 and also When Will AI Be Created? by Luke Muehlhauser. The discussion will go live at 6pm Pacific time next Monday 22 September. Sign up to be notified here.

A Guide to Rational Investing

19 ColbyDavis 15 September 2014 02:36AM

Hello Less Wrong, I don't post here much but I've been involved in the Bay Area Less Wrong community for several years, where many of you know me from. The following is a white paper I wrote earlier this year for my firm, RHS Financial, a San Francisco based private wealth management practice. A few months ago I presented it at a South Bay Less Wrong meetup. Since then many of you have encouraged me to post it here for the rest of the community to see. The original can be found here, please refer to the disclosures, especially if you are the SEC. I have added an afterword here beneath the citations to address some criticisms I have encountered since writing it. As a company white paper intended for a general audience, please forgive me if the following is a little too self-promoting or spends too much time on grounds already well-tread here, but I think many of you will find it of value. Hope you enjoy!



Executive Summary: Capital markets have created enormous amounts of wealth for the world and reward disciplined, long-term investors for their contribution to the productive capacity of the economy. Most individuals would do well to invest most of their wealth in the capital market assets, particularly equities. Most investors, however, consistently make poor investment decisions as a result of a poor theoretical understanding of financial markets as well as cognitive and emotional biases, leading to inferior investment returns and inefficient allocation of capital. Using an empirically rigorous approach, a rational investor may reasonably expect to exploit inefficiencies in the market and earn excess returns in so doing.





Most people understand that they need to save money for their future, and surveys consistently find a large majority of Americans expressing a desire to save and invest more than they currently are. Yet the savings rate and percentage of people who report owning stocks has trended down in recent years,1 despite the increasing ease with which individuals can participate in financial markets, thanks to the spread of discount brokers and employer 401(k) plans. Part of the reason for this is likely the unrealistically pessimistic expectations of would-be investors. According to a recent poll barely one third of Americans consider equities to be a good way to build wealth over time.2 The verdict of history, however, is against the skeptics.

The Greatest Deal of all Time

Equity ownership is probably the easiest, most powerful means of accumulating wealth over time, and people regularly forego millions of dollars over the course of their lifetimes letting their wealth sit in cash. Since its inception in 1926, the annualized total return on the S&P 500 has been 9.8% as of the end of 2012.3 $1 invested back then would be worth $3,533 by the end of the period. More saliently, a 25 year old investor investing $5,000 per year at that rate would have about $2.1 million upon retirement at 65.

The strong performance of stock markets is robust to different times and places. Though the most accurate data on the US stock market goes back to 1926, financial historians have gathered information going back to 1802 and find the average annualized real return in earlier periods is remarkably close to the more recent official records. Looking at rolling 30 year returns between 1802 and 2006, the lowest and highest annualized real returns have been 2.6% and 10.6%, respectively.4 The United States is not unique in its experience, either. In a massive study of the sixteen countries that had data on local stock, bond, and cash returns available for every year of the twentieth century, the stock market in every one had significant, positive real returns that exceeded those of cash and fixed income alternatives.5 The historical returns of US stocks only slightly exceed those of the global average.

The opportunity cost of not holding stocks is enormous. Historically the interest earned on cash equivalent investments like savings accounts has barely kept up with inflation - over the same since-1926 period inflation has averaged 3.0% while the return on 30-day treasury bills (a good proxy for bank savings rates) has been 3.5%.6 That 3.5% rate would only earn an investor $422k over the same $5k/year scenario above. The situation today is even worse. Most banks are currently paying about 0.05% on savings.

Similarly, investment grade bonds, such as those issued by the US Treasury and highly rated corporations, though often an important component of a diversified portfolio, have offered returns only modestly better than cash over the long run. The average return on 10-year treasury bonds has been 5.1%,7 earning an investor $619k over the same 40 year scenario. The yield on the 10-year treasury is currently about 3%.

Homeownership has long been a part of the American dream, and many have been taught that building equity in your home is the safest and most prudent way to save for the future. The fact of the matter, however, is that residential housing is more of a consumption good than an investment. Over the last century the value of houses have barely kept up with inflation,8 and as the recent mortgage crisis demonstrated, home prices can crash just as any other market.

In virtually every time and place we look, equities are the best performing asset available, a fact which is consistent with the economic theory that risky assets must offer a premium to their investors to compensate them for the additional uncertainty they bear. What has puzzled economists for decades is why the so-called equity risk premium is so large and why so many individuals invest so little in stocks.9

Your Own Worst Enemy

Recent insights from multidisciplinary approaches in cognitive science have shed light on the issue, demonstrating that instead of rationally optimizing between various trade-offs, human beings regularly rely on heuristics - mental shortcuts that require little cognitive effort - when making decisions.10 These heuristics lead to taking biased approaches to problems that deviate from optimal decision making in systematic and predictable ways. Such biases affect financial decisions in a large number of ways, one of the most profound and pervasive being the tendency of myopic loss aversion.

Myopic loss aversion refers to the combined result of two observed regularities in the way people think: that losses feel bad to a greater extent than equivalent gains feel good, and that people rely too heavily (anchor) on recent and readily available information. 11Taken together, it is easy to see how these mental errors could bias an individual against holding stocks. Though the historical and expected return on equities greatly exceeds those of bonds and cash, over short time horizons they can suffer significant losses. And while the loss of one’s home equity is generally a nebulous abstraction that may not manifest itself consciously for years, stock market losses are highly visible, drawing attention to themselves in brokerage statements and newspaper headlines. Not surprisingly, then, an all too common pattern among investors is to start investing at a time when the headlines are replete with stories of the riches being made in markets, only to suffer a pullback and quickly sell out at ten, twenty, thirty plus percent losses and sit on cash for years until the next bull market is again near its peak in a vicious circle of capital destruction. Indeed, in the 20 year period ending 2012, the S&P 500 returned 8.2% and investment grade bonds returned 6.3% annualized. The inflation rate was 2.5%, and the average retail investor earned an annualized rate of 2.3%.12

Even when investors can overcome their myopic loss aversion and stay in the stock market for the long haul, investment success is far from assured. The methods by which investors choose which stocks or stock managers to buy, hold, and sell are also subject to a host of biases which consistently lead to suboptimal investing and performance. Chief among these is overconfidence, the belief that one’s judgements and skills are reliably superior.

Overconfidence is endemic to the human experience. The vast majority of people think of themselves as more intelligent, attractive, and competent than most of their peers,13 even in the face of proof to the contrary. 93% of people consider themselves to be above-average drivers,14 for example, and that percentage decreases only slightly if you ask people to evaluate their driving skill after being admitted to a hospital following a traffic accident.15 Similarly, most investors are confident they can consistently beat the market. One survey found 74% of mutual fund investors believed the funds they held would “consistently beat the S&P 500 every year” in spite of the statistical reality that more than half of US stock funds underperform in a given year and virtually none will outperform it each and every year. Many investors will even report having beaten the index despite having verifiably underperformed it by several percentage points.16

Overconfidence leads investors to take outsized bets on what they know and are familiar with. Investors around the world commonly hold 80% or more of their portfolios in investments from their own country,17 and one third of 401(k) assets are invested in participants’ own employer’s stock.18 Such concentrated portfolios are demonstrably riskier than a broadly diversified portfolio, yet investors regularly evaluate their investments as less risky than the general market, even if their securities had recently lost significantly more than the overall market.

If an investor believes himself to possess superior talent in selecting investments, he is likely to trade more as a result in an attempt to capitalize on each new opportunity that presents itself. In this endeavor, the harder investors try, the worse they do. In one major study, the quintile of investors who traded the most over a five year period earned an average annualized 7.1 percentage points less than the quintile that traded the least.19

The Folly of Wall Street

Relying on experts does little to help. Wall Street employs an army of analysts to follow the every move of all the major companies traded on the market, predicting their earnings and their expected performance relative to peers, but on the whole they are about as effective as a strategy of throwing darts. Burton Malkiel explains in his book A Random Walk Down Wall Street how he tracked the one and five year earnings forecasts on companies in the S&P 500 from analysts at 19 Wall Street firms and found that in aggregate the estimates had no more predictive power than if you had just assumed a given company’s earnings would grow at the same rate as the long-term average rate of growth in the economy. This is consistent with a much broader body of literature demonstrating that the predictions of statistical prediction rules - formulas that make predictions based on simple statistical rules - reliably outperform those of human experts. Statistical prediction rules have been used to predict the auction price of bordeaux better than expert wine tasters,20 marital happiness better than marriage counselors,21 academic performance better than admissions officers,22 criminal recidivism better than criminologists,23 and bankruptcy better than loan officers,24 to name just a few examples. This is an incredible finding that’s difficult to overstate. When considering complex issues such as these our natural intuition is to trust experts who can carefully weigh all the relevant information in determining the best course of action. But in reality experts are simply humans who have had more time to reinforce their preconceived notions on a particular topic and are more likely to anchor their attention on items that only introduce statistical noise.

Back in the world of finance, It turns out that to a first approximation the best estimate on the return to expect from a given stock is the long-run historical average of the stock market, and the best estimate of the return to expect from a stock picking mutual fund is the long-run historical average of the stock market minus its fees. The active stock pickers who manage mutual funds have on the whole demonstrated little ability to outperform the market. To be sure, at any given time there are plenty of managers who have recently beaten the market smartly, and if you look around you will even find a few with records that have been terrific over ten years or more. But just as a coin-flipping contest between thousands of contestants would no doubt yield a few who had uncannily “called it” a dozen or more times in a row, the number of market beating mutual fund managers is no greater than what you should expect as a result of pure luck.25

Expert and amatuer investors alike underestimate how competitive the capital markets are. News is readily available and quickly acted upon, and any fact you know about that you think gives you an edge is probably already a value in the cells of thousands of spreadsheets of analysts trading billions of dollars. Professor of Finance at Yale and Nobel Laureate Robert Shiller makes this point in a lecture using an example of a hypothetical drug company that announces it has received FDA approval to market a new drug:

Suppose you then, the next day, read in The Wall Street Journal about this new announcement. Do you think you have any chance of beating the market by trading on it? I mean, you're like twenty-four hours late, but I hear people tell me — I hear, "I read in Business Week that there was a new announcement, so I'm thinking of buying." I say, "Well, Business Week — that information is probably a week old." Even other people will talk about trading on information that's years old, so you kind of think that maybe these people are naïve. First of all, you're not a drug company expert or whatever it is that's needed. Secondly, you don't know the math — you don't know how to calculate present values, probably. Thirdly, you're a month late. You get the impression that a lot of people shouldn't be trying to beat the market. You might say, to a first approximation, the market has it all right so don't even try.26

In that last sentence Shiller hints at one of the most profound and powerful ideas in finance: the efficient market hypothesis. The core of the efficient market hypothesis is that when news that impacts the value of a company is released, stock prices will adjust instantly to account for the new information and bring it back to equilibrium where it’s no longer a “good” or “bad” investment but simply a fair one for its risk level. Because news is unpredictable by definition, it is impossible then to reliably outperform the market as a whole, and the seemingly ingenious investors on the latest cover of Forbes or Fortune are simply lucky.

A Noble Lie

In the 50s, 60s, and 70s several economists who would go on to win Nobel prizes worked out the implications of the efficient market hypothesis and created a new intellectual framework known as modern portfolio theory.27 The upshot is that capital markets reward investors for taking risk, and the more risk you take, the higher your return should be (in expectation, it might not turn out to be the case, which is why it’s risky). But the market doesn’t reward unnecessary risk, such as taking out a second mortgage to invest in your friend’s hot dog stand. It only rewards systematic risk, the risks associated with being exposed to the vagaries of the entire economy, such as interest rates, inflation, and productivity growth.28 Stock of small companies are riskier and have a higher expected return than stocks of large companies, which are riskier than corporate bonds, which are riskier than Treasury bonds. But owning one small cap stock doesn’t offer a higher expected return than another small cap stock, or a portfolio of hundreds of small caps for that matter. Owning more of a particular stock merely exposes you to the idiosyncratic risks that particular company faces and for which you are not compensated. Diversifying assets across as many securities as possible, it is possible to reduce the volatility of your portfolio without lowering its expected return.

This approach to investing dictates that you should determine an acceptable level of risk for your portfolio, then buy the largest basket of securities possible that targets that risk, ideally while paying the least amount possible in fees. Academic activism in favor of this passive approach gained momentum through the 70s, culminating in the launch of the first commercially available index fund in 1976, offered by The Vanguard Group. The typical index fund seeks to replicate the overall market performance of a broad class of investments such as large US stocks by owning all the securities in that market in proportion to their market weights. Thus if XYZ stock makes up 2% of the value of the relevant asset class, the index fund will allocate 2% of its funds to that stock. Because index funds only seek to replicate the market instead of beating it, they save costs on research and management teams and pass the savings along to investors through lower fees.

Index funds were originally derided and attracted little investment, but years of passionate advocacy by popularizers such as Jack Bogle and Burton Malkiel as well as the consensus of the economics profession has helped to lift them into the mainstream. Index funds now command trillions of dollars of assets and cover every segment of the market in stocks, bonds, and alternative assets in the US and abroad. In 2003 Vanguard launched its target retirement funds, which took the logic of passive investing even further by providing a single fund that would automatically shift from more aggressive to more conservative index investments as its investors approached retirement. Target retirement funds have since become especially popular options in 401(k) plans.

The rise of index investing has been a boon to individual investors, who have clearly benefited from the lower fees and greater diversification they offer. To the extent that investors have bought into the idea of passive investing over market timing and active security selection they have collectively saved themselves a fortune by not giving in to their value-destroying biases. For all the good index funds have done though, since their birth in the 70s, the intellectual foundation upon which they stand, the efficient market hypothesis, has been all but disproved.

The EMH is now the noble lie of the economics profession; while economists usually teach their students and the public that the capital markets are efficient and unbeatable, their research over the last few decades has shown otherwise. In a telling example, Paul Samuelson, who helped originate the EMH and advocated it in his best selling textbook, was a large, early investor in Berkshire Hathaway, Warren Buffett’s active investment holding company.29 But real people regularly ruin their lives through sloppy investing, and for them perhaps it is better just to say that beating the market can’t be done, so just buy, hold, and forget about it. We, on the other hand, believe a more nuanced understanding of the facts can be helpful.

Premium Investing

Shortly after the efficient market hypothesis was first put forth researchers realized the idea had serious theoretical shortcomings.30 Beginning as early as 1977 they also found empirical “anomalies,” factors other than systematic risk that seemed to predict returns.31 Most of the early findings focused on valuation ratios - measures of a firm’s market price in relation to an accounting measure such as book value or earnings - and found that “cheap” stocks on average outperformed “expensive” stocks, confirming the value investment philosophy first promulgated by the legendary Depression-era investor Benjamin Graham and popularized by his most famous student, Warren Buffett. In 1992 Eugene Fama, one of the fathers of the efficient market hypothesis, published, along with Ken French, a groundbreaking paper demonstrating that the cheapest decile stocks in the US, as measured by the price to book ratio, outperformed the highest decile stocks by an astounding 11.9% per year, despite there being little difference in risk between them.32

A year later, researchers found convincing evidence of a momentum anomaly in US stocks: stocks that had the highest performance over the last 3-12 months continued to outperform relative to those with the lowest performance. The effect size was comparable to that of the value anomaly and again the discrepancy could not be explained with any conventional measure of risk.33

Since then, researchers have replicated the value and momentum effects across larger and deeper datasets, finding comparably large effect sizes in different times, regions, and asset classes. In a highly ambitious 2012 paper, Clifford Asness (a former student of Fama’s) and Tobias Moskowitz documented the significance of value and momentum across 18 national equity markets, 10 currencies, 10 government bonds, and 27 commodity futures.

Though value and momentum are the most pervasive and best documented of the market anomalies, many others have been discovered across the capital markets. Others include the small-cap premium34 (small company stocks tend to outperform large company stocks even in excess of what should be expected by their risk), the liquidity premium35 (less frequently traded securities tend to outperform more frequently traded securities), short-term reversal36 (equities with the lowest one-week to one-month performance tend to outperform over short time horizons), carry37 (high-yielding currencies tend to appreciate against low-yielding currencies), roll yield38,39 (bonds and futures at steeply negatively sloped points along the yield curve tend to outperform those at flatter or positively sloped points), profitability40 (equities of firms with higher proportions of profits over assets or equity tend to outperform those with lower profitability), calendar effects41 (stocks tend to have stronger returns in January and weaker returns on Mondays), and corporate action premia42 (securities of corporations that will, currently are, or have recently engaged in mergers, acquisitions, spin-offs, and other events tend to consistently under or outperform relative to what would be expected by their risk).

Most of these market anomalies appear remarkably robust compared to findings in other social sciences,43 especially considering that they seem to imply trillions of dollars of easy money is being overlooked in plain sight. Intelligent observers often question how such inefficiencies could possibly persist in the face of such strong incentives to exploit them until they disappear. Several explanations have been put forth, some of which are conflicting but which all probably have some explanatory power.

The first interpretation of the anomalies is to deny that they are actually anomalous, but rather are compensation for risk that isn’t captured by the standard asset pricing models. This is the view of Eugene Fama, who first postulated that the value premium was compensation for assuming risk of financial distress and bankruptcy that was not fully captured by simply measuring the standard deviation of a value stock’s returns.44 Subsequent research, however, disproved that the value effect was explained by exposure to financial distress.45 More sophisticated arguments point to the fact that the excess returns of value, momentum, and many other premiums exhibit greater skewness, kurtosis, or other statistical moments than the broad market: subtle statistical indications of greater risk, but the differences hardly seem large enough to justify the large return premiums observed.46

The only sense in which e.g. value and momentum stocks seem genuinely “riskier” is in career risk; though the factor premiums are significant and robust in the long term, they are not consistent or predictable along short time horizons. Reaping their rewards requires patience, and an analyst or portfolio manager who recommends an investment for his clients based on these factors may end up waiting years before it pays off, typically more than enough time to be fired.47 Though any investment strategy is bound to underperform at times, strategies that seek to exploit the factors most predictive of excess returns are especially susceptible to reputational hazard. Value stocks tend to be from unpopular companies in boring, slow growth industries. Momentum stocks are often from unproven companies with uncertain prospects or are from fallen angels who have only recently experienced a turn of luck. Conversely, stocks that score low on value and momentum factors are typically reputable companies with popular products that are growing rapidly and forging new industry standards in their wake.

Consider then, two companies in the same industry: Ol’Timer Industries, which has been around for decades and is consistently profitable but whose product lines are increasingly considered uncool and outdated. Recent attempts to revamp the company’s image by the firm’s new CEO have had modest success but consumers and industry experts expect this to be just delaying further inevitable loss of market share to, founded eight years ago and posting exponential revenue growth and rapid adoption by the coveted 18-35 year old demographic, who typically describe its products using a wide selection of contemporary idioms and slang indicating superior social status and functionality. Ol’Timer Industries’ stock will likely score highly on value on momentum factors relative to and so have a higher expected return. But consider the incentives of the investment professional choosing between the two: if he chooses Ol’Timer and it outperforms he may be congratulated and rewarded perhaps slightly more than if he had chosen and it outperforms, but if he chooses Ol’Timer and it underperforms he is a fool and a laughingstock who wasted clients’ money on his pet theory when “everyone knew” was going to win. At least if he chooses and it underperforms it was a fluke that none of his peers saw coming, save for a few wingnuts who keep yammering about the arcane theories of Gene Fama and Benjamin Graham.

For most investors, “it is better for reputation to fail conventionally than to succeed unconventionally” as John Maynard Keynes observed in his General Theory. Not that this is at all restricted to investors, professional or amateur. In a similar vein, professional soccer goalkeepers continue to jump left or right on penalty kicks when statistics show they’d block more shots standing still.48 But standing in place while the ball soars into the upper right corner makes the goalkeeper look incompetent. The proclivity of middle managers and bureaucrats to default to uncontroversial decisions formed by groupthink is familiar enough to be the stuff of popular culture; nobody ever got fired for buying IBM, as the saying goes. Psychological experiments have shown that people will often affirm an obviously false observation about simple facts such as the relative lengths of straight lines on a board if others have affirmed it before them.49

We find ourselves back to the nature of human thinking and the biases and other cognitive errors that afflict it. This is what most interpretations of the market anomalies focuses on. Both amatuer and professional investors are human beings that are apt to make investment decisions not through a methodical application of modern portfolio theory but based rather on stories, anecdotes, hunches, and ideologies. Most of the anomalies make sense in light of an understanding of some of the most common biases such as anchoring and availability bias, status quo bias, and herd behavior.50 Rational investors seeking to exploit these inefficiencies may be able to do so to a limited extent, but if they are using other peoples’ money then they are constrained by the biases of their clients. The more aggressively they attempt to exploit market inefficiencies, the more they risk badly underperforming the market long enough to suffer devastating withdrawals of capital.51

It is no surprise then, that the most successful investors have found ways to rely on “sticky” capital unlikely to slip out of their control at the worst time. Warren Buffett invests the float of his insurance company holdings, which behaves in actuarially predictable ways; David Swensen manages the Yale endowment fund, which has an explicitly indefinite time horizon and a rules based spending rate; Renaissance Technologies, arguably the most successful hedge fund ever, only invests its own money; Dimensional Fund Advisors, one of the only mutual fund companies that has consistently earned excess returns through factor premiums, only sells through independent financial advisors who undergo a due diligence process to ensure they share similar investment philosophies.

Building a Better Portfolio

So what is an investor to do? The prospect of delicately crafting a portfolio that’s adequately diversified while taking advantage of return premiums may seem daunting, and one may be tempted to simply buy a Vanguard target retirement fund appropriate for their age and be done with it. Doing so is certainly a reasonable option. But we believe that with a disciplined investment strategy informed by the findings discussed above superior results are possible.

The first place to start is an assessment of your risk tolerance. How far can your portfolio fall before it adversely affects your quality of life? For investors saving for retirement with many more years of work ahead of them, the answer will likely be “quite a lot.” With ten years or more to work with, your portfolio will likely recover from even the most extreme bear markets. But people do not naturally think in ten-year increments, and many must live off their portfolio principal; accept that in the short term your portfolio will sometimes be in the red and consider what percentage decline over a period of a few months to a year you are comfortable enduring. Over a one year period the “worst case scenario” on diversified stock portfolios is historically about a 40% decline. For a traditional “moderate” portfolio of 60% stocks, 40% bonds it has been about a 25% decline.52

With a target on how much risk to accept in your portfolio, modern portfolio theory shows us a technique for achieving the most efficient tradeoff between risk and return possible called mean-variance optimization. An adequate treatment of MVO is beyond the scope of this paper,53 but essentially the task is to forecast expected returns on the major asset classes (e.g. US Stocks, International Stocks, and Investment Grade Bonds) then compute the weights for each that will achieve the highest expected return for a given amount of risk. We use an approach to mean variance optimization known as the Black-Litterman model54 and estimate expected returns using a limited number of simple inputs; for example, the expected return on an index of stocks can be closely approximated using the current dividend yield plus the long run growth rate of the economy.55

With optimal portfolio weights determined, next the investor must select the investment vehicles to use to gain exposure to the various asset classes. Though traditional index funds are a reasonable option, in recent years several “enhanced index” mutual fund and ETFs have been released that provide inexpensive, broad exposure to the hundreds or thousands of securities in a given asset classes while enhancing exposure to one or more of the major factor premiums discussed above such as value, profitability, or momentum. Research Affiliates, for example, licences a “fundamental index” that has been shown to provide efficient exposure to value and small-cap stocks across many markets.56 These “RAFI” indexes have been licensed to the asset management firms Charles Schwab and PowerShares to be made available through mutual funds and ETFs to the general investing public, and have generally outperformed their traditional index fund counterparts since inception.

Over the course of time, portfolio allocations will drift from their optimized allocations as particular asset classes inevitably outperform relative to other ones. Leaving this unchecked can lead to a portfolio that is no longer risk-return efficient. The investor must periodically rebalance the portfolio by selling securities that have become overweight and buying others that are underweight. Research suggests that by setting “tolerance bands” around target asset allocations, monitoring the portfolio frequently and trading when weights drift outside tolerance, investors can take further advantage of inter-asset-class value and momentum effects and boost return while reducing risk.57

Most investors, however, do not rebalance systematically, perhaps in part because it can be psychologically distressing. Rebalancing necessarily entails regularly selling assets that have been performing well in order to buy ones that have been laggards, exactly when your cognitive biases are most likely to tell you that it’s a bad idea. Indeed, neuroscientists have observed in laboratory experiments that when individuals consider the prospect of buying more of a risky asset that has lost them money, it activates the modules in the brain associated with anticipation of physical pain and anxiety.58 Dealing with investment losses is literally painful for investors.

Many investors may find it helpful to their peace of mind as well as their portfolio to outsource the entire process to a party with less emotional attachment in their portfolio. Realistically, most investors have neither the time nor the motivation necessary to attain a firm understanding of modern portfolio theory, research the capital market expectations on various asset classes and securities, and regularly monitor and rebalance their portfolio, all with enough rigor to make it worth the effort compared to a simple indexing strategy. By utilizing the skills of a good financial advisor, however, an investor can leverage the expertise of a professional with the bandwidth to execute these tactics in a cost-efficient manner.

A financial advisor should be able to engage you as an investor and acquire a firm understanding of your goals, needs, and attitudes towards risk, money, and markets. Because he or she will have an entire practice over which to efficiently dedicate time and resources on portfolio research, optimization, and trading, the financial advisor should be able to craft a portfolio that’s optimized for your personal situation. Financial advisors, as institutional investors, generally have access to institutional class funds that retail investors do not, including many of those that have demonstrated the greatest dedication to exploiting the factor premiums. Notably, DFA and AQR, the two fund families with the greatest academic support, are generally only available to individual investors through a financial advisor. Should your professionally managed portfolio provide a better risk adjusted return than a comparable do-it-yourself index fund approach, the FA’s fees have paid for themselves.

Furthermore, a good financial advisor will make sure your investments are tax efficient and that you are making the most of tax-preferred accounts. Researchers have shown that after asset allocation, asset location, the strategic placement of investments in accounts with different tax treatment, is one of the most important factors in net portfolio returns,59 yet most individual investors largely ignore these effects.60 Advisor’s fees can generally be paid with pre-tax funds as well, further enhancing tax efficiency.

Invest with Purpose

There is something of a paradox involved in investing. Finance is a highly specialized and technical field, but money is a very personal and emotional topic. Achieving the joy and fulfillment associated with financial success requires a large measure of emotional detachment and impersonal pragmatism. Far too often people suffer great loss by confusing loyalties and aspirations, fears and regrets with the efficient allocation of their portfolio assets. We as advisors hate to see this happen; there is nothing to celebrate about the needless destruction of capital, it is truly a loss for us all. One of the greatest misconceptions about finance is that investing is just a zero-sum game, that one trader’s gain is another’s loss. Nothing could be further from the truth. Economists have shown that one of the greatest predictors of a nation’s well being is its financial development.61 The more liquid and active our capital markets, the greater our society’s capacity for innovation and progress. When you invest in the stock market, you are contributing your share to the productive capacity of our world, your return is your reward for helping make it better, outperformance is a sign that you have steered capital to those with the greatest use for it.


With the right accounts and investments in place and a process for managing them effectively, you the investor are freed to focus on what you are working and investing for, and an advisor can work with you to help get you there. Whether you want to travel the world, buy the house of your dreams, send your children to the best college, maximize your philanthropic giving, or simply retire early, an advisor can help you develop a financial plan to turn the dollars and cents of your portfolio into the life you want to live, building more health, wealth, and happiness for you, your loved ones, and the world.




1. “U.S. Stock Ownership Stays at Record Low,” Gallup.

2. U.S. Investors Not Sold on Stock Market as Wealth Creator,” Gallup.

3. Data provided by Morningstar.

4. Siegel, Stocks for the Long Run, 5-25

5. Dimson et al, Triumph of the Optimists.

6. Ibid. 3

7. Ibid

8. Shiller, “Understanding Recent Trends in House Prices and Home Ownership.”

9. Mankiw and Zeldes, for example, find that to justify the historical equity risk premium observed, investors would in aggregate need to be indifferent between a certain payoff of $51,209 and a 50/50 bet paying either $50,000 or $100,000. Mankiw and Zeldes, “The consumption of stockholders and nonstockholders,” 8.

10. For a highly readable introduction to the idea of cognitive biases, see Daniel Kahneman’s book “Thinking: Fast and Slow.” Kahneman has been a pioneer in the field and for his work won the 2002 Nobel prize in economics.

11. Benartzi and Thaler, “Myopic Loss Aversion and the Equity Premium Puzzle.”

12. “Guide to the Markets,” J.P. Morgan Asset Management

13. See, for example, Kruger and Dunning,  "Unskilled and Unaware of It: How Difficulties in Recognizing One's Own Incompetence Lead to Inflated Self-Assessments" and Zuckerman and Jost,  "What Makes You Think You're So Popular? Self Evaluation Maintenance and the Subjective Side of the ‘Friendship Paradox’"

14. Svenson, “Are We All Less Risky and More Skillful than Our Fellow Drivers?”

15. Preston and Harris, “Psychology of Drivers in Traffic Accidents.”

16. Zweig, Your Money and Your Brain. 88-91.

17. French and Poterba, “Investor Diversification and International Equity Markets.”

18. Ibid. 14. p. 98-99.

19. Barber and Odean, “Trading is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors.”

20. Ashenfelter et al, “Predicting the Quality and Prices of Bordeaux Wine.”

21. Thornton, "Toward a Linear Prediction of Marital Happiness."

22. Swets et al, "Psychological Science Can Improve Diagnostic Decisions."

23. Carroll et al, "Evaluation, Diagnosis, and Prediction in Parole Decision-Making."

24. Stillwell et al, "Evaluating Credit Applications: A Validation of Multiattribute Utility Weight Elicitation Techniques"

25. See Fama and French, “Luck versus Skill in the Cross-Section of Mutual Fund Returns.” They do find modest evidence of skill at the right tail end of the distribution under the capital asset pricing model. After controlling for the value, size, and momentum factor premiums (discussed below), however, evidence of net-of-fee skill is not significantly different than zero.

26. Shiller, “Efficient Markets vs. Excess Volatility.”

27. Professor Goetzmann of the Yale School of Management has a introductory hyper-text textbook on modern portfolio theory available on his website, “An Introduction to Investment Theory.”

28. In the language of modern portfolio theory this risk is known at a security’s beta. Mathematically it is the covariance of the security’s returns with the market’s returns, divided by the variance of the market’s returns.

29. Setton, “The Berkshire Bunch.”

30. For example, Grossman and Stiglitz prove in “On the Impossibility of Informationally Efficient Markets” that market efficiency cannot be an equilibrium because without excess returns there is no incentive for arbitrageurs to correct mispricings. More recently, Markowitz, one of fathers of modern portfolio theory, showed in “Market Efficiency: A Theoretical Distinction and So What” that if a couple key assumptions of MPT are relaxed, the market portfolio is no longer optimal for most investors.

31. Basu, “Investment Performance of Common Stocks in Relation to their Price-Earnings Ratios: A Test of the Efficient Market Hypothesis.”

32. Fama and French, “The Cross-Section of Expected Stock Returns.”

33. Jegadeesh and Titman, “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency”

34. Ibid. 31.

35. Pastor and Stambaugh, “Liquidity Risk and Expected Stock Returns.”

36. Jegadeesh, “Evidence of Predictable Behavior or Security Returns.”

37. Froot and Thaler, “Anomalies: Foreign Exchange.”

38. Campbell and Shiller, “Yield Spreads and Interest Rate Movements: A Bird’s Eye View.”

39. Erb and Harvey, “The Tactical and Strategic Value of Commodity Futures.”

40. Novy-Marx, “The Other Side of Value: The Gross Profitability Premium.”

41. Thaler, “Seasonal Movements in Security Prices.”

42. Mitchell and Pulvino, “Characteristics of Risk and Return in Risk Arbitrage.”

43. See McLean and Pontiff, “Does Academic Research Destroy Stock Return Predictability?” A meta analysis of 82 equity return factors was able to replicate 72 using out of sample data.

44. Fama and French, “Size and Book-to-Market Factors in Earnings and Returns.”

45. Daniel and Titman, “Evidence on the Characteristics of Cross Sectional Variation in Stock Returns.”

46. Hwang and Rubesam, “Is Value Really Riskier than Growth?”

47. Numerous investor profiles have expounded on the difficulty of being a rational investor in an irrational market. In a recent article in Institutional Investor, Asness and Liew give a highly readable overview of the risk vs. mispricing debate and discuss the problems they encountered launching a value-oriented hedge fund in the middle of the dot-com bubble.

48. Bar-Eli, “Action Bias Among Elite Soccer Goalkeepers: The Case of Penalty Kicks. Journal of Economic Psychology.”

49. Asch, “Opinions and Social Pressure.”

50. Daniel et al provides one of the most thorough theoretical discussions on how certain common cognitive biases can result in systematically biased security prices in “Investor Psychology and Security Market Under- and Overreaction.”

51. Schleifer and Vishny, “The Limits of Arbitrage.”

52. Data provided by Vanguard.

53. Chapter 2 of Goetzmann’s “An Introduction to Investment Theory” provides an introductory discussion.

54. The Black-Litterman model allows investors to combine their estimates of expected returns with equilibrium implied returns in a Bayesian framework that largely overcomes the input-sensitivity problems associated with traditional mean-variance optimization. Idzorek offers a thorough introduction in “A Step-By-Step Guide to the Black-Litterman Model.”

55. Ilmanen’s “Expected Returns on Major Asset Classes” provides a detailed explanation of the theory and evidence of forecasting expected returns.

56. Walkshausl and Lobe, “Fundamental Indexing Around the World.”

57. Buetow et al, “The Benefits of Rebalancing.”

58. Kuhnen and Knutson, “The Neural Basis of Financial Risk Taking.”

59. Dammon et al, “Optimal Asset Location and Allocation with Taxable and Tax-Deferred Investing.”

60. Bodie and Crane, “Personal Investing: Advice, Theory, and Evidence from a Survey of TIAA-CREF Participants.”

61. Yongseok Shin of the Federal Reserve provides a brief review of the literature on this research in “Financial Markets: An Engine for Economic Growth.”



Works Cited



Asch, Solomon E. "Opinions and Social Pressure." Scientific American 193, no. 5 (12 1955).

Ashenfelter, Orley. "Predicting the Quality and Prices of Bordeaux Wine*." The Economic Journal 118, no. 529 (12 2008).

Asness, Clifford and Liew, John. “The Great Divide over Market Efficiency.” Institutional Investor, March 3, 2014.

Asness, Clifford, Moskowitz, Tobias, and Pedersen, Lasse. “Value and Momentum Everywhere.” The Journal of Finance 68, no. 3 (6, 2013).

Bar-Eli, Michael, Ofer H. Azar, Ilana Ritov, Yael Keidar-Levin, and Galit Schein. "Action Bias among Elite Soccer Goalkeepers: The Case of Penalty Kicks." Journal of Economic Psychology 28, no. 5 (12 2007).

Barber, Brad M., and Terrance Odean. "Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors." The Journal of Finance 55, no. 2 (12 2000).

Basu, S. "Investment Performance of Common Stocks in Relation to Their Price-Earnings Ratios: A Test of the Efficient Market Hypothesis."The Journal of Finance 32, no. 3 (12 1977).

Benartzi, S., and R. H. Thaler. "Myopic Loss Aversion and the Equity Premium Puzzle." The Quarterly Journal of Economics110, no. 1 (12, 1995).

Bodie, Zvi, and Dwight B. Crane. "Personal Investing: Advice, Theory, and Evidence." Financial Analysts Journal 53, no. 6 (12 1997).

Buetow, Gerald W., Ronald Sellers, Donald Trotter, Elaine Hunt, and Willie A. Whipple. "The Benefits of Rebalancing." The Journal of Portfolio Management 28, no. 2 (12 2002).

Campbell, John and Shiller, Robert. “Yield Spreads and Interest Rate Movements: A Bird’s Eye View.” The Econometrics of Financial Markets, 58 no. 3 (1991).

Carroll, John S., Richard L. Wiener, Dan Coates, Jolene Galegher, and James J. Alibrio. "Evaluation, Diagnosis, and Prediction in Parole Decision Making." Law & Society Review 17, no. 1 (12 1982).

Dammon, Robert M., Chester S. Spatt, and Harold H. Zhang. "Optimal Asset Location and Allocation with Taxable and Tax-Deferred Investing." The Journal of Finance 59, no. 3 (12 2004).

Daniel, Kent, and Sheridan Titman. "Evidence on the Characteristics of Cross Sectional Variation in Stock Returns." The Journal of Finance52, no. 1 (12 1997).

Daniel, Kent, Hirshleifer, David, and Subrahmanyam, Avanidhar. “Investor Psychology and Security Market Under- and Overreactions.” The Journal of Finance, 53 no. 6 (1998).

Dimson, Elroy, Marsh, Paul, and Staunton, Mike. Triumph of the Optimists. Princeton: Princeton University Press, 2002.

Erb, Cfa Claude B., and Campbell R. Harvey. "The Strategic and Tactical Value of Commodity Futures." CFA Digest 36, no. 3 (12 2006).

Fama, Eugene F., and Kenneth R. French. "The Cross-Section of Expected Stock Returns." The Journal of Finance 47, no. 2 (12 1992).

Fama, Eugene F., and Kenneth R. French. "Luck versus Skill in the Cross-Section of Mutual Fund Returns." The Journal of Finance65, no. 5 (12 2010).

Fama, Eugene F., and Kenneth R. French. "Size and Book-to-Market Factors in Earnings and Returns."The Journal of Finance 50, no. 1 (12 1995).

French, Kenneth and Poterba, James. “Investor Diversification and International Equity Markets.” American Economic Review (1991).

Froot, Kenneth A., and Richard H. Thaler. "Anomalies: Foreign Exchange." Journal of Economic Perspectives 4, no. 3 (12 1990).

“Guide to the Markets.” J.P. Morgan Asset Management. 2014

Goetzmann, William. An Introduction to Investment Theory. Yale School of Management. Accessed April 09, 2014.

Grossman, Sanford and Stiglitz, Joseph. “On the Impossibility of Informationally Efficent Markets.” The American Economic Review 70, no. 3 (6, 1980).

Hwang, Soosung and Rubesam, Alexandre. “Is Value Really Riskier Than Growth? An Answer with Time-Varying Return Reversal.” Journal of Banking and Finance, 37 no. 7 (2013).

Idzorek, Thomas. “A Step-by-Step Guide to the Black-Litterman Model.” Ibbotson Associates (2005).

Ilmanen, Antti. “Expected Returns on Major Asset Classes.” Research Foundation of CFA Institute (2012).

Jegadeesh, Narasimhan, and Sheridan Titman. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency." The Journal of Finance48, no. 1 (12 1993).

Kahneman, Daniel. Thinking, Fast and Slow. New York: Farrar, Straus and Giroux, 2011.

Kruger, Justin, and David Dunning. "Unskilled and Unaware of It: How Difficulties in Recognizing One's Own Incompetence Lead to Inflated Self-assessments." Journal of Personality and Social Psychology77, no. 6 (12 1999).

Kuhnen, Camelia M., and Brian Knutson. "The Neural Basis of Financial Risk Taking." Neuron 47, no. 5 (12 2005).

Malkiel, Burton. A Random Walk Down Wall Street: Time-Tested Strategies for Successful Investing (Tenth Edition). New York: W.W. Norton & Company, 2012.

Mankiw, N.gregory, and Stephen P. Zeldes. "The Consumption of Stockholders and Nonstockholders." Journal of Financial Economics 29, no. 1 (12 1991).

Markowitz, Harry M. "Market Efficiency: A Theoretical Distinction and So What?" Financial Analysts Journal 61, no. 5 (12 2005).

McLean, David and Pontiff, Jeffrey. “Does Academic Research Destroy Stock Return Predictability?” Working Paper, (2013).

Mitchell, Mark, and Todd Pulvino. "Characteristics of Risk and Return in Risk Arbitrage." The Journal of Finance 56, no. 6 (12 2001).

Novy-Marx, Robert. "The Other Side of Value: The Gross Profitability Premium." Journal of Financial Economics 108, no. 1 (12 2013).

Pastor, Lubos and Stambaugh, Robert. “Liquidity Risk and Expected Stock Returns.” The Journal of Political Economy, 111 no. 3 (6, 2003).

Preston, Caroline E., and Stanley Harris. "Psychology of Drivers in Traffic Accidents." Journal of Applied Psychology 49, no. 4 (12 1965).

Setton, Dolly. The Berkshire Bunch.” Forbes, October 12, 1998.

Shleifer, Andrei, and Robert W. Vishny. "The Limits of Arbitrage."The Journal of Finance 52, no. 1 (12 1997).

Siegel, Jeremy J. Stocks for the Long Run: The Definitive Guide to Financial Market Returns and Long-term Investment Strategies (Forth Edition). New York: McGraw-Hill, 2008.

Shiller, Robert. “Understanding Recent Trends in House Prices and Homeownership.” Housing, Housing Finance and Monetary Policy, Jackson Hole Conference Series, Federal Reserve Bank of Kansas City, 2008, pp. 85-123

Shiller, Robert. “Efficient Markets vs. Excess Volatility.” Yale. Accessed April 09, 2014.

Shin, Yongseok. “Financial Markets: An Engine for Economic Growth.” The Regional Economist (July 2013).

Stillwell, William G., F.hutton Barron, and Ward Edwards. "Evaluating Credit Applications: A Validation of Multiattribute Utility Weight Elicitation Techniques."Organizational Behavior and Human Performance 32, no. 1 (12 1983).

Svenson, Ola. "Are We All Less Risky and More Skillful than Our Fellow Drivers?" Acta Psychologica47, no. 2 (12 1981).

Swets, J. A., R. M. Dawes, and J. Monahan. "Psychological Science Can Improve Diagnostic Decisions."Psychological Science in the Public Interest 1, no. 1 (12, 2000).

Thaler, Richard. "Anomalies: Seasonal Movements in Security Prices II: Weekend, Holiday, Turn of the Month, and Intraday Effects."Journal of Economic Perspectives1, no. 2 (12 1987).

Thornton, B. "Toward a Linear Prediction Model of Marital Happiness." Personality and Social Psychology Bulletin 3, no. 4 (12, 1977).

"U.S. Stock Ownership Stays at Record Low." Gallup. Accessed April 09, 2014.

Walkshäusl, Christian, and Sebastian Lobe. "Fundamental Indexing around the World." Review of Financial Economics 19, no. 3 (12 2010).

Zuckerman, Ezra W., and John T. Jost. "What Makes You Think You're so Popular? Self-Evaluation Maintenance and the Subjective Side of the "Friendship Paradox""Social Psychology Quarterly 64, no. 3 (12 2001).


Zweig, Jason. Your Money and Your Brain: How the New Science of Neuroeconomics Can Help Make You Rich. New York: Simon & Schuster, 2007.




I wish to thank Romeo Stevens for the feedback and proofreading he provided for early drafts of this paper. You should go buy his Mealsquares (just look how happy I look eating them there!)


If the section on statistical prediction rules sounded familiar it's probably because I stole all the examples from this Less Wrong article by lukeprog about them. After you're done giving this article karma you should go give that one some more.


After I made my South Bay meetup presentation Peter McCluskey wrote on the Bay Area LW mailing list that "Your paper's report of 'a massive study of the sixteen countries that had data on local stock, bond, and cash returns available for every year of the twentieth century' could be considered a study of survivorship bias, in that it uses criteria which exclude countries where stocks lost 100% at some point (Russia, Poland, China, Hungary)." This is a good point and is worth addressing, which some researchers have done in recent years. Dimson, Marsh, and Staunton (2006) find that the surviving markets of the 20th century I cite in my paper dominated the global market capitalization in 1900 and the effect of national stock-market implosions was mostly negligible on worldwide averages. Peter did go on to say that "I don't know of better advice for the average person than to invest in equities, and I have most of my wealth in equities..." so I think we're mostly on the same page at least in terms of practical advice.


In a conversation with Alyssa Vance she similarly expressed skepticism that the equity risk premium has been significantly greater than zero due to the fact that at some point in the 20th century most major economies experienced double-digit inflation and very high marginal rates of taxation on capital income. It is true that taxes and inflation significantly dilute an investor's return, and one would be foolish to ignore their effects. But while they may reduce the absolute attractiveness of equities, the effects of taxes and inflation actually make stocks look more attractive relative to the alternatives of bonds and cash investments. In the US and most jurisdictions, the dividends and capital gains earned on stocks are taxed at preferential rates relative to the interest earned on fixed income investments, which is typically taxed as ordinary income. Furthermore, the majority of individual investors hold a large fraction of their investments in tax-sheltered accounts (such as 401(k)s and IRAs in the US).


At my South Bay meetup presentation, Patrick LaVictoire (among others) expressed incredulity at my claim that retail investors have on average badly underperformed relevant benchmarks and that by implication institutional investors have outperformed. The source I cite in my paper is gated but there is plenty of research on actual investor performance. Morningstar regularly publishes info on how investors routinely underperform the mutual funds they invest in by buying into and selling out of them at the wrong times. Finding data on institutional investors is a little trickier but Busse, Goyal, and Wahal (2010) find that institutional investors managing e.g. pensions, foundations, and endowments on average outperform the broad US equity market in the US equity sleeve of their portfolios. (the language of that paper sounds much more pessimistic, with "alphas are statistically indistinguishable from zero" in the abstract. The key is that they are controlling for the size, value, and momentum effects discussed in my paper. In other words, once we account for the fact that institutional investors are taking advantage of the factor premiums that have been shown to most consistently outperform a simple index strategy, they aren't providing any extra value. This ties in with the idea of "shrinking alpha" or "smart beta" that is currently en vogue in my industry.)


I'm happy to address further questions and criticisms in the comments.

Should people be writing more or fewer LW posts?

13 John_Maxwell_IV 14 September 2014 07:40AM

It's unlikely that by pure chance we are currently writing the correct number of LW posts.  So it might be useful to try to figure out if we're currently writing too few or too many LW posts.  If commenters are evenly divided on this question then we're probably close to the optimal number; otherwise we have an opportunity to improve.  Here's my case for why we should be writing more posts.

Let's say you came up with a new and useful life hack, you have a novel line of argument on an important topic, or you stumbled across some academic research that seems valuable and isn't frequently discussed on Less Wrong.  How valuable would it be for you to share your findings by writing up at post for Less Wrong?

Recently I visited a friend of mine and commented on the extremely bright lights he had in his room.  He referenced this LW post written over a year ago.  That got me thinking.  The bright lights in my friend's room make his life better every day, for a small upfront cost.  And my friend is probably just one of tens or hundreds of people to use bright lights this way as a result of that post.  Given that the technique seems to be effective, that number will probably continue going up, and will grow exponentially via word of mouth (useful memes tend to spread).  So by my reckoning, chaosmage has created and will create a lot of utility.  If they had kept that idea to themselves, I suspect they would have captured less than 1% of the total value to be had from the idea.

You can reach orders of magnitude more people writing an obscure Less Wrong comment than you can talking to a few people at a party in person.  For example, at least 100 logged in users read this fairly obscure comment of mine.  So if you're going to discuss an important topic, it's often best to do it online.  Given enough eyeballs, all bugs in human reasoning are shallow.

Yes, peoples' time does have opportunity costs.  But people are on Less Wrong because they need a break anyway.  (If you're a LW addict, you might try the technique I describe in this post for dealing with your addiction.  If you're dealing with serious cravings, for LW or video games or drugs or anything else, perhaps look at N-acetylcysteine... a variety of studies suggest it helps reduce cravings (behavioral addictions are pretty similar to drug addictions neurologically btw), it has a good safety profile, and you can buy it on Amazon.  Not prescribed by doctors because it's not approved by the FDA.  Yes, you could use willpower (it's worked so well in the past...) or you could hit the "stop craving things as much" button, and then try using willpower.  Amazing what you can learn on Less Wrong isn't it?)

And LW does a good job of indexing content by how much utility people are going to get out of it.  It's easy to look at a post's keywords and score and guess if it's worth reading.  If your post is bad it will vanish in to obscurity and few will be significantly harmed.  (Unless it's bad and inflammatory, or bad with a linkbait title... please don't write posts like that.)  If your post is good, it will spread virally on its own and you'll generate untold utility.

Given that above-average posts get read much more than below-average posts, if you're post's expected quality is average, sharing it on Less Wrong has a high positive expected utility.  Like Paul Graham, I think we should be spreading our net wide and trying to capture all of the winners we can.

I'm going to call out a particular subset of LW commenters in particular.  If you're a commenter and you (a) have at least 100 karma, (b) it's over 80% positive, and (c) you have a draft post with valuable new ideas you've been sitting on for a while, you should totally polish it off and share it with us!  In general, the better your track record, the more you should be inclined to share ideas that seem valuable.  Worst case you can delete your post and cut your losses.

Everybody's talking about machine ethics

12 sbenthall 17 September 2014 05:20PM

There is a lot of mainstream interest in machine ethics now. Here are some links to some popular articles on this topic.

By Zeynep Tufecki, a professor at the I School at UNC, on Facebook's algorithmic newsfeed curation and why Twitter should not implement the same.

By danah boyd, claiming that 'tech folks' are designing systems that implement an idea of fairness that comes from neoliberal ideology.

danah boyd (who spells her name with no capitalization) runs the Data & Society, a "think/do tank" that aims to study this stuff. They've recently gotten MacArthur Foundation funding for studying the ethical and political impact of intelligent systems. 

A few observations:

First, there is no mention of superintelligence or recursively self-modifying anything. These scholars are interested in how, in the near future, the already comparatively powerful machines have moral and political impact on the world.

Second, these groups are quite bad at thinking in a formal or mechanically implementable way about ethics. They mainly seem to recapitulate the same tired tropes that have been resonating through academia for literally decades. On the contrary, mathematical formulation of ethical positions appears to be ya'll's specialty.

Third, however much the one-true-morality may be indeterminate or presently unknowable, progress towards implementable descriptions of various plausible moral positions could at least be incremental steps forward towards an understanding of how to achieve something better. Considering a slow take-off possible future, iterative testing and design of ethical machines with high computational power seems like low-hanging fruit that could only better inform longer-term futurist thought.

Personally, I try to do work in this area and find the lack of serious formal work in this area deeply disappointing. This post is a combination heads up and request to step up your game. It's go time.


Sebastian Benthall

PhD Candidate

UC Berkeley School of Infromation

What are you learning?

12 Viliam_Bur 15 September 2014 10:50AM

This is a thread to connect rationalists who are learning the same thing, so they can cooperate.

The "learning" doesn't necessarily mean "I am reading a textbook / learning an online course right now". It can be something you are interested in long-term, and still want to learn more.



Top-level comments contain only the topic to learn. (Plus one comment for "meta" debate.) Only one topic per comment, for easier search. Try to find a reasonable level of specificity: too narrow topic means less people; too wide topic means more people who actually are interested in something different than you are.

Use the second-level comments if you are learning that topic. (Or if you are going to learn it now, not merely in the far future.) Technically, "me too" is okay in this thread, but providing more info is probably more useful. For example: What are you focusing on? What learning materials you use? What is your goal?

Third- and deeper-level comments, that's debate as usual.

What are your contrarian views?

10 Metus 15 September 2014 09:17AM

As per a recent comment this thread is meant to voice contrarian opinions, that is anything this community tends not to agree with. Thus I ask you to post your contrarian views and upvote anything you do not agree with based on personal beliefs. Spam and trolling still needs to be downvoted.

Proposal: Use logical depth relative to human history as objective function for superintelligence

8 sbenthall 14 September 2014 08:00PM

I attended Nick Bostrom's talk at UC Berkeley last Friday and got intrigued by these problems again. I wanted to pitch an idea here, with the question: Have any of you seen work along these lines before? Can you recommend any papers or posts? Are you interested in collaborating on this angle in further depth?

The problem I'm thinking about (surely naively, relative to y'all) is: What would you want to program an omnipotent machine to optimize?

For the sake of avoiding some baggage, I'm not going to assume this machine is "superintelligent" or an AGI. Rather, I'm going to call it a supercontroller, just something omnipotently effective at optimizing some function of what it perceives in its environment.

As has been noted in other arguments, a supercontroller that optimizes the number of paperclips in the universe would be a disaster. Maybe any supercontroller that was insensitive to human values would be a disaster. What constitutes a disaster? An end of human history. If we're all killed and our memories wiped out to make more efficient paperclip-making machines, then it's as if we never existed. That is existential risk.

The challenge is: how can one formulate an abstract objective function that would preserve human history and its evolving continuity?

I'd like to propose an answer that depends on the notion of logical depth as proposed by C.H. Bennett and outlined in section 7.7 of Li and Vitanyi's An Introduction to Kolmogorov Complexity and Its Applications which I'm sure many of you have handy. Logical depth is a super fascinating complexity measure that Li and Vitanyi summarize thusly:

Logical depth is the necessary number of steps in the deductive or causal path connecting an object with its plausible origin. Formally, it is the time required by a universal computer to compute the object from its compressed original description.

The mathematics is fascinating and better read in the original Bennett paper than here. Suffice it presently to summarize some of its interesting properties, for the sake of intuition.

  • "Plausible origins" here are incompressible, i.e. algorithmically random.
  • As a first pass, the depth D(x) of a string x is the least amount of time it takes to output the string from an incompressible program.
  • There's a free parameter that has to do with precision that I won't get into here. 
  • Both a string of length n that is comprised entirely of 1's, and a string of length n of independent random bits are both shallow. The first is shallow because it can be produced by a constant-sized program in time n. The second is shallow because there exists an incompressible program that is the output string plus a constant sized print function that produces the output in time n.
  • An example of a deeper string is the string of length n that for each digit i encodes the answer to the ith enumerated satisfiability problem. Very deep strings can involve diagonalization.
  • Like Kolmogorov complexity, there is an absolute and a relative version. Let D(x/w) be the least time it takes to output x from a program that is incompressible relative to w,
That's logical depth. Here is the conceptual leap to history-preserving objective functions. Suppose you have a digital representation of all of human society at some time step t, calling this ht. And suppose you have some representation of the future state of the universe u that you want to build an objective function around. What's important, I posit, is the preservation of the logical depth of human history in its computational continuation in the future.

We have a tension between two values. First, we want there to be an interesting, evolving future. We would perhaps like to optimize D(u).

However, we want that future to be our future. If the supercontroller maximizes logical depth by chopping all the humans up and turning them into better computers and erasing everything we've accomplished as a species, that would be sad. However, if the supercontroller takes human history as an input and then expands on it, that's much better. D(u/ht) is the logical depth of the universe as computed by a machine that takes human history at time slice t as input.

Working on intuitions here--and your mileage may vary, so bear with me--I think we are interested in deep futures and especially those futures that are deep with respect to human progress so far. As a conjecture, I submit that those will be futures most shaped by human will.

So, here's my proposed objective for the supercontroller, as a function of the state of the universe. The objective is to maximize:

f(u) = D(u/ht) / D(u)

I've been rather fast and loose here and expect there to be serious problems with this formulation. I invite your feedback! I'd like to conclude by noting some properties of this function:
  • It can be updated with observed progress in human history at time t' by replacing ht with ht'. You could imagine generalizing this to something that dynamically updated in real time.
  • This is a quite conservative function, in that it severely punishes computation that does not depend on human history for its input. It is so conservative that it might result in, just to throw it out there, unnecessary militancy against extra-terrestrial life.
  • There are lots of devils in the details. The precision parameter I glossed over. The problem of representing human history and the state of the universe. The incomputability of logical depth (of course it's incomputable!). My purpose here is to contribute to the formal framework for modeling these kinds of problems. The difficult work, like in most machine learning problems, becomes feature representation, sensing, and efficient convergence on the objective.
Thank you for your interest.

Sebastian Benthall
PhD Candidate
UC Berkeley School of Information


Should EA's be Superrational cooperators?

7 diegocaleiro 16 September 2014 09:41PM

Back in 2012 when visiting Leverage Research, I was amazed by the level of cooperation in daily situations I got from Mark. Mark wasn't just nice, or kind, or generous. Mark seemed to be playing a different game than everyone else.

If someone needed X, and Mark had X, he would provide X to them. This was true for lending, but also for giving away.

If there was a situation in which someone needed to direct attention to a particular topic, Mark would do it.

You get the picture. Faced with prisoner dilemmas, Mark would cooperate. Faced with tragedy of the commons, Mark would cooperate. Faced with non-egalitarian distributions of resources, time or luck (which are convoluted forms of the dictator game), Mark would rearrange resources without any indexical evaluation. The action would be the same, and the consequentialist one, regardless of which side of a dispute was the Mark side.

I never got over that impression. The impression that I could try to be as cooperative as my idealized fiction of Mark was.

In game theoretic terms, Mark was a Cooperational agent.

  1. Altruistic - MaxOther
  2. Cooperational - MaxSum
  3. Individualist - MaxOwn
  4. Equalitarian - MinDiff
  5. Competitive - MaxDiff
  6. Aggressive - MinOther

Under these definitions of kinds of agents used in research on game theoretical scenarios, what we call Effective Altruism would be called Effective Cooperation. The reason why we call it "altruism" is because even the most parochial EA's care about a set containing a minimum of 7 billion minds, where to a first approximation MaxSum ≈ MaxOther.

Locally however the distinction makes sense. In biology Altruism usually refers to a third concept, different from both the "A" in EA, and Alt, it means acting in such a way that Other>Own without reference to maximizing or minimizing, since evolution designs adaptation executors, not maximizers.

A globally Cooperational agent acts as a consequentialist globally. So does an Alt agent.

The question then is,

How should a consequentialist act locally?

The mathematical response is obviously as a Coo. What real people do is a mix of Coo and Ind.

My suggestion is that we use our undesirable yet unavoidable moral tribe distinction instinct, the one that separates Us from Them, and act always as Coos with Effective Altruists and mix Coo and Ind only with non EAs. That is what Mark did.


You’re Entitled to Everyone’s Opinion

6 satt 20 September 2014 03:39PM

Over the past year, I've noticed a topic where Less Wrong might have a blind spot: public opinion. Since last September I've had (or butted into) five conversations here where someone's written something which made me think, "you wouldn't be saying that if you'd looked up surveys where people were actually asked about this". The following list includes six findings I've brought up in those LW threads. All of the findings come from surveys of public opinion in the United States, though some of the results are so obvious that polls scarcely seem necessary to establish their truth.

  1. The public's view of the harms and benefits from scientific research has consistently become more pessimistic since the National Science Foundation began its surveys in 1979. (In the wake of repeated misconduct scandals, and controversies like those over vaccination, global warming, fluoridation, animal research, stem cells, and genetic modification, people consider scientists less objective and less trustworthy.)
  2. Most adults identify as neither Republican nor Democrat. (Although the public is far from apolitical, lots of people are unhappy with how politics currently works, and also recognize that their beliefs align imperfectly with the simplistic left-right axis. This dissuades them from identifying with mainstream parties.)
  3. Adults under 30 are less likely to believe that abortion should be illegal than the middle-aged. (Younger adults tend to be more socially liberal in general than their parents' generation.)
  4. In the 1960s, those under 30 were less likely than the middle-aged to think the US made a mistake in sending troops to fight in Vietnam. (The under-30s were more likely to be students and/or highly educated, and more educated people were less likely to think sending troops to Vietnam was a mistake.)
  5. The Harris Survey asked, in November 1969, "as far as their objectives are concerned, do you sympathize with the goals of the people who are demonstrating, marching, and protesting against the war in Vietnam, or do you disagree with their goals?" Most respondents aged 50+ sympathized with the protesters' goals, whereas only 28% of under-35s did. (Despite the specific wording of the question, the younger respondents worried that the protests reflected badly on their demographic, whereas older respondents were more often glad to see their own dissent voiced.)
  6. A 2002 survey found that about 90% of adult smokers agreed with the statement, "If you had to do it over again, you would not have started smoking." (While most smokers derive enjoyment from smoking, many weight smoking's negative consequences strongly enough that they'd rather not smoke; they continue smoking because of habit or addiction.)

continue reading »

Link: The trap of "optimal conditions"

6 polymathwannabe 18 September 2014 06:37PM

"the next time you’re stopping yourself from trying something because the conditions are not optimal, remember that those optimal conditions may not have been the reason it worked. They may not be the cause. They may not even be correlated. They may just be a myth you’ve bought into or sold yourself that limits you from breaking out and exceeding your expectations."

More at:

[Link] Learning how to exert self-control

6 pinyaka 14 September 2014 11:07PM

Here's a link to a short op-ed about some tips to develop self-control. The author get them from talking with Walter Mischel, a researcher who correlated impulsiveness as a child (measured by ability to delay eating sweets) and various metrics as an adult (education attainment/cocaine use/weight). Mischel has a new book coming out, but this is not a review of the book. I thought this might be of interest because it talks a little about how self-control is a skill that can be developed and even gave some specific things to do.

1. If possible remove unhelpful triggers from your environment. If not possible, try to reduce the emotional appeal of the trigger by mentally associating it with something unpleasant. One example he gives is imagining a cockroach crawling on the chocolate mousse that a server at a restaurant offers.

2. Develop specific if-then plans such as "if it is before noon, I won't check email" or "If I feel angry, I will count backward from ten." The goal of these kinds of checks is to introduce a delay between impulse and action during which you are reminded of your goal and have a chance to consider the impact of following the impulse on that goal.

3. Link the behavior that you want to modify to a "burning goal" so that you have emotional impetus to actually make the desired change.

A kind or reverse "tragedy of the commons" - any solution ideas?

6 D_Alex 14 September 2014 04:42AM

I have recently come across a very practical example of a kind of "tragedy of the commons" - the unwillingness to invest in assets that benefit stakeholders indiscriminately. Specifically, on large strata-title apartment projects there is a reluctance to implement such measures as:

- central hot water heating (~ 10% lower all-up costs, ~20% lower operating costs)

- Solar hot water heating (>20% ROI)

- Solar electric power (~10% ROI)

UNLESS some kind of user-pays system is implemented, which would use up pretty much all of the gains.


The concern is of course that providing the above systems would create a "commons" that would tend to be exploited.


I am curious if there are any ideas on a usable solutions, perhaps some kind of workable protocol that would enable the above, or existing success stories - what made them work?

Open thread, September 15-21, 2014

5 gjm 15 September 2014 12:24PM

If it's worth saying, but not worth its own post (even in Discussion), then it goes here.

Notes for future OT posters:

1. Please add the 'open_thread' tag.

2. Check if there is an active Open Thread before posting a new one. (Immediately before; refresh the list-of-threads page before posting.)

3. Open Threads should be posted in Discussion, and not Main.

4. Open Threads should start on Monday, and end on Sunday.

Ways to improve LessWrong

5 adamzerner 14 September 2014 02:25AM

I think it'd be a good idea to keep a list of the ways we'd like to see LessWrong improve, sorted by popularity. Ie. email alerts for new responses.

So if you have an idea for how LessWrong could be better, post it in the comments. As people up/downvote, we'll get a sense for what the consensus opinions are.

I think there's a pretty good amount to be gained by improving LessWrong.

  • I think there's a lot of low-hanging fruit (like email alerts for new responses).
  • Conversations here are actually useful and productive. Facilitating conversation should thus lead to more of these useful and productive conversations (as opposed to leading to more of an unproductive type of conversation). (Sorry, I didn't word this well; hopefully you guys know what I mean.)
  • Perhaps something big would come out of this list (like meet-ups). Perhaps rationality hack-a-thons (whatever that means)? 
I'm not a good enough coder now, but once I am, I think I'd like to do what I can to make LessWrong better. I'm starting a coding bootcamp (Fullstack Academy) on Monday. By the end of it, I should at least be competent.


Note: I say "ways to improve" instead of "features" because "ways to improve" is more general.

Link: How Community Feedback Shapes User Behavior

4 Tyrrell_McAllister 17 September 2014 01:49PM

This article discusses how upvotes and downvotes influence the quality of posts on online communities.  The article claims that downvotes lead to more posts of lower quality from the downvoted commenter.

From the abstract:

Social media systems rely on user feedback and rating mechanisms for personalization, ranking, and content filtering. [...] This paper investigates how ratings on a piece of content affect its author’s future behavior. [...] [W]e find that negative feedback leads to significant behavioral changes that are detrimental to the community.  Not only do authors of negatively-evaluated content contribute more, but also their future posts are of lower quality, and are perceived by the community as such.  In contrast, positive feedback does not carry similar effects, and neither encourages rewarded authors to write more, nor improves the quality of their posts.

The authors of the article are Justin Cheng, Cristian Danescu-Niculescu-Mizil, and Jure Leskovec.

Edited to add: NancyLebovitz already posted about this study in the Open Thread from September 8-14, 2014.

Any LessWrong readers at the University of Michigan?

3 Asymmetric 16 September 2014 01:39PM

I'm interested in gauging interest in a LessWrong group at UM -- probably a Facebook group, as opposed to an official University club.

Group Rationality Diary, September 16-30

3 therufs 16 September 2014 01:33PM

This is the public group instrumental rationality diary for September 16-30.

It's a place to record and chat about it if you have done, or are actively doing, things like: 

  • Established a useful new habit
  • Obtained new evidence that made you change your mind about some belief
  • Decided to behave in a different way in some set of situations
  • Optimized some part of a common routine or cached behavior
  • Consciously changed your emotions or affect with respect to something
  • Consciously pursued new valuable information about something that could make a big difference in your life
  • Learned something new about your beliefs, behavior, or life that surprised you
  • Tried doing any of the above and failed

Or anything else interesting which you want to share, so that other people can think about it, and perhaps be inspired to take action themselves. Try to include enough details so that everyone can use each other's experiences to learn about what tends to work out, and what doesn't tend to work out.

Thanks to cata for starting the Group Rationality Diary posts, and to commenters for participating.

Previous diary: September 1-15

Rationality diaries archive

LessWrong's attitude towards AI research

2 Florian_Dietz 20 September 2014 03:02PM

AI friendliness is an important goal and it would be insanely dangerous to build an AI without researching this issue first. I think this is pretty much the consensus view, and that is perfectly sensible.

However, I believe that we are making the wrong inferences from this.

The straightforward inference is "we should ensure that we completely understand AI friendliness before starting to build an AI". This leads to a strongly negative view of AI researchers and scares them away. But unfortunately reality isn't that simple. The goal isn't "build a friendly AI", but "make sure that whoever builds the first AI makes it friendly".

It seems to me that it is vastly more likely that the first AI will be built by a large company, or as a large government project, than by a group of university researchers, who just don't have the funding for that.

I therefore think that we should try to take a more pragmatic approach. The way to do this would be to focus more on outreach and less on research. It won't do anyone any good if we find the perfect formula for AI friendliness on the same day that someone who has never heard of AI friendliness before finishes his paperclip maximizer.

What is your opinion on this?

Discussion of "What are your contrarian views?"

2 Metus 20 September 2014 12:09PM

I'd like to use this thread to review the "What are your contrarian views?" thread as the meta discussion there was drowned out by the intended content I feel. What can be done better with the voting system? Should threads like these be a regular occurence? What have you specifically learned from that thread? Did you like it at all?


Usual voting rules apply.

[question] What edutainment apps do you recommend?

2 Gunnar_Zarncke 20 September 2014 08:55AM

Follow up to: Rationality Games Apps

In the spirit of: Games for rationalists

My son (10) wants a smartphone and I reasonably expect that he wants to and will play games with it. He appears to be the right age to use it. I don't want to prevent him from playing games nor do I think that possible or helpful. But I'd like to suggest and promote a few apps and games that *are* helpful or from which he can learn something. 

Obvious candidates are 

There are lots of low profile apps filed under learning in the app stores but most of this is crap and it takes lots of time to explore these. 

I also found some recommendation for learning with Android apps and will point my son to these. 

I'd like to hear what apps do you or yours children use. Which apps and esp. games do you recommend for future rationalists?

Link: quotas-microaggression-and-meritocracy

2 Lexico 19 September 2014 10:18PM


I remember seeing a talk of the concept of privilege show up in the discussion thread on contrarian views.

Some discussion got started from "Feminism is a good thing. Privilege is real."

This is an article that presents some of those ideas in a way that might be approachable for LW.

One of the ideas I take out of this is that these issues can be examined as the result of unconscious cognitive bias. IE sexism isn't the result of any conscious thought, but can be the result as a failure mode where we don't rationality correctly in these social situations.

Of course a broad view of these issues exist, and many people have different ways of looking at these issues, but I think it would be good to focus on the case presented in this article rather than your other associations.

Weekly LW Meetups

1 FrankAdamek 19 September 2014 04:43PM

This summary was posted to LW Main on September 12th. The following week's summary is here.

Irregularly scheduled Less Wrong meetups are taking place in:

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:

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

continue reading »

Street action "Stop existential risks!", Union square, San Francisco, September 27, 2014 at 2:00 PM

-3 turchin 20 September 2014 02:08PM

Existential risks are the risks of human extinction. A global catastrophe will happen most likely because of the new technologies such as biotech, nanotech, and AI, along with several other risks: runaway global warming, and nuclear war. Sir Martin Rees estimates these risks to have a fifty percent probability in the 21st century.

We must raise the awareness of impending doom and make the first ever street action against the possibility of human extinction. Our efforts could help to prevent these global catastrophes from taking place. I suggest we meet in Union square, San Francisco, September 27, 2014 at 2:00 PM in order to make a short and intense photo session with the following slogans:

Stop Existential Risks!

No Human Extinction!

AI must be Friendly!

No Doomsday Weapons!

Ebola must die!

Prevent Global Catastrophe!

These slogans will be printed in advance, but more banners are welcome. I have previous experience with organizing actions for immortality and funding of life extension near Googleplex, the White house in DC, and Burning Man, and I know this street action, taking place on September 27th,  is both legal and a fun way to express our points of view.

Organized by Alexey Turchin and Longevity Party.

Friendliness in Natural Intelligences

-4 Slider 18 September 2014 10:33PM

The challenge of friendliness in Artificial Intelligence is to ensure how a general intelligence will be of utility instead of being destructive or pathologically indifferent to the values of existing individuals or aims and goals of their creation. The current provision of computer science is likely to yield bugs and way too technical and inflexible guidelines of action. It is known to be inadequate to handle the job sufficiently. However the challenge of friendliness is also faced by natural intelligences, those that are not designed by an intelligence but molded into being by natural selection.

We know that natural intelligences do the job adequately enough that we do not think that natural intelligence unfriendliness is a significant existential threat. Like plants do solar energy capturing way more efficently and maybe utilising quantum effects that humans can't harness, we know that natural intelligences are using friendliness technology that is of higher caliber that we can build into machines. However as we progress this technology maybe lacking dangerously behind and we need to be able to apply it to hardware in addition to wetware and potentially boost it to new levels.

The earliest concrete example of a natural intelligence being controlled for friendliness I can think of is Socrates. He was charged for "corruption of the heart of the societys youngters". He defended that his stance of questioning everything was without fault. He was however found quilty even thought the trial could be identified with faults. The jury might have been politically motivated or persuaded and the citizens might have expected the results to not be taken seriously. While Socrates was given a very real possibility of escaping imprisonment and capital punishment he did not circumvent his society operation. In fact he was obidient enough that he acted as his own executioner drinking the poison himself. Because of the kind of farce his teachers death had been Plato lost hope for the principles that lead to such an absurd result him becoming skeptical of democrasy.

However if the situation would have been about a artificial intelligence a lot of things went very right. The intelligences society became scared of him and asked it to die. There was dialog about how the deciders were ignorant and stupid and that nothing questionable had been done. However ultimately when issues of miscommunications had been cleared and the society insisted upon its expression of will instead of circumventing the intervention the intelligence pulled its own plug voluntarily. Therefore Socrates was propably the first friendly (natural) intelligence.

The mechanism used in this case was that of a juridical system. That is a human society recognises that certain acts and individuals are worth restraining for the danger that they pose to the common good. A common method is incarcenation and the threat of it. That is certain bad acts can be tolerated in the wild and corrective action can then be employed. When there is reason to expect bad acts or no reason to expect good acts individuals can be restricted in never being able to act in the first place. Whether a criminal is released early can depend on whether there is reason to expect not to be a repeat offender. That is understanding how an agent acts makes it easier to grant operating priviledges. Such hearings are very analogous to a gatekeeper and a AI in a AI-boxing situation.

However when a new human is created it is not assumed hostile until proven friendly. Rather humans are born innocent but powerless. A fully educated and socialised intelligence is assigned for multiple year observation and control period. These so called "parents" have a very wide freedom on programming principles. However human psychology also has peroid of "peer guidedness" where the opinion of peers becomes important. When a youngter grows his thinking is constantly being monitored and things like time of onset of speech are monitored with interest. They also gain guidance on very trivial thinking skills. While this has culture passing effect it also keeps the parent very updated on what is the mental status of the child. Never is a child allowed to grow or reason extended amounts of time isolated. Thus the task of evaluating whether an unknown individual is friendly or not is not encountered. There is never a need to turing-test that a child "works". There is always a maintainer and it has the equivalent of psychological growth logs.

However despite all these measures we know that small children can be cruel and have little empathy. However instead of shelving them as rejects we either accomodate them with an environment that minimises the harm or direct them to a more responcible path. When a child ask a question on how they should approach a particular kind of situation this can be challenging for the parent to answer. The parent might also resort to giving a best-effort answer that might not be entirely satisfactory or even wrong advice may be given. However children have dialog with their parents and other peers.

An interesting question is does parenting break down if the child is intellectually too developed compared to the parent or parenting environment? It's also worth noting that children are not equipped with a "constitution of morality". Some things they infer from experience. Some ethical rules are thougth them explicitly. They learn to apply the rules and interpret them in different situations. Some rules might be contradictory and some moral authorities trusted more.

Beoynd the individual level groups of people have an mechanism of acccepting other groups. This doesn't always happen without conditions. However here things seem to work much less efficently. If two groups of people differ in values enough they might start a war of ideology against each other. This kind of war usually concludes with physical action instead of arguments. Suppression of Nazi Germany can be seen as friendliness immune reaction. Normally divergent values and issues having countries wanted and could unite against a different set of values that was tried to be imposed by force. However the success Nazis had can debatably be attributed for a lousy conclusion of world war I. The effort extended to build peace varies and contests with other values.

Friendliness migth also have an important component that it is relative to a set of values. A society will support the upring of certain kinds of children with the suppression of certain other kinds. USSR had officers that's sole job was to protect that things were going according to party line. At this point we have trouble getting a computer to follow anyones values. However it might be important to ask "friendly to whom?". The exploration of friendliness is also an exploration in hostility. We want to be hostile towards UFAIs. It would be awful for a AI to be friendly only towards it's inventor, or only towards it's company. However we have been hostile to neardentals. Was that wrong? Would it be a signficant loss to developed sentience if AIs were less than friendly to humans?

If we ask our grandgrandgrandparents on how we should conduct things they might give a different version than we have. It's expectable that our children are capable of going beyond our morality. Ensuring that a societys values are never violated would be to freeze them in time indefinately. In this way there can be danger in developing a too friendly AI. For that AI could never be truly superhuman. In a way if my child asks me a morally challenging question and I change my opinion about it by the result of that conversation it might be a friendliness failure. Instead of imparting values I receive them with the values causal history being in the inside of a young head instead of a cultural heritage of a longlived civilization.

As a civilizaton we have mapped a variety of thoughts and psyche- and organizational strucutres on how they work. The thought space on how an AI might think is poorly mapped. However we are spreading our understandig on cognitive diversity learning about how austistic persons think as well as dolphins. We can establish things liek that some savants are really good with dates and that askingn about dates from that kind of person is more realiable than an ordinary person. To be able to use AI thinking we need to understand what AI thought is. Up to now we have not needed to study in detail how humans think. We can just adapt to the way they do without attending to how it works. But in similar that we need to know the structure of a particle accelerator to be able to say that it provides information about particle behaviour we need to know why it would make sense to take what an AI says seriously. The challenge would be the same if we were asked to listen seriously to a natural intelligence from a foreign culture. Thus the enemy is inferential distance itself rather than the resultant thought processes. For we know that we can create things we don't understand. Thus it's important to understand that doing things you don't understand is a recipe for disaster. And we must not fool ourself that we understand what a machine thinking would be. Only once we have convinced our fellow natural intelligences that we know what we are doing can it make sense to listen to our creations. Socrates could not explain himself so his effect on others was unsafe. If you need to influence others you need to explain why you are doing so.