Hi all,

As part of my PhD I've written a paper developing a new approach to decision theory that I call Meta Decision Theory. The idea is that decision theory should take into account decision-theoretic uncertainty as well as empirical uncertainty, and that, once we acknowledge this, we can explain some puzzles to do with Newcomb problems, and can come up with new arguments to adjudicate the causal vs evidential debate. Nozick raised this idea of taking decision-theoretic uncertainty into account, but he did not defend the idea at length, and did not discuss implications of the idea.

I'm not yet happy to post this paper publicly, so I'll just write a short abstract of the paper below. However, I would appreciate written comments on the paper. If you'd like to read it and/or comment on it, please e-mail me at will dot crouch at 80000hours.org. And, of course, comments in the thread on the idea sketched below are also welcome.

 

Abstract

First, I show that our judgments concerning Newcomb problems are stakes-sensitive. By altering the relative amounts of value in  the transparent box and the opaque box, one can construct situations in which one should clearly one-box, and one can construct situations in which one should clearly two-box. A plausible explanation of this phenomenon is that our intuitive judgments are sensitive to decision-theoretic uncertainty as well as empirical uncertainty: if the stakes are very high for evidential decision theory (EDT) but not for Causal Decision theory (CDT) then we go with EDT's recommendation, and vice-versa for CDT over EDT.

Second, I show that, if we 'go meta' and take decision-theoretic uncertainty into account, we can get the right answer in both the Smoking Lesion case and the Psychopath Button case.

Third, I distinguish Causal MDT (CMDT) and Evidential MDT (EMDT). I look at what I consider to be the two strongest arguments in favour of EDT, and show that these arguments do not work at the meta level. First, I consider the argument that EDT gets the right answer in certain cases. In response to this, I show that one only needs to have small credence in EDT in order to get the right answer in such cases. The second is the "Why Ain'cha Rich?" argument. In response to this, I give a case where EMDT recommends two-boxing, even though two-boxing has a lower average return than one-boxing.

Fourth, I respond to objections. First, I consider and reject alternative explanations of the stakes-sensitivity of our judgments about particular cases, including Nozick's explanation. Second, I consider the worry that 'going meta' leads one into a vicious regress. I accept that there is a regress, but argue that the regress is non-vicious.

In an appendix, I give an axiomatisation of CMDT.

I just finished the first draft of my essay, "Are Sunk Costs Fallacies?"; there is still material I need to go through, but the bulk of the material is now there. The formatting is too gnarly to post here, so I ask everyone's forgiveness in clicking through.

To summarize:

1. sunk costs are probably issues in big organizations
   • but maybe not ones that can be helped
2. sunk costs are not issues in animals
3. they appear to be in children & adults
   • but many apparent problems can be explained as part of a learning strategy
4. there are few clear indications sunk costs are genuine problems
5. much of what we call 'sunk cost' looks like simple carelessness & thoughtlessness

(If any of that seems unlikely or absurd to you, click through. I've worked very hard to provide multiple citations where possible, and fulltext for practically everything.)

I started this a while ago; but Luke/SIAI paid for much of the work, and that motivation plus academic library access made this essay more comprehensive than it would have been and finished months in advance.

 

Followup to: The Savage theorem and the Ellsberg paradox

In the previous post, I presented a simple version of Savage's theorem, and I introduced the Ellsberg paradox. At the end of the post, I mentioned a strong Bayesian thesis, which can be summarised: "There is always a price to pay for leaving the Bayesian Way."¹ But not always, it turns out. I claimed that there was a method that is Ellsberg-paradoxical, therefore non-Bayesian, but can't be money-pumped (or "Dutch booked"). I will present the method in this post.

I'm afraid this is another long post. There's a short summary of the method at the very end, if you want to skip the jibba jabba and get right to the specification. Before trying to money-pump it, I'd suggest reading at least the two highlighted dialogues.

Ambiguity aversion

To recap the Ellsberg paradox: there's an urn with 30 red balls and 60 other balls that are either green or blue, in unknown proportions. Most people, when asked to choose between betting on red or on green, choose red, but, when asked to choose between betting on red-or-blue or on green-or-blue, choose green-or-blue. For some people this behaviour persists even after due calculation and reflection. This behaviour is non-Bayesian, and is the prototypical example of ambiguity aversion.

There were some major themes that came out in the comments on that post. One theme was that I Fail Technical Writing Forever. I'll try to redeem myself.

Another theme was that the setup given may be a bit too symmetrical. The Bayesian answer would be indifference, and really, you can break ties however you want. However the paradoxical preferences are typically strict, rather than just tie-breaking behaviour. (And when it's not strict, we shouldn't call it ambiguity aversion.) One suggestion was to add or remove a couple of red balls. Speaking for myself, I would still make the paradoxical choices.

A third theme was that ambiguity aversion might be a good heuristic if betting against someone who may know something you don't. Now, no such opponent was specified, and speaking for myself, I'm not inferring one when I make the paradoxical choices. Still, let me admit that it's not contrived to infer a mischievous experimenter from the Ellsberg setup. One commentator puts it better than me:

Betting generally includes an adversary who wants you to lose money so they win in. Possibly in psychology experiments [this might not apply] ... But generally, ignoring the possibility of someone wanting to win money off you when they offer you a bet is a bad idea.

Now betting is supposed to be a metaphor for options with possibly unknown results. In which case sometimes you still need to account for the possibility that the options were made available by an adversary who wants you to choose badly, but less often. And you should also account for the possibility that they were from other people who wanted you to choose well, or that the options were not determined by any intelligent being or process trying to predict your choices, so you don't need to account for an anticorrelation between your choice and the best choice. Except for your own biases.

We can take betting on the Ellsberg urn as a stand-in for various decisions under ambiguous circumstances. Ambiguity aversion can be Bayesian if we assume the right sort of correlation between the options offered and the state of the world, or the right sort of correlation between the choice made and the state of the world. In that case just about anything can Bayesian. But sometimes the opponent will not have extra information, nor extra power. There might not even be any opponent as such. If we assume there are no such correlations, then ambiguity aversion is non-Bayesian.

The final theme was: so what? Ambiguity aversion is just another cognitive bias. One commentator specifically complained that I spent too much time talking about various abstractions and not enough time talking about how ambiguity aversion could be money-pumped. I will fix that now: I claim that ambiguity aversion cannot be money-pumped, and the rest of this post is about my claim.

I'll start with a bit of name-dropping and some whig history, to make myself sound more credible than I really am². In the last twenty years or so many models of ambiguity averse reasoning have been constructed. Choquet expected utility³ and maxmin expected utility⁴ were early proposed models of ambiguity aversion. Later multiplier preferences⁵ were the result of applying the ideas of robust control to macroeconomic models. This results in ambiguity aversion, though it was not explicitly motivated by the Ellsberg paradox. More recently, variational preferences⁶ generalises both multiplier preferences and maxmin expected utility. What I'm going to present is a finitary case of variational preferences, with some of my own amateur mathematical fiddling for rhetorical purposes.

Probability intervals

The starting idea is simple enough, and may have already occurred to some LW readers. Instead of using a prior probability for events, can we not use an interval of probabilities? What should our betting behaviour be for an event with probability 50%, plus or minus 10%?

There are some different ways of filling in the details. So to be quite clear, I'm not proposing the following as the One True Probability Theory, and I am not claiming that the following is descriptive of many people's behaviour. What follows is just one way of making ambiguity aversion work, and perhaps the simplest way. This makes sense, given my aim: I should just describe a simple method that leaves the Bayesian Way, but does not pay.

Now, sometimes disjoint ambiguous events together make an event with known probability. Or even a certainty, as in an event and its negation. If we want probability intervals to be additive (and let's say that we do) then what we really want are oriented intervals. I'll use +- or -+ (pronounced: plus-or-minus, minus-or-plus) to indicate two opposite orientations. So, if P(X) = 1/2 +- 1/10, then P(not X) = 1/2 -+ 1/10, and these add up to 1 exactly.

Such oriented intervals are equivalent to ordered pairs of numbers. Sometimes it's more helpful to think of them as oriented intervals, but sometimes it's more helpful to think of them as pairs. So 1/2 +- 1/10 is the pair (3/5,2/5). And 1/2 -+ 1/10 is (2/5,3/5), the same numbers in the opposite order. The sum of these is (1,1), which is 1 exactly.

You may wonder, if we can use ordered pairs, can we use triples, or longer lists? Yes, this method can be made to work with those too. And we can still think in terms of centre, length, and orientation. The orientation can go off in all sorts of directions, instead of just two. But for my purposes, I'll just stick with two.

You might also ask, can we set P(X) = 1/2 +- 1/2? No, this method just won't handle it. A restriction of this method is that neither of the pair can be 0 or 1, except when they're both 0 or both 1. The way we will be using these intervals, 1/2 +- 1/2 would be the extreme case of ambiguity aversion. 1/2 +- 1/10 represents a lesser amount of ambiguity aversion, a sort of compromise between worst-case and average-case behaviour.

To decide among bets (having the same two outcomes), compute their probability intervals. Sometimes, the intervals will not overlap. Then it's unambiguous which is more likely, so it's clear what to pick. In general, whether they overlap or not, pick the one with the largest minimum -- though we will see there are three caveats when they do overlap. If P(X) = 1/2 +- 1/10, we would be indifferent between a bet on X and on not X: the minimum is 2/5 in either case. If P(Y) = 1/2 exactly, then we would strictly prefer a bet on Y to a bet on X.

Which leads to the first caveat: sometimes, given two options, it's strictly better to randomise. Let's suppose Y represents a fair coin. So P(Y) = 1/2 exactly, as we said. But also, Y is independent of X. P(X and Y) = 1/4 +- 1/20, and so on. This means that P((X and not Y) or (Y and not X)) = 1/2 exactly also. So we're indifferent between a bet on X and a bet on not X, but we strictly prefer the randomised bet.

In general, randomisation will be strictly better if you have two choices with overlapping intervals of opposite orientations. The best randomisation ratio will be the one that gives a bet with zero-length interval.

Now let us reconsider the Ellsberg urn. We did say the urn can be a metaphor for various situations. Generally these situations will not be symmetrical. But, even in symmetrical scenarios, we should still re-think how we apply the principle of indifference. I argue that the underlying idea is really this: if our information has a symmetry, then our decisions should have that same symmetry. If we switch green and blue, our information about the Ellsberg urn doesn't change. The situation is indistinguishable, so we should behave the same way. It follows that we should be indifferent between a bet on green and a bet on blue. Then, for the Bayesian, it follows that P(red) = P(green) = P(blue) = 1/3. Period.

But for us, there is a degree of freedom, even in this symmetrical situation. We know what the probability of red is, so of course P(red) = 1/3 exactly. But we can set, say⁷, P(green) = 1/3 +- 1/9, and P(blue) = 1/3 -+ 1/9. So we get P(red or green) = 2/3 +- 1/9, P(red or blue) = 2/3 -+ 1/9, P(green or blue) = 2/3 exactly, and of course P(red or green or blue) = 1 exactly.

So: red is 1/3 exactly, but the minimum of green is 2/9. (green or blue) is 2/3 exactly, but the minimum of (red or blue) is 5/9. So choose red over green, and (green or blue) over (red or blue). That's the paradoxical behaviour. Note that neither pair of choices offered in the Ellsberg paradox has the type of overlap that favours randomisation.

Once we have a decision procedure for the two-outcome case, then we can tack on any utility function, as I explained in the previous post. The result here is what you would expect: we get oriented expected utility intervals, obtained by multiplying the oriented probability intervals by the utility. When deciding, we pick the one whose interval has the largest minimum. So for example, a bet which pays 15U on red (using U for "utils", the abstract units of measurement of the utility function) has expected utility 5U exactly. A bet which pays 18U on green has expected utility 6U +- 2U, the minimum is 4U. So pick the bet on red over that.

Operationally, probability is associated with the "fair price" at which we are willing to bet. A probability interval indicates that there is no fair price. Instead we have a spread: we buy bets at their low price and sell at their high price. At least, we do that if we have no outstanding bets, or more generally, if the expected utility interval on our outstanding bets has zero-length. The second caveat is that if this interval has length, then it affects our price: we also sell bets of the same orientation at their low price, and buy bets of the opposite orientation at their high price, until the length of this interval is used up. The midpoint of the expected utility interval on our outstanding bets will be irrelevant.

This can be confusing, so it's time for an analogy.

Bootsianism

If you are Bayesian and risk-neutral (and if bets pay in "utils" rather than cash, you are risk-neutral by definition) then outstanding bets have no effect on further betting behaviour. However, if you are risk-averse, as is the most common case, then this is no longer true. The more money you've already got on the line, the less willing you will be to bet.

But besides risk attitude, there could also be interference effects from non-monetary payouts. For example, if you are dealing in boots, then you wouldn't buy a single boot for half the price of a pair, and neither would you sell one of your boots for half the price of a pair. Unless you happened to already have unmatched boots, then you would sell those at a lower price, or buy boots of the opposite orientation at a higher price, until you had no more unmatched boots. If you were otherwise risk-neutral with respect to boots, then your behaviour would not depend on the number of pairs you have, just on the number and orientation of your unmatched boots.

This closely resembles the non-Bayesian behaviour above. In fact, for the Ellsberg urn, we could just say that a bet on red is worth a pair of boots, a bet on green is worth two left boots, and a bet on blue is worth two right boots. Without saying anything further, it's clear that we would strictly prefer red (a pair) over green (two lefts), but we would also strictly prefer green-or-blue (two pairs) over red-or-blue (one left and three rights). That's the paradoxical behaviour, but you know you can't money-pump boots.

A: I'll buy that pair of boots for 30 zorkmids. B: Okay, here's your pair of boots. A: And here's your 30 zorkmids. Thank you. B: Thank you. Say, didn't you just buy an identical pair this morning? A: Yeah, I did. Then a dingo ate the right one. I've got the left one here. Never worn. B: How narratively convenient! How much would you sell it for? A: Hmm, how about 10 zorkmids? B: Really, 10 zorkmids? So, do you think right boots are more valuable than left boots? A: No, of course not. Why? B: Arbitrage! A: Gesundheit. B: Thanks. I'll buy a left boot from you for 10 zorkmids. A: Great! Here's your left boot. B: And here's your 10 zorkmids. Thank you. A: Thank you! B: And I'll buy a right boot from you for 10 zorkmids. A: Errrm... Sorry? Why would I agree to that? B: You just sold me a left boot for 10 zorkmids. Well, you yourself said rights aren't more valuable than lefts. So, logically, you should be willing to sell me a right boot for 10 zorkmids. A: What? No.

Boots' rule

So much for the static case. But what do we do with new information? How do we handle conditional probabilities?

We still get P(A|B) by dividing P(A and B) by P(B). It will be easier to think in terms of pairs here. So for example P(red) = 1/3 exactly = (1/3,1/3) and P(red or green) = 2/3 +- 1/9 = (7/9,5/9), so P(red|red or green) = (3/7,3/5) = 18/35 -+ 3/35. And similarly P(green|red or green) = (1/3 +- 1/9)/(2/3 +- 1/9) = 17/35 +- 3/35.

This rule covers the dynamic passive case, where we update probabilities based on what we observe, before betting. The third and final caveat is in the active case, when information comes in between bets. Now, we saw that the length and orientation of the interval on expected utility of outstanding bets affects further betting behaviour. There is actually a separate update rule for this quantity. It is about as simple as it gets: do nothing. The interval can change when we make choices, and its midpoint can shift due to external events, but its length and orientation do not update.

You might expect the update rule for this quantity to follow from the way the expected utility updates, which follows from the way probability updates. But it has a mind of its own. So even if we are keeping track of our bets, we'd still need to keep track of this extra variable separately.

Sometimes it may be easier to think in terms of the total expected utility interval of our outstanding bets, but sometimes it may be easier to think of this in terms of having a "virtual" interval that cancels the change in the length and orientation of the "real" expected utility interval. The midpoint of this virtual interval is irrelevant and can be taken to always be zero. So, on update, compute the prior expected utility interval of outstanding bets, subtract the posterior expected utility interval from it, and add this difference to the virtual interval. Reset its midpoint to zero, keeping only the length and orientation.

That can also be confusing, so let's have another analogy.

Yo' mama's so illogical...

I recently came across this example by Mark Machina:

M: Children, I only have one treat, I can only give it to one of you. I: Me, mama! J: No, give it to me! M: No. Rather than give it to either of you, it's better if I toss a coin. Heads, it goes to Irina, tails, it goes to Joey. ... M: Heads. Irina gets it. J: But mama! M: Fair is fair. I: Yeah Joey! J: But mama, you yourself said it's better to toss a coin than to give it to either of us. So, logically, instead of giving it to Irina you should toss a coin again. M: Nice try, Joey.

Instead of giving the treat to either child, she strictly prefers to toss a coin and give the treat to the winner. But after the coin is tossed, she strictly prefers to give the treat to the winner rather than toss again.

This cannot be explained in terms of maximising expected utility, in the typical sense of "utility". And of course only known probabilities are involved here, so there's no question as to whether her beliefs are probabilistically sophisticated or not. But it could be said that she is still maximising the expected value of an extended objective function. This extended objective function does not just consider who gets a treat, but also considers who "had a fair chance". She is unfair if she gives the treat to either child outright, but fair if she tosses a coin. That fairness doesn't go away when the result of the coin toss is known.

Or something like that. There are surely other ways of dissecting the mother's behaviour. But no matter what, it's going to have to take the coin toss into account, even though the coin, in and of itself, has no relevance to the situation.

Let's go back to the urn. Green and blue have the type of overlap that favours randomisation: P((green and heads) or (blue and tails)) = 1/3 exactly. A bet paying 9U on this event has expected utility of 3U exactly. Let's say we took this bet. Now say the coin comes up heads. We can update the probabilities as per above. The answer is that P(green) = 1/3 +- 1/9 as it was before. That makes sense because it's an independent event: knowing the result of the coin toss gives no information about the urn. The difference is that we now have an outstanding bet that pays 9U if the ball is green. The expected utility would therefore be 3U +- 1U. Except, the expected utility interval was zero-length before the coin was tossed, so it remains zero-length. Equivalently, the virtual interval becomes -+ 1U, so that the effective total is 3U exactly. (In this example, the midpoint of the expected utility interval didn't change either. That's not generally the case.) A bet randomised on a new coin toss would have expected utility 3U, plus the virtual interval of -+ 1U, for an effective total of 3U -+ 1U. So we would strictly prefer to keep the bet on green rather than re-randomise.

Let's compare this with a trivial example: let's say we took a bet that pays 9U if the ball drawn from the urn is green. The expected utility of this bet is 3U +- 1U. For some unrelated reason, a coin is tossed, and it comes up heads. The coin has also nothing to do with the urn or my bet. I still have a bet of 9U on green, and its expected utility is still 3U +- 1U.

But the difference between these two examples is just in the counterfactual: if the coin had come up tails, in the first example I would have had a bet of 9U on blue, and in the second example I would have had a bet of 9U on green. But the coin came up heads, and in both examples I end up with a bet of 9U on green. The virtual interval has some spooky dependency on what could have happened, just like "had a fair chance". It is the ghost of a departed bet.

I expect many on LW are wondering what happened. There was supposed to be a proof that anything that isn't Bayesian can be punished. Actually, this threat comes with some hidden assumptions, which I hope these analogies have helped to illustrate. A boot is an example of something which has no fair price, even if a pair of boots has one. A mother with two children and one treat is an example where some counterfactuals are not forgotten. The hidden assumptions fail in our case, just as they can fail in these other contexts where Bayesianism is not at issue. This can be stated more rigorously⁸, but that is basically how it's possible. Now We Know. And Knowing is Half the Battle.

Notes

1. Taken almost verbatim from Eliezer Yudkowsky's post on the Allais paradox.
2. And footnotes pointing to some tangentially relevant journal articles make me sound extra credible.
3. For Choquet expected utility see: D. Schmeidler, Subjective probability and expected utility without additivity, Econometrica 57 (1989) pp 571-587.
4. For maxmin expected utility see: I. Gilboa and D. Schmeidler, Maxmin expected utility with a non-unique prior, J. Math. Econ. 18 (1989) pp 141-153.
5. For multiplier preferences see: L.P. Hansen and T.J. Sargeant, Robust control and model uncertainty, Amer. Econ. Rev. 91 (2001) pp 60-66.
6. For variational preferences see: F. Maccheroni, M. Marinacci, and A. Rustichini, Dynamic variational preferences, J. Econ. Theory 128 (2006) pp 4-44.
7. Any length between 0 and 1/3 works. But here's where I pulled 1/9 from: a Bayesian might assign exactly 1/61 prior probability to the 61 possible urn compositions, and the result is roughly approximated by the Laplacian rule of succession, which prescribes a pseudocount of one green and one blue ball. A similar thing with probability intervals is roughly approximated by using a pseudocount of 3/2 +- 1/2 green and 3/2 -+ 1/2 blue balls.
8. To quickly relate this back to Savage's rules: rules 1 and 3 guarantee that there's no static money pump. Rule 2 then is supposed to guarantee that there is no dynamic money pump. But it is stronger than necessary for that purpose. I claim that this method obeys rules 1, 3, and a weaker version of rule 2, and that it is dynamically consistent. For dynamic consistency of variational preferences in general, see footnotes above. This method is a special case, for which I wrote up a simpler proof.

Appendix A: method summary

Events are assigned a pair of prior probabilities, which can also be thought of as an oriented probability interval. e.g. (3/5,2/5) can also be thought of as 1/2 +- 1/10. Neither side of the pair can be 0 or 1, except when they're both 0 or both 1. Each side of the pair is additive: if A and B are disjoint, and P(A) = (x,y), and P(B) = (u,v), then P(A or B) = (x+u,y+v). Each side of the pair updates by Bayes' rule: if P(A and B) = (x,y), and P(B) = (u,v), then P(A|B) = (x/u,y/v). Given a utility function, each bet will then have an expected utility interval: multiply the probability intervals by the utility for each possible outcome. There is also a virtual expected utility interval to keep track of. The midpoint of this interval is always zero. When updating the virtual expected utility interval, compute the prior expected utility interval of the outstanding bet(s), subtract the posterior expected utility interval from it, and add this difference to the virtual expected utility interval. Throw away the midpoint (reset the midpoint of the interval to zero, keeping just the length and orientation). To decide among bets: compute the expected utility intervals of each of them -- including already outstanding bets, and including the virtual expected utility interval. Rank them according to the minimum values of the intervals. Implicitly when presented with options we are also presented with the option to randomise among them, and sometimes this is strictly better than any of the pure options.

Appendix B: obligatory image for LW posts on this topic

Followup to: A summary of Savage's foundation for probability and utility.

In 1961, Daniel Ellsberg, most famous for leaking the Pentagon Papers, published the decision-theoretic paradox which is now named after him ¹. It is a cousin to the Allais paradox. They both involve violations of an independence or separability principle. But they go off in different directions: one is a violation of expected utility, while the other is a violation of subjective probability. The Allais paradox has been discussed on LW before, but when I do a search it seems that the first discussion of the Ellsberg paradox on LW was my comments on the previous post ². It seems to me that from a Bayesian point of view, the Ellsberg paradox is the greater evil.

But I should first explain what I mean by a violation of expected utility versus subjective probability, and for that matter, what I mean by Bayesian. I will explain a special case of Savage's representation theorem, which focuses on the subjective probability side only. Then I will describe Ellsberg's paradox. In the next episode, I will give an example of how not to be Bayesian. If I don't get voted off the island at the end of this episode.

Rationality and Bayesianism

Bayesianism is often taken to involve the maximisation of expected utility with respect to a subjective probability distribution. I would argue this label only sticks to the subjective probability side. But mainly, I wish to make a clear division between the two sides, so I can focus on one.

Subjective probability and expected utility are certainly related, but they're still independent. You could be perfectly willing and able to assign belief numbers to all possible events as if they were probabilities. That is, your belief assignment obeys all the laws of probability, including Bayes' rule, which is, after all, what the -ism is named for. You could do all that, but still maximise something other than expected utility. In particular, you could combine subjective probabilities with prospect theory, which has also been discussed on LW before. In that case you may display Allais-paradoxical behaviour but, as we will see, not Ellsberg-paradoxical behaviour. The rationalists might excommunicate you, but it seems to me you should keep your Bayesianist card.

On the other hand your behaviour could be incompatible with any subjective probability distribution. But you could still maximise utility with respect to something other than subjective probability. In particular, when faced with known probabilities, you would be maximising expected utility in the normal sense. So you can not exhibit any Allais-paradoxical behaviour, because the Allais paradox involves only objective lotteries. But you may exhibit, as we will see, Ellsberg-paradoxical behaviour. I would say you are not Bayesian.

So a non-Bayesian, even the strictest frequentist, can still be an expected utility maximiser, and a perfect Bayesian need not be an expected utility maximiser. What I'm calling Bayesianist is just the idea that we should reason with our subjective beliefs the same way that we reason with objective probabilities. This also has been called having "probabilistically sophisticated" beliefs, if you prefer to avoid the B-word, or don't like the way I'm using it.

In a lot of what follows, I will bypass utility by only considering two outcomes. Utility functions are only unique up to a constant offset and a positive scale factor. With two outcomes, they evaporate entirely. The question of maximising expected utility with respect to a subjective probability distribution reduces to the question of maximising the probability, according to that distribution, of getting the better of the two outcomes. (And if the two outcomes are equal, there is nothing to maximise.)

And on the flip side, if we have a decision method for the two-outcome case, Bayesian or otherwise, then we can always tack on a utility function. The idea of utility is just that any intermediate outcome is equivalent to an objective lottery between better and worse outcomes. So if we want, we can use a utility function to reduce a decision problem with any (finite) number of outcomes to a decision problem over the best and worst outcomes in question.

Savage's representation theorem

Let me recap some of the previous post on Savage's theorem. How might we defend Bayesianism? We could invoke Cox's theorem. This starts by assuming possible events can be assigned real numbers corresponding to some sort of belief level on someone's part, and that there are certain functions over these numbers corresponding to logical operations. It can be proven that, if someone's belief functions obey some simple rules, then that person acts as if they were reasoning with subjective probability. Now, while the rules for belief functions are intuitive, the background assumptions are pretty sketchy. It is not at all clear why these mathematical constructs are requirements of rationality.

One way to justify those constructs is to argue in terms of choices a rational person must make. We imagine someone is presented with choices among various bets on uncertain events. Their level of belief in these events can be gauged by which bets they choose. But if we're going to do that anyway, then, as it turns out, we can just give some simple rules directly about these choices, and bypass the belief functions entirely. This was Leonard Savage's approach ³. To quote a comment on the previous post: "This is important because agents in general don't have to use beliefs or goals, but they do all have to choose actions."

Savage's approach actually covers both subjective probability and expected utility. The previous post discusses both, whereas I am focusing on the former. This lets me give a shorter exposition, and I think a clearer one.

We start by assuming some abstract collection of possible bets. We suppose that when you are offered two bets from this collection, you will choose one over the other, or express indifference.

As discussed, we will only consider two outcomes. So all bets have the same payout, the difference among them is just their winning conditions. It is not specified what it is that you win. But it is assumed that, given the choice between winning unconditionally and losing unconditionally, you would choose to win.

It is assumed that the collection of bets form what is called a boolean algebra. This just means we can consider combinations of bets under boolean operators like "and", "or", or "not". Here I will use brackets to indicate these combinations. (A or B) is a bet that wins under the conditions that make either A win, or B win, or both win. (A but not B) wins whenever A wins but B doesn't. And so on.

If you are rational, your choices must, it is claimed, obey some simple rules. If so, it can be proven that you are choosing as if you had a assigned subjective probabilities to bets. Savage's axioms for choosing among bets are ⁴:

1. If you choose A over B, you shall not choose B over A; and, if you do not choose A over B, and do not choose B over C, you shall not choose A over C.
2. If you choose A over B, you shall also choose (A but not B) over (B but not A); and conversely, if you choose (A but not B) over (B but not A), you shall also choose A over B.
3. You shall not choose A over (A or B).
4. If you choose A over B, then you shall be able to specify a finite sequence of bets C₁, C₂, ..., C_n, such that it is guaranteed that one and only one of the C's will win, and such that, for any one of the C's, you shall still choose (A but not C) over (B or C).

Rule 1 is a coherence requirement on rational choice. It is requires your preferences to be a total pre-order. One objection to Cox's theorem is that levels of belief could be incomparable. This objection does not apply to rule 1 in this context because, as we discussed above, we're talking about choices of bets, not beliefs. Faced with choices, we choose. A rational person's choices must be non-circular.

Rule 2 is an independence requirement. It demands that when you compare two bets, you ignore the possibilty that they could both win. In those circumstances you would be indifferent between the two anyway. The only possibilities that are relevant to the comparison are the ones where one bet wins and the other doesn't. So, you ought to compare A to B the same way you compare (A but not B) to (B but not A). Savage called this rule the Sure-thing principle.

Rule 3 is a dominance requirement on rational choice. It demands that you not choose something that cannot do better under any circumstance: whenever A would win, so would (A or B). Note that you might judge (B but not A) to be impossible a priori. So, you might legitimately express indifference between A and (A or B). We can only say it is never legitimate to choose A over (A or B).

Rule 4 is the most complicated. Luckily it's not going to be relevant to the Ellsberg paradox. Call it Mostly Harmless and forget this bit if you want.

What rule 4 says is that if you choose A over B, you must be willing to pay a premium for your choice. Now, we said there are only two outcomes in this context. Here, the premium is paid in terms of other bets. Rule 4 demands that you give a finite list of mutually exclusive and exhaustive events, and still be willing to choose A over B if we take any event on your list, cut it from A, and paste it to B. You can list as many events as you need to, but it must be a finite list.

For example, if you thought A was much more likely than B, you might pull out a die, and list the 6 possible outcomes of one roll. You would also be willing to choose (A but not a roll of 1) over (B or a roll of 1), (A but not a roll of 2) over (B or a roll of 2), and so on. If not, you might list the 36 possible outcomes of two consecutive rolls, and be willing to choose (A but not two rolls of 1) over (B or two rolls of 1), and so on. You could go to any finite number of rolls.

In fact rule 4 is pretty liberal, it doesn't even demand that every event on your list be equiprobable, or even independent of the A and B in question. It just demands that the events be mutually exclusive and exhaustive. If you are not willing to specify some such list of events, then you ought to express indifference between A and B.

If you obey rules 1-3, then that is sufficient for us construct a sort of qualitative subjective probability out of your choices. It might not be quantitative: for one thing, there could be infinitessimally likely beliefs. Another thing is that there might be more than one way to assign numbers to beliefs. Rule 4 takes care of these things. If you obey rule 4 also, then we can assign a subjective probability to every possible bet, prove that you choose among bets as if you were using those probabilities, and also prove that it is the only probability assignment that matches your choices. And, on the flip side, if you are choosing among bets based on a subjective probability assignment, then it is easy to prove you obey rules 1-3, as well as rule 4 if the collection of bets is suitably infinite, like if a fair die is avaialble to bet on.

Savage's theorem is impressive. The background assumptions involve just the concept of choice, and no numbers at all. There are only a few simple rules. Even rule 4 isn't really all that hard to understand and accept. A subjective probability distribution appears seemingly out of nowhere. In the full version, a utility function appears out of nowhere too. This theorem has been called the crowning glory of decision theory.

The Ellsberg paradox

Let's imagine there is an urn containing 90 balls. 30 of them are red, and the other 60 are either green or blue, in unknown proportion. We will draw a ball from the urn at random. Let us bet on the colour of this ball. As above, all bets have the same payout. To be specific, let's say you get pie if you win, and a boot to the head if you lose. The first question is: do you prefer to bet that the colour will be red, or that it will be green? The second question is: do you prefer to bet that it will be (red or blue), or that it will be (green or blue)?

The most common response⁵ is to choose red over green, and (green or blue) over (red or blue). And that's all there is to it. Paradox! ⁶

	30	60
	Red	Green	Blue
A	pie	BOOT	BOOT		A is preferred to B
B	BOOT	pie	BOOT
C	pie	BOOT	pie		D is preferred to C
D	BOOT	pie	pie
					Paradox!

 

If choices were based solely on an assignment of subjective probability, then because the three colours are mutually exclusive, P(red or blue) = P(red) + P(blue), and P(green or blue) = P(green) + P(blue). So, since P(red) > P(green) then P (red or blue) > P(green or blue), but instead we have P(red or blue) < P(green or blue).

Knowing Savage's representation theorem, we expect to get a formal contradiction from the 4 rules above plus the 2 expressed choices. Something has to give, so we'd like to know which rules are really involved. You can see that we are talking only about rule 2, the Sure-thing principle. It says we shall compare (red or blue) to (green or blue) the same way as we compare red to green.

This behaviour has been called ambiguity aversion. Now, perhaps this is just a cognitive bias. It wouldn't be the first time that people behave a certain way, but the analysis of their decisions shows a clear error. And indeed, when explained, some people do repent of their sins against Bayes. They change their choices to obey rule 2. But others don't. To quote Ellsberg:

...after rethinking all their 'offending' decisions in light of [Savage's] axioms, a number of people who are not only sophisticated but reasonable decide that they wish to persist in their choices. This includes people who previously felt a 'first order commitment' to the axioms, many of them surprised and some dismayed to find that they wished, in these situations, to violate the Sure-thing Principle. Since this group included L.J. Savage, when last tested by me (I have been reluctant to try him again), it seems to deserve respectful consideration.

I include myself in the group that thinks rule 2 is what should be dropped. But I don't have any dramatic (de-)conversion story to tell. I was somewhat surprised, but not at all dismayed, and I can't say I felt much if any prior commitment to the rules. And as to whether I'm sophisticated or reasonable, well never mind! Even if there are a number of other people who are all of the above, and even if Savage himself may have been one of them for a while, I do realise that smart people can be Just Plain Wrong. So I'd better have something more to say for myself.

Well, red obviously has a probability of 1/3. Our best guess is to apply the principle of indifference to also assign probability 1/3 to green or blue. But our best guess is not necessarily a good guess. The probabilities we assign to red, and to (green or blue), are objective. We're guessing the probability of green, and of (red or blue). It seems wise to take this difference into account when choosing what to bet on, doesn't it? And surely it will be all the more wise when dealing with real-life, non- symetrical situations where we can't even appeal to the principle of indifference.

Or maybe I'm just some fool talking jibba jabba. Against this sort of talk, the LW post on the Allais paradox presents a version of Howard Raiffa's dynamic inconsistency argument. This makes no references to internal thought processes, it is a purely external argument about the decisions themselves. As stated in that post, "There is always a price to pay for leaving the Bayesian Way." ⁷ This is expanded upon in an earlier post:

Sometimes you must seek an approximation; often, indeed. This doesn't mean that probability theory has ceased to apply, any more than your inability to calculate the aerodynamics of a 747 on an atom-by-atom basis implies that the 747 is not made out of atoms. Whatever approximation you use, it works to the extent that it approximates the ideal Bayesian calculation - and fails to the extent that it departs.

Bayesianism's coherence and uniqueness proofs cut both ways ... anything that is not Bayesian must fail one of the coherency tests. This, in turn, opens you to punishments like Dutch-booking (accepting combinations of bets that are sure losses, or rejecting combinations of bets that are sure gains).

Now even if you believe this about the Allais paradox, I've argued that this doesn't really have much to do with Bayesianism one way or the other. The Ellsberg paradox is what actually strays from the Path. So, does God also punish ambiguity aversion?

Tune in next time⁸, when I present a two-outcome decision method that obeys rules 1, 3, and 4, and even a weaker form of rule 2. But it exhibits ambiguity aversion, in gross violation of the original rule 2, so that it's not even approximately Bayesian. I will try to present it in a way that advocates for its internal cognitive merit. But the main thing ⁹ is that, externally, it is dynamically consistent. We do not get booked, by the Dutch or any other nationality.

Notes

 

1. Ellsberg's original paper is: Risk, ambiguity, and the Savage axioms, Quarterly Journal of Economics 75 (1961) pp 643-669
2. Some discussion followed, in which I did rather poorly. Actually I had to admit defeat. Twice. But, as they say: fool me once, shame on me; fool me twice, won't get fooled again!
3. Savage presents his theorem in his book: The Foundations of Statistics, Wiley, New York, 1954.
4. To compare to Savage's setup: for the two outcome case, we deal directly with "actions" or equivalently "events", here called "bets". We can dispense with "states"; in particular we don't have to demand that the collection of bets be countably complete, or even a power-set algebra of states, just that it be some boolean algebra. Savage's axioms of course have a descriptive interpretation, but it is their normativity that is at issue here, so I state them as "you shall". Rules 1-3 are his P1-P3, and 4 is P6. P4 and P7 are irrelevant in the two- outcome case. P5 is included in the background assumption that you would choose to win. I do not call this normative, because the payoff wasn't specified.
5. Ellsberg originally proposed this just as a thought experiment, and canvassed various victims for their thoughts under what he called "absolutely non-expiremental conditions". He used $100 and $0 instead of pie and a boot to the head. Which is dull of course, but it shouldn't make a difference¹⁰. The experiment has since been repeated under more experimental conditions. The expirementers also invariably opt for the more boring cash payouts.
6. Some people will say this isn't "really" a paradox. Meh.
7. Actually, I inserted "to pay". It wasn't in the original post. But it should have been.
8. Sneak preview
9. As a great decision theorist once said, "Stupid is as stupid does."
10. ...or should it? Savage's rule P4 demands that it shall not. And the method I have in mind obeys this rule. But it turns out this is another rule that God won't enforce. And that's yet another post, if I get to it at all.

 

As I've been reading through various articles and their comments on Less Wrong, I've noticed a theme that has appeared repeatedly: a frustration that we are not seeing more practical benefits from studying rationality. For example, Eliezer writes in A Sense that More Is Possible,

Why aren't "rationalists" surrounded by a visible aura of formidability? Why aren't they found at the top level of every elite selected on any basis that has anything to do with thought? Why do most "rationalists" just seem like ordinary people...

Yvain writes in Extreme Rationality: It's Not That Great,

...I've gotten countless clarity-of-mind benefits from Overcoming Bias' x-rationality, but practical benefits? Aside from some peripheral disciplines, I can't think of any.

patrissimo wrote in a comment on another article,

Sorry, folks, but compared to the self-help/self-development community, Less Wrong is currently UTTERLY LOSING at self-improvement and life optimization.

These writers have also offered some suggestions for improving the situation. Eliezer writes,

Of this [question] there are several answers; but one of them, surely, is that they have received less systematic training of rationality in a less systematic context than a first-dan black belt gets in hitting people.

patrissimo describes what he thinks an effective rationality practice would look like.

1. It is a group of people who gather in person to train specific skills.
2. While there are some theoreticians of the art, most people participate by learning it and doing it, not theorizing about it.
3. Thus the main focus is on local practice groups, along with the global coordination to maximize their effectiveness (marketing, branding, integration of knowledge, common infrastructure). As a result, it is driven by the needs of the learners [emphasis added].
4. You have to sweat, but the result is you get stronger.
5. You improve by learning from those better than you, competing with those at your level, and teaching those below you.
6. It is run by a professional, or at least someone getting paid [emphasis added] for their hobby. The practicants receive personal benefit from their practice, in particular from the value-added of the coach, enough to pay for talented coaches.

Dan Nuffer and I have decided that it's time to stop talking and start doing. We are in the very early stages of creating a business to help people improve their lives by training them in instrumental rationality. We've done some preliminary market research to get an idea of where the opportunities might lie. In fact, this venture got started when, on a whim, I ran a poll on ask500people.com asking,

Would you pay $75 for an interactive online course teaching effective decision-making skills?

I got 299 responses in total. These are the numbers that responded with "likely" or "very likely":

• 23.4% (62) overall.
• 49% (49 of 100) of the respondents from India.
• 10.6% (21 of 199) of the respondents not from India.
• 9.0% (8 of 89) of the respondents from the U.S.

These numbers were much higher than I expected, especially the numbers from India, which still puzzle me. Googling around a bit, though, I found an instructor-led online decision-making course for $130, and a one-day decision-making workshop offered in the UK for £200 (over $350)... and the Google keyword tool returns a large number of search terms (800) related to "decision-making", many of them with a high number of monthly searches.

So it appears that there may be a market for training in effective decision-making -- something that could be the first step towards a more comprehensive training program in instrumental rationality. Some obvious market segments to consider are business decision makers, small business owners, and intelligent people of an analytical bent (e.g., the kind of people who find Less Wrong interesting). An important subset of this last group are INTJ personality types; I don't know if there is an effective way to find and market to specific Meyers-Briggs personality types, but I'm looking into it.

"Life coaching" is a proven business, and its growing popularity suggests the potential for a "decision coaching" service; in fact, helping people with big decisions is one of the things a life coach does. One life coach of 12 years described a typical client as age 35 to 55, who is "at a crossroads, must make a decision and is sick of choosing out of safety and fear." Life coaches working with individuals typically charge around $100 to $300 per hour. As far as I can tell, training in decision analysis / instrumental rationality is not commonly found among life coaches. Surely we can do better.

Can we do effective training online? patrissimo thinks that gathering in person is necessary, but I'm not so sure. His evidence is that "all the people who have replied to me so far saying they get useful rationality practice out of the LW community said the growth came through attending local meetups." To me this is weak evidence -- it seems to say more about the effectiveness of local meetups vs. just reading about rationality. In any event, it's worth testing whether online training can work, since

• not everyone can go to meetups,
• it should be easier to scale up, and
• not to put too fine a point on it, but online training is probably more profitable.

To conclude, one of the things an entrepreneur needs to do is "get out of the building" and talk to members of the target market. We're interested in hearing what you think. What ideas do you think would be most effective in training for instrumental rationality, and why? What would you personally want from a rationality training program? What kinds of products / services related to rationality training would you be interesting in buying?

tl;dr: I present four axioms for anthropic reasoning under copying/deleting/merging, and show that these result in a unique way of doing it: averaging non-indexical utility across copies, adding indexical utility, and having all copies being mutually altruistic.

Some time ago, Eliezer constructed an anthropic trilemma, where standard theories of anthropic reasoning seemed to come into conflict with subjective anticipation. rwallace subsequently argued that subjective anticipation was not ontologically fundamental, so we should not expect it to work out of the narrow confines of everyday experience, and Wei illustrated some of the difficulties inherent in "copy-delete-merge" types of reasoning.

Wei also made the point that UDT shifts the difficulty in anthropic reasoning away from probability and onto the utility function, and ata argued that neither the probabilities nor the utility function are fundamental, that it was the decisions that resulted from them that were important - after all, if two theories give the same behaviour in all cases, what grounds do we have for distinguishing them? I then noted that this argument could be extended to subjective anticipation: instead of talking about feelings of subjective anticipation, we could replace it by questions such as "would I give up a chocolate bar now for one of my copies to have two in these circumstances?"

I then made a post where I applied by current intuitions to the anthropic trilemma, and showed how this results in complete nonsense, despite the fact that I used a bona fide utility function. What we need are some sensible criteria for which to divide utility and probability between copies, and this post is an attempt to figure that out. The approach is similar to expected utility, where a quadruped of natural axioms forced all decision processes to have a single format.

The assumptions are:

1. No intrinsic value in the number of copies
2. No preference reversals
3. All copies make the same personal indexical decisions
4. No special status to any copy.

For a community that endorses Bayesian epistemology we have had surprisingly few discussions about the most famous Bayesian contribution to epistemology: the Dutch Book arguments. In this post I present the arguments, but it is far from clear yet what the right way to interpret them is or even if they prove what they set out to. The Dutch Book arguments attempt to justify the Bayesian approach to science and belief; I will also suggest that any successful Dutch Book defense of Bayesianism cannot be disentangled from decision theory. But mostly this post is to introduce people to the argument and to get people thinking about a solution. The literature is scant enough that it is plausible people here could actually make genuine progress, especially since the problem is related to decision theory.¹

Bayesianism fits together. Like a well-tailored jacket it feels comfortable and looks good. It's an appealing, functional aesthetic for those with cultivated epistemic taste. But sleekness is not a rigourous justification and so we should ask: why must the rational agent adopt the axioms of probability as conditions for her degrees of belief? Further, why should agents accept the principle conditionalization as a rule of inference? These are the questions the Dutch Book arguments try to answer.

The arguments begin with an assumption about the connection between degrees of belief and willingness to wager. An agent with degree of belief b in hypothesis h is assumed to be willing to buy wager up to and including $b in a unit wager on h and sell a unit wager on h down to and including $b. For example, if my degree of belief that I can drink ten eggnogs without passing out is .3 I am willing to bet $0.30 on the proposition that I can drink the nog without passing out when the stakes of the bet are $1. Call this the Will-to-wager Assumption. As we will see it is problematic.

The trolley problem is one of the more famous thought experiments in moral philosophy, and studies by psychologists and anthropologists suggest that the response distributions to its major permutations remain roughly the same throughout all human cultures. Most people will permit pulling the lever to redirect the trolley so that it will kill one person rather than five, but will balk at pushing one fat person in front of the trolley to save the five if that is the only available option of stopping it.

However, in informal settings, where the dilemma is posed by a peer rather than a teacher or researcher, it has been my observation that there is another major category which accounts for a significant proportion of respondents' answers. Rather than choosing to flip the switch, push the fat man, or remain passive, many people will reject the question outright. They will attack the improbability of the premise, attempt to invent third options, or appeal to their emotional state in the provided scenario ("I would be too panicked to do anything",) or some combination of the above, in order to opt out of answering the question on its own terms.

Related to Belief In Belief

Suppose that a neighbor comes to you one day and tells you “There’s a dragon in my garage!” Since all of us have been through this before at some point or another, you may be inclined to save time and ask “Is the dragon by any chance invisible, inaudible, intangible, and does it convert oxygen to carbon dioxide when it breathes?”

The neighbor, however, is a scientific minded fellow and responds “Yes, yes, no, and maybe, I haven’t checked. This is an idea with testable consequences. If I try to touch the dragon it gets out of the way, but it leaves footprints in flour when I sprinkle it on the garage floor, and whenever it gets hungry, it comes out of my garage and eats a nearby animal. It always chooses something weighing over thirty pounds, and you can see the animals get snatched up and mangled to a pulp in its invisible jaws. It’s actually pretty horrible. You may have noticed that there have been fewer dogs around the neighborhood lately.”

This triggers a tremendous number of your skepticism filters, and so the only thing you can think of to say is “I think I’m going to need to see this.”

“Of course,” replies the neighbor, and he sets off across the street, opens the garage door, and is promptly eaten by the invisible dragon.