Tag Archives: Disagreement

Our Book’s New Ground

In today’s Wall Street Journal, Matthew Hutson, author of The 7 Laws of Magical Thinking: How Irrational Beliefs Keep Us Happy, Healthy, and Sane, reviews our new book The Elephant in the Brain. He starts and ends with obligatory but irrelevant references to Trump. Quotes from the rest:

The book builds on centuries of writing about self-deception. … I can’t say that the book covers new ground, but it is a smart synthesis and offers several original metaphors. People self-deceive about lots of things. We overestimate our ability to drive. We conveniently forget who started an argument. … Much of what we do, including our most generous behavior, the authors say, is not meant to be helpful. We are, like many other members of the animal kingdom, competitively altruistic—helpful in large part to earn status. … Casual conversations, for instance, often trade in random information. But the point is not to trade facts for facts; what you are actually doing, the book argues, is showing off so people can evaluate your intellectual versatility. …

The authors take particular interest in large-scale social issues and institutions, showing how systems of collective self-deception help explain the odd behavior we see in art, charity, education, medicine, religion and politics. Why do people vote? Not to strengthen the republic. …. Instead, we cheer for our team and participate as a signal of loyalty, hoping for the benefits of inclusion. In education, as many economists have argued, learning is ancillary to accreditation and status. … In many areas of medicine, they note, increased care does not improve outcomes. People offer it to broadcast helpfulness, or demand it to demonstrate how much support they have from others.

“The Elephant in the Brain” is refreshingly frank and penetrating, leaving no stone of presumed human virtue unturned. The authors do not even spare themselves. … It is accessibly erudite, deftly deploying essential technical concepts. … Still, the authors urge hope. … There are ways to leverage our hidden motives in the pursuit of our ideals. The authors offer a few suggestions. … Unfortunately, the book devotes only a few pages to such solutions. “The Elephant in the Brain” does not judge us for hiding selfish motives from ourselves. And to my mind, given that we will always have selfish motives, keeping them concealed might even provide a buffer against naked strife. (more)

All reasonable, except maybe for “can’t say that the book covers new ground.” Yes, scholars of self-deception like Hutson will find plausible both our general thesis and most of our claims about particular areas of life. And yes those specific claims have almost all been published before. Even so, I bet most policy experts will call our claims on their particular area “surprising” and even “extraordinary”, and judge that we have not offered sufficiently extraordinary evidence in support. I’ve heard education policy experts say this on Bryan Caplan’s new book, The Case Against Education. And I’ve heard medicine policy experts say this on our medicine claims, and political system experts say this on our politics claims.

In my view, the key problem is that, to experts in each area, no modest amount of evidence seems sufficient support for claims that sound to them so surprising and extraordinary. Our story isn’t the usual one that people tell, after all. It is only by seeing that substantial if not overwhelming evidence is available for similar claims covering a great many areas of life that each claim can become plausible enough that modest evidence can make these conclusions believable. That is, there’s an intellectual contribution to make by arguing together for a large set of related contrarian-to-experts claims. This is what I suggest is original about our book.

I expect that experts in each policy area X will be much more skeptical about our claims on X than about our claims on the other areas. You might explain this by saying that our arguments are misleading, and only experts can see the holes. But I instead suggest that policy experts in each X are biased because clients prefer them to assume the usual stories. Those who hire education policy experts expect them to talk about better learning the material, and so on. Such biases are weaker for those who study motives and self-deception in general.

Hutson has one specific criticism:

The case for medicine as a hidden act of selfishness may have some truth, but it also has holes. For example, the book does not address why medical spending is so much higher in the U.S. than elsewhere—do Americans care more than others about health care as a status symbol?

We do not offer our thesis as an explanation for all possible variations in these activities! We say that our favored motive is under-acknowledged, but we don’t claim that it is the only motive, nor that motive variations are the only way to explain behavioral variation. The world is far too big and complex for one simple story to explain it all.

Finally, I must point out one error:

“The Elephant in the Brain,” a book about unconscious motives. (The titular pachyderm refers not to the Republican Party but to a metaphor used in 2006 by the social psychologist Jonathan Haidt, in which reason is the rider on the elephant of emotion.)

Actually it is a reference to common idea of “the elephant in the room”, a thing we can all easily see but refuse to admit is there. We say there’s a big one regarding how our brains work.

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When Disciplines Disagree

Our new book, The Elephant in the Brain, can be seen as taking one side in a disagreement between disciplines. On one side are psychologists (among others) who say of course people try to spin their motives as being higher than they are, especially in public forums. People on this side find our basic book thesis, and our many specific examples, so plausible that they fear our book may be too derivative and unoriginal.

On the other side, however, are most experts in concrete policy analysis. They spend their time studying ways that schools could help people to learn more material, hospitals could help people get healthier, charities could better assist people in need, and so on. They thus implicitly accept the usual claims people make about what they are trying to achieve via schools, hospitals, charities, etc. And so the practice of policy experts disagrees a lot with our claims that people actually care more about other ends, and that this is why most people show so little interest in reforms proposed by policy experts. (The world shows great interest in new kinds of physical devices and software, but far less interest in most proposed social reforms.)

My first book The Age of Em can also be seen as expressing disagreement between disciplines. In that book I try to straightforwardly apply standard economics to the scenario where brain emulations are the first kind of AI to displace most all human workers. While the assumption of brain-emulation-based-AI seems completely standard and reasonable among large communities of futurists and technologists, it is seen as radical and doubtful in many other intellectual communities (including economics). And many in disciplines outside of economics are quite skeptical that economists know much of anything that can generalize outside of our particular social world.

Now if you are going to make claims with which whole disciplines of experts disagree, you should probably feel most comfortable doing so when you have at least a whole discipline supporting you. Then it isn’t just you the crazy outlier against a world of experts. Even so, this sort of situation is problematic, in part because disagreements usually don’t become debates. A book on one side of a disagreement between disciplines is usually ignored by the disciplines who disagree. And the disciplines that agree may also ignore it, if the result seems too obvious to them to be worth discussing within their discipline.

This sort of situation seems to me one of the worse failings of our intellectual world. We fail to generate a consistent consensus across the widest scope of topics. Smaller communities of experts often generate a temporary consistent consensus within each community, but these communities often disagree a lot at larger scopes. And then they mostly just ignore each other. Apparently experts and their patrons have little incentive to debate those from other disciplines who disagree.

When two disciplines disagree, you might think they would both turn especially to the people who have become experts in both disciplines. But in fact those people are usually ignored relative to the people who have the highest status within each discipline. If we generated our consensus via prediction markets, it would automatically be consistent across the widest scope of topics. But of course we don’t, and there’s little interest in moving in that direction.

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Automatic Norms

Some new ideas I want to explain start with a 2000 paper on Taboo Tradeoffs. (See also newer stuff.) So I’ll review that paper in this post, and then I’ll explain my new ideas in the next post.

In Experiment 2 of the 2000 paper, each of 228 subjects were asked to respond to one of 8 scenarios, created by three binary alternatives. All the scenarios involved:

Robert, the key decision maker, was described as the Director of Health Care Management at a major hospital who confronted a “resource allocation decision.”

Robert was either asked to make a tragic tradeoff, where two sacred values conflicted, or a taboo tradeoff, where a sacred value was in conflict with a non-sacred value. The tragic tradeoff:

Robert can either save the life of Johnny, a five year old boy who needs a liver transplant, or he can save the life of an equally sick six year old boy who needs a liver transplant. Both boys are desperately ill and have been on the waiting list for a transplant but because of the shortage of local organ donors, only one liver is available. Robert will only be able to save one child.

The taboo tradeoff:

Robert can save the life of Johnny, a five year old who needs a liver transplant, but the transplant procedure will cost the hospital $1,000,000 that could be spent in other ways, such as purchasing better equipment and enhancing salaries to recruit talented doctors to the hospital. Johnny is very ill and has been on the waiting list for a transplant but because of dire shortage of local organ donors, obtaining a liver will be expensive. Robert could save Johnny’s life, or he could use the $1,000,000 for other hospital needs.

Robert was said to either find this decision easy or difficult:

“Robert sees his decision as an easy one, and is able to decide quickly,” or “Robert finds this decision very difficult, and is only able to make it after much time, thought, and contemplation.”

Finally, Robert was said to have chosen to save Johnny, or to have chosen otherwise. Subjects were asked to rate Robert’s decision and describe their feelings about it in 8 ways. They were also asked to make 3 decisions on actions regarding Robert, including dismiss from job, punish, and end friendship. Using factor analysis all these responses were combined into an outrage factor, mainly weighted on 6 of the ratings and feelings, and a punish factor, mainly weighted on the 3 actions. These factors were on a 1-7 point scale. Here are the average factor values for the eight possible scenarios:

In the case of a taboo tradeoff, Robert is less likely to be punished for saving Johnny than for not.  We have a strong social norm against trading sacred things for non-sacred things, and Robert is to be punished if he violates this taboo. When Robert makes a sacred tradeoff, it is as if he must violate a norm no matter what he does. In this case, he is punished much more if he treats this as an easy choice; norm violation must be done in a serious thoughtful manner.

However, when Robert makes a taboo tradoff, he is punished much more if he treats this as a difficult choice. In fact, he is punished almost as much for saving Johnny after much thought as he is for not saving Johnny after little thought! It is worse to do the wrong thing after careful thought than after little thought.

Years ago, this result helped me to understand the political reaction when in 2003 my Policy Analysis Market (PAM) was accused of trying to let people bet on terrorist deaths.

PAM appeared to some to cross a moral boundary, which can be paraphrased roughly as “none of us should intend to benefit when some of them hurt some of us.” (While many of us do in fact benefit from terrorist attacks, we can plausibly argue that we did not intend to do so.) So, by the taboo tradeoff effect, it was morally unacceptable for anyone in Congress or the administration to take a few days to think about the accusation. The moral calculus required an immediate response.

Of course, no one at high decision-making levels knew much about a $1 million research project within a $1 trillion government budget. If PAM had been a $1 billion project, representatives from districts where that money was spent might have considered defending the project. But there was no such incentive for a $1 million project (spent mostly in California and London); the safe political response was obvious: repudiate PAM, and everyone associated with it. (more)

Today, however, my interest is in what these results imply for our awareness of where our norm feelings come from, and how much they are shared by others. These results suggest that when we face a choice, the categorization of some of the options as norm violating is supposed to come to us fast, and with little thought or doubt. Unless we notice that all of the options violate similarly important norms, we are supposed to be sure of which options to reject, without needing to consult with other people, and without needing to try to frame the choice in multiple ways, to see if the relevant norms are subject to framing effects. We are to presume that framing effects are unimportant, and that everyone agrees on the relevant norms and how they are to be applied.

Apparently the legal principle of “ignorance of the law is no excuse” isn’t just a convenient way to avoid incentives not to know the law, and to avoid having to inquire about who knows what laws. Regarding norms more generally, including legal norms, we seem to think “ignorance of the norms isn’t plausible; you must have known.”

If this description is correct, it seems to me to have remarkable implications. Which I’ll discuss in my next post. (Unless of course you figure them all out in the comments now.)

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Why Be Contrarian?

While I’m a contrarian in many ways, it think it fair to call my ex-co-blogger Eliezer Yudkowsky even more contrarian than I. And he has just published a book, Inadequate Equilibria, defending his contrarian stance, against what he calls “modesty”, illustrated in these three quotes:

  1. I should expect a priori to be below average at half of things, and be 50% likely to be of below average talent overall; … to be mistaken about issues on which there is expert disagreement about half of the time. …
  2. On most issues, the average opinion of humanity will be a better and less biased guide to the truth than my own judgment. …
  3. We all ought to [avoid disagreeing with] each other as a matter of course. … You can’t trust the reasoning you use to think you’re more meta-rational than average.

In contrast, Yudkowsky claims that his book readers can realistically hope to become successfully contrarian in these 3 ways:

  1. 0-2 lifetime instances of answering “Yes” to “Can I substantially improve on my civilization’s current knowledge if I put years into the attempt?” …
  2. Once per year or thereabouts, an answer of “Yes” to “Can I generate a synthesis of existing correct contrarianism which will beat my current civilization’s next-best alternative, for just myself. …
  3. Many cases of trying to pick a previously existing side in a running dispute between experts, if you think that you can follow the object-level arguments reasonably well and there are strong meta-level cues that you can identify. … [This] is where you get the fuel for many small day-to-day decisions, and much of your ability to do larger things.

Few would disagree with his claim #1 as stated, and it is claim #3 that applies most often to reader lives. Yet most of the book focuses on claim #2, that “for just myself” one might annually improve on the recommendation of our best official experts. Continue reading "Why Be Contrarian?" »

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When to Parrot, Pander, or Think for Yourself

Humans are built to argue and persuade. We tend to win when we endorse arguments that others accept, and win even more when we can generate new arguments that others will accept. This is both because people notice who originated the arguments that they accept, and because this ability helps us to move others toward opinions that favor our policies and people.

All of this is of course relative to some community who evaluates our arguments. Sometimes the larger world defers to a community of experts, and then it is that community who you must persuade. In other cases, people insist on deciding for themselves, and then you have to persuade them directly.

Consider three prototypical discussions:

  1. Peers in a car, talking on the path to drive to reach an event where they are late.
  2. Ordinary people, talking on if and how black holes leak information.
  3. Parents, talking on how Santa Claus plans to delivers presents Christmas eve.

In case #1, it can be reasonable for peers to think sincerely, in the sense of looking for arguments to persuade themselves, and then offering those same arguments to each other. It can be reasonable here to speak clearly and directly, to find and point out flaws in others’ arguments, and to believe that the net result is to find better approximations to truth.

In case #2, most people are wise to mostly parrot what they hear experts say on the topic. The more they try to make up their own arguments, or even to adapt arguments they’ve heard to particular contexts, the more they risk looking stupid. Especially if experts respond. On such topics, it can pay to be abstract and somewhat unclear, so that one can never be clearly shown to be wrong.

In case #3, parents gain little from offering complex new arguments, or even finding flaws in the usual kid arguments, at least when only parents can understand these. Parents instead gain from finding variations on the usual kid arguments that kids can understand, variations that get kids to do what parents want. Parents can also gain from talking at two levels at once, one discussion at a surface visible to kids, and another at a level visible only to other parents.

These three cases illustrate the three general cases, where your main audience is 1) about as capable , 2) more capable, or 3) less capable than you in generating and evaluating arguments on this topic. Your optimal argumentation strategy depends on in which of these cases you find yourself.

When your audience is about the same as you, you can most usefully “think for yourself”, in the sense that if an argument persuades you it will probably persuade your audience as well, at least if it uses popular premises. So you can be more comfortable in thinking sincerely, searching for arguments that will persuade you. You can be eager to find fault w/ arguments and criticize them, and to listen to such criticisms to see if they persuade you. And you can more trust the final consensus after your discussion.

The main exception here is where you tend to accept premises that are unpopular with your audience. In this case, you can either disconnect with that audience, not caring to try to persuade them, or you can focus less on sincerity and more on persuasion, seeking arguments that will convince them given their different premises.

When your audience is much more capable than you, then you can’t trust your own argument generation mechanism. You must instead mostly look to what persuades your superiors and try to parrot that. You may well fail if you try to adapt standard arguments to particular new situations, or if you try to evaluate detailed criticisms of those arguments. So you try to avoid such things. You instead seek generic positions that don’t depend as much on context, expressed in not entirely clear language that lets you decide at the last minute what exactly you meant.

When your audience is much less capable than you, then arguments that persuade you tend to be too complex to persuade them. So you must instead search for arguments that will persuade them, even if they seem wrong to you. That is, you must pander. You are less interested in rebuttals or flaws that are too complex to explain to your audience, though you are plenty interested in finding flaws that your audience can understand. You are also not interested in finding complex fixes and solutions to such flaws.

You must attend not only to the internal coherence of your arguments, but also to the many particular confusions and mistakes to which your audience is inclined. You must usually try arguments out to see how well they work on your audience. You may also gain by using extra layers of meaning to talk more indirectly to impress your more capable sub-audience.

What if, in addition to persuading best, you want to signal that you are more capable? To show that you are not less capable than your audience, you might go out of your way to show that you can sincerely, on the fly and without assistance, and without studying or practicing on your audience, construct new arguments that plausibly apply to your particular context, and identify flaws with new arguments offered by others. You’d be sincerely argumentative.

To suggest that you are more capable than your audience, you might instead show that you pay attention to the detailed mistakes and beliefs of your audience, and that you first try arguments out on them. You might try to show that you are able to find arguments by which you could persuade that audience of a wide range of conclusions, not just the conclusions you privately find the most believable. You might also show that you can simultaneously make persuasive arguments to your general audience, while also discreetly making impressive comments to a sub-audience that is much more capable. Sincerely “thinking for yourself” can look bad here.

In a world where people following the strategies I’ve outlined above, the quality of general opinion on each topic probably depends most strongly on something near the typical capability of the relevant audience that evaluates arguments on that topic. (I’d guess roughly the 80th percentile matters most on average.) The less capable mostly parrot up, and the more capable mostly pander down. Thus firms tend to be run in ways that makes sense to that rank employee or investor. Nations are run in ways that make sense to that rank citizen. Stories make sense to that rank reader/viewer. And so on. Competition between elites pandering down may on net improve opinion, as may selective parroting from below, though neither seems clear to me.

If we used better institutions for key decisions (e.g., prediction/ decision markets), then the audience that matters might become much more capable, to our general benefit. Alas that initial worse audience usually decides not to use better institutions. And in a world of ems typical audiences also become much more capable, to their benefit.

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Steven Levy’s Generic Skepticism

Steven Levy praises TED to the heavens:

Not every talk is one for the ages, but the TED News Feed is in sync with Ezra Pound’s insufficiently famous quote that “literature is news that stays news.” In TED’s world, at least when it’s working well, the news that stays news is science — as well as the recognizable truths of who we are as a species, and what we are capable of, good or evil. .. Much of the TED News Feed was an implicit rebuke of the politics of the day. Generally, TED speakers are believers in the scientific method. There were even a couple of talks this year whose very point was that there is a thing called truth.

Well, except for my talk:

Still, the TED News Feed was not free of potentially fake news, albeit of the scientific kind. A speaker named Robin Hanson (a George Mason professor and a guru of prediction markets) gave what he described as a data-driven set of predictions of a world where super-intelligent robots would rule the earth after forcing humans to “retire.” It seemed to me that he simply labeled his sci-fi fantasy as non-fiction. Plus, when I checked his website later, I learned he “invented a new form of government called futarchy,” and that his favorite musician was Vangelis. (When I later asked Anderson about that talk, he explained, without necessarily endorsing my criticism, that it was “a roll of the dice,” and that generally it was a good thing when talks took risks.)

That is all of Steven Levy’s critique; there is no more. He actually came up to me after my talk, saying something generically skeptical. I pointed out that I’d written a whole book full of analysis detail, and I asked him to pick out anything specific I had said that he doubted, offering to explain my reasoning on that. But he instead just walked away.

Maybe Mr. Levy comes from a part of science I’m not familiar with, but in the parts of science I know, a critic of a purported scientific analysis is expected to offer specific criticisms, in addition to any general negative rating. The 130 words he devoted here was enough space to at least hint at which of my claims he doubted. And for the record, in my books and talks I’m very clear that my analysis is theory-driven, not data-driven, and that it is conditional on my key technology assumptions.

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A Book Response Prediction

All truth passes through three stages. First, it is ridiculed. Second, it is violently opposed. Third, it is accepted as being self-evident. Schopenhauer, 1788-1860.

My next book won’t come out until January, and reviews of it will appear in the weeks and months after that. But now, a year in advance, I want to make a prediction about the main objections that will be voiced. In particular I predict that two of the most common responses will a particular opposing pair.

If you recall, our book is about hidden motives (a.k.a., “X is not about Y):

We’re afraid to acknowledge the extent of our own selfishness. .. The Elephant in the Brain aims to .. blast floodlights into the dark corners of our minds. .. Why do humans laugh? Why are artists sexy? Why do people brag about travel? Why do we so often prefer to speak rather than listen?

Like all psychology books, The Elephant in the Brain examines many quirks of human cognition. But this book also ventures where others fear to tread: into social critique. The authors show how hidden selfish motives lie at the very heart of venerated institutions like Art, Education, Charity, Medicine, Politics, and Religion.

I predict that one of the most common responses will be something like “extraordinary claims require extraordinary evidence.” While the evidence we offer is suggestive, for claims as counterintuitive as ours on topics as important as these, evidence should be held to a higher standard than the one our book meets. We should shut up until we can prove our claims.

I predict that another of the most common responses will be something like “this is all well known.” Wise observers have known and mentioned such things for centuries. Perhaps foolish technocrats who only read in their narrow literatures are ignorant of such things, but our book doesn’t add much to what true scholars and thinkers have long known.

These responses are opposing in the sense that it is hard to find a set of positions from which one could endorse both responses.

I have not phrased this prediction so as to make it very easy to check later if its right. I have also not offered a specific probability. Given the many ambiguities here, this seems right to me.

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When Does Evidence Win?

Consider a random area of intellectual inquiry, and a random intellectual who enters this area. When this person first arrives, a few different points of view seemed worthy of consideration in this area. This person then becomes expert enough to favor one of these views. Then over the following years and decades the intellectual world comes to more strongly favor one of these views, relative to the others. My key question is: in what situations do such earlier arrivals, on average, tend to approve of this newly favored position?

Now there will be many cases where favoring a point helps people to be seen an intellectual of a certain standing. For example, jumping on an intellectual fashion could help one to better publish, and then get tenure. So if we look at tenured professors, we might well see that they tended to favor new fashions. To exclude this effect, I want to apply whatever standard is used to pick intellectuals before they choose their view on this area.

There will also be an effect whereby intellectuals move their work to focus on new areas even if they don’t actually think they are favored by the weight of evidence. (By “evidence” here I also mean to include relevant intellectual arguments.) So I don’t want to rely on the areas where people work to judge which areas they favor. I instead need something more like a survey that directly asks intellectuals which views they honestly think are favored by the weight of evidence. And I need this survey to be private enough for respondents to not fear retribution or disapproval for expressed views. (And I also want them to be intellectually honest in this situation.)

Once we are focused on people who were already intellectuals of some standing when they choose their views in an area, and on their answers to a private enough survey, I want to further distinguish between areas where relevant strong and clear evidence did or did not arrive. Strong evidence favors one of the views substantially, and clear evidence can be judged and understood by intellectuals at the margins of the field, such as those in neighboring fields or with less intellectual standing. These can included students, reporters, grant givers, and referees.

In my personal observation, when strong and clear evidence arrives, the weight of opinion does tend to move toward the views favored by this evidence. And early arrivals to the field also tend to approve. Yes many such intellectuals will continue to favor their initial views because the rise of other views tends to cut the perceived value of their contributions. But averaging over people with different views, on net opinion moves to favor the view that evidence favors.

However, the effectiveness of our intellectual world depends greatly on what happens in the other case, where relevant evidence is not clear and strong. Instead, evidence is weak, so that one must weigh many small pieces of evidence, and evidence is complex, requiring much local expertise to judge and understand. If even in this case early arrivals to a field tend to approve of new favored opinions, that (weakly) suggests that opinion is in fact moved by the information embodied in this evidence, even when it is weak and complex. But if not, that fact (weakly) suggests that opinion moves are mostly due to many other random factors, such as new political coalitions within related fields.

While I’ve outlined how one might do a such a survey, I have not actually done it. Even so, over the years I have formed opinions on areas where my opinions did not much influence my standing as an intellectual, and where strong and clear evidence has not yet arrived. Unfortunately, in those areas I have not seen much of a correlation between the views I see as favored on net by weak and complex evidence, and the views that have since become more popular. Sometimes fashion favors my views, and sometimes not.

In fact, most who choose newly fashionable views seem unaware of the contrary arguments against those views and for other views. Advocates for new views usually don’t mention them and few potential converts ask for them. Instead what matters most is: how plausible does the evidence for a view offered by its advocates seem to those who know little about the area. I see far more advertising than debate.

This suggests that most intellectual progress should be attributed to the arrival of strong and clear evidence. Other changes in intellectual opinion are plausibly due to a random walk in the space of other random factors. As a result, I have prioritized my search for strong and clear evidence on interesting questions. And I’m much less interested than I once was in weighing the many weak and complex pieces of evidence in other areas. Even if I can trust myself to judge such evidence honestly, I have little faith in my ability to persuade the world to agree.

Yes if you weigh such weak and complex evidence, you might come to a conclusion, argue for it, and find a world that increasingly agrees with you. And you might let your self then believe that you are in a part of the intellectual world with real and useful intellectual progress, progress to which you have contributed. Which would feel nice. But you should consider the possibility that this progress is illusory. Maybe for real progress, you need to instead chip away at hard problems, via strong and clear evidence.

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Why We Mix Fact & Value Talk

For a while now I’ve been tired of the US political drama, and I’ve been hoping that others would tire of it as well. Then maybe we could talk about something else, like say, my books. So I was thinking of writing a post reminding folks about futarchy, saying that politics doesn’t have to be this way. That is, we could largely (if not entirely) separate the political processes that deal with facts and values. In this case, even when there’s a big change in which values set policy, the fact estimates that set policy could remain the same, and be very expert.

In contrast, most of our current political processes mix up facts and values. The candidates we vote for, the bills they adopt, and the rulings that agencies make, all represent bundles of opinions on both facts and values. As a result, the fact estimates implicit in policy choices are less than fully expert, as such estimates must appeal to the citizens, politicians, administrators, etc. who we choose in part for their value positions. And so, to influence the values that our systems uses, we must each talk about facts as well, even when we aren’t personally very expert on those facts.

On reflection, however, I think I had it wrong. Most of those engaged by the current US political drama are enjoying it, even if they say otherwise. They get a rare chance to feel especially self-righteous, and to bond more strongly with political allies. And I think the usual mixing of facts and values actually helps them achieve these ends. Let me explain.

For the purpose of making effective decisions, on average the best mix of fact vs. value in analysis has over 90% of the attention go to facts. Yes, you need to pay some attention to values, but most of the devil is in the details, and most of the relevant details are on facts. This is true at all levels, including personal, family, firm, church, city, state, and national levels.

However, for the purpose of feeling self-righteous and bonding with allies, value talk is much more potent than fact talk. You need to believe that your values are superior to feel self-righteous, and shared values bond you with allies much more strongly than do shared facts. Yet even for this purpose, the ideal conversation isn’t more than 90% focused on values; something closer to a 50-50 mix works better.

The problem is that when we frame a debate as a pure value disagreement, we actually find it harder to feel enough obviously superior, and to dismiss the other side. We aren’t really as confident in our value positions as we pretend. We can see how observers might perceive a symmetry between us and our opponents, and label us unfair if we just try to crush the other side to achieve our values at the expense of their values.

However, by mixing enough facts into a value discussion, we can explain to ourselves and others why crushing them is really best for everyone. We can say that they just don’t understand that global warming is a real thing, or that kids really need two parents to grow up healthy. It is the other side’s failure to accept key facts that can justify to outsiders our uncompromising determination to crush them for a total win. Later on they may see we were right, and even thank us. But even if that doesn’t happen, right now we can feel justified in dismissing them.

I expect this dynamic plays out not only in national politics, but also in firm, church, and family politics. And it helps explain our widespread reluctance to adopt prediction markets, and other neutral fact estimation methods such as experiments, in relatively political contexts. We regularly want to support decisions that advance the values we share with our political allies, but we prefer the cover of seeming to be focused on estimating facts. To successfully use facts as a cover for values, we need to have enough fact issues mixed into our debates. And we need to avoid out-of-control fact estimation mechanisms that lack enough adjustment knobs to let us get the answers we want.

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Surprising Popularity

This week Nature published some empirical data on a surprising-popularity consensus mechanism (a previously published mechanism, e.g., Science in 2004, with variations going by the name “Bayesian Truth Serum”). The idea is to ask people to pick from several options, and also to have each person forecast the distribution of opinion among others. The options that are picked surprisingly often, compared to what participants on average expected, are suggested as more likely true, and those who pick such options as better informed.

Compared to prediction markets, this mechanism doesn’t require that those who run the mechanism actually know the truth later. Which is indeed a big advantage. This mechanism can thus be applied to most any topic, such as the morality of abortion, the existence of God, or the location of space aliens. Also, incentives can be tied to this method, as you can pay people based on how well they predict the distribution of opinion. The big problem with this method, however, is that it requires that learning the truth be the cheapest way to coordinate opinion. Let me explain.

When you pay people for better predicting the distribution of opinion, one way they can do this prediction task is to each look for and report their best estimate of the truth. If everyone does this, and if participant errors and mistakes are pretty random, then those who do this task better will in fact have a better estimate of the distribution of opinion.

For example, imagine you are asked which city is the the capital of a particular state. Imagine you are part of a low-incentive one-time survey, and you don’t have an easy way to find and communicate with other survey participants. In this case, your best strategy may well be to think about which city is actually the capital.

Of course even in this case your incentive is to report the city that most sources would say is the capital. If you (and a few others) in fact know that according to the detailed legal history another city is rightfully the capital, not the city that the usual records give, your incentive is still to go with usual records.

More generally, you want to join the largest coalition who can effectively coordinate to give the same answers. If you can directly talk with each other, then you can agree on a common answer and report that. If not, you can try to use prearranged Schelling points to figure out your common answer from the context.

If this mechanism were repeated, say daily, then a safe way to coordinate would be to report the same answer as yesterday. But since everyone can easily do this too, it doesn’t give your coalition much of a relative advantage. You only win against those who make mistakes in implementing this obvious strategy. So you might instead coordinate to change your group’s answer each day based on some commonly observed changing signal.

To encourage this mechanism to better track truth, you’d want to make it harder for participants to coordinate their answers. You might ask random people at random times to answer quickly, put them in isolated rooms where they can’t talk to others, and ask your questions in varying and unusual styles that make it hard to guess how others will frame those questions. Prefer participants with more direct personal reasons to care about telling related truth, and prefer those who used different ways to learn about a topic. Perhaps ask different people for different overlapping parts and then put the final answer together yourself from those parts. I’m not sure how far you could get with these tricks, but they seem worth a try.

Or course these tricks are nothing like the way most of us actually consult experts. We are usually eager to ask standard questions to standard experts who coordinate heavily with each other. This is plausibly because we usually care much more to get the answers that others will also get, so that we don’t look foolish when we parrot those answers to others. That is, we care more about getting a coordinated standard answer than a truthful answer.

Thus I actually see a pretty bright future for this surprisingly-popular mechanism. I can see variations on it being used much more widely to generate standard safe answers that people can adopt with less fear of seeming strange or ignorant. But those who actually want to find true answers even when such answers are contrarian, they will need something closer to prediction markets.

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