Monthly Archives: May 2014

Who/What Should Get Votes?

Alex T. asks Should the Future Get a Vote? He dislikes suggestions to give more votes to “civic organizations” who claim to represent future folks, since prediction markets could be more trustworthy:

Through a suitable choice of what is to be traded, prediction markets can be designed to be credibly motivated by a variety of goals including the interests of future generations. … If all we cared about was future GDP, a good rule would be to pass a policy if prediction markets estimate that future GDP will be higher with the policy than without the policy. Of course, we care about more than future GDP; perhaps we also care about environmental quality, risk, inequality, liberty and so forth. What Hanson’s futarchy proposes is to incorporate all these ideas into a weighted measure of welfare. … Note, however, that even this assumes that we know what people in the future will care about. Here then is the final meta-twist. We can also incorporate into our measure of welfare predictions of how future generations will define welfare. (more)

For example, we could implement a 2% discount rate by having official welfare be 2% times welfare this next year plus 98% times welfare however it will be defined a year from now. Applied recursively, this can let future folks keep changing their minds about what they care about, even future discount rates.

We could also give votes to people in the past. While one can’t change the experiences of past folks, one can still satisfy their preferences. If past folks expressed particular preferences regarding future outcomes, those preferences could also be given weight in an overall welfare definition.

We could even give votes to animals. One way is to make some assumptions about what outcomes animals seem to care about, pick ways to measure such outcomes, and then include weights on those measures in the welfare definition. Another way is to assume that eventually we’ll “uplift” such animals so that they can talk to us, and put weights on what those uplifted animals will eventually say about the outcomes their ancestors cared about.

We might even put weights on aliens, or on angels. We might just put a weight on what they say about what they want, if they ever show up to tell us. If they never show up, those weights stay set at zero.

Of course just because we could give votes to future folks, past folks, animals, aliens, and angels doesn’t mean we will ever want to do so.

GD Star Rating
Tagged as: , , ,

Let’s Talk About Race

A Post OpEd by Jonathan Capehart:

That honest conversation about race everyone wants? We can’t handle it. … We say we want the conversation. But we just can’t handle it — especially in public. … [In 2008,] I would have wanted to hear a white Southern Republican such as Barbour give an honest speech on race from his perspective, in an effort to explain and heal. It might have proved uncomfortable, but we would have listened, learned and moved forward with the knowledge gained. But I also understand Barbour’s reticence. To deliver such a speech, with power and nuance, would mean putting one’s livelihood — in politics and business — on the line. It would require a bravery and selflessness few could muster. (more)

Capehart dares us to prove him wrong. So let me try. (At least at a meta-level.)

Today academia has a pecking order. For example, math is high while education studies are low. Academics sometimes argue about this order, mentioning arguments for and against each discipline. Sometimes people invoke misleading stereotypes, and sometimes others correct them. While misconceptions remain common, we probably still have more accurate beliefs on how disciplines differ than we would if these conversations were forbidden.

Long ago when issues of race and gender equality were first raised in TV shows, I remember (as a kid) seeing characters argue about the differing features of various races, genders, etc. Claims were made, rebutted, etc. This helped I think. But today it is never ok, even in private, to describe any negative tendencies of “low” races, nor any positive tendencies of “high” races, at least if that suggests others have those tendencies less. And this basically bans the sort of useful talk that academics now have about their pecking order. A similar ban holds for much of gender talk.

The reason that such talk is useful is that it is generally harder to evaluate behaviors and people outside of the cultures and roles that you know best. In the cultures I know best, such as academic economics or research software, I feel at least modestly competent to evaluate behaviors and people, especially for people who take on the same roles that I have taken.

Yes, even there people vary greatly in personality, smarts, experience, etc., but I have collected many standard tricks for discerning such things. The fact that folks from another race or gender might have somewhat different means or variances doesn’t matter that much, as long as my standard tricks work similarly for them. It hasn’t seemed hard for me to deal fairly with folks from other races and genders, as long they stayed close to roles I knew well, centered within cultures I knew well.

However, the further that people and contexts get from the cultures and roles that I know best, the less reliable are my standard tricks. People from other races and genders often have experienced substantially differing cultures and roles than the ones I’m most familiar with. So to make sense of behavior in such cases, I have to fall back somewhat onto beliefs about which of my usual tricks degrade how fast as various parameters change with cultures and roles. That is, I must rely on stereotypes about what tends to vary by cultures and roles, and it is too easy to be wrong about those. In particular I must rely on my best guesses about how many things differ for the different cultures and roles associated with different races and genders.

Sometimes people say you shouldn’t use stereotypes, but should instead just “judge each person and situation by itself.” But you just can’t do that if you don’t know how to interpret what you see. Since behaviors and features change with cultures, you need some sense of the cultural origins of what you see in order to interpret it. And since we all can’t immerse ourselves in depth in many different cultures, we need to talk to each other to share what we’ve seen.

If academics weren’t allowed to say bad things about the culture of education studies, nor good things about the culture of math, I expect we’d mostly just stop talking how these cultures differ. But we’d be pretty sure that there are differences, and that all cultures have both good and bad aspects. So we’d have stereotypes, and use them when doing so wasn’t overly visible. Similarly, our effective ban on race and gender talk doesn’t stop us from believing that many important things change with the differing cultures and roles that have correlated with races and genders. Nor does it keep us from often acting on such beliefs.

Our choice to ban saying bad things about “low” races and genders, or saying good things about “high” races and genders, was clearly a costly signal, and it did send the message “we care enough about keep good relations with you to pay this cost.” But part of the cost was to make it harder to use talk to reduce the impact of misleading race and gender stereotypes on our actions. We might have been better off to instead pay a different kind of cost, such as cash transfers.

I’m basically invoking the usual argument for the info value of free speech here. It is an argument that is often given lip service, but alas our commitment to it is far weaker than our lip service would suggest.

Added 14May: Maybe when people say they want a “conversation about race”, they don’t mean that old white men should do any talking beyond nodding agreement and sympathy with other speakers.

GD Star Rating
Tagged as: , , ,

Why Do Firms Buy Ads?

Firms almost never have enough data to justify their belief that ads work:

Classical theories assume the firm has access to reliable signals to measure the causal impact of choice variables on profit. For advertising expenditure we show, using twenty-five online field experiments with major U.S. retailers and brokerages ($2.8 million expenditure), that this assumption typically does not hold. Evidence from the randomized trials is very weak because individual-level sales are incredibly volatile relative to the per capita cost of a campaign — a “small” impact on a noisy dependent variable can generate positive returns. A calibrated statistical argument shows that the required sample size for an experiment to generate informative confidence intervals is typically in excess of ten million person-weeks. This also implies that selection bias unaccounted for by observational methods only needs to explain a tiny fraction of sales variation to severely bias observational estimates. We discuss how weak informational feedback has shaped the current marketplace and the impact of technological advances moving forward. (more; HT Bo Cowgill)

More striking quotes below. The paper offers management consulting and nutrition supplements as examples of other products that people rarely have sufficient evidence to justify. In fact, I wouldn’t be surprised if this applied to a large fraction of what we and firms buy: we buy because others say it works, and we don’t have data to disprove them.

More striking quotes:  Continue reading "Why Do Firms Buy Ads?" »

GD Star Rating
Tagged as:

Info As Excuse

When we try to justify our actions, we prefer to do so by citing a common general good that results from our actions. But of course we often have other stronger motives for our actions, motives that we are less eager to highlight.

One big category of examples here are info justifications. When we endorse a policy, we often point out how it may tend to encourage info to be generated, spread, or aggregated. After all, who could be against more info? But the details of the policies we endorse often belie that appearance, as we pick details that reduce and discourage info. Because we have other agendas.

For example:

  1. We say free speech is to elicit more better info, but for that it should instead be free hearing.
  2. We say meetings are to gain info, but they are more to show who controls, who allied with whom.
  3. We say we hire college grads because of all they’ve learned, but they don’t learn much there.
  4. We say court proceedings are to get info to decide guilt, but then rules of evidence cut out info.
  5. We say managers are to collect info to make key decisions, but they are more motivators and politicians.
  6. We say diverse groups are good as they get diverse info, but most kinds don’t, they just make distance.
  7. We say voting is to get info on better policies, but the better informed don’t get more votes.
  8. We say voting is to get info on better policies, but we don’t use random juries of voters, who would get more info.
  9. We say we travel to learn, but we can usually learn lots cheaper at home.
  10. We say we read news to gain useful info, but very little of it has much use to us.

Have more good examples?

GD Star Rating
Tagged as:

Michael Covel Interview

Investment advisor Michael Covel interviewed me on prediction markets for his podcast show here. I couldn’t be very encouraging about his main strategy of trend-following, but we covered many interesting issues.

GD Star Rating
Tagged as:

Fixing Academia Via Prediction Markets

When I first got into prediction markets twenty five years ago, I called them “idea futures”, and I focused on using them to reform how we deal with controversies in science and academia (see here, herehere, here). Lately I’ve focused on what I see as the much higher value application of advising decisions and reforming governance (see herehere, here, here). I’ve also talked a lot lately about what I see as the main social functions of academia (see here, here, here, here). Since prediction markets don’t much help to achieve these functions, I’m not optimistic about the demand for using prediction markets to reform academia.

But periodically people do consider using prediction markets to reform academia, as did Andrew Gelman a few months ago. And a few days ago Scott Alexander, who I once praised for his understanding of prediction markets, posted a utopian proposal for using prediction markets to reform academia. These discussions suggest that I revisit the issue of how one might use prediction markets to reform academia, if in fact enough people cared enough about gaining accurate academic beliefs. So let me start by summarizing and critiquing Alexander’s proposal.

Alexander proposes prediction markets where anyone can post any “theory” broadly conceived, like “grapes cure cancer.” (Key quotes below.) Winning payouts in such market suffer a roughly 10% tax to fund experiments to test their theories, and in addition some such markets are subsidized by science patron orgs like the NSF. Bettors in each market vote on representatives who then negotiate to pick someone to pay to test the bet-on theory. This tester, who must not have a strong position on the subject, publishes a detailed test design, at which point bettors could leave the market and avoid the test tax. “Everyone in the field” must make a public prediction on the test. Then the test is done, winners paid, and a new market set up for a new test of the same question. Somewhere along the line private hedge funds would also pay for academic work in order to learn where they should bet.

That was the summary; here are some critiques. First, people willing to bet on theories are not a good source of revenue to pay for research. There aren’t many of them and they should in general be subsidized not taxed. You’d have to legally prohibit other markets to bet on these without the tax, and even then you’d get few takers.

Second, Alexander says to subsidize markets the same way they’d be taxed, by adding money to the betting pot. But while this can work fine to cancel the penalty imposed by a tax, it does not offer an additional incentive to learn about the question. Any net subsidy could be taken by anyone who put money in the pot, regardless of their info efforts. As I’ve discussed often before, the right way to subsidize info efforts for a speculative market is to subsidize a market maker to have a low bid-ask spread.

Third, Alexander’s plan to have bettors vote to agree on a question tester seems quite unworkable to me. It would be expensive, rarely satisfy both sides, and seems easy to game by buying up bets just before the vote. More important, most interesting theories just don’t have very direct ways to test them, and most tests are of whole bundles of theories, not just one theory. Fourth, for most claim tests there is no obvious definition of “everyone in the field,” nor is it obvious that everyone should have opinion on those tests. Forcing a large group to all express a public opinion seems a huge cost with unclear benefits.

OK, now let me review my proposal, the result of twenty five years of thinking about this. The market maker subsidy is a very general and robust mechanism by which research patrons can pay for accurate info on specified questions, at least when answers to those questions will eventually be known. It allows patrons to vary subsidies by questions, answers, time, and conditions.

Of course this approach does require that such markets be legal, and it doesn’t do well at the main academic function of credentialing some folks as having the impressive academic-style mental features with which others like to associate. So only the customers of academia who mainly want accurate info would want to pay for this. And alas such customers seem rare today.

For research patrons using this market-maker subsidy mechanism, their main issues are about which questions to subsidize how much when. One issue is topic. For example, how much does particle physics matter relative to anthropology? This mostly seems to be a matter of patron taste, though if the issue were what topics should be researched to best promote economic growth, decision markets might be used to set priorities.

The biggest issue, I think, is abstraction vs. concreteness. At one extreme one can ask very specific questions like what will be the result of this very specific experiment or future empirical measurement. At the other extreme, one can ask very abstract questions like “do grapes cure cancer” or “is the universe infinite”.

Very specific questions offer bettors the most protection against corruption in the judging process. Bettors need worry less about how a very specific question will be interpreted. However, subsidies of specific questions also target specific researchers pretty directly for funding. For example, subsidizing bets on the results of a very specific experiment mainly subsidizes the people doing that experiment. Also, since the interest of research patrons in very specific questions mainly results from their interest in more general questions, patrons should prefer to directly target the more general questions directly of interest to them.

Fortunately, compared to other areas where one might apply prediction markets, academia offers especially high hopes for using abstract questions. This is because academia tends to house society’s most abstract conversations. That is, academia specializes in talking about abstract topics in ways that let answers be consistent and comparable across wide scopes of time, space, and discipline. This offers hope that one could often simply bet on the long term academic consensus on a question.

That is, one can plausibly just directly express a claim in direct and clear abstract language, and then bet on what the consensus will be on that claim in a century or two, if in fact there is any strong consensus on that claim then. Today we have a strong academic consensus on many claims that were hotly debated centuries ago. And we have good reasons to believe that this process of intellectual progress will continue long into the future.

Of course future consensus is hardly guaranteed. There are many past debates that we’d still find to hard to judge today. But for research patrons interested in creating accurate info, the lack of a future consensus would usually be a good sign that info efforts in that area less were valuable than in other areas. So by subsidizing markets that bet on future consensus conditional on such a consensus existing, patrons could more directly target their funding at topics where info will actually be found.

Large subsidies for market-makers on abstract questions would indirectly result in large subsidies on related specific questions. This is because some bettors would specialize in maintaining coherence relationships between the prices on abstract and specific questions. And this would create incentives for many specific efforts to collect info relevant to answering the many specific questions related to the fewer big abstract questions.

Yes, we’d  probably end up with some politics and corruption on who qualifies to judge later consensus on any given question – good judges should know the field of the question as well as a bit of history to help them understand what the question meant when it was created. But there’d probably be less politics and lobbying than if research patrons choose very specific questions to subsidize. And that would still probably be less politics than with today’s grant-based research funding.

Of course the real problem, the harder problem, is how to add mechanisms like this to academia in order to please the customers who want accuracy, while not detracting from or interfering too much with the other mechanisms that give the other customers of academia what they want. For example, should we subsidize high relevant prestige participants in the prediction markets, or tax those with low prestige?

Those promised quotes: Continue reading "Fixing Academia Via Prediction Markets" »

GD Star Rating
Tagged as: , ,

Factory+Files Future

The difficulty of practical interstellar travel is horrendously underestimated. … Known physics will never deposit living people on Earth-like planets around other stars. (more)

That was Donald Brownlee, who said something similar in our film. It occurs to me that skepticism about cryonics and interstellar travel have similar roots, and that understanding this is useful. So let me explain.

Imagine that one tried to take a rock, say this fossil:


and put it somewhere on Earth so that it could be found in a million years. Or that one tried to throw this fossil rock so that it would pass close to a particular distant star in a million years. Few would claim that doing so is impossible. Most would accept that these are possible, even if we require that the rock (plus casing) remain largely unchanged, i.e., retain its shape and maybe even most of its embedded DNA snips.

So skepticism about making people last a long time via cryonics, or about getting people to distant stars, is mainly about how people differ from rocks. People are fragile biological systems than slowly degrade with time, and that can be easily disrupted by environmental disturbances. Which justifies some doubt on if the human body can survive long difficult paths in space-time.

So why am I more hopeful? Because there are (at least) two ways to ensure that a certain kind of object exists at certain destination in space-time. One way is to have an object of that kind exist at a prior point in space-time, and then move it from that prior point to the destination. The other way is to build the desired object at the destination. That is, have a spec file that describes the object, and have a factory at the destination follow that spec file to create the object. One factory can make many objects, factories and files can be lighter and hardier than other objects, and you might even be able to make all the particular factories you need from one smaller hardier general factory. Thus it can be much easier to get one factory+files to a distant destination than to get many desired objects there.

Yes, today we don’t have factories that can make humans from a spec file. But if our society continues to grow in size and abilities, it should be able to do the next best thing: make an android emulation of a human from a spec file. And we should be able to make a spec file from a frozen brain plus a generic spec file.

If so, a frozen brain will serve as a temporary spec file, and we will be able to send many people to distant stars by sending just one hardy factory there, and then transmitting lots of spec files. The ability to encode a person in a spec file will make it far easier to send a person to a wide range of places and times in the universe.

See David Brin’s novel Existence for an elaboration on the throwing rocks with files theme.

GD Star Rating
Tagged as: , ,

Should You Kiss Ass?

Consider two possible work strategies. One strategy is just to try to do a good job. The other is to try to kiss ass and please your boss any way you can. Of course you can try either strategy, both, or neither. Which makes four different kinds of workers. Now ask yourself, of these four kinds of workers, which ones do you think achieve the most career success? Which ones have the most job and life satisfaction?

I came across a fascinating paper (ungated here) from 1994 that asked exactly this question. Looking at 500 ex students of industrial relations, they compared the effect of ass-kissing to doing a good job on success and job satisfaction.

Supervisor-focused tactics … include: agree with your immediate supervisor’s ideas; praise your immediate supervisor on his or her accomplishments; agree with your supervisor’s major opinions outwardly even when you disagree inwardly. Job-focused tactics … include: make others aware of your accomplishments in your job; try to take responsibility for positive events even when you are not solely responsible; arrive at work early in order to look good in front of others.

The result: workers who try to please their boss are more successful in their careers, and workers who try to seem good at their jobs are less successful. Boss-pleasers are also more satisfied with their job and life, while good-jobbers aren’t any more or less satisfied.

The only other thing that predicted satisfaction: being married. Other things that predicted job success: being married, being on the job many years, working more hours per week, and not having a PhD.

We like to act like we just want to do a good job, and would rather not have bosses breathing down our necks. But what if, we actually like kissing ass?

Please speak up if you know of any more recent that might confirm or disconfirm these results.

GD Star Rating
Tagged as: , ,

How Important Is Inequality?

My post last August on “Inequality Talk is About Grabbing” got 215 comments, which may be a record. In that post I tried to explain why most inequality talk focuses on the small part of inequality that is financial inequality at a given time between the families of a nation. I suggested that the reason is that we think we could grab lots of money from the rich without much pain to the rest of us.

Recently Piketty’s book has induced lots of folks to express deep concern about the same sort of inequality. Piketty says rising inequality is due to owners of capital getting higher returns than the economic growth rate, and recommends taxing capital. Yes that would greatly reduce the total amount of capital in the long run, and therefore also cut future wages and wealth, but gosh darn it inequality is harmful enough to be worth paying such a huge price to reduce it. This is a crisis and something must be done! So say Piketty’s loud chorus of fans.

But consider what a more expert source says is the real reason of rising (between-family within-nation at-a-time) inequality:

In summary, the authors attribute the growth of household inequality to three interacting forces. The first is rising returns to education. Earnings across educational classes have become more polarized. The second factor is increased positive assortative mating. People with similar socioeconomic backgrounds tend increasingly to marry each other, exacerbating income inequality. Third, the increase in married female labor force participation has heightened inequality, and has also made women’s earnings an increasingly important determinant of household income inequality. (more)

Do you think those who thought rising inequality was a crisis justifying our destroying lots of long run capital also think it important enough to justify reducing education, assortative mating, and female work, if those are the causes? Yeah, me neither. And that’s what you’d expect if grabbing is more the motive here than cutting inequality.

GD Star Rating
Tagged as: , ,

Open Thread

This is our monthly place to discuss related topics that have not appeared in recent posts.

GD Star Rating
Tagged as: