Variety Is Shallow

I haven’t read that much in the field of marketing, but what I have read so far has tended to confirm what I’ve read and taught in economics industrial organization: firms try hard to make products have distinctive feature packages in order to gain market power over customers whose ideal product is closer to that package. Even if some of those features are symbolic and created via how ads make people see products.

Reading the book How Brands Grow: What Marketers Don’t Know, by Byron Sharp, leads me to doubt this usual story. Sharp presents a lot of data (some shown in these figures for the Audible version) in support of these points (from this summary):

1. Penetration is key .. all brands have similar levels of loyalty.
2. Light users are as or even more important as heavy ones
3. Leading brands are distinctive, not different
4. Create memory structure to build “mental availability”
5. The power of “physical availability”
6. People don’t want a love affair with most brands

Most price promotions, and ads that don’t reinforce easy-to-recall product cues, are wasted money. We can model the distribution of purchase choices pretty well via these assumptions:

A. People vary a lot in how often they buy from any given product category; most buys are from very infrequent buyers, who mostly buy from other brands.
B. People have a some “loyalty” in having a better than random (but far from certain) chance to buy the same brand choice as last time.
C. If they don’t pick the same brand as last time, buyers pick a brand at random in proportion to product popularity.

Product categories are surprisingly large. For example, to a first approximation all fast food competes nearly equally with all other fast food. It isn’t that pizza places compete mainly pizza places while burger places compete mainly with other burger places. There are some exceptions, such a rich people tending more to buy expensive brands, or people with kids tending to buy books for kids, but these are rare and weak. Mostly there is no “space” of product features; there is just a set of distinct but equally different options, some more popular than others.

Over the last century consumers have moved to choosing a LOT more product variety, which ends up being expensive because of all the fixed costs to support all those different products. We like to tell ourselves that we do this because the new products we pick are closer to the ideal points of our complex authentic identity. Marketers like to tell the same thing to us, and to the firms who buy marketing services. But in fact we just want the appearance of having specific feature packages we like that fit who we are; we mostly don’t actually have coherent identities, but instead just wander around in the space of available products.

This greatly strengthens the welfare case for reducing product variety, in order to reduce unit costs. It seems we are mostly trying to gain a zero-sum status via showing off our wealth and not-actually-there distinct identity.

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Cities As Harems

Many animal species are organized into harems, wherein a single male dominates a group of females and their children. When males become adult, they must leave home and wander singly or in small male groups hoping to tempt harem females into liaisons or to start new harems.

I’ve heard that polygamous sects are often run this way today, with older men kicking out young men when they come of age. But re-reading Montaillou on rural 1300 France makes me realize that humanity has long has related harem-like gender patterns.

Back in 1300 France, centrality gave status. The biggest cities were at the top, above towns and then villages. At the bottom were woodcutters and shepards, all male, who spent most of their time wandering far from villages or towns. Along with soldiers and sailors, these men lived dangerous low-status high-mobility lives in sparse areas. They sometimes tempted women into liaisons, or made it rich enough to start a family in a village. Such mating strategies may explain why such men moved so often even they were poor and moving is expensive.

Back in the high status centers, there remained a few high status men and women, many low status women, but fewer low status men. The lower status women were often servants to high status males, and often had affairs with them.

In the US today, the states with the most men relative to women are Alaska, Wyoming, North Dakota, Nevada, Utah, and Montana — mostly harsher low density areas. In contrast, the states with the most women relative to men are District of Columbia, Rhode Island, Maryland, Massachusetts, New York, near some of our biggest high status cities. Most big US cities have more women than men. The exceptions are San Jose, San Francisco, Las Vegas, Honolulu, Austin, Seattle, San Diego, places with new booming, mostly tech, industries. Men are more willing to move to try new often-harsher industries and places.

We hear college-educated women complain today that there aren’t enough college-educated men to go around, either during college itself or afterward. Of course there are plenty of other men around, but these women mostly consider such men beneath them. Seems to me this isn’t that different from 1300 France; women are more eager to locate near high status people. They focus on high status men, and lament there aren’t enough to go around.

Sometimes people fear today that low status men unhappy from being unable to find women will cause havoc. But in the past men avoided such feelings successfully by just avoiding women. By rarely seeing women they less often felt the envy that might cause havoc. If there’s a bigger problem today it might be because low status men more often come into contact with attractive but unavailable women. From this perspective, maybe low status men avoiding women via male-oriented video games isn’t such a bad thing?

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Age of Em in Amsterdam

At 6pm on Tuesday, 24 November 2015, I’ll speak at Amsterdam University College on:

The Age of Em: Work, Love and Life when Robots Rule the Earth

Robots may one day rule the world, but what is a robot-ruled earth like? Many think the first truly smart robots will be brain emulations or ems. Scan a human brain, then run a model with the same connections on a fast computer and you have a robot brain, but recognisably human. Ems make us question common assumptions of moral progress because they reject many of the values we hold dear. Applying decades of expertise in physics, computer science and economics, Robin Hanson uses standard theories to paint a detailed picture of a world dominated by ems. (more)

The day before I’ll speak on the same subject at an invitation-only session of CIO Day. Added: I’ll also be on a panel on Enterprise Prediction Markets during the more open session on Tuesday.

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Hive Mind

Some people like murder mystery novels. I much prefer intellectual mysteries like that in Garett Jones’ new book Hive Mind: How Your Nation’s IQ Matters So Much More Than Your Own:

Over a decade ago I began my research into how IQ matters for nations. I soon found that the strong link between average IQ and national productivity couldn’t be explained with just the conventional finding that IQ predicts higher wages. IQ apparently mattered far more for nations than for individuals. In my early work, I estimated that IQ mattered about six times more for nations than for individuals: your nation’s IQ mattered so much more than your own. That puzzle, that paradox of IQ, is what set me on my intellectual journey. …

I’ll lay out five major channels for how IQ can pay off more for nations than for you as an individual: Continue reading "Hive Mind" »

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Science Fiction Is Fantasy

Why do people like fantasy novels? One obvious explanation is that “magic” relaxes the usual constraints on the stories one can tell. Story-tellers can either use this freedom to explore a wider range of possible worlds, and so feed reader hungers for variety and strangeness, or they can focus repeatedly on particular story settings that seem ideal places for telling engaging stories, settings that are just not feasible without magic.

It is widely acknowledged that science fiction is by far the closest literary genre to fantasy. One plausible explanation for this is that future technology serves the same function in science fiction that magic serves in fantasy: it can be an “anything goes” sauce to escape the usual story constraints. So future tech can either let story tellers explore a wider space of strangeness, or return repeatedly to settings that feel particularly attractive, and are infeasible without future tech.

Of course it might be that some readers actually care about the real future, and want to hear stories set in that real future. But the overwhelming levels of implausible unrealism I find in almost all science fiction (and fantasy) suggest that this is a negligible fraction of readers, a faction writers rarely specialize in targeting. Oh writers will try to add a gloss of realism to the extent that it doesn’t cost them much in terms of other key story criteria. But when there are conflicts, other criteria win.

My forthcoming book The Age of Em, tries to describe a realistic future setting in great detail. I expect some of those who use science fiction in order to consume strange variety will enjoy the strangeness of my scenario, at least if they can get over the fact that it doesn’t come packaged with plot and characters. But they are unlikely to want to return to that setting repeatedly, as it just can’t compete with places designed to be especially compelling for stories. My setting is designed to be realistic, and I’ll just have to see how many readers I can attract to that unusual feature.

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Could Gambling Save Psychology?

A new PNAS paper:

Prediction markets set up to estimate the reproducibility of 44 studies published in prominent psychology journals and replicated in The Reproducibility Project: Psychology predict the outcomes of the replications well and outperform a survey of individual forecasts. … Hypotheses being tested in psychology typically have low prior probabilities of being true (median, 9%). … Prediction markets could be used to obtain speedy information about reproducibility at low cost and could potentially even be used to determine which studies to replicate to optimally allocate limited resources into replications. (more; see also coverage at 538AtlanticScience, Gelman)

We’ve had enough experiments with prediction markets over the years, both lab and field experiments, to not be at all surprised by these findings of calibration and superior accuracy. If so, you might ask: what is the intellectual contribution of this paper?

When one is trying to persuade groups to try prediction markets, one encounters consistent skepticism about experiment data that is not on topics very close to the proposed topics. So one value of this new data is to help persuade academic psychologists to use prediction markets to forecast lab experiment replications. Of course for this purpose the key question is whether enough academic psychologists were close enough to the edge of making such markets a continuing practice that it was worth the cost of a demonstration project to create closely related data, and so push them over the edge.

I expect that most ordinary academic psychologists need stronger incentives than personal curiosity to participate often enough in prediction markets on whether key psychology results will be replicated (conditional on such replication being attempted). Such additional incentives could come from:

  1. direct monetary subsidies for market trading, such as via subsidized market makers,
  2. traders with higher than average trading records bragging about it on their vitae, and getting hired etc. more because of that, or
  3. prediction market prices influencing key decisions such as what articles get published where, who gets what grants, or who gets what jobs.

For example, imagine that one or more top psychology journals used prediction market chances that an empirical paper’s main result(s) would be confirmed (conditional on an attempt) as part of deciding whether to publish that paper. In this case the authors of a paper and their rivals would have incentives to trade in such markets, and others could be enticed to trade if they expected trades by insiders and rivals alone to produce biased estimates. This seems a self-reinforcing equilibrium; if good people think hard before participating in such markets, others could see those market prices as deserving of attention and deference, including in the journal review process.

However, the existing equilibrium also seems possible, where there are few or small markets on such topics off to the side, markets that few pay much attention to and where there is little resources or status to be won. This equilibrium arguably results in less intellectual progress for any given level of research funding, but of course progress-inefficient academic equilibria are quite common.

Bottom line: someone is going to have to pony up some substantial scarce academic resources to fund an attempt to move this part of academia to a better equilibria. If whomever funded this study didn’t plan on funding this next step, I could have told them ahead of time that they were mostly wasting their money in funding this study. This next move won’t happen without a push.

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Investors Not Barking

Detective: “Is there any other point to which you would wish to draw my attention?”

Holmes: “To the curious incident of the dog in the night-time.”

Detective: “The dog did nothing in the night-time.”

Holmes: “That was the curious incident.”

We’ve seen several centuries of continuing economic growth enabled by improving tech (broadly conceived). Some of that tech can be seen as “automation” where machines displace humans on valued tasks.

The economy has consistently found new tasks for humans, to make up for displaced tasks. But while the rate of overall economic growth has be relatively steady, we have seen fluctuations in the degree of automation displacement in any given industry and region. This has often led to local anxiety about whether we are seeing the start of a big trend deviation – are machines about to suddenly take over most human jobs fast?

Of course so far such fears have not yet been realized. But around the year 2000, near the peak of the dotcom tech boom, we arguably did see substantial evidence of investors suspecting a big trend-deviating disruption. During a big burst of computer-assisted task displacement, the tech sector should soon see a big increase in revenue. So anticipating a substantial chance of such a burst justifies bigger stock values for related firms. And this graph of the sector breakdown of the S&P500 over the last few decades shows that investors then put their money where their mouths were regarding such a possible big burst:


In the last few years, we’ve heard another burst of anxiety about an upcoming big burst of automation displacing humans on tasks. It is one of our anxieties du jour. But if you look at the right side of the graph above you’ll note that are not now seeing a boom in the relative value of tech sector stocks.

We see the same signal if we look at majors chosen by college graduates. A big burst of automation not only justifies bigger tech stock values, it also justifies more students majoring in tech. And during the dotcom boom we did see a big increase in students choosing to major in computer science. But we have not seen such an increase during the last decade.

So the actions of both stock investors and college students suggest that they do not believe we are at substantial risk of a big burst of automation soon. These dogs are not barking. Even if robots taking jobs is what lots of talking heads are talking about. Because talking heads aren’t putting their money, or their time, where their mouths are.

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Require Legal Liability Insurance

The point of liability law is mainly to induce good behavior by having courts threaten to make related people pay cash later if a bad thing happens. The law tries to set who would pay how much to whom after what events in order to induce such people to take good care, so as to minimize the sum of the costs of bad things happening, care taken to avoid them, and the legal process itself.

One thing that limits the ability of law to make these choices well is the fact that most people have limited amounts of the kinds of assets that the legal system is willing to grab to settle a lawsuit. Like cash, stocks, and on. Some people are “judgement proof”, meaning they have none of these things. Most others have some assets, but substantially less than the law might want to make them pay in some situations.

Because most people have limited assets in this sense, those who bring lawsuits typically focus their attention on related parties with “deep pockets”, i.e., those who have far more assets. If such parties have any involvement at all in some bad event, lawsuits focus on blaming them and trying to make them pay.

This focus on deep pockets seems a clear failure of the system. Liability should instead be chosen based on the usual legal criteria of who could have most cheaply prevented the bad event, who could have reasonably foreseen the event in order to target their prevention efforts, and who did or did not take sufficient levels of care given such things.

If people had more assets that they could pay in the event they were held liable for a bad event, the law would have more options. It wouldn’t have to make them pay more, but it could do so if the situation seemed to warrant it.

One kind of solution is to allow the legal system to touch more kinds of assets. For example, in many ancient societies you could be sold into slavery to pay legal debts. Or your larger family clan might be held liable for your actions. While many places today have a homestead exemption that prevents some kinds of creditors from taking a primary home to cover debts, the law could have fewer such exceptions. However, many people feel uncomfortable with such approaches.

A different solution, one that should induce less of this discomfort, is to require people to buy general legal liability insurance. In many places today all drivers are required to buy insurance for auto accident liability up to stated amounts. The idea here is to just generalize that to all legal liability. We’d pick some minimum amount everyone should be ready to pay, say one million dollars. Then everyone would have to find an insurance company willing to cover them for that amount. If they were held liable by a court, they’d personally pay what they could out of their personal assets, and then the insurance firm would pay the rest.

Insurance firms would of course charge you different premiums, based on their estimates of how many assets you have and your likelihood of being held liable for bad events. To convince them you are a low risk, you could show them many things about yourself, and even let them continually monitor you in many ways.

Of course there is a cost to the insurance process, and there would remain some hidden info and actions which would produce some transfers between people who look alike to insurance firms, and make most people not quite as careful as they ideally would. But surely this should move care in the right direction, compared to a system where people get sued less because they don’t have enough money to pay.

Well yes, it is possible that the whole legal system is just making everyone pay too much across the board for all bad events. In which case making people able to pay more just makes things worse. But if we think this is the situation we should just cut back on the legal system, starting by making it harder to sue parties with deep pockets. Maybe we should limit all parties to a max liability of a few tens of thousands of dollars.

But if you don’t want to cut back on the liability of those with deep pockets, and if you accept that deep pocket folks aren’t actually as more responsible for bad events, surely not enough to explain how much more often they are sued, then you gotta think it would be good if other people could be held more liable than they are today.

So you should want to require general legal liability insurance. And then we’d all have to pay a bit more, but we’d all have fewer bad events. Which should be worth the trade, if the legal system is close to doing the right thing now with our limited abilities to pay.

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Testing Moral Progress

Mike Huemer just published his version of the familiar argument that changing moral views is evidence for moral realism. Here is the progress datum he seeks to explain:

Mainstream illiberal views of earlier centuries are shocking and absurd to modern readers. The trend is consistent across many issues: war, murder, slavery, democracy, women’s suffrage, racial segregation, torture, execution, colonization. It is difficult to think of any issue on which attitudes have moved in the other direction. This trend has been ongoing for millennia, accelerating in the last two centuries, and even the last 50 years, and it affects virtually every country on Earth. … All the changes are consistent with a certain coherent ethical standpoint. Furthermore, the change has been proceeding in the same direction for centuries, and the changes have affected nearly all societies across the globe. This is not a random walk.

Huemer’s favored explanation:

If there are objective ethical truths to which human beings have some epistemic access, then we should expect moral beliefs across societies to converge over time, if only very slowly.

But note three other implications of this moral-learning process, at least if we assume the usual (e.g., Bayesian) rational belief framework:

  1. The rate at which moral beliefs have been changing should track the rate at which we get relevant info, such as via life experience or careful thought. If we’ve seen a lot more change recently than thousands of years ago, we need a reason to think we’ve had a lot more thinking or experience lately.
  2. If people are at least crudely aware of the moral beliefs of others in the world, then they should be learning from each other much more than from their personal thoughts and experience. Thus moral learning should be a worldwide phenomena; it might explain average world moral beliefs, but it can’t explain much of belief differences at a time.
  3. Rational learning of any expected value via a stream of info should produce a random walk in those expectations, not a steady trend. But as Huemer notes, what we mostly see lately are steady trends.

For Age of Em, I read a lot about cultural value variation, and related factor analyses. One of the two main factors by which national values vary correlates strongly with average national wealth. At each point in time, richer nations have more of this factor, over time nations get more of it as they get richer, and when a nation has an unusual jump in wealth it gets an unusual jump in this factor. And this favor explains an awful lot of the value choices Huemer seeks to explain. All this even though people within a nation that have these values more are not richer on average.

The usual view in this field is that the direction of causation here is mostly from wealth to this value factor. This makes sense because this is the usual situation for variables that correlate with wealth. For example, if length of roads or number of TVs correlate with wealth, that is much more because wealth causes roads and TVs, and much less because roads and TV cause wealth. Since wealth is the main “power” factor of a society, this main factor tends to cause other small things more than they cause it.

This is as close as Hummer gets to addressing this usual view:

Perhaps there is a gene that inclines one toward illiberal beliefs if one’s society as a whole is primitive and poor, but inclines one toward liberal beliefs if one’s society is advanced and prosperous. Again, it is unclear why such a gene would be especially advantageous, as compared with a gene that causes one to be liberal in all conditions, or illiberal in all conditions. Even if such a gene would be advantageous, there has not been sufficient opportunity for it to be selected, since for almost all of the history of the species, human beings have lived in poor, primitive societies.

Well if you insist on explaining things in terms of genes, everything is “unclear”; we just don’t have good full explanations to take us all the way from genes to how values vary with cultural context. I’ve suggested that we industry folks are reverting to forager values in many ways with increasing wealth, because wealth cuts the fear that made foragers into farmers. But you don’t have to buy my story to find it plausible that humans are just built so that their values vary as their society gets rich. (This change need not at all be adaptive in today’s environment.)

Note that we already see many variables that change between rich vs. poor societies, but which don’t change the same way between rich and poor people within a society. For example rich people in a society save more, but rich societies don’t save more. Richer societies spend a larger fraction of income on medicine, but richer people spend a smaller fraction. And rich societies have much lower fertility even when rich people have about the same fertility.

Also not that “convergence” is about variance of opinion; it isn’t obvious to me that variance is lower now than it was thousands of years. What we’ve seen is change, not convergence.

Bottom line: the usual social science story that increasing wealth causes certain predictable value changes fits the value variation data a lot better than the theory that the world is slowly learning moral truth. Even if we accepted moral learning as explaining some of the variation, we’ll need wealth causes values to explain a lot of the rest of the variation. So why not let it explain all? Maybe someone can come up with variations on the moral learning theory that fit the data better. But at the moment, the choice isn’t even close.

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Super-Factor Scenario

A man always has two reasons for doing anything: a good reason and the real reason. J. P. Morgan

In economics today, as in many related fields, data analysis is king, and theory takes a back seat, at least as far as status goes. When people celebrate particular exemplary data analyses, they usually point to a use of difficult statistical techniques, or more commonly to some clever idea for how certain data could speak to an important question. They point far less often to what is more often the real limiting factor: access to relevant data, and to resources (such as time and student assistance) to process that data. Organizations with data are far more willing to show them to academics from prestigious institutions.

This is part of a more general pattern: when we give people status, the criteria we claim to use to choose who gets status often differs substantially from our real criteria. Let’s see how that might play out regarding the strong claim I posted on Saturday:

If we put together a huge super-dataset describing many individual people in as many ways as possible, a factor analysis of this dataset may find important new super-factors that span many of these features domains. Such super-factors would be promising candidates to use in a wide range of social research, and social policy. (more)

When someone finally does this data analysis that I’ve proposed, and finds some super-factors, they will be rightly celebrated. But what will they be celebrated for? Their main actual contribution will have been to get some organization to pony up enough resources to look for super-factors. But that’s not the sort of thing for which we like to celebrate intellectuals. So I predict that such people will instead be celebrated for the very idea of looking for super factors, for looking for a certain kind of super-factor, or for a clever computational or statistical technique used in the search.

There isn’t much risk of people finding my post and using that to undercut this celebration. I know of many cases where prestigious academics were celebrated for “insights” that others had expressed beforehand. As long as those others and/or their venues were of sufficiently lower status, academics see no conflict. Should anyone make an issue of it, there are always differing details that can be seized on to explain why the two ideas were really quite different.

If we had prediction markets on such things, and used them as the main way we allocated credit on such claims, well then in that case I might be able to lock in great rewards now, rewards that others couldn’t steal later. But that is one of the reasons we don’t want prediction-market-based rewards. In the end we like most of our hypocrisies, including those involving giving people status for different reasons than we claim.

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