Bryan Caplan visited Malaysia:
I only spent three days in Malaysia. I suspect that rural areas would be less moderate than major cities like KL and Malacca. Nevertheless, I think that most Westerners would be shocked by the pluralism and tolerance that I saw.
A commenter note:
Re anti-Semitism, it is actually quite blatant in Malaysia. It's nowhere as public as you might expect, but that's primarily because there are hardly any Jews in the country, so hardly anything spurs a discussion of them. However, the leader of the opposition is often frequently tarred by the ruling party with a number of slurs which should give you an idea of Malaysian taboos - he has been called a homosexual, a Jew-lover, a pawn of the CIA, a pawn of the Chinese/Indians, and a Zionist. While racism is actually pretty blatant throughout Malaysian society, it's so blatant towards the Jews that it is taken for granted. Members of Parliament have frequently raised the issue of the opposition leader's fraternizing with American Jews in Parliament, arguing it clearly disqualifies him from any leadership position. The closest Malaysians have come to a political consensus is that Zionism (and by extension Jews/Judaism) is bad, and that the Iraq war was unjust. (And you'll find plenty of people willing to pin the Iraq war on the Jews/Zionists too.) Zhuge Liang is spot on re the Protocols of Zion, and as a Malaysian, I'm really quite appalled that they are such a staple in our bookstores. The trendy malls in the Kuala Lumpur city centre probably don't stock them, but most bookshops in the suburbs - even small niche operations - carry them.
When we were there last (2007), all I saw were divisions, divisions, divisions - Muslim/Chinese/Indian divisions.
Friday, December 5, 2008
Daylight savings time or when should you trust economics papers
I was a little irked to see this again especially in the context of Felix Salmon's post on When Can You Trust Economics Papers? My response would be never.
This is a reprise of an earlier New Economist post and my own comments. From New Economist:
In my naivety I had assumed most social scientists understood how to model interaction effects. Then I read this post by Omar on orgtheory.net, and realised maybe I was wrong:
So we are agreed interaction models are awesome. However, as your stats 101 teacher told you, you have to be careful about two things: (1) never omit the main effects. Thus you don’t test hypothesis 1 using any of these specifications:
Y=a+b1X+b2XZ+e
Y=a+b1Z+b2XZ+e
or Alanis forbid:
Y=a+b1XZ+e
And (2) when interpreting b1 and b2 in the fully specified model, remember that those effects are conditional on the value of the other variables. b1 is now the effect of X on Y when Z=0 and b2 is now the effect of Z on Y when X=0. If your variables don’t have a meaningful zero point (like a racial attitudes scale), center them at their mean so that you can say “b2 is the effect of being Southern on voting republican for those who have average levels of racial animus towards blacks.”
Seems simple. Everybody knows this. Why am I even explaining this to you? Well, as noted by Brambor, Clark and Golder (2006) in a recent article in Political Analysis, a survey of 156 articles published in the major Political Science journals shows that only 10% of researchers specified their interaction models correctly. A large chunk of them outright omitted main effects, which can lead to incorrect significance tests of the interaction term. In some of these articles the entire contribution was riding on the interaction term. So things are not so simple. Consider the horror:
In an award-winning article in the American Political Science Review, Boix (1999) examines the factors that determine electoral system choice in advanced democracies. He makes two main conclusions. First, ethnic or religious fragmentation encourages the adoption of proportional representation in small and medium-sized countries (621). He draws this conclusion based on a model that includes an interaction term between ethnoreligious fragmentation and country size. However, he does not include either of the constitutive terms. When these terms are included, there is no longer any evidence that ethno-religious fragmentation ever affects the adoption of proportional representation (italics added).
You should read the article to see other horror stories. The lesson: if your dissertation/paper is riding on an interaction effect, don’t be a fool. Estimate a fully specified model.
It looks as though the Daylight Savings Time paper by Grant and Kotchen did not do this (or at the very least, if they did, it is not at all obvious that they did). See their Equation 2, Table 4 and 5. The treatment variable needs to be included as a main effect which they did not. Their conclusions rest entirely on the interaction of the treatment with year.
This is a reprise of an earlier New Economist post and my own comments. From New Economist:
In my naivety I had assumed most social scientists understood how to model interaction effects. Then I read this post by Omar on orgtheory.net, and realised maybe I was wrong:
So we are agreed interaction models are awesome. However, as your stats 101 teacher told you, you have to be careful about two things: (1) never omit the main effects. Thus you don’t test hypothesis 1 using any of these specifications:
Y=a+b1X+b2XZ+e
Y=a+b1Z+b2XZ+e
or Alanis forbid:
Y=a+b1XZ+e
And (2) when interpreting b1 and b2 in the fully specified model, remember that those effects are conditional on the value of the other variables. b1 is now the effect of X on Y when Z=0 and b2 is now the effect of Z on Y when X=0. If your variables don’t have a meaningful zero point (like a racial attitudes scale), center them at their mean so that you can say “b2 is the effect of being Southern on voting republican for those who have average levels of racial animus towards blacks.”
Seems simple. Everybody knows this. Why am I even explaining this to you? Well, as noted by Brambor, Clark and Golder (2006) in a recent article in Political Analysis, a survey of 156 articles published in the major Political Science journals shows that only 10% of researchers specified their interaction models correctly. A large chunk of them outright omitted main effects, which can lead to incorrect significance tests of the interaction term. In some of these articles the entire contribution was riding on the interaction term. So things are not so simple. Consider the horror:
In an award-winning article in the American Political Science Review, Boix (1999) examines the factors that determine electoral system choice in advanced democracies. He makes two main conclusions. First, ethnic or religious fragmentation encourages the adoption of proportional representation in small and medium-sized countries (621). He draws this conclusion based on a model that includes an interaction term between ethnoreligious fragmentation and country size. However, he does not include either of the constitutive terms. When these terms are included, there is no longer any evidence that ethno-religious fragmentation ever affects the adoption of proportional representation (italics added).
You should read the article to see other horror stories. The lesson: if your dissertation/paper is riding on an interaction effect, don’t be a fool. Estimate a fully specified model.
It looks as though the Daylight Savings Time paper by Grant and Kotchen did not do this (or at the very least, if they did, it is not at all obvious that they did). See their Equation 2, Table 4 and 5. The treatment variable needs to be included as a main effect which they did not. Their conclusions rest entirely on the interaction of the treatment with year.
Price formation and the airline industry
The following is an excerpt from an analysis of airline troubles (emphasis mine):
... former American Airlines C.E.O. Bob Crandall gave a speech to a group of airline executives in New York City in June that sounded a little like heresy: Crandall essentially called for reregulating the sector. “We have failed to confront the reality that unfettered competition just doesn’t work very well in certain industries,” he said.
... Crandall ran American Airlines from 1980 to 1998, and he’s a bit of an iconoclast. He has long hated the intense competition in the business and the resulting fare wars and turf battles. He once said that in commercial aviation, prices are set not by a company’s costs but by its “dumbest competitor.” Crandall was also known for ruthlessly cutting costs to keep American profitable; in the mid-1980s, he ordered olives removed from the salads served in-flight, a move that saved the company about $100,000 a year.
... on the subject of reregulation, Crandall may be right. “Airlines work with a very distorted supply-demand equation,” he said, and right now that’s manifesting itself in the form of too many flights, too much traffic, and fares that don’t come close to covering an airline’s costs. Worse, the infrastructure—planes, fuel, gates—is extremely expensive and too inflexible to quickly adapt to changes. So any market corrections involve a lot of pain for consumers and a lot of destroyed capital for airlines.
As pointed out by Yet Another Sheep, economists really do not know how prices are formed. My thoughts are:
1. Gee, if airline companies keep following the dumbest competitor, no wonder they are constantly going bankrupt. Which led to - gee - barrier to entries must be really low if airlines keep pricing below cost to contest the market or deter entry.
2. If airline companies play a pricing game is the Nash Equilibrium the same as the equilibrium in a tatonement process? Or when does Debrey's proof equilibrium in competitive markets break down? Or maybe the airline industry is not a competitive market in the Arrow-Debreu sense.
... former American Airlines C.E.O. Bob Crandall gave a speech to a group of airline executives in New York City in June that sounded a little like heresy: Crandall essentially called for reregulating the sector. “We have failed to confront the reality that unfettered competition just doesn’t work very well in certain industries,” he said.
... Crandall ran American Airlines from 1980 to 1998, and he’s a bit of an iconoclast. He has long hated the intense competition in the business and the resulting fare wars and turf battles. He once said that in commercial aviation, prices are set not by a company’s costs but by its “dumbest competitor.” Crandall was also known for ruthlessly cutting costs to keep American profitable; in the mid-1980s, he ordered olives removed from the salads served in-flight, a move that saved the company about $100,000 a year.
... on the subject of reregulation, Crandall may be right. “Airlines work with a very distorted supply-demand equation,” he said, and right now that’s manifesting itself in the form of too many flights, too much traffic, and fares that don’t come close to covering an airline’s costs. Worse, the infrastructure—planes, fuel, gates—is extremely expensive and too inflexible to quickly adapt to changes. So any market corrections involve a lot of pain for consumers and a lot of destroyed capital for airlines.
As pointed out by Yet Another Sheep, economists really do not know how prices are formed. My thoughts are:
1. Gee, if airline companies keep following the dumbest competitor, no wonder they are constantly going bankrupt. Which led to - gee - barrier to entries must be really low if airlines keep pricing below cost to contest the market or deter entry.
2. If airline companies play a pricing game is the Nash Equilibrium the same as the equilibrium in a tatonement process? Or when does Debrey's proof equilibrium in competitive markets break down? Or maybe the airline industry is not a competitive market in the Arrow-Debreu sense.
Michael Lewis' subprime parable
Michael Lewis decides to live beyond his means (in a manner of speaking) - he rented rather than bought a house that he couldn't afford (emphases mine). I don't fully agree with him that:
Americans feel a deep urge to live in houses that are bigger than they can afford. This desire cuts so cleanly through the population that it touches just about everyone. It’s the acceptable lust.
... But the real moral is that when a middle-class couple buys a house they can’t afford, defaults on their mortgage, and then sits down to explain it to a reporter from the New York Times, they can be confident that he will overlook the reason for their financial distress: the peculiar willingness of Americans to risk it all for a house above their station. People who buy something they cannot afford usually hear a little voice warning them away or prodding them to feel guilty. But when the item in question is a house, all the signals in American life conspire to drown out the little voice. The tax code tells people like the Garcias that while their interest payments are now gargantuan relative to their income, they’re deductible. Their friends tell them how impressed they are—and they mean it. Their family tells them that while theirs is indeed a big house, they have worked hard, and Americans who work hard deserve to own a dream house. Their kids love them for it.
... It’s no good pretending that Americans didn’t know they couldn’t afford such properties, or that they were seduced into believing they could afford them by mendacious mortgage brokers or Wall Street traders. If they hadn’t lusted after the bigger house, they never would have met the mortgage brokers in the first place. The money-lending business didn’t create the American desire for unaffordable housing. It simply facilitated it.
The best passage was:
The pool was another example. Because we moved in during the winter, we didn’t pay that much attention to it at first. Had we bothered to dip our fingers in, we’d have discovered that it was not merely heated but was saltwater. It was a full six weeks before we really even noticed the pool house. Full bathroom, full kitchen, shiny new Viking range, and a fridge stuffed with 24 bottles of champagne. For a few weeks I felt that all of this was excessive. Then one day I became aware of the inconvenience of having to walk, dripping wet, from the pool back into the main house. This is what you need a pool house for—so you can make the transition from water to dry land without the trouble of walking the whole 15 yards back into the house and climbing a long flight of stairs to the giant dressing room. From that moment on, it seemed to me terribly inconvenient to not have a pool house. How on earth did people with pools, but no special house adjacent to them, cope?
Americans feel a deep urge to live in houses that are bigger than they can afford. This desire cuts so cleanly through the population that it touches just about everyone. It’s the acceptable lust.
... But the real moral is that when a middle-class couple buys a house they can’t afford, defaults on their mortgage, and then sits down to explain it to a reporter from the New York Times, they can be confident that he will overlook the reason for their financial distress: the peculiar willingness of Americans to risk it all for a house above their station. People who buy something they cannot afford usually hear a little voice warning them away or prodding them to feel guilty. But when the item in question is a house, all the signals in American life conspire to drown out the little voice. The tax code tells people like the Garcias that while their interest payments are now gargantuan relative to their income, they’re deductible. Their friends tell them how impressed they are—and they mean it. Their family tells them that while theirs is indeed a big house, they have worked hard, and Americans who work hard deserve to own a dream house. Their kids love them for it.
... It’s no good pretending that Americans didn’t know they couldn’t afford such properties, or that they were seduced into believing they could afford them by mendacious mortgage brokers or Wall Street traders. If they hadn’t lusted after the bigger house, they never would have met the mortgage brokers in the first place. The money-lending business didn’t create the American desire for unaffordable housing. It simply facilitated it.
The best passage was:
The pool was another example. Because we moved in during the winter, we didn’t pay that much attention to it at first. Had we bothered to dip our fingers in, we’d have discovered that it was not merely heated but was saltwater. It was a full six weeks before we really even noticed the pool house. Full bathroom, full kitchen, shiny new Viking range, and a fridge stuffed with 24 bottles of champagne. For a few weeks I felt that all of this was excessive. Then one day I became aware of the inconvenience of having to walk, dripping wet, from the pool back into the main house. This is what you need a pool house for—so you can make the transition from water to dry land without the trouble of walking the whole 15 yards back into the house and climbing a long flight of stairs to the giant dressing room. From that moment on, it seemed to me terribly inconvenient to not have a pool house. How on earth did people with pools, but no special house adjacent to them, cope?
Thursday, December 4, 2008
Alternative energy
Claims:
The U.S. economy wastes 55 percent of the energy it consumes, and while American companies have ruthlessly wrung out other forms of inefficiency, that figure hasn’t changed much in recent decades. The amount lost by electric utilities alone could power all of Japan.
A 2005 report by the Lawrence Berkeley National Laboratory found that U.S. industry could profitably recycle enough waste energy—including steam, furnace gases, heat, and pressure—to reduce the country’s fossil-fuel use (and greenhouse-gas emissions) by nearly a fifth. A 2007 study by the McKinsey Global Institute sounded largely the same note; it concluded that domestic industry could use 19 percent less energy than it does today—and make more money as a result.
Economists like to say that rational markets don’t “leave $100 bills on the ground,” but according to McKinsey’s figures, more than $50 billion floats into the air each year, unclaimed by American businesses. What’s more, the technologies required to save that money are, for the most part, not new or unproven or even particularly expensive. By and large, they’ve been around since the 19th century. The question is: Why aren’t we using them?
The answer seems to be some kind of coordination failure:
The first barrier is obvious from a trip through ArcelorMittal’s [steel mill] four miles of interconnected pipes, wires, and buildings. Steel mills are noisy, hot, and smelly—all signs of enormous interdependent energy systems at work. In many cases, putting waste energy to use requires mixing the exhaust of one process with the intake of another, demanding coordination. But engineers have largely been trained to focus only on their own processes; many tend to resist changes that make those processes more complex. Whereas European and Japanese corporate cultures emphasize energy-saving as a strategy that enhances their competitiveness, U.S. companies generally do not. (DuPont and Dow, which have saved billions on energy costs in the past decade, are notable exceptions. ArcelorMittal’s ownership is European.)
In some industries, investments in energy efficiency also suffer because of the nature of the business cycle. When demand is strong, managers tend to invest first in new capacity; but when demand is weak, they withhold investment for fear that plants will be closed. The timing just never seems to work out. McKinsey found that three-quarters of American companies will not invest in efficiency upgrades that take just two years to pay for themselves. ...
The answer seems to be better regulation and more competition (which are not as paradoxical as they appear):
... industry’s inertia is reinforced by regulation. The Clean Air Act has succeeded spectacularly in reducing some forms of air pollution, but perversely, it has chilled efforts to reuse energy: because many of these efforts involve tinkering with industrial exhaust systems, they can trigger a federal or local review of the plant, opening a can of worms some plant managers would rather keep closed.
Much more problematic are the regulations surrounding utilities. Several waves of deregulation have resulted in a hodgepodge of rules without providing full competition among power generators. Though it’s cheaper and cleaner to produce power at Casten’s projects than to build new coal-fired capacity, many industrial plants cannot themselves use all the electricity they could produce: they can’t profit from aggressive energy recycling unless they can sell the electricity to other consumers. Yet byzantine regulations make that difficult, stifling many independent energy recyclers. Some of these competitive disadvantages have been addressed in the latest energy bill, but many remain.
Ultimately, making better use of energy will require revamping our operation of the electrical grid itself, an undertaking considerably more complicated than, say, creating a carbon tax. For the better part of a century, we’ve gotten electricity from large, central generators, which waste nearly 70 percent of the energy they burn. They face little competition and are allowed to simply pass energy costs on to their customers. Distributing generators across the grid would reduce waste, improve reliability, and provide at least some competition.
Opening the grid to competition is one of the more important steps to take if we’re serious about reducing fossil-fuel use and carbon emissions, yet no one’s talking about doing that. Democratic legislators are nervous about creating incentives for cleaner, cheaper generation that may also benefit nuclear power. Neither party wants to do the dirty work of shutting down old, wasteful generators. And of course the Enron debacle looms over everything.
If markets were efficient wouldn't someone have come in and picked up all the $100 bills that purportedly lie on the ground. Perhaps given the complications, the $100 bills are illusory.
Meanwhile, China has also moved forward with recycling energy:
... it was a surprise to drive toward a coal-cement complex in Zibo, a modest city of 4 or 5 million people in Shandong province, 230 miles southeast of Beijing, and see … no white haze. True, miners trudging along the street had blackened faces, and the city was dotted with 100-foot-high mounds of low-grade coal, previously trash but now worth picking over because of soaring world demand. But no white powder mixed with the black, and only wispy plumes of steam wafted from the fat, high smokestacks of the Sunnsy cement company (its name is from the Chinese shansui, or “mountain water”). Indeed, the fattest and somewhat rusty-looking central exhaust stack had been fitted with elaborate ductwork of obviously newer metal, which captured everything coming out of the stack and shunted it to a nearby new building.
Inside the new building was an electricity-generating plant, and what I was seeing was the handiwork of a Chinese engineer in his mid-40s named Tang Jinquan. Tang had never intended to get into the cement business. But when he graduated from the technical university in Harbin, in far northern China, the government was still assigning jobs to graduates—and his assignment was a cement-research institute in his hometown of Tianjin. “I am interested in heat generation, this place is about cement—no match!” he told me (through an interpreter) at the factory in Zibo. He spent nearly the next 20 years of his career in a long effort to make the dirty, wasteful, fast-growing cement industry less environmentally destructive.
The heart of his idea—easy to describe, tricky to implement—is capturing the enormous amount of heat normally wasted in cement making and using it to run turbines that generate electric power. This power can then be fed back into the factory, doing work that would otherwise require burning even more coal. The reduction of dust is a visible indicator of the more fundamental reduction of waste. Over the course of a long day, I heard about the many, many refinements Tang had made to this “co-generation” system since he first started working on it, in the mid-1980s. The punch line is that it now works well enough to cut the energy (mainly from coal) required to make clinker by 60 percent, and the overall power demands of the cement production line by 30 percent.
The U.S. economy wastes 55 percent of the energy it consumes, and while American companies have ruthlessly wrung out other forms of inefficiency, that figure hasn’t changed much in recent decades. The amount lost by electric utilities alone could power all of Japan.
A 2005 report by the Lawrence Berkeley National Laboratory found that U.S. industry could profitably recycle enough waste energy—including steam, furnace gases, heat, and pressure—to reduce the country’s fossil-fuel use (and greenhouse-gas emissions) by nearly a fifth. A 2007 study by the McKinsey Global Institute sounded largely the same note; it concluded that domestic industry could use 19 percent less energy than it does today—and make more money as a result.
Economists like to say that rational markets don’t “leave $100 bills on the ground,” but according to McKinsey’s figures, more than $50 billion floats into the air each year, unclaimed by American businesses. What’s more, the technologies required to save that money are, for the most part, not new or unproven or even particularly expensive. By and large, they’ve been around since the 19th century. The question is: Why aren’t we using them?
The answer seems to be some kind of coordination failure:
The first barrier is obvious from a trip through ArcelorMittal’s [steel mill] four miles of interconnected pipes, wires, and buildings. Steel mills are noisy, hot, and smelly—all signs of enormous interdependent energy systems at work. In many cases, putting waste energy to use requires mixing the exhaust of one process with the intake of another, demanding coordination. But engineers have largely been trained to focus only on their own processes; many tend to resist changes that make those processes more complex. Whereas European and Japanese corporate cultures emphasize energy-saving as a strategy that enhances their competitiveness, U.S. companies generally do not. (DuPont and Dow, which have saved billions on energy costs in the past decade, are notable exceptions. ArcelorMittal’s ownership is European.)
In some industries, investments in energy efficiency also suffer because of the nature of the business cycle. When demand is strong, managers tend to invest first in new capacity; but when demand is weak, they withhold investment for fear that plants will be closed. The timing just never seems to work out. McKinsey found that three-quarters of American companies will not invest in efficiency upgrades that take just two years to pay for themselves. ...
The answer seems to be better regulation and more competition (which are not as paradoxical as they appear):
... industry’s inertia is reinforced by regulation. The Clean Air Act has succeeded spectacularly in reducing some forms of air pollution, but perversely, it has chilled efforts to reuse energy: because many of these efforts involve tinkering with industrial exhaust systems, they can trigger a federal or local review of the plant, opening a can of worms some plant managers would rather keep closed.
Much more problematic are the regulations surrounding utilities. Several waves of deregulation have resulted in a hodgepodge of rules without providing full competition among power generators. Though it’s cheaper and cleaner to produce power at Casten’s projects than to build new coal-fired capacity, many industrial plants cannot themselves use all the electricity they could produce: they can’t profit from aggressive energy recycling unless they can sell the electricity to other consumers. Yet byzantine regulations make that difficult, stifling many independent energy recyclers. Some of these competitive disadvantages have been addressed in the latest energy bill, but many remain.
Ultimately, making better use of energy will require revamping our operation of the electrical grid itself, an undertaking considerably more complicated than, say, creating a carbon tax. For the better part of a century, we’ve gotten electricity from large, central generators, which waste nearly 70 percent of the energy they burn. They face little competition and are allowed to simply pass energy costs on to their customers. Distributing generators across the grid would reduce waste, improve reliability, and provide at least some competition.
Opening the grid to competition is one of the more important steps to take if we’re serious about reducing fossil-fuel use and carbon emissions, yet no one’s talking about doing that. Democratic legislators are nervous about creating incentives for cleaner, cheaper generation that may also benefit nuclear power. Neither party wants to do the dirty work of shutting down old, wasteful generators. And of course the Enron debacle looms over everything.
If markets were efficient wouldn't someone have come in and picked up all the $100 bills that purportedly lie on the ground. Perhaps given the complications, the $100 bills are illusory.
Meanwhile, China has also moved forward with recycling energy:
... it was a surprise to drive toward a coal-cement complex in Zibo, a modest city of 4 or 5 million people in Shandong province, 230 miles southeast of Beijing, and see … no white haze. True, miners trudging along the street had blackened faces, and the city was dotted with 100-foot-high mounds of low-grade coal, previously trash but now worth picking over because of soaring world demand. But no white powder mixed with the black, and only wispy plumes of steam wafted from the fat, high smokestacks of the Sunnsy cement company (its name is from the Chinese shansui, or “mountain water”). Indeed, the fattest and somewhat rusty-looking central exhaust stack had been fitted with elaborate ductwork of obviously newer metal, which captured everything coming out of the stack and shunted it to a nearby new building.
Inside the new building was an electricity-generating plant, and what I was seeing was the handiwork of a Chinese engineer in his mid-40s named Tang Jinquan. Tang had never intended to get into the cement business. But when he graduated from the technical university in Harbin, in far northern China, the government was still assigning jobs to graduates—and his assignment was a cement-research institute in his hometown of Tianjin. “I am interested in heat generation, this place is about cement—no match!” he told me (through an interpreter) at the factory in Zibo. He spent nearly the next 20 years of his career in a long effort to make the dirty, wasteful, fast-growing cement industry less environmentally destructive.
The heart of his idea—easy to describe, tricky to implement—is capturing the enormous amount of heat normally wasted in cement making and using it to run turbines that generate electric power. This power can then be fed back into the factory, doing work that would otherwise require burning even more coal. The reduction of dust is a visible indicator of the more fundamental reduction of waste. Over the course of a long day, I heard about the many, many refinements Tang had made to this “co-generation” system since he first started working on it, in the mid-1980s. The punch line is that it now works well enough to cut the energy (mainly from coal) required to make clinker by 60 percent, and the overall power demands of the cement production line by 30 percent.
How reliable is TripAdvisor?
Here I had posted that I relied a lot on TripAdvisor ratings in selecting hotels. One bad review can really turn me off to a place. So how reliable are these ratings?
Wayne Curtis decided to find out the following:
... with Web 2.0 and the ubiquity of user-generated information, someone setting off on a trip can dredge up all manner of suggestions and insider tips online, to the great annoyance of professional travel writers. Travel bees everywhere, it seems, are gathering nectar and bringing it back to the hive.
Which leaves one to wonder: How sweet is their honey?
Curious, during a four-day trip to Seattle last fall I relied solely on user-generated information. Seattle seemed the perfect destination for this experiment—I was unfamiliar with the city, and I figured that its hypercaffeinated, digitally literate residents (the region is home to Microsoft, Amazon, and Expedia) should make for a complex online ecology. I’d leave guidebooks at home, ignore the racks of tourist brochures in hotel lobbies, and not so much as make eye contact with a concierge. All my decisions would be based on advice from TripAdvisor, Yelp, Chowhound, Wikitravel, and other online travel communities.
... The van eventually let me off at the Sixth Avenue Inn. I had chosen this hotel after reading through exhaustive user write-ups on TripAdvisor, which has more than 2 million reviews posted by everyday travelers. It piqued my interest not because the travelers raved about the place—it was ranked 80th out of 115 Seattle hotels—but because the conditions some described were so colorfully deplorable. (Also, one review was titled “Stinky and dirty, but otherwise great,” which appealed to me with its koan-like quality.) I was curious whether TripAdvisors could be trusted to know a bad hotel room when they saw one.
... Of the Sixth Avenue Inn, for instance, one reviewer reported that the guest rooms smelled of “a mixture of smoke and various bodily odors that apparently mixed into the establishment over the years.” Another wrote, “I mistook a bath towel for a hand towel because the towels were so small.” On they went: “My first night I awoke to water pouring through the ceiling in my bathroom”; “I slept fully clothed and wore socks at all times. The shower was gross”; and “If Todd happens to be your waiter, you’ll also get entertainment” (it was unclear whether this was a good or a bad thing). A reviewer also noted that the pillows were “uncomfortably flat.”
The information online is often piping fresh—some of these reviews had been written just days before I arrived. (Indeed, I had decided against another hotel based on a recently posted account of a child sneezing lavishly in the whirlpool, “using the water as his tissue.”) What’s more, the site allows you to post “candid traveler photos.” Future historians will be pleased to discover that no hotel carpet stain has gone undocumented.
I opened the door to my room with mild trepidation. But it turns out that the Sixth Avenue Inn is absolutely fine. Not fancy, but fine. The shower was not gross. I could detect no unfamiliar bodily odors. The pillows did not strike me as unusually flat. I met no one named Todd. And the end of the toilet paper was even folded into a crisp equilateral triangle, the international symbol of hygienic attention. I slept well.
Wayne Curtis decided to find out the following:
... with Web 2.0 and the ubiquity of user-generated information, someone setting off on a trip can dredge up all manner of suggestions and insider tips online, to the great annoyance of professional travel writers. Travel bees everywhere, it seems, are gathering nectar and bringing it back to the hive.
Which leaves one to wonder: How sweet is their honey?
Curious, during a four-day trip to Seattle last fall I relied solely on user-generated information. Seattle seemed the perfect destination for this experiment—I was unfamiliar with the city, and I figured that its hypercaffeinated, digitally literate residents (the region is home to Microsoft, Amazon, and Expedia) should make for a complex online ecology. I’d leave guidebooks at home, ignore the racks of tourist brochures in hotel lobbies, and not so much as make eye contact with a concierge. All my decisions would be based on advice from TripAdvisor, Yelp, Chowhound, Wikitravel, and other online travel communities.
... The van eventually let me off at the Sixth Avenue Inn. I had chosen this hotel after reading through exhaustive user write-ups on TripAdvisor, which has more than 2 million reviews posted by everyday travelers. It piqued my interest not because the travelers raved about the place—it was ranked 80th out of 115 Seattle hotels—but because the conditions some described were so colorfully deplorable. (Also, one review was titled “Stinky and dirty, but otherwise great,” which appealed to me with its koan-like quality.) I was curious whether TripAdvisors could be trusted to know a bad hotel room when they saw one.
... Of the Sixth Avenue Inn, for instance, one reviewer reported that the guest rooms smelled of “a mixture of smoke and various bodily odors that apparently mixed into the establishment over the years.” Another wrote, “I mistook a bath towel for a hand towel because the towels were so small.” On they went: “My first night I awoke to water pouring through the ceiling in my bathroom”; “I slept fully clothed and wore socks at all times. The shower was gross”; and “If Todd happens to be your waiter, you’ll also get entertainment” (it was unclear whether this was a good or a bad thing). A reviewer also noted that the pillows were “uncomfortably flat.”
The information online is often piping fresh—some of these reviews had been written just days before I arrived. (Indeed, I had decided against another hotel based on a recently posted account of a child sneezing lavishly in the whirlpool, “using the water as his tissue.”) What’s more, the site allows you to post “candid traveler photos.” Future historians will be pleased to discover that no hotel carpet stain has gone undocumented.
I opened the door to my room with mild trepidation. But it turns out that the Sixth Avenue Inn is absolutely fine. Not fancy, but fine. The shower was not gross. I could detect no unfamiliar bodily odors. The pillows did not strike me as unusually flat. I met no one named Todd. And the end of the toilet paper was even folded into a crisp equilateral triangle, the international symbol of hygienic attention. I slept well.
Wednesday, December 3, 2008
Agent based modeling
I came across two articles on the "wisdom of swarms":
1. Swarm Theory in NGS:
"Ants aren't smart," Gordon says. "Ant colonies are." A colony can solve problems unthinkable for individual ants, such as finding the shortest path to the best food source, allocating workers to different tasks, or defending a territory from neighbors. As individuals, ants might be tiny dummies, but as colonies they respond quickly and effectively to their environment. They do it with something called swarm intelligence.
Where this intelligence comes from raises a fundamental question in nature: How do the simple actions of individuals add up to the complex behavior of a group? How do hundreds of honeybees make a critical decision about their hive if many of them disagree? What enables a school of herring to coordinate its movements so precisely it can change direction in a flash, like a single, silvery organism? The collective abilities of such animals—none of which grasps the big picture, but each of which contributes to the group's success—seem miraculous even to the biologists who know them best. Yet during the past few decades, researchers have come up with intriguing insights.
One key to an ant colony, for example, is that no one's in charge. No generals command ant warriors. No managers boss ant workers. The queen plays no role except to lay eggs. Even with half a million ants, a colony functions just fine with no management at all—at least none that we would recognize. It relies instead upon countless interactions between individual ants, each of which is following simple rules of thumb. Scientists describe such a system as self-organizing.
...
That's how swarm intelligence works: simple creatures following simple rules, each one acting on local information. No ant sees the big picture. No ant tells any other ant what to do. Some ant species may go about this with more sophistication than others. (Temnothorax albipennis, for example, can rate the quality of a potential nest site using multiple criteria.) But the bottom line, says Iain Couzin, a biologist at Oxford and Princeton Universities, is that no leadership is required. "Even complex behavior may be coordinated by relatively simple interactions," he says.
Inspired by the elegance of this idea, Marco Dorigo, a computer scientist at the Université Libre in Brussels, used his knowledge of ant behavior in 1991 to create mathematical procedures for solving particularly complex human problems, such as routing trucks, scheduling airlines, or guiding military robots.
In Houston, for example, a company named American Air Liquide has been using an ant-based strategy to manage a complex business problem. The company produces industrial and medical gases, mostly nitrogen, oxygen, and hydrogen, at about a hundred locations in the United States and delivers them to 6,000 sites, using pipelines, railcars, and 400 trucks. Deregulated power markets in some regions (the price of electricity changes every 15 minutes in parts of Texas) add yet another layer of complexity.
...
Ants had evolved an efficient method to find the best routes in their neighborhoods. Why not follow their example? So Air Liquide combined the ant approach with other artificial intelligence techniques to consider every permutation of plant scheduling, weather, and truck routing—millions of possible decisions and outcomes a day. Every night, forecasts of customer demand and manufacturing costs are fed into the model.
"It takes four hours to run, even with the biggest computers we have," Harper says. "But at six o'clock every morning we get a solution that says how we're going to manage our day."
For truck drivers, the new system took some getting used to. Instead of delivering gas from the plant closest to a customer, as they used to do, drivers were now asked to pick up shipments from whichever plant was making gas at the lowest delivered price, even if it was farther away.
"You want me to drive a hundred miles? To the drivers, it wasn't intuitive," Harper says. But for the company, the savings have been impressive. "It's huge. It's actually huge."
Other companies also have profited by imitating ants. In Italy and Switzerland, fleets of trucks carrying milk and dairy products, heating oil, and groceries all use ant-foraging rules to find the best routes for deliveries. In England and France, telephone companies have made calls go through faster on their networks by programming messages to deposit virtual pheromones at switching stations, just as ants leave signals for other ants to show them the best trails.
In the U.S., Southwest Airlines has tested an ant-based model to improve service at Sky Harbor International Airport in Phoenix. With about 200 aircraft a day taking off and landing on two runways and using gates at three concourses, the company wanted to make sure that each plane got in and out as quickly as possible, even if it arrived early or late.
"People don't like being only 500 yards away from a gate and having to sit out there until another aircraft leaves," says Doug Lawson of Southwest. So Lawson created a computer model of the airport, giving each aircraft the ability to remember how long it took to get into and away from each gate. Then he set the model in motion to simulate a day's activity.
"The planes are like ants searching for the best gate," he says. But rather than leaving virtual pheromones along the way, each aircraft remembers the faster gates and forgets the slower ones. After many simulations, using real data to vary arrival and departure times, each plane learned how to avoid an intolerable wait on the tarmac. Southwest was so pleased with the outcome, it may use a similar model to study the ticket counter area.
2. James Fallows on Dayjet which has recently filed for bankruptcy:
Jim Herriott and Bruce Sawhill, computer scientists in their 40s, are the ant farmers. They have worked together for 10 years—the first five at the Santa Fe Institute in New Mexico and the past five at DayJet. When we met, they had a comedy-team manner, with Herriott playing the straight man, carefully explaining the principles, and Sawhill the mad scientist, exclaiming about the elegance of the underlying math. Their job has been to determine exactly how many people might pay to use an air taxi, and where they would want to go. Their answers have come through ant farming, which could less colorfully be called inductive reasoning.
For instance, to predict how many Floridians would pay to fly from Pensacola to Naples, they start not by gathering gross-travel or population figures but by trying to simulate the decisions that hundreds of thousands of individual travelers will make. Their computer models resemble a much more complex version of an “artificial life” computerized game like SimCity or SimLife—or, to explain the nickname they gave themselves, programs that simulate the paths a colony of ants will take across a floor as they discover and retrieve pieces of food. This process is also known as “agent-based modeling.” The ants, or agents, in DayJet’s model are the 500,000 people per day in the seven southeastern states who take business trips of 100 miles or more. Some 80 percent of these trips are now made by car. Commercial airlines account for most of the rest, with trains, buses, charter flights, etc. making up the remainder. (In the Northeast, commercial airlines represent less of the total, and trains more.)
Herriott and Sawhill have developed a model to simulate the individual decisions that go into every one of these business trips. The model starts with the likelihood that a person in any one city, let’s say Mobile, will want to go to another, say Savannah, on any given weekday (for now, DayJet is a weekday-only service). These predictions are based on average income in each city, business relations, and other factors, and are constantly tuned to reflect real data. “It’s like the pull between two planetary bodies,” Herriott said. “Almost a Newtonian law!” (He was joking.)
The DayJet model factors in all relevant variables that could affect the traveler’s decision—something that is hard enough for a real person in real time. It contains up-to-date listings of all flights offered by all commercial airlines serving the region, and the prices for short-term bookings and seven- or 14-day advance-purchase fares. It has average highway-speed and congestion data for the routes people would drive between any two cities, and real-world travel time from different parts of a city to the nearest airport. It includes lodging and restaurant costs, if a driving trip means an overnight stay, and rental-car and gas rates.
Also, crucially, it tries to place some value on people’s time. Time value obviously varies: being three hours late for a wedding is different from being three hours late for a meeting on a Thursday afternoon. Because its target customers are business travelers earning from $75,000 to several hundred thousand dollars a year, income levels at which the time savings are worth the cost, the model uses salary to approximate the business value of time. (People making even more, it assumes, might use “normal” corporate jets.)
With this information and more plugged in, the ant farmers run the model—over and over and over. While we watched on a big-screen map projection from Herriott’s computer, the whole possible range of trips taken by one typical day’s 500,000 business travelers whizzed by in a few minutes: Miami-Atlanta, Key West–Jacksonville, Savannah-Biloxi. The point was not to predict exactly which trips travelers would take on any particular day but instead to see which patterns of travel emerged and where there might be a market for air taxis. In theory, DayJet could offer service from any airport to any other airport within the plane’s 650-mile nonstop range. But to minimize the number of empty “deadhead” legs its planes might have to fly back from remote locations and to maximize the number of paying flights each plane could make per day, the company planned to start in a concentrated area and then expand as it became sure there was more demand.
For every simulated trip, the computer was comparing all the alternatives—take the whole trip by car, take a train if there was one, drive to the nearest major airport and take Delta or JetBlue—and predicting whether a traveler would choose any of them over a possible flight with DayJet. A counter continuously tallied how many trips would be made using each option. For people taking one of the “trunk routes,” like Atlanta to Miami, the airlines were the obvious choice. But enough people heading from one small place to another created a market DayJet could tap.
(I had hopes for this company because I thought their model could stand the market test.)
1. Swarm Theory in NGS:
"Ants aren't smart," Gordon says. "Ant colonies are." A colony can solve problems unthinkable for individual ants, such as finding the shortest path to the best food source, allocating workers to different tasks, or defending a territory from neighbors. As individuals, ants might be tiny dummies, but as colonies they respond quickly and effectively to their environment. They do it with something called swarm intelligence.
Where this intelligence comes from raises a fundamental question in nature: How do the simple actions of individuals add up to the complex behavior of a group? How do hundreds of honeybees make a critical decision about their hive if many of them disagree? What enables a school of herring to coordinate its movements so precisely it can change direction in a flash, like a single, silvery organism? The collective abilities of such animals—none of which grasps the big picture, but each of which contributes to the group's success—seem miraculous even to the biologists who know them best. Yet during the past few decades, researchers have come up with intriguing insights.
One key to an ant colony, for example, is that no one's in charge. No generals command ant warriors. No managers boss ant workers. The queen plays no role except to lay eggs. Even with half a million ants, a colony functions just fine with no management at all—at least none that we would recognize. It relies instead upon countless interactions between individual ants, each of which is following simple rules of thumb. Scientists describe such a system as self-organizing.
...
That's how swarm intelligence works: simple creatures following simple rules, each one acting on local information. No ant sees the big picture. No ant tells any other ant what to do. Some ant species may go about this with more sophistication than others. (Temnothorax albipennis, for example, can rate the quality of a potential nest site using multiple criteria.) But the bottom line, says Iain Couzin, a biologist at Oxford and Princeton Universities, is that no leadership is required. "Even complex behavior may be coordinated by relatively simple interactions," he says.
Inspired by the elegance of this idea, Marco Dorigo, a computer scientist at the Université Libre in Brussels, used his knowledge of ant behavior in 1991 to create mathematical procedures for solving particularly complex human problems, such as routing trucks, scheduling airlines, or guiding military robots.
In Houston, for example, a company named American Air Liquide has been using an ant-based strategy to manage a complex business problem. The company produces industrial and medical gases, mostly nitrogen, oxygen, and hydrogen, at about a hundred locations in the United States and delivers them to 6,000 sites, using pipelines, railcars, and 400 trucks. Deregulated power markets in some regions (the price of electricity changes every 15 minutes in parts of Texas) add yet another layer of complexity.
...
Ants had evolved an efficient method to find the best routes in their neighborhoods. Why not follow their example? So Air Liquide combined the ant approach with other artificial intelligence techniques to consider every permutation of plant scheduling, weather, and truck routing—millions of possible decisions and outcomes a day. Every night, forecasts of customer demand and manufacturing costs are fed into the model.
"It takes four hours to run, even with the biggest computers we have," Harper says. "But at six o'clock every morning we get a solution that says how we're going to manage our day."
For truck drivers, the new system took some getting used to. Instead of delivering gas from the plant closest to a customer, as they used to do, drivers were now asked to pick up shipments from whichever plant was making gas at the lowest delivered price, even if it was farther away.
"You want me to drive a hundred miles? To the drivers, it wasn't intuitive," Harper says. But for the company, the savings have been impressive. "It's huge. It's actually huge."
Other companies also have profited by imitating ants. In Italy and Switzerland, fleets of trucks carrying milk and dairy products, heating oil, and groceries all use ant-foraging rules to find the best routes for deliveries. In England and France, telephone companies have made calls go through faster on their networks by programming messages to deposit virtual pheromones at switching stations, just as ants leave signals for other ants to show them the best trails.
In the U.S., Southwest Airlines has tested an ant-based model to improve service at Sky Harbor International Airport in Phoenix. With about 200 aircraft a day taking off and landing on two runways and using gates at three concourses, the company wanted to make sure that each plane got in and out as quickly as possible, even if it arrived early or late.
"People don't like being only 500 yards away from a gate and having to sit out there until another aircraft leaves," says Doug Lawson of Southwest. So Lawson created a computer model of the airport, giving each aircraft the ability to remember how long it took to get into and away from each gate. Then he set the model in motion to simulate a day's activity.
"The planes are like ants searching for the best gate," he says. But rather than leaving virtual pheromones along the way, each aircraft remembers the faster gates and forgets the slower ones. After many simulations, using real data to vary arrival and departure times, each plane learned how to avoid an intolerable wait on the tarmac. Southwest was so pleased with the outcome, it may use a similar model to study the ticket counter area.
2. James Fallows on Dayjet which has recently filed for bankruptcy:
Jim Herriott and Bruce Sawhill, computer scientists in their 40s, are the ant farmers. They have worked together for 10 years—the first five at the Santa Fe Institute in New Mexico and the past five at DayJet. When we met, they had a comedy-team manner, with Herriott playing the straight man, carefully explaining the principles, and Sawhill the mad scientist, exclaiming about the elegance of the underlying math. Their job has been to determine exactly how many people might pay to use an air taxi, and where they would want to go. Their answers have come through ant farming, which could less colorfully be called inductive reasoning.
For instance, to predict how many Floridians would pay to fly from Pensacola to Naples, they start not by gathering gross-travel or population figures but by trying to simulate the decisions that hundreds of thousands of individual travelers will make. Their computer models resemble a much more complex version of an “artificial life” computerized game like SimCity or SimLife—or, to explain the nickname they gave themselves, programs that simulate the paths a colony of ants will take across a floor as they discover and retrieve pieces of food. This process is also known as “agent-based modeling.” The ants, or agents, in DayJet’s model are the 500,000 people per day in the seven southeastern states who take business trips of 100 miles or more. Some 80 percent of these trips are now made by car. Commercial airlines account for most of the rest, with trains, buses, charter flights, etc. making up the remainder. (In the Northeast, commercial airlines represent less of the total, and trains more.)
Herriott and Sawhill have developed a model to simulate the individual decisions that go into every one of these business trips. The model starts with the likelihood that a person in any one city, let’s say Mobile, will want to go to another, say Savannah, on any given weekday (for now, DayJet is a weekday-only service). These predictions are based on average income in each city, business relations, and other factors, and are constantly tuned to reflect real data. “It’s like the pull between two planetary bodies,” Herriott said. “Almost a Newtonian law!” (He was joking.)
The DayJet model factors in all relevant variables that could affect the traveler’s decision—something that is hard enough for a real person in real time. It contains up-to-date listings of all flights offered by all commercial airlines serving the region, and the prices for short-term bookings and seven- or 14-day advance-purchase fares. It has average highway-speed and congestion data for the routes people would drive between any two cities, and real-world travel time from different parts of a city to the nearest airport. It includes lodging and restaurant costs, if a driving trip means an overnight stay, and rental-car and gas rates.
Also, crucially, it tries to place some value on people’s time. Time value obviously varies: being three hours late for a wedding is different from being three hours late for a meeting on a Thursday afternoon. Because its target customers are business travelers earning from $75,000 to several hundred thousand dollars a year, income levels at which the time savings are worth the cost, the model uses salary to approximate the business value of time. (People making even more, it assumes, might use “normal” corporate jets.)
With this information and more plugged in, the ant farmers run the model—over and over and over. While we watched on a big-screen map projection from Herriott’s computer, the whole possible range of trips taken by one typical day’s 500,000 business travelers whizzed by in a few minutes: Miami-Atlanta, Key West–Jacksonville, Savannah-Biloxi. The point was not to predict exactly which trips travelers would take on any particular day but instead to see which patterns of travel emerged and where there might be a market for air taxis. In theory, DayJet could offer service from any airport to any other airport within the plane’s 650-mile nonstop range. But to minimize the number of empty “deadhead” legs its planes might have to fly back from remote locations and to maximize the number of paying flights each plane could make per day, the company planned to start in a concentrated area and then expand as it became sure there was more demand.
For every simulated trip, the computer was comparing all the alternatives—take the whole trip by car, take a train if there was one, drive to the nearest major airport and take Delta or JetBlue—and predicting whether a traveler would choose any of them over a possible flight with DayJet. A counter continuously tallied how many trips would be made using each option. For people taking one of the “trunk routes,” like Atlanta to Miami, the airlines were the obvious choice. But enough people heading from one small place to another created a market DayJet could tap.
(I had hopes for this company because I thought their model could stand the market test.)
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