The benefits of hindsight

The current financial crisis has its roots in Greenspan's decision to keep interest rates very low in 2002 and 2003 to head off the danger of a deflation-induced double-dip recession, and his subsequent decision that the costs of cleaning up after a housing bubble were likely to be less than the costs of the high unemployment that would be generated by a preemptive attempt to pop a housing-speculation bubble. Two years ago, I would have said that Greenspan's judgment here was correct. Six months ago, I would have said that his judgment was probably correct. Today -- in the middle of the largest nationalizations in history -- I can no longer state that Greenspan made the right calls with respect to the level of interest rates and the housing bubble in the 2000s. (emphasis mine)

From Brad DeLong.

This is the nice thing about economics (and blogging) being a spectator sport - we get to be right all the time.

Regulating banks

1. New financial products are like new drugs and should be treated as such. New financial products have to undergo "trials" and data collected extensively before being offered to the general public.

Unlike new drugs, financial products need only be safe and not necessarily effective.

2. MR comments on the Volcker banking plan that regulates bank size and limits proprietary trading. As has been mentioned previously here, and here that limits on bank size is in effect a limit on profits. Perhaps the best way to go is to treat all financial institutions as public service companies such as water companies. Hearings are held as to whether new products can be launched, how much pay top management should receive and how much products should be charged. (No, seriously!)

The public service commission approach will answer most (if not all but #7) in a positive manner:

1. Do its restrictions apply to subsidaries, affiliates, and holding companies in a meaningful way? Can they apply?

2. How do the restrictions apply to off-balance sheet activities, if at all? Keep in mind the various lessons about the construction of synthetic asset positions.

3. How will Congressional oversight committees apply and interpret the plan? This is a big one.

4. Can a financial institution avoid or sidestep the restrictions by changing its status as a commercial bank, legally speaking?

5. If you cap bank size, are the new and smaller banks still "too big to fail" by prevailing standards?

6. How does the proposal treat bank leverage, including implicit forms of leverage through off-balance sheet activities? Does leverage get redistributed elsewhere?

7. How does it affect the political economy of bank lobbying?

The above most thoughtful questions are from MR.

Quantum economics

No doubt in my mind that there are parallels between quantum physics (many dimension worlds) and economic modeling. These excerpts are from The Great Beyond by Paul Halpern which was a great read until I had to struggle to understand tensor calculus and SU(3) symmetry and the such. I'd be surprised if I can find an accessible (read: idiot's guide) to these concepts.

Now that mathematicians had taken over relativity, he [Einstein] bemoaned, he could barely understand it himself. (pg. 79)

A main problem for us theoreticians rather resembles that represented Charybdis and Scylla between which Odysseus was forced to steer. ... Speculation is certainly a necessary part of the theoretical work, just as much as building on experimental facts. Still, it drags many of us into a mental whirlpool not unlike the hydrodynamical one of Charybdis, from which the escape feels like a miracle. On the other hand, sticking too closely to the facts - Scylla had six hard ones - may be equally deadly when using them as building stones for theory. - OSKAR KLEIN, From My Life of Physics (pg. 114)

I was surprised how dogmatic Einstein's view could be - on page 171:

As he [Einstein] guided his assistants during their exhibitions into unknown territories, it became clear to them that he had very fixed ideas about what features should or should not become part of a unified theory. ... Knowing his taste, they would strive hard to make their models more "virtuous" and less "sinful." One of the cardinal sins, for example, was bringing any notion of probability into the theories.

Supersymmetry is one of the most audacious proposals in the history of modern scientific thought. Year after year since it was postulated, experiments have failed to demonstrate its existence. Accelerators have smashed countless particles, producing not a single supersymmetric companion in their debris. Yet many theorists find it so compelling that they can scarcely believe the world could survive without it. ... No other physical theory has won so many supporters with so little experimental support, surviving instead on the basis of its own mathematical beauty and internal consistency. (pg 231, emphasis mine)

... [physicist, Steinhardt] sees considerable danger in relying on one particular model. "When you get down only to a single competitor it's not always a healthy situation," he advises. "It's much better to have two or more competing models, forcing you to think more carefully about your theories, your predictions and the observations." (pg. 284)

Replicating experiments with propensity score matching

I've been trying to learn some propensity score matching and consequently have been perusing some papers. The Smith-Todd paper "Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators?" was a good useful starting point for me. I was a little perplexed by the desire of the authors to "hit" the experimental estimate though. Presumably, if the experiement were repeated, it would not achieve the same treatment effect as the original - the treatment effect has a standard error or confidence interval around it.

Agodini and Dynarski's paper "Are Experiments the Only Option? A Look at Dropout Prevention Programs" was also interesting. The authors don't try to match the experimental effects but ask if the direction of the experimental effect can be concluded based on propensity score methods. Also interesting was the whole question surrounding the standard error of the propensity score estimate - whether the simple random sample estimate is "close" to the bootstrapped estimate or not since there have been claims that the standard error from the propensity score estimator is from an estimate based on nonlinear methods and hence not reliable. They find that the bootstrapped estimates are similar to an SRS standard error.

Replicating Econbrowser's replication of CEA analysis

There was an interesting post on Econbrowser where the impact of the fiscal stimulus done by the CEA was replicated.

options nocenter;
filename gdp 'C:\ReplicateCEA\GDPC1.txt';
/* File downloaded from FRED St. Louis */
data GDP;
infile gdp firstobs = 14;
/* Read two variables: date and GDP */
input date value;
informat date yymmdd10.;
format date yymmdd10.;
lngdp = log(value);
y=lngdp;
/* Calculate the first difference of y */
diffy = dif(y);
run;

data gdp4q;
set GDP end=last;
output;
if last then do;
date = '01Oct2009'd;
value = value * 1.04;
lngdp = log(value);
y=lngdp;
output;
end;
run;

data gdp4q;
set gdp4q;
dateq = put(date,yyq6.);
run;

filename nfp 'C:SVAR\ReplicateCEA\PAYEMS.txt';
/* File downloaded from FRED St. Louis */
data NFP;
infile nfp firstobs = 16;
/* Read two variables: date and GDP */
input date value;
informat date yymmdd10.;
format date yymmdd10.;
lnnfp = log(value);
e=lnnfp;
/* Calculate the first difference of y */
diffnfp = dif(e);
dateq = put(date,yyq6.);
run;

data nfp2;
set nfp; by dateq date;

if last.dateq then output;
run;

data gdp_nfp(drop = ln: diff:);
merge gdp4q(in=a rename = (value = gdp)) nfp2(in=b rename = (value = nfp));

by dateq;
if a and b;
run;

proc print data = gdp_nfp noobs;
where year(date)>=2007;
run;

ods output ParameterEstimates=pe;
proc varmax data = gdp_nfp;
where 1990<=year(date)<=2007;
id date interval = qtr;
model y e /p=4 ;
output lead=12 out = for;
run;

proc transpose data = pe out = pe_ty;
where equation = 'y';
var estimate;
id parameter;
run;

data pe_ty;
set pe_ty(rename = (const1 = intercept) drop = _name_);
_type_ = 'PARMS';
_model_ = 'Baseline';
_depvar_ = 'y';
y = -1;
RUN;

proc transpose data = pe out = pe_te;
where equation = 'e';
var estimate;
id parameter;
run;

data pe_te;
set pe_te(rename = (const2 = intercept) drop = _name_);
_type_ = 'PARMS';
_model_ = 'Baseline';
_depvar_ = 'e';
e = -1;
RUN;

%macro genar(lag=,eq1=,eq2=,var1=,var2=);
%do i=1 %to &lag;
%do e=1 %to &eq2;
ar&i._&eq1._&e=lag&i(&&var&e);
%end;
%end;
%mend genar;

options mprint;
data gdp2;
set gdp_nfp;
%genar(lag=4,eq1=1,eq2=2,var1=y,var2=e);
run;

data e2;
set gdp_nfp;
%genar(lag=4,eq1=2,eq2=2,var1=y,var2=e);
run;

proc score data = gdp2 score = pe_ty out=gdp2_score type=parms;
var ar1_1_1 ar1_1_2 ar2_1_1 ar2_1_2 ar3_1_1 ar3_1_2 ar4_1_1 ar4_1_2;
run;

proc score data = e2 score = pe_te out=e2_score type=parms;
var ar1_2_1 ar1_2_2 ar2_2_1 ar2_2_2 ar3_2_1 ar3_2_2 ar4_2_1 ar4_2_2;
run;

symbol1 value=none i=join;
symbol2 value=none i=join;
proc gplot data = gdp2_Score;
where year(date)>=2008;
plot (baseline y) * date / overlay;
run;
quit;

proc gplot data = e2_score;
where year(date)>=2008;
plot (baseline e) * date / overlay;
run;
quit;

What I've been reading

1. Three Scientists and their Gods: Looking for Meaning in an Age of Information by Robert Wright: Was more interesting than I expected. One review is here. The book features (1) computer scientist Ed Fredkin who believes that the universe IS a computer, (2) sociobiologist E. O. Wilson, and (3) Quaker economist Kenneth Boulding. The book presents their views on how they view their world and what shaped their views.

2. Swoosh: The Unauthorized Story of Nike and the Men Who Played There by JB Strasser and Laurie Becklund. Another book which was more interesting than I had expected. It was NOT a story of Phil Knight who declined to be interviewed for the book. What is interesting about the book is the absence of Knight after Nike went public and plunged into a product crisis lurching from apparel to uninspired shoe after uninspired shoe (except for the success of Air Jordan) and how it was overtaken by Reebok. This seems to be a story of another company with 'founderitis' -the inability of its founders to deal with the changing marketplace perhaps as a direct result of the wealth brought on by the IPO.

Interesting titbit: None of the original people of Nike liked the Swoosh logo and none (except for Jeff Johnson who came up with the name) liked calling the company Nike. Missing from the book is probably a good description of the shoe making process. This is discussed mainly toward the latter part of the book as Nike was foundering and unable to come up with innovative products and the extremem difficulties they encountered when trying to put sacs filled with air in the soles.

3. Two Park Street: A Publishing Memoir by Robert Brooks about his time as editor-in-chief of the Trade department at Houghton Mifflin. He recounts the role he played in bringing to publication the Peterson Field Guides, Rachel Carson's Silent Spring and Winston Churchills 6 volume work on World War 2 among others (Lord of the Rings was published in the UK and Houghton Mifflin bough the rights for U.S.). The memoirs paint a romantic view of book publishing during his tenure (from the 1930s to 60s).

Average monthly condo fees in the U.S.

Data source:

Steven Ruggles, Matthew Sobek, Trent Alexander, Catherine A. Fitch, Ronald Goeken, Patricia Kelly Hall, Miriam King, and Chad Ronnander. Integrated Public Use Microdata Series: Version 4.0 [Machine-readable database]. Minneapolis, MN: Minnesota Population Center [producer and distributor], 2008.

We've been looking at condos around the DC area and it got me wondering as to what the average condo fees were like around the country. The above is a chart of the average condo fees tabulated from IPUMS. Because of topcoding the average is below the true average. The mean and max are labeled at the end of the bars. Unfortunately, I'm unable to improve on the resolution of the jpeg.

The SAS code for this is:

proc means data = cf.hh2008 N mean std min max;
where year = 2008 and condofee > 0;
class stateicp;
var condofee;
weight hhwt;
output out = summ mean = meancondofee min = mincondofee max=maxcondofee;
run;

data summ2;
set summ;
proc sort; by descending meancondofee;
run;

data annosum;
set summ2;
%annomac;
%dclanno;
%system(2,2,3);
midpoint = stateicp;
x = round(meancondofee,1);
lbl = compress(put(meancondofee, 8.)"/"put(maxcondofee, 8.));
%label(x,.,lbl,black,0,0,1.8,swissb,6);
run;

filename grafout 'chart1.jpeg';
goptions device = jpeg targetdevice = jpeg ftext="Verdana" gsfname = grafout xmax=8 in ymax=6 in xpixels=4000 ypixels=3000 vpos=40 hpos=40 lfactor=1 ;
axis1 label = none;
title1 h=1 'Average monthly condominium fees in 2008: U.S. States';
proc gchart data=summ2;
where _type_ ^=0;
hbar stateicp / sumvar=meancondofee maxis=axis1 discrete descending nostat anno=annosum(where=(_type_^=0));
run;
quit;

Seals as predictors of earthquakes?

This news item in early January caught my eye:

The sea lions of San Francisco are almost as famous as the city's cable cars or even the Golden Gate bridge, says the BBC's Peter Bowes in Los Angeles.

Twenty years ago, for no apparent reason, the smelly, noisy animals took up residence in the docks at Pier 39.

Their numbers grew rapidly to about 1,700 animals, and they became a popular tourist attraction.

But then most of them disappeared.
....
One outlandish suggestion was that they were fleeing the bay because of an imminent earthquake, our correspondent says.

The seals were found off the coast of Oregon. But did they really sense the imminent earthquake on January 9?

Lost decade of retirement

This post by Jim Hamilton on the lost decade for stocks made me check on my mutual fund returns:

Fund _______________5 years_____________10 years/inception
TRP Retirement ................ -2.92% ............................... -1.83%
TRP Equity Index...............0.79%................................. -0.40%
TRP Growth........................2.45%................................. 2.03%
TRP International...............6.19%.................................. 1.54%
TRP Science & Tech ...........4.62%..................................-5.8%

Assuming inflation rate of about 3% on average I may have been better off putting the money under my mattress. The lost decade is testing my faith in buy and hold.

Haunting first lines

"Last night I dreamt I went to Manderley again."
Rebecca, by Daphne Du Maurier

"I came to Comala because I had been told that my father, a man named Pedro Paramo, lived there. It was my mother who told me. And I had promised her that after she died I would go see him."
Pedro Paramo by Juan Rolfo

Entropy and free trade

Is it possible to use entropy as an analogy to free trade? Contrary to intuition, maximum entropy is not totally free trade but but some point between free trade and autarky (and perhaps speculatively close to autarky itself).

This analogy is used to illustrate why free trade is not a stable equilibrium. (As free market liberals would say - if free trade were so great we'd already be there. There would be no need for the WTO.)

1. Energy has to be expended to bring the world closer to free trade. If this energy is insufficient the trading system would tend toward autarky.
2. Free trade cannot spontaneously happen. Work is required to achieve it.
3. The energy/work that is applied to achieving free trade can dissipate quickly into a lot of hot air.

Is it rational to sell books on Amazon for 0.01

There are a lot of these listed.

Fees by Amazon:
Closing fee $1.35
Listing fee $0.99
Commission 0.15 * 0.01 = 0.0015

Total fees: $2.34

Amount paid by buyer = $0.01 + $3.99 (S+H) = $4.00

Amount to seller = $4.00 - $2.34 = $2.66

Unless S+H is less than $2.66 (including costs of packaging and postage) then it really isn't rational to sell at $0.01.

The USPS shipping rates for media mail at up to 1lb is $2.38 and up to 2lbs is $2.77.

Greenhouse gas abate and the Jacksonia Mode of Discourse

Ted Gayer's piece seems to be a possible example of non-Jacksonian mode of discourse which I would applaud. There is a puzzle about the negative MC of abatement in the McKinsey report:

The main point of the McKinsey study is provided in their Exhibit B, which illustrates a rather peculiar finding that there are a significant number of pollution abatement options that can be achieved at “negative cost.” This finding violates the basic principles of economics. If firms (or consumers) could reduce emissions at negative cost, then they would do so. To say otherwise is to say that they are willingly or ignorantly passing up profits.

What, then, can explain a finding of negative cost? There are four possibilities:

First, the estimated costs of abatement are simply incorrect. ...
Second, the costs of abatement are incompletely estimated. ...
Third, the private discount rate is incorrectly estimated. ...
The fourth possible reason for the negative cost finding is that firms do indeed irrationally forego profit-maximizing activities, or they are ignorant of such activities.

To the category of second falls costs such as information and coordination costs. From the McKinsey report:

Unlocking the negative cost options would require overcoming persistent barriers to market efficiency such as mismatches between who pays the cost of an option and who gains the benefit (e.g. homebuilder versus homeowner), lack of information about the impact of individual decisions, and consumer desire for a rapid payback (typically 2 to 3 years) whn incremental up front investment is required.

Here is a concrete (but not necessarily correct) example:
I'd like to install solar panels. Say for that the upfront cost is $20,000 and the life of the panels is 30 years in which I may recoup the cost in 20 years. But I don't plan to be in the house for 20 years.

Perhaps the panels add value to the house - but how much. Here the cost of acquiring the information is not included. The added uncertainty of this option makes me decide not to install solar panels. There is a role for the government - they could step in and say that when I sell my house they will pay me the difference (in PV terms assuming some "correct" discount rate) of my up-front cost and the amount I've recouped in terms of electricity savings. How do we calculate this savings? Again some cost of information is left out of this equation which needs to be made clear.

These are what Ted Gayer may be saying about incomplete costs and is perhaps a reason why a carbon tax is much easier to implement.

How transparent is transparency

Or are car invoice prices really invoice prices? I applaud the Internet for making invoice prices widely available. Before the age of the Internet we had to comb through special subscription magazines or look through car buying books to get the same information. Yet is this really the invoice price?

From Edmunds:

The pricing of cars is a complicated process. To simplify things, consumers have been told to look at the invoice price of a car and assume that's what the dealer paid for the car. Offer a small amount over the invoice and you have a great deal. While invoice provides a valuable reference point, both holdback and dealer cash increase the dealer's profit with financial sleight of hand.

Holdback is usually either 2 or 3 percent of either the invoice or the sticker price of the car. On a $20,000 car that's either $400 or $600 that is held out of the initial deal until after the car is sold. This allows dealers to sell a car at invoice price and still make a profit. Check the holdback percentage before going shopping but don't try to negotiate on holdback, since dealers consider this money sacred. Still, knowing it is there will help you press for a better price.

Dealer cash is even more significant. When a car isn't selling well, the manufacturer will sometimes offer an incentive — often as much as $2,000 — but only let the dealer know about it. This is like a wild card in negotiating, and it lets the dealer claim he's taking a loss while still actually making a nice profit. Dealer cash is listed on Edmunds.com under Latest Incentives.

While transparency is generally a good thing at some point there is a pushback when invoice will no longer be the "true" invoice as is the case now and transparent is no longer quite as transparent.

When meetings are open to the public in the name of transparency there are generally pre-meeting meetings that are not.

On forecasting (yet again)

Rajiv Sethi points out much more eloquently than I can the complications with forecasting or why we did not predict the financial crisis. (He uses prediction markets as an example.)

... the very same market characteristics that serve to enhance predictive accuracy in the case of exogenous events could undermine accuracy in forecasting endogenous events. Accurate forecasting of exogneous events requires broad participation and high levels of market visibility and liquidity, so that decentralized information can be effectively aggregated. But in the case of endogenous events, the more reliable a market is perceived by the public to be, the greater the incentives to manipulate prices at the margin. The problem is especially severe when there is a positive feedback loop between subjective beliefs and objective probabilities, ...

Thus, the more likely the event is going to occur, the more skewed my incentive would be to make it occur (if it rewards me). If everyone were like me (a representative agent) then it would likely become self-fulfilling. If there are less of me but more of the opposite then the outcome would not occur therefore making the prediction and the forecast invalid. Since very few people had an interest in seeing the crisis occur the forecast that would be one is then invalidated.

This seems to represent a paradox in forecasting or why as some have claimed:

... economists did something even better than predict the crisis. We correctly predicted that we would not be able to predict it. The most important part of the much-maligned Efficient Markets Hypothesis (EMH) is that nobody can systematically beat the stock market. Which implies nobody can predict a market crash, because if you could, then you would obviously beat the market.

The above argument does not rely on EMH but then again, I don't really believe that the crisis was not forecasteable because people were consciously trying to not make it so. This is why I think that the gleeful economist who proclaims that crises are unpredictable and that economists even predict that they cannot predict it is like a fool who says he is infinitely wise because he knows that he knows nothing.

However, I believe that there is a feedback in financial markets forecasting and behavior of actors in the markets that make forecasts inaccurate.

Is medicine a science?

Related to an earlier post on what constitutes a science is The Checklist from Atul Gawande:

“The fundamental problem with the quality of American medicine is that we’ve failed to view delivery of health care as a science. The tasks of medical science fall into three buckets. One is understanding disease biology. One is finding effective therapies. And one is insuring those therapies are delivered effectively. That third bucket has been almost totally ignored by research funders, government, and academia. It’s viewed as the art of medicine. That’s a mistake, a huge mistake. And from a taxpayer’s perspective it’s outrageous.”

This third bucket can be made into a science by the introduction of a checklist.

In 2001, though, a critical-care specialist at Johns Hopkins Hospital named Peter Pronovost decided to give it a try. He didn’t attempt to make the checklist cover everything; he designed it to tackle just one problem, the one that nearly killed Anthony DeFilippo: line infections. On a sheet of plain paper, he plotted out the steps to take in order to avoid infections when putting a line in. Doctors are supposed to (1) wash their hands with soap, (2) clean the patient’s skin with chlorhexidine antiseptic, (3) put sterile drapes over the entire patient, (4) wear a sterile mask, hat, gown, and gloves, and (5) put a sterile dressing over the catheter site once the line is in. Check, check, check, check, check. These steps are no-brainers; they have been known and taught for years. So it seemed silly to make a checklist just for them. Still, Pronovost asked the nurses in his I.C.U. to observe the doctors for a month as they put lines into patients, and record how often they completed each step. In more than a third of patients, they skipped at least one.

The next month, he and his team persuaded the hospital administration to authorize nurses to stop doctors if they saw them skipping a step on the checklist; nurses were also to ask them each day whether any lines ought to be removed, so as not to leave them in longer than necessary. This was revolutionary. Nurses have always had their ways of nudging a doctor into doing the right thing, ranging from the gentle reminder (“Um, did you forget to put on your mask, doctor?”) to more forceful methods (I’ve had a nurse bodycheck me when she thought I hadn’t put enough drapes on a patient). But many nurses aren’t sure whether this is their place, or whether a given step is worth a confrontation. (Does it really matter whether a patient’s legs are draped for a line going into the chest?) The new rule made it clear: if doctors didn’t follow every step on the checklist, the nurses would have backup from the administration to intervene.

Pronovost and his colleagues monitored what happened for a year afterward. The results were so dramatic that they weren’t sure whether to believe them: the ten-day line-infection rate went from eleven per cent to zero. So they followed patients for fifteen more months. Only two line infections occurred during the entire period. They calculated that, in this one hospital, the checklist had prevented forty-three infections and eight deaths, and saved two million dollars in costs.

... The checklists provided two main benefits, Pronovost observed. First, they helped with memory recall, especially with mundane matters that are easily overlooked in patients undergoing more drastic events. (When you’re worrying about what treatment to give a woman who won’t stop seizing, it’s hard to remember to make sure that the head of her bed is in the right position.) A second effect was to make explicit the minimum, expected steps in complex processes. Pronovost was surprised to discover how often even experienced personnel failed to grasp the importance of certain precautions. In a survey of I.C.U. staff taken before introducing the ventilator checklists, he found that half hadn’t realized that there was evidence strongly supporting giving ventilated patients antacid medication. Checklists established a higher standard of baseline performance.

... In December, 2006, the Keystone Initiative published its findings in a landmark article in The New England Journal of Medicine. Within the first three months of the project, the infection rate in Michigan’s I.C.U.s decreased by sixty-six per cent. The typical I.C.U.—including the ones at Sinai-Grace Hospital—cut its quarterly infection rate to zero. Michigan’s infection rates fell so low that its average I.C.U. outperformed ninety per cent of I.C.U.s nationwide. In the Keystone Initiative’s first eighteen months, the hospitals saved an estimated hundred and seventy-five million dollars in costs and more than fifteen hundred lives. The successes have been sustained for almost four years—all because of a stupid little checklist.

And in the same article, it also establishes why aeronautical engineering is not always science - because of the human element:

On October 30, 1935, at Wright Air Field in Dayton, Ohio, the U.S. Army Air Corps held a flight competition for airplane manufacturers vying to build its next-generation long-range bomber. It wasn’t supposed to be much of a competition. In early evaluations, the Boeing Corporation’s gleaming aluminum-alloy Model 299 had trounced the designs of Martin and Douglas. Boeing’s plane could carry five times as many bombs as the Army had requested; it could fly faster than previous bombers, and almost twice as far. A Seattle newspaperman who had glimpsed the plane called it the “flying fortress,” and the name stuck. The flight “competition,” according to the military historian Phillip Meilinger, was regarded as a mere formality. The Army planned to order at least sixty-five of the aircraft.

A small crowd of Army brass and manufacturing executives watched as the Model 299 test plane taxied onto the runway. It was sleek and impressive, with a hundred-and-three-foot wingspan and four engines jutting out from the wings, rather than the usual two. The plane roared down the tarmac, lifted off smoothly, and climbed sharply to three hundred feet. Then it stalled, turned on one wing, and crashed in a fiery explosion. Two of the five crew members died, including the pilot, Major Ployer P. Hill.

An investigation revealed that nothing mechanical had gone wrong. The crash had been due to “pilot error,” the report said. Substantially more complex than previous aircraft, the new plane required the pilot to attend to the four engines, a retractable landing gear, new wing flaps, electric trim tabs that needed adjustment to maintain control at different airspeeds, and constant-speed propellers whose pitch had to be regulated with hydraulic controls, among other features. While doing all this, Hill had forgotten to release a new locking mechanism on the elevator and rudder controls. The Boeing model was deemed, as a newspaper put it, “too much airplane for one man to fly.” The Army Air Corps declared Douglas’s smaller design the winner. Boeing nearly went bankrupt.

Still, the Army purchased a few aircraft from Boeing as test planes, and some insiders remained convinced that the aircraft was flyable. So a group of test pilots got together and considered what to do.

They could have required Model 299 pilots to undergo more training. But it was hard to imagine having more experience and expertise than Major Hill, who had been the U.S. Army Air Corps’ chief of flight testing. Instead, they came up with an ingeniously simple approach: they created a pilot’s checklist, with step-by-step checks for takeoff, flight, landing, and taxiing. Its mere existence indicated how far aeronautics had advanced. In the early years of flight, getting an aircraft into the air might have been nerve-racking, but it was hardly complex. Using a checklist for takeoff would no more have occurred to a pilot than to a driver backing a car out of the garage. But this new plane was too complicated to be left to the memory of any pilot, however expert.

With the checklist in hand, the pilots went on to fly the Model 299 a total of 1.8 million miles without one accident. The Army ultimately ordered almost thirteen thousand of the aircraft, which it dubbed the B-17. And, because flying the behemoth was now possible, the Army gained a decisive air advantage in the Second World War which enabled its devastating bombing campaign across Nazi Germany.

Medicine today has entered its B-17 phase. Substantial parts of what hospitals do—most notably, intensive care—are now too complex for clinicians to carry them out reliably from memory alone. I.C.U. life support has become too much medicine for one person to fly.

And I've always wondered - could a computer make a better House? There are so many symptoms that correlate with different diseases - would it not be more efficient to use a computer to narrow and eliminate possible candidates? Perhaps it is hero worship that we are after and if something as mundane as a computer could quickly narrow down the possibilities for us we would not be watching the series.

Gold

Some fascinating facts on gold from NGS:

... Descending into an icy tunnel 17,000 feet up in the Peruvian Andes, the 44-year-old miner stuffs a wad of coca leaves into his mouth to brace himself for the inevitable hunger and fatigue. For 30 days each month Apaza toils, without pay, deep inside this mine dug down under a glacier above the world's highest town, La Rinconada. For 30 days he faces the dangers that have killed many of his fellow miners—explosives, toxic gases, tunnel collapses—to extract the gold that the world demands. Apaza does all this, without pay, so that he can make it to today, the 31st day, when he and his fellow miners are given a single shift, four hours or maybe a little more, to haul out and keep as much rock as their weary shoulders can bear. Under the ancient lottery system that still prevails in the high Andes, known as the cachorreo, this is what passes for a paycheck: a sack of rocks that may contain a small fortune in gold or, far more often, very little at all. ...

... In all of history, only 161,000 tons of gold have been mined, barely enough to fill two Olympic-size swimming pools. More than half of that has been extracted in the past 50 years. ...

... As a girl growing up on the remote Indonesian island of Sumbawa, Nur Piah heard tales about vast quantities of gold buried beneath the moun­tain rain forests. They were legends—until geol­ogists from an American company, Newmont Mining Corporation, discovered a curious green rock near a dormant volcano eight miles from her home. ...

Nur Piah, then 24, replied to a Newmont ad seeking "operators," figuring her friendly manner would get her a job answering phones. When the daughter of a Muslim cleric arrived for training, though, her boss showed her a different operating booth—the cab of a Caterpillar 793 haul truck, one of the world's largest trucks. Standing 21 feet tall and 43 feet long, the truck was bigger than her family home. Its wheels alone were double her height. "The truck terrified me," Nur Piah recalls. Another shock soon followed when she saw the first cut of the mine itself. "They had peeled the skin off the Earth!" she says. "I thought, Whatever force can do that must be very powerful."

Ten years later, Nur Piah is part of that force herself. Pulling a pink head scarf close around her face, the mother of two smiles demurely as she revs the Caterpillar's 2,337-horsepower engine and rumbles into the pit at Batu Hijau. Her truck is part of a 111-vehicle fleet that hauls close to a hundred million tons of rock out of the ground every year. The 1,800-foot volcano that stood here for millions of years? No hint of it remains. The space it once occupied has been turned into a mile-wide pit that reaches 345 feet below sea level. By the time the seam at Batu Hijau is exhausted in 20 years or so, the pit will bottom out at 1,500 feet below sea level. The environmental wreckage doesn't concern Nur Piah anymore. "I only think about getting my salary," she says.

... Nowhere is the gold obsession more culturally entrenched than it is in India. Per capita income in this country of a billion people is $2,700, but it has been the world's runaway leader in gold demand for several decades. In 2007, India consumed 773.6 tons of gold, about 20 percent of the world gold market and more than double that purchased by either of its closest followers, China (363.3 tons) and the U.S. (278.1 tons). India produces very little gold of its own, but its citizens have hoarded up to 18,000 tons of the yellow metal—more than 40 times the amount held in the country's central bank.

... As the price of the metal goes up, however, poor Indian families are having a harder time raising the gold they need for dowries. Though the dowry retains a social function—balancing the wealth between the families of bride and groom—the rising price of gold has only fueled its abusive side. In the neighboring state of Tamil Nadu, the struggle to acquire gold has led to dowry-related domestic violence (usually when grooms' families beat the brides for bringing too little gold) and selective abortions (committed by families desperate to avoid the financial burden of a daughter).

The Decision Makers

This is the title of Robert Heller's book which I found to be ponderous and confusing. The takeaway I got from this is that not all decision makers are right all the time (Duh!). Examples are Akio Morita's decision to go with the Walkman (success) and Betamax (failure). If he had kept the stories to a narrative instead of trying to reduce them to case studies it would have been a more interesting book. Instead it is just downright confusing. I would not be surprised if he is now a management consultant. As the book stands I could barely read it much less skim it.

The best part of the book turned out to be the beginning:

pg 26:
The basic question is: What kind of decision am I considering? With that answer in hand, at least fifteen other questions follow:
1. What decisions am I, consciously or unconsciously, not taking that I ought to take?
2. (Very Important) What is the question that this decision will answer?
3. How many realistic alternatives are there as an answer to the question?
4. Does this decision have to be taken at all?
5. If it is not taken, what consequences will follow?
6. What objective is the decision intended to achieve?
7. What results if that aim is not achieved?
8. What is the perfect infrmation that will enable the decision to be taken in near perfectness?
9. How near can I et to that perfect information?
10. Is the degree of imperfection so great as to undermine the basis for rational decision?
11. How is the decision to be executed? By whom? Monitored in what way and against what criteria?
12.What can go wrong?
13. In the event that Murphy's Law operates, and what can go wrong does, what will be the response?
14. What can go too well?
15. If the results of this decision flow broadly to plan what further decisions will have to be taken - and when?

pg. 45

1. Involve all relevant people from the start.
2. Have a single, fully-worked-out objective in view - aim to kill one bird with many stones, not two birds with one.
3. Having obtained thebest possible information and counsel in concert, act on it in concert.
4. Be governed by what you rather than what you fear.
5. Embody the decisions in a comprehensive plan that everybody knows and that wil cover the expected consequences of setback or success.
6. Entrust execution to competent people with no conflicting responsibilities.
7. Leave operational people to operate.
8. In the event of serious failure, start again to review and renew the decisions.
9. Only abandon the decision when it is plain to all that is objective cannot be achieved.

These rules more or less end each chapter (there are 8 chapters in all) yet as I went along these rules are sometimes consistent across the book and depends on the situation the decision maker finds himself to be in - e.g. is this an expansion, a salvation, an innovation, an attempt to outcompete, etc. While these can be useful rules of thumb they seem