As of today, I am officially furloughed without pay since the NIH is officially closed and nonessential employees like myself are barred from working without pay by the Antideficiency Act of 1884. However, given that blogging is not considered an official duty, I can continue to post to Scientific Clearing House. Those who are not up on American politics may be wondering why the US government has shutdown. The reason is that the US fiscal year begins on Oct 1 and according to the the US Constitution, only Congress can appropriate funds for the functioning of government and they did not pass a budget for the new fiscal year by midnight of September 30. Actually, Congress has not passed a budget on time in recent years but has passed Continuing Resolutions that to keep the government going. So why have they not passed a budget or a CR this year? Well, currently the US government is divided with the Democratic party controlling the Senate and Presidency and the Republican party controlling the House of Representatives. All three entities must agree for a law to pass. Three years ago, the Democrats controlled the Congress, which includes both the House and Senate, and passed the Affordable Care Act, also known as Obamacare, which the President signed into law. The Republicans took control of the House in 2011 and have been trying to repeal the ACA ever since but have been stopped by the Senate. This year they decided to try a new tactic, which was to pass a budget that withholds funding for the ACA. The Senate did not agree, passed a budget with the ACA and sent it back to the House, which then took out funding for the ACA again with some modifications and sent it back. This went on back and forth without converging to an agreement and thus we are closed today.
I guess this is just a week for sadness. My high school friend, David Wright, died this morning at the age of 50. He collapsed at his desk, probably of a heart attack. Dave was a natural athlete and story teller. He was always there for you if you needed someone. He will be missed.
Well, I spoke too soon in my earlier post on the America’s Cup. Oracle Team USA has since won 7 races in a row and now it is 8-8 in the best of 17 match (although they have already had 18 races). The final race to determine the winner is today. Check out the action here. In the past, America’s Cup races had usually been best of 3 or best of 5 matches. In this new format, the races are much shorter, taking less than an hour rather than several, and they try to get in two a day if the weather permits. In the beginning New Zealand had the faster boat. They had already been racing for over a month in the challenger series and were just better than Oracle. However, the long format and some weather delays has given Oracle a chance to get up to speed and now they are definitely the faster boat. Yesterday, they flew by New Zealand on the upwind leg. The only chance New Zealand has to win today is if Oracle makes a mistake.
I was saddened to learn that Richard “Dick” Azuma, who was a professor in the University of Toronto Physics department from 1961 to 1994 and emeritus after that, passed yesterday. He was a nuclear physicist par excellence and chair of the department when I was there as an undergraduate in the early 80′s. I was in the Engineering Science (physics option) program, which was an enriched engineering program at UofT. I took a class in nuclear physics with Professor Azuma during my third year. He brought great energy and intuition to the topic. He was one of the few professors I would talk to outside of class and one day I asked if he had any open summer jobs. He went out of his way to secure a position for me at the nuclear physics laboratory TRIUMF in Vancouver in 1984. That was the best summer of my life. The lab was full of students from all over Canada and I remain good friends with many of them today. I worked on a meson scattering experiment and although I wasn’t of much use to the experiment I did get to see first hand what happens in a lab. I wrote a 4th year thesis on some of the results from that experiment. I last saw Dick in 2010 when I went to Toronto to give a physics colloquium. He was still very energetic and as engaged in physics as ever. We will all miss him greatly.
Today may be the last race for the America’s Cup yacht series between the US and New Zealand. Here are the highlights from the last race.
It is a best of 17 series and New Zealand has 8 wins so today may be the last chance to watch these hundred million dollar multihull yachts fly around San Francisco harbour at close to 50 miles per hour. All the races are posted on You Tube.
Economist and Noble Laureate Ronald Coase died earlier this month just three months short of his 103rd birthday. Coase is mostly famous for two papers: “The Nature of the Firm” (1937) and “The Problem of Social Cost” (1960). He came up with many of the ideas for the first paper when he was just 21. Coase asked the simple question of why companies exist. According to Adam Smith it should actually be more cost-effective for a person to contract out work rather than hire people. Coases’s answer was that there are always transaction costs or frictions that make a firm more cost-effective. In other words, the market (i.e. price mechanism) is not always the most efficient way to organize production. The size of a firm is determined by the point when the extra (marginal) cost of organizing an extra employee balances the transaction costs of obtaining her services on the free market. Hence, the great irony of modern capitalism is that its main pillar, the firm, is a paragon of central planning. Firms in essence are totalitarian regimes where the citizens are free to leave.
Conservative and libertarian leaning individuals generally prize private companies and free markets over governments. They argue that many of the functions of government, such as schools and healthcare, would be more efficient if privatized. The question then is why are private firms more efficient than government? When we hand over functions formerly performed by a democratically elected government, we are in essence making society less democratic. One could argue that firms are more efficient because they are subject to competition. That is why we want to break up monopolies. However, that should be true of government too. If we don’t like the government we have then we can always elect another one. We can even change the constitution to our liking. In principle, no one is under more competition than our elected officials. It is the job of the citizenry to ensure that they are doing their job.
The Science Show did a feature story recently about the discovery of streptomycin, the first antibiotic to treat tuberculosis, which had killed 2 billion people in the 18th and 19th centuries. Streptomycin was discovered by graduate student Albert Schatz in 1943, who worked in the lab of Professor Selman Waksman at Rutgers. Waksman was the sole winner of the 1952 Nobel Prize for this work. The story is narrated by the author of the book Experiment Eleven, who paints Waksman as the villain and Schatz as the victim. Evidently, Waksman convinced Schatz to sign away his patent rights to Rutgers but secretly negotiated a deal to obtain 20% of the royalties. When Schatz discovered this, he sued Waksman and obtained a settlement. However, this turned the scientific community against him and he forced him out of microbiology into science education. To me, this is just more evidence that prizes and patents are incentives for malfeasance.
James Lee and I just published a paper entitled “The causal meaning of Fisher’s average effect” in the journal Genetics Research. The paper can be obtained here. This paper is the brainchild of James and I just helped him out with some of the proofs. James’s take on the paper can be read here. The paper resolves a puzzle about the incommensurability of Ronald Fisher’s two definitions of the average effect noted by population geneticist D.S. Falconer three decades ago.
Fisher was well known for both brilliance and obscurity and people have long puzzled over the meaning of some of his work. The concept of the average effect is extremely important for population genetics but it is not very well understood. The field of population genetics was invented in the early twentieth century by luminaries such as Fisher, Sewall Wright, and JBS Haldane to reconcile Darwin’s theory of evolution with Mendelian genetics. This is a very rich field that has been somewhat forgotten. People in mathematical, systems, computational, and quantitative biology really should be fully acquainted with the field.
For those who are unacquainted with genetics, here is a quick primer to understand the paper. Certain traits, like eye colour or the ability to roll your tongue, are affected by your genes. Prior to the discovery of the structure of DNA, it was not clear what genes were, except that they were the primary discrete unit of genetic inheritance. These days it usually refers to some region on the genome. Mendel’s great insight was that genes come in pairs, which we now know to correspond to the two copies of each of the 23 chromosomes we have. A variant of a particular gene is called an allele. Traits can depend on genes (or more accurately genetic loci) linearly or nonlinearly. Consider a quantitative trait that depends on a single genetic locus that has two alleles, which we will call a and A. This means that a person will have one of three possible genotypes: 1) homozygous in A (i.e. have two A alleles), 2) heterozygous (have one of each), or 3) homozygous in a (i.e. have no A alleles). If the locus is linear then if you plot the measure of the trait (e.g. height) against the number of A alleles, you will get a straight line. For example, suppose allele A contributes a tenth of a centimetre to height. Then people with one A allele will be on average one tenth of a centimetre taller than those with no A alleles and those with two A alleles will be two tenths taller. The familiar notion of dominance is a nonlinear effect. So for example, the ability to roll your tongue is controlled by a single gene. There is a dominant rolling allele and a recessive nonrolling allele. If you have at least one rolling allele, you can roll your tongue.
The average effect of a gene substitution is the average change in a trait if one allele is substituted for another. A crucial part of population genetics is that you always need to consider averages. This is because genes are rarely completely deterministic. They can be influenced by the environment or other genes. Thus, in order to define the effect of the gene, you need to average over these other influences. This then leads to a somewhat ambiguous definition of average effect and Fisher actually came up with two. The first, and as James would argue the primary definition, is a causal one in that we want to measure the average effect of a gene if you experimentally substituted one allele for another prior to development and influence by the environment. A second correlation definition would simply be to plot the trait against the number of alleles as in the example above. The slope would then be the average effect. This second definition looks at the correlation between the gene and the trait but as the old saying goes “correlation does not imply causation”. For example, the genetic loci may not have any effect on the trait but happens to be strongly correlated with a true causal locus (in the population you happen to be examining). Distinguishing between genes that are merely associated with a trait from ones that are actually causal remains an open problem in genome wide association studies.
Our paper goes over some of the history and philosophy of the tension between these two definitions. We wrote the paper because these two definitions do not always agree and we show under what conditions they do agree. The main reason they don’t agree is that averages will depend on the background over which you average. For a biallelic gene, there are 2 alleles but 3 genotypes. The distribution of alleles in a population is governed by two parameters. It’s not enough to specify the frequency of one allele. You also need to know the correlation between alleles. The regression definition matches the causal definition if a particular function representing this correlation is held fixed while the experimental allele substitutions under the causal definition are carried out. We also considered the multi-allele and multi-loci case in as much generality as we could.
Winston Churchill once said that “Democracy is the worst form of government, except for all those other forms that have been tried from time to time.” The current effectiveness of the US government does make one wonder if that is even true. The principle behind democracy is essentially utilitarian – a majority or at least a plurality decides on the course of the state. However, implicit in this assumption is that the utility function for individuals match their participation function.
For example, consider environmental regulation. The utility function for the amount of allowable emissions of some harmful pollutant like mercury for most people will be downward sloping – most people would increase their utility the less the pollutant is emitted. However, for a small minority of polluters it will be upward sloping with a much steeper slope. Let’s say that the sum of the utility gained for the bulk of the population for strong regulation is greater than that gained by the few polluters for weak regulation. If the democratic voice one has in affecting policy is proportional to the summed utility then the smaller gain for the many will outweigh the larger gain to the few. Unfortunately, this is not usually case. More often, the translation of utility to legislation and regulation is not proportional but passes through a very nonlinear participation function with a sharp threshold. The bulk of the population is below the threshold so they provide little or no voice on the issue. The minority utility is above the threshold and provides a very loud voice which dominates the result. Our laws are thus systematically biased to protecting the interests of special interest groups.
The way out of this trap is to either align everyone’s utility functions or to linearize the participation functions. We could try to use regulation to dampen the effectiveness of minority participation functions or use public information campaigns to change utility functions or increase the participation functions of the silent majority. Variations of these methods have been tried with varying degrees of success. Then there is always the old install a benevolent dictator who respects the views of the majority. That one really doesn’t have a good track record though.
Before you take that job programming at an investment bank or hedge fund you may want to read Felix Salmon’s post and Michael Lewis’s article on the case of Sergey Aleynikov. He was a top programmer at Goldman Sachs, who was then prosecuted and convicted of stealing proprietary computer code. The conviction was eventually overturned but he has now been charged again for the same crime under a different law. According to Lewis, the code was mostly modified open source stuff that Aleynikov emailed to himself for future reference of what he had done and had little value outside of Goldman. Salmon thinks that Goldman aggressively pursued this case because in order for the directors of the programming division to justify their bonuses, they need to make it look like the code, which they don’t understand, is important. If Goldman Sachs had a public relations problem before, the Lewis article will really put it over the top. This case certainly makes me think that we should change the criminal code and leave cases of intellectual theft by employees to the civil courts and not force the taxpayer to pick up the tab. Also, what is the point for putting a harmless nonviolent programmer in jail for 8 years. We could at least have him serve his sentence doing something useful like writing code to improve city traffic flow. Finally, the open software foundation may have a case against Goldman and other firms who use open source code and then violate the open source license agreement. I’m sure it wouldn’t be too hard to find a backer with deep pockets to pursue the case.
Kevin D Hall, Nancy F Butte, Boyd A Swinburn, Carson C Chow. Dynamics of childhood growth and obesity: development and validation of a quantitative mathematical model. Lancet Diabetes and Endocrinology 2013 .
You can read the press release here.
In order to curb childhood obesity, we need a good measure of how much food kids should eat. Although people like Claire Wang have proposed quantitative models in the past that are plausible, Kevin Hall and I have insisted that this is a hard problem because we don’t fully understand childhood growth. Unlike adults, who are more or less in steady state, growing children are a moving target. After a few fits and starts we finally came up with a satisfactory model that modifies our two compartment adult body composition model to incorporate growth. That previous model partitioned excess energy intake into fat and lean compartments according to the Forbes rule, which basically says that the ratio of added fat to lean is proportional to how much fat you have so the more fat you have the more excess Calories go to fat. The odd consequence of that model is that the steady state body weight is not unique but falls on a one dimensional curve. Thus there is a whole continuum of possible body weights for a fixed diet and lifestyle. I actually don’t believe this and have a modification to fix it but that is a future story.
What puzzled me about childhood growth was how do we know how much more to eat as we grow? After some thought, I realized that what we could do is to eat enough to maintain the fraction of body fat at some level, using leptin as a signal perhaps, and then tap off the energy stored in fat when we needed to grow. So just like we know how much gasoline (petrol) to add by simply filling the tank when it’s empty, we simply eat to keep our fat reserves at some level. In terms of the model, this is a symmetry breaking term that transfers energy from the fat compartment to the lean compartment. In my original model, I made this term a constant and had food intake increase to maintain the fat to lean ratio and showed using singular perturbation theory that his would yield growth that was qualitatively similar to the real thing. This then sat languishing until Kevin had the brilliant idea to make the growth term time dependent and fit it to actual data that Nancy Butte and Boyd Swinburn had taken. We could then fit the model to normal weight and obese kids to quantify how much more obese kids eat, which is more than previously believed. Another nice thing is that when the child stops growing the model is automatically the adult model!
I think one of the things that tends to lead us astray when we try to understand complex phenomena like evolution, disease, or the economy, is that we have this idea that they must have a single explanation. For example, recently two papers have been published in high profile journals trying to explain mammal monogamy. Although monogamy is quite common in birds it only occurs in 5% of mammals. Here is Carl Zimmer’s summary. The study in Science, which surveyed 2545 mammal species, argued that monogamy arises when females are solitary and sparse. Males must then commit to one since dates are so hard to find. The study in PNAS examined 230 primate species, for which monogamy occurs at the higher rate of 27%, and used Bayesian inference to argue that monogamy arises to prevent male infanticide. It’s better to help out at home rather than go around killing other men’s babies. Although both of these arguments are plausible, there need not be a single universal explanation. Each species could have its own set of circumstances that led to monogamy involving these two explanations and others. However, while we should not be biased towards a single explanation, we shouldn’t also throw up our hands like Hayek and argue that no complex phenomenon can be understood. Some phenomena will have simpler explanations than others but since the Kolmogorov complexity is undecidable there is no algorithm that can tell you which is which. We will just have to struggle with each problem as it comes.
I’m currently in Mt. Snow, Vermont to give a talk at the Gordon Research Conference on Computer Aided Drug Design. Yes, I know nothing about drug design. I am here because the organizer, Anthony Nicholls, asked me to give a pedagogical talk on Bayesian Inference. My slides are here. I only arrived yesterday but the few talks I’ve seen have been quite interesting. One interesting aspect of this conference is that many of the participants are from industry. The evening sessions are meant to be of more general interest. Last night were two talks about how to make science more reproducible. As I’ve posted before, many published results are simply wrong. The very enterprising Elizabeth Iorns has started something called the Reproducibility Initiative. I am not completely clear about how it works but it is part of another entity she started called Science Exchange, which helps to facilitate collaborations with a fee-for-service model. The Reproducibility Initiative piggy backs on Science Exchange by providing a service (for a fee) to validate any particular result. Papers that pass approval get a stamp of approval. It is expected that pharma would be interested in using this service so they can inexpensively check if possible drug targets actually hold up. Many drugs fail at phase three of clinical trials because they’ve been shown to be ineffective and this may be due to the target being wrong to start with.
On a final note, I flew to Albany and drove here. Unlike in the past when I would have printed out a map, I simply assumed that I could use Google Maps on my smart phone to get here. However, Google Maps doesn’t really know where Mt. Snow is. It tried to take me up a dirt road to the back of the ski resort. Also, just after I turned up the road, the phone signal disappeared so I was blind and had no paper backup. I was suspicious that this was the right way to go so I turned back to the main highway in hopes of finding a signal or a gas station to ask for directions. A few miles down Route 9, I finally did get a signal and also found a sign that led me the way. Google Maps still tried to take me the wrong way. I should have followed what I always tell my daughter – Always have a backup plan.
A Coulon, CC Chow, RH Singer, DR Larson Eukaryotic transcriptional dynamics: from single molecules to cell populations. Nat Gen Reviews (2013).
Abstract | Transcriptional regulation is achieved through combinatorial interactions between regulatory elements in the human genome and a vast range of factors that modulate the recruitment and activity of RNA polymerase. Experimental approaches for studying transcription in vivo now extend from single-molecule techniques to genome-wide measurements. Parallel to these developments is the need for testable quantitative and predictive models for understanding gene regulation. These conceptual models must also provide insight into the dynamics of transcription and the variability that is observed at the single-cell level. In this Review, we discuss recent results on transcriptional regulation and also the models those results engender. We show how a non-equilibrium description informs our view of transcription by explicitly considering time- and energy-dependence at the molecular level.
This paper started many years ago when Steve Wank, of the Digestive Diseases Branch of NIDDK, had this idea to use this new wireless PH detecting SmartPill that you could swallow to determine how much acid your stomach was producing. There really was no noninvasive way to monitor how well medications would work for certain reflux diseases. What he wanted was a model of gastric acid secretion output based on the dynamics of PH when a buffer was added to design a protocol for the experiment. I came up with a simple mass-action model of acid buffering and made some graphs for him. We then tested the model out in a beaker. He thought the model worked better than I did but it was somewhat useful to him in designing the experiment.
BACKGROUND:Gastro-oesophageal reflux disease (GERD) and gastric acid hypersecretion respond well to suppression of gastric acid secretion. However, clinical management and research in diseases of acid secretion have been hindered by the lack of a non-invasive, accurate and reproducible tool to measure gastric acid output (GAO). Thus, symptoms or, in refractory cases, invasive testing may guide acid suppression therapy.
AIM:To present and validate a novel, non-invasive method of GAO analysis in healthy subjects using a wireless pH sensor, SmartPill (SP) (SmartPill Corporation, Buffalo, NY, USA).
METHODS:Twenty healthy subjects underwent conventional GAO studies with a nasogastric tube. Variables impacting liquid meal-stimulated GAO analysis were assessed by modelling and in vitro verification. Buffering capacity of Ensure Plus was empirically determined. SP GAO was calculated using the rate of acidification of the Ensure Plus meal. Gastric emptying scintigraphy and GAO studies with radiolabelled Ensure Plus and SP assessed emptying time, acidification rate and mixing. Twelve subjects had a second SP GAO study to assess reproducibility.
RESULTS:Meal-stimulated SP GAO analysis was dependent on acid secretion rate and meal-buffering capacity, but not on gastric emptying time. On repeated studies, SP GAO strongly correlated with conventional basal acid output (BAO) (r = 0.51, P = 0.02), maximal acid output (MAO) (r = 0.72, P = 0.0004) and peak acid output (PAO) (r = 0.60, P = 0.006). The SP sampled the stomach well during meal acidification.
CONCLUSIONS:SP GAO analysis is a non-invasive, accurate and reproducible method for the quantitative measurement of GAO in healthy subjects. SP GAO analysis could facilitate research and clinical management of GERD and other disorders of gastric acid secretion.
I recently wrote about Michael Houghton declining the prestigious Gairdner prize because it left out two critical contributors to the discovery of the Hepatitis C virus. Houghton has now written an opinion piece in Nature Medicine arguing that prizes relax the restriction to three awardees, an arbitrary number I’ve never understood. After all, one could argue that Freeman Dyson had a reasonable claim on the Nobel Prize awarded to Feynman, Schwinger, and Tomonaga for QED. I’ve quoted the entire piece below.
Nature Medicine: Earlier this year, I was greatly honored with the offer of a 2013 Canada Gairdner International Award for my contributions to the discovery of the hepatitis C virus (HCV). I was selected along with Harvey Alter, chief of clinical studies in the Department of Transfusion Medicine at the US National Institutes of Health’s Clinical Center in Bethesda, Maryland, and Daniel Bradley, a consultant at the US Centers for Disease Control and Prevention in Atlanta, both of whom had a vital role in the research that eventually led to the identification and characterization of the virus.
My colleagues accepted their awards. However, I declined my C$100,000 ($98,000) prize because it excluded two other key contributors who worked with me closely to successfully isolate the viral genome for the first time. I felt that given their crucial inputs, it would be wrong of me to keep accepting major prizes just ‘on their behalf’, a situation that has developed because major award foundations and committees around the world insist that prizes be limited to no more than three recipients per topic.
HCV was identified in 1989 in my laboratory at the Chiron Corporation, a California biotechnology firm since purchased by the Swiss drug company Novartis. The discovery was the result of seven years of research in which I worked closely, both intellectually and experimentally, with Qui-Lim Choo, a member of my own laboratory, and George Kuo, who had his own laboratory next door to mine at Chiron. We finally identified the virus using a technically risky DNA-expression screening technique through which we isolated a single small nucleic acid clone from among many millions of such clones from different recombinant libraries. This was achieved without the aid of the still-evolving PCR technology to amplify the miniscule amounts of viral nucleic acid present in blood. We ultimately proved that this clone derived from a positive-stranded viral RNA genome intimately associated with hepatitis, but one not linked to either the hepatitis A or B viruses1, 2. The finding represented the first time any virus had been identified without either prior visualization of the virus itself, characterization of its antigens or viral propagation in cell culture.
The high-titer infectious chimpanzee plasma used for our molecular analyses at Chiron was provided in 1985 by Bradley, an expert in chimpanzee transmission of HCV and in the virus’s basic properties and cellular responses, with whom I had an active collaboration since 1982. The proposed aim of the collaboration was for my laboratory to apply contemporary molecular cloning methodologies to a problem that had proven intractable since the mid-1970s, when Alter and his colleagues first demonstrated the existence of non-A, non-B hepatitis (NANBH), as it was then known. Alter’s team went on to define the high incidence and medical importance of NANBH, including the virus’s propensity to cause liver fibrosis, cirrhosis and cancer. They also identified high-titer infectious human plasma in 1980 and were instrumental in promoting the adoption of surrogate tests for NANBH by blood banks to reduce the incidence of post-transfusion infection.
With regrets to the Gairdner Foundation—a generous and altruistic organization—I felt compelled to decline the International Gairdner Award without the addition of Kuo and Choo to the trio of scientists offered the award. In 1992, all five of us received the Karl Landsteiner Memorial Award from the American Association of Blood Banks. But subsequent accolades given in honor of HCV’s discovery have omitted key members of the group: only Bradley and I received the 1993 Robert Koch Prize, and only Alter and I won the 2000 Albert Lasker Award for Clinical Medical Research—in both cases, despite my repeated requests that the other scientists involved in the discovery be recognized. With the exclusion once more of Kuo and Choo from this year’s Gairdner Award, I decided that I should not continue to accept major awards without them. In doing so, I became the first person since the Gairdner’s inception in 1959 to turn down the prize.
I hope that my decision helps bring attention to a fundamental problem with many scientific prizes today. Although some awards, such as the Landsteiner, are inclusionary and emphasize outstanding team accomplishments, the majority of the world’s prestigious scientific awards—including the Gairdner, Lasker and Shaw prizes, which all seem to be modeled on the Nobel Prize and indeed are sometimes known as the ‘baby Nobels’—are usually restricted to at most three individuals per discovery. Unsurprisingly, this limitation often leads to controversy, when one or more worthy recipients are omitted from the winners list.
Perhaps what may help this situation is for awards committees to solicit, and then be responsive to, input from potential recipients themselves prior to making their final decisions. Some of the recipients are best placed to know the full and often intricate history of the discovery and collaborative efforts, and such input should help committees better understand the size of the contributing team from which they can then choose recipients according to each award’s particular policy.
With this information in hand, award organizers should be willing to award more than three researchers. As knowledge and technology grows exponentially around the world and with an increasing need for multidisciplinary collaborations to address complex questions and problems, there is a case to be made for award committees adjusting to this changing paradigm. Moreover, it is inherently unfair to exclude individuals who played a key part in the discovery. Why should they and their families suffer such great disappointment after contributing such crucial input? Some award restructuring could also be inspirational to young scientists, encouraging them to be highly interactive and collaborative in the knowledge that when a novel, long-shot idea or approach actually translates to scientific success, all key parties will be acknowledged appropriately.
In this vein, I am happy to note that the inaugural Queen Elizabeth Prize for Engineering, a new £1 million ($1.6 million) prize from the UK government, was awarded at a formal ceremony last month to five individuals who helped create the internet and the World Wide Web, even though the original guidelines stipulated a maximum of three recipients. If the Queen of England—the very emblem of tradition—can cast protocol aside, clearly other institutions can too. I hope more awards committees will follow Her Majesty’s lead.
The Obama administration has decided to delay by one year the implementation of the employer health care mandate for businesses with more than 50 employees. The fear was that companies with slightly more than 50 employees would simply lay off or convert to part-time the workers above the threshold to avoid the penalties. I think in general thresholds are a bad idea for economic and tax policy as they provide an incentive to game the system. They should be replaced by smooth scales. US federal income taxes have a small number of rigid brackets for which income above a certain amount is taxed at a higher rate. This should be replaced by a smooth function so the tax rate for an extra dollar earned will only be slightly higher than the previous dollar. The shape of the function can be debated but a smooth one would certainly work better than the current discontinuous one. In terms of the employer mandate for providing health care, a smooth phased-in penalty would avoid the incentives for companies to manipulate the number of employees they have.
The blogosphere is aflutter over Harvard economist and former chairman of the Council of Economic Advisors to Bush 43, Greg Mankiw‘s recent article “Defending the One Percent“. Mankiw’s paper mostly argues against the classic utilitarian reason for redistribution – a dollar is more useful to a poor person than a rich one. However, near the end of the paper he proposes that an alternative basis for fair income distribution should be the just desserts principle where everyone is compensated according to how much they contribute. Mankiw believes that the recent surge in income inequality is due to changes in technology that favour superstars who create much more value for the economy than the rest. He then argues that the superstars are superstars because of heritable innate qualities like IQ and not because the economy is rigged in their favour.
The problem with this idea is that genetic ability is a shared natural resource that came through a long process of evolution and everyone who has ever lived has contributed to this process. In many ways, we’re like a huge random Monte Carlo simulation where we randomly try out lots of different gene variants to see what works best. Mankiw’s superstars are the Monte Carlo trials that happen to be successful in our current system. However, the world could change and other qualities could become more important just as physical strength was more important in the pre-information age. The ninety-nine percent are reservoirs of genetic variability that we all need to prosper. Some impoverished person alive today may possess the genetic variant to resist some future plague and save humanity. She is providing immense uncompensated economic value. The just desserts world is really nothing more than a random world; a world where you are handed a lottery ticket and you hope you win. This would be fine but one shouldn’t couch it in terms of some deeper rationale. A world with a more equitable distribution is one where we compensate the less successful for their contribution to economic progress. However, that doesn’t mean we should have a world with completely equal income distribution. Unfortunately, the human mind needs incentives to try hard so for maximal economic growth, the lottery winners must always get at least a small bonus.
For those interested, here is a four page summary of my research activities that I wrote for my upcoming quadrennial review at NIH. It doesn’t include everything I’ve done in the past four years, just the main lines of research.
June 24, 2013: Corrected a small typo in the summary.
The body weight simulator, originally a web based java application, is now also an iPhone app (see here in iTunes). The simulator is based on the human metabolism model developed by Kevin Hall, myself, and collaborators. The exact model is given in detail in our Lancet paper, which is listed here along with other related references. The app predicts the time course of your body weight given your baseline parameters and your new diet and/or new physical activity. It will also give a suggested daily caloric intake to attain a new weight over a specified period of time along with the diet required to maintain that weight. The model uses parameters calibrated to the average American so your own mileage will vary. Also, I basically wrote the app in my spare time over the past year so it is pretty primitive as far as apps go but it does the job. Please try it out and give me feedback.