I have noticed that panpsychism, which is the idea that some or all elements of  matter possess some form of consciousness, subjective experience, mental awareness, or whatever you would like to call it, seems to be gaining favour these days. Noted neuroscientist Christoff Koch has recently suggested that consciousness may be a property of matter like mass or charge. I was just listening to a Philosophy Bites podcast where philosopher Galen Strawson (listen here) was forcefully arguing that panpsychism or micropsychism was in fact the most plausible prior if one is a physicalist or monist (i.e. someone who believes that everything is made of the same stuff).  He argued that it was much more plausible for electrons to possess some tiny amount of consciousness then for it to emerge from the interactions of a large number of neurons.

What I want to point out  is that panpsychism is a closeted form of dualism (i.e. mind is different from matter). I believe philosopher David Chalmers, who coined the term “The hard problem of consciousness“, would agree.  Unlike consciousness, mass and charge can be measured and obey well-defined rules. If I were to make a computer simulation of the universe, I could incorporate mass and charge into the physical laws, be they Newton’s Laws and Maxwell’s equations, the Standard Model of particle physics, String theory, or whatever will replace that.  However, I have no idea how to incorporate consciousness into any simulation. Deeming consciousness to be a property of matter is no different from Cartesian dualism.  Both off-load the problem to a separate realm. You can be a monist or a panpsychist but you cannot be both.

Log normal

A comment to my previous post correctly points out that the income distribution is approximately log-normal. What this means is that while income itself is not normally distributed, the logarithm of income is.  The log-normal distribution has a pretty fat tail for high incomes. A variable will be log-normal if it is the product of a lot of random variables, since the log of a product is a sum. It has been argued for many years that achievement should be log-normal because it involves the product of many independent events. This is why a good programmer can be hundreds of times better than a mediocre one.  I even gave a version of this argument here. Hence, small differences in innate ability can lead to potentially large differences in outcome. However, despite the fact that income may deviate from log-normality in some cases and in particular between sectors of the economy (e.g. finance vs. philosophy), there is still a question of whether the compensation scheme needs to follow log-normal even if productivity does. After all, if small differences in innate ability are magnified to such a large extent, one could argue that income should be pegged to the log of productivity.

Nonlinearity in your wallet

Many human traits like height, IQ, and 50 metre dash times are very close to being normally distributed. The normal distribution (more technically the normal probability density function) or Gaussian function

f(x) = \frac{1}{\sqrt{2\pi}\sigma}e^{-(x-\mu)^2/2\sigma^2}

is the famous bell shaped curve that the histogram of class grades fall on. The shape of the Gaussian is specified by two parameters the mean \mu, which coincides with the peak of the bell, and the standard deviation \sigma, which is a measure of how wide the Gaussian is. Let’s take height as an example. There is a 68% chance that any person will be within one standard deviation of the mean and a little more than 95% that you will be within two standard deviations. The tallest one percent are about 2.3 standard deviations from the mean.

The fact that lots of things are normally distributed  is not an accident but a consequence of the central limit theorem (CLT), which may be the most important mathematical law in your life. The theorem says that the probability distribution of a sum of a large number of random things will be normal (i.e. a Gaussian). In the example of height, it suggests that there are perhaps hundreds or thousands of genetic and environmental factors that determine your height, each contributing a little amount. When you add them together you get your height and the distribution is normal.

Now, the one major thing in your life that bucks the normal trend is income and especially wealth distribution. Incomes are extremely non-normal. They have what are called fat tails, meaning that the income of the top earners are much higher than would be expected by a normal distribution. A general rule of thumb called the Pareto Principle is that 20% of the population controls 80% of the wealth. It may even be more skewed these days.

There are many theories as to why income and wealth is distributed the way it is and I won’t go into any of these. What I want to point out is that whatever it is that governs income and wealth, it is definitely nonlinear. The key ingredient in the CLT is that the factors add linearly. If there were some nonlinear combination of the variables then the result need not be normal. It has been argued that some amount of inequality is unavoidable given that we are born with unequal innate traits but the translation of those differences into  income inequality is a social choice to some degree. If we rewarded the contributors to income more linearly, then incomes would be distributed more normally (there would be some inherent skew because incomes must be positive). In some sense, the fact that some sectors of the economy seem to have much higher incomes than other sectors implies a market failure.

Obesity references

I’ve been asked about references to papers on which my New York Times interview is based so I’ve listed them below.  You can find summaries for some of them as well as the slides for my talks and posts related to obesity here.

K.D. Hall, G.Sacks, D. Chandramohan, C.C Chow, C. Wang; S. Gortmaker; B. Swinburn, `Quantifying the effect of energy imbalance on body weight change.’ The Lancet 378:826-37 (2011).

K.D. Hall and C.C. Chow, `Estimating changes of free-living energy intake and its confidence interval,’ Am J Clin Nutr 94:66-74 (2011).

K.D. Hall, M. Dore, J. Guo, and C.C. Chow, ‘The progressive increase of food waste in America’, PLoS ONE 4(11): e7940 (2009).

C.C. Chow and K.D. Hall, `The dynamics of human body weight change’, PLoS Computational Biology , e1000045 (2008).

K.D. Hall, H.L. Bain and C.C. Chow, `How adaptations of substrate utilization regulate body composition’, International Journal of Obesity, 31 , 1378-83 (2007). [PDF]

V. Periwal and C.C. Chow, ‘Patterns in food intake correlate with body mass index’, American Journal of Physiology: Endocrinology and Metabolism, 291 929-936 (2006) [PDF]

Causality and obesity

The standard adage for complex systems as seen in biology and economics is that “correlation does not imply causation.”  The question then is how do you ever prove that something causes something. In the example of obesity, I stated in my New York Times interview that the obesity epidemic was caused by an increase in food availability.  What does that mean? If you strictly follow formal logic then this means that a) an increase in food supply will lead to an increase in obesity (i.e. modus ponens) and b) if there were no obesity epidemic then there would not have been an increase in food availability (i.e. modus tollens). It doesn’t mean that if there were not an increase in food availability then there would be no obesity epidemic.  This is where many people seem to be confused.  The obesity epidemic could have been caused by many things.  Some argue that it was a decline in physical activity. Some say that it is due to some unknown environmental agent. Some believe it is caused by an overconsumption of sugar and high fructose corn syrup. They could all be true and that still doesn’t mean that increased food supply was not a causal factor. Our validated model shows that if you feed the US population the extra food then there will be an  increase in body weight that more than compensates for the observed rise.  We have thus satisfied a) and thus I can claim that the obesity epidemic was caused by an increase in food supply.

Stating that obesity is a complex phenomenon that involves lots of different factors and that there cannot be a simple explanation is not an argument against my assertion. This is what I called hiding behind complexity. Yes, it is true that obesity is complex but that is not an argument for saying that food is not a causal factor. If you want to disprove my assertion then what you need to do is to find a country that does not have an obesity epidemic but did exhibit an increase in food supply that was sufficient to cause it. My plan is to do this by applying our model to other nations as soon as I am able to get ahold of data of body weights over time. This has proved more difficult than I expected. The US should be commended for having good easily accessible data. Another important point to consider is that even if increased food supply caused the obesity epidemic, this does not mean that reducing food supply will reverse it. There could be other effects that maintain it even in the absence of excess food.  As we all know, it’s complicated.

FACM12 talk at NJIT

I’m currently at the New Jersey Institute of Technology for the ninth annual Frontiers in Applied and Computational Mathematics conference.  Here are the slides for my talk.  It’s on computational neuroscience and has nothing to do with obesity.  Also, it only seems like lots of slides because of the animations.

The calorie debate

The day after I appeared on the radio show The Takeaway, Scientific American editor Michael Moyer came on to criticize me.  I welcome the debate and my response is below.

Here is a link to The Takeaway’s series on obesity, which has his audio file.  Moyer also writes in his blog:

Unfortunately Chow’s outsider’s perspective on the obesity crisis isn’t really an outsider’s perspective at all: it is the physicist’s perspective. Physicists have a long history of marching into other sciences with grand plans of stripping complex phenomena down to the essentials with the hope of uncovering simple fundamental laws. Occasionally this works. More often, they tend to overlook the very biochemistry at the heart of the process in question.

Chow’s conclusion is not just obvious—it’s a tautology. Because for Chow, a calorie is just a unit of energy. Eat more calories than you burn, and the energy must go somewhere. That somewhere is fat cells. The conclusion is built into the assumptions.

But perhaps a calorie is not just a calorie. Perhaps, as some prominent researchers argue, the body processes calories from sugar in a fundamentally unique and harmful way. According to this hypothesis, we’re not getting fat because we’re eating more. We’re getting fat because of what we’re eating more of. The biochemistry that explains why this would happen is complex—certainly difficult to include in a computer model—but that doesn’t make it wrong.

Ultimately experiments will decide if this hypothesis is true, or if it is not true, or if it is true but just one part of a nuanced understanding of obesity that includes biochemistry, microbiology, neurobiology, politics, economics and much more. The obesity crisis isn’t rocket science. It’s complicated.

Moyer’s criticism of me is ironic in two ways. The first is with regards to his claim that biology is not like physics. I fully agree and have posted on this very topic here. Additionally, while I have been frustrated in the past trying to get biologists to pay attention to my work, this is the one area where I am not a complete outsider and have access to and input from some of the very best clinicians, experimentalists, and public health scientists in the field.

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On the radio

I was a guest on two radio shows this morning.  At 7:45 this morning I was on The Takeaway, a nationally syndicated show on NPR (audio file available on website) and then at 10:30 I was on the Kathleen Dunn Show on Wisconsin Public Radio (audio file available here.  I am on halfway into the show). You can hear clearly that I’m not anywhere near as eloquent as Arif and Sebastian.

New York Times Interview

My conversation with Claudia Dreifus of the New Times can be found here.  I have to commend Claudia for putting in a great deal of effort on this piece. I never realized that they were so much work.  The published interview is a very condensed version of our many conversations.  Claudia did her very best to make sure everything was accurate but some nuance had to be sacrificed for space.  For example, I was engaged but not yet married when I moved to NIH.  Also, I want to point out that I did know that a calorie was a unit of energy but I had no idea that the food Calorie is really a kilocalorie nor how many Calories are contained in common food items.

One of the things that got cut from the story was that I heard about the job at NIDDK from John Rinzel.  The Laboratory of Biological Modeling that I’m now part of, used to be called the Mathematical Research Branch and John was its chief for twenty years. Wilfrid Rall was the chief before John. A brief history of the lab can be found here. One could argue that this was where computational neuroscience was established. Bard Ermentrout is among the many computational neuroscientists that passed through the lab. The branch actually predates the NIDDK and was put there for administrative reasons even though it focused on neuroscience.  However, near the end of John’s tenure as chief, the institute had less enthusiasm for the lab and resources were reduced.  John ended up leaving for NYU. Marvin Gershengorn came in as the new scientific director in the early 2000’s and he wanted to rebuild the lab. I have no doubt that I got the job because of the input Marvin received from John. Although Marvin was interested in obesity, he didn’t compel me to work on the topic.  He was very good about giving me and the lab freedom to work on anything interesting. Right now there are four PIs in the lab – Artie Sherman, Kevin Hall, Vipul Periwal and myself, and we work on a variety of biological topics although mostly with some connection to diabetes and metabolism. One thing that worried me about the piece, aside from a backlash from the food industry, was that it would pigeonhole me as an obesity researcher. I’m still very much interested in many topics including neuroscience, genetics and gene induction.

The last thing that doesn’t really make it through is that our argument for excess food causing the obesity epidemic is not just based on correlations between the increase in food supply and average body weight.  What we did was to take the actual USDA reported food availability per person, feed it to our calibrated model and showed that it more than explained the weight increase.  It may be that other factors liked decreases in physical activity are involved but they are not necessary to explain the obesity epidemic.  Those that doubt it was caused by excess food must show that all of it was thrown away.  We are already arguing that most of it was wasted.  Finally, I don’t really know how to stem the obesity epidemic.  I’m not sure that making food more expensive through taxation is the correct solution since it would cause hardship for low-income people.  I do think that curtailing food marketing to children would help but I’m not hopeful that it would ever happen.


Correction: Jun 7, 2012.  Will Rall was a member of the MRB but was never the chief.

Marginal economics

The recent financial crisis and  great recession has spurred an ongoing economics blog war.  The blog Noahpinion keeps a running summary. The battle is largely between the Keynesian view of macroeconomics, spearheaded by Paul Krugman, versus the Chicago school, represented by the likes of  John Cochrane. In very simple terms, the Keynesian explanation of recessions are that they are due to decreases in aggregate demand. By that, it simply means that economic activity shrinks due to some shock like the bursting of the real estate bubble or an inexplicable decrease in consumer confidence (Keynes called them animal spirits). Keynesian’s think in terms of reduced phenomenological models with names like the IS/LM (Investment-savings/Liquidity preference-Money supply) model or the AD/AS (aggregate demand/ aggregate supply) model. These models apply supply and demand ideas of microeconomics to the entire economy. Although the models are simple, they can give very specific prescriptions for what to do about certain economic situations. The AD/AS model is analogous to demand/supply curves of microeconomics for single products. It consists of an AD curve and an AS curve on a graph of global price level versus global output (i.e. GDP).   The AD curve slopes down to the right since the higher the price the lower the demand for goods and services while the AS curve slopes up to the right since the higher the aggregate price the higher the aggregate supply.

Unlike in microeconomics, the reasons for why these curves do what they do is not completely self-evident and takes some reasoning to justify.  The problem in a recession is that the entire demand curve shifts to the left so the equilibrium GDP also shifts to a lower level. Keynesians argue that in order to get out of a recession, we need to move the demand curve back to the right and the simplest way to do that is to increase demand through government spending. This was the rationale for the 2009 stimulus although many of the Keynesians like Paul Krugman and Christina Romer (who was in the Obama administration) cautioned beforehand that the amount was too small to get us completely out of the recession. Hence, the fact that we are still in recession is not evidence that the stimulus failed.

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Errata recap

I want to stress that there is nothing wrong with the results in the paper. The mistakes are typographical in the sense that the formulas in the methods were transcribed incorrectly from our code.  This was just pointed out to me that the errata could be misinterpreted.  What happened was that MS Word kept turning our equations into pictures so we couldn’t edit them so we retyped them over and over again.  Transcription errors then started to creep in and we were so adapted to the equations that we didn’t notice anymore.  Not a good excuse but unfortunately that is what happened.


I attended a conference on Criticality in Neural Systems at NIH this week.  I thought I would write a pedagogical post on the history of critical phenomena and phase transitions since it is a long and somewhat convoluted line of thought to link criticality as it was originally defined in physics to neuroscience.  Some of this is a recapitulation of a previous post.

Criticality is about phase transitions, which is a change in the state of matter, such as between gas and liquid. The classic paradigm of phase transitions and critical phenomena is the Ising model of magnetization. In this model, a bunch of spins that can be either up or down (north or south) sit on lattice points. The lattice is said to be magnetized if all the spins are aligned and unmagnetized or disordered if they are randomly oriented. This is a simplification of a magnet where each atom has a magnetic moment which is aligned with a spin degree of freedom of the atom. Bulk magnetism arises when the spins are all aligned.  The lowest energy state of the Ising model is for all the spins to be aligned and hence magnetized. If the only thing that spins had to deal with was the interaction energy then we would be done.  What makes the Ising model interesting and for that matter all of statistical mechanics is that the spins are also coupled to a heat bath. This means that the spins are subjected to random noise and the size of this noise is given by the temperature. The noise wants to randomize the spins. The presence of randomness is why there is the word “statistical” in statistical mechanics. What this means is that we can never say for certain what the configuration of a system is but only assign probabilities and compute moments of the probability distribution. Statistical mechanics really should have been called probabilistic mechanics.

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Errata for PLoS Genetics paper

I just discovered some glaring typographical errors in the Methods Section of our recent paper: Heritability and genetic correlations explained by common SNPS for metabolic syndrome traits.  The corrected Methods can be obtained here.  I will see if I can include an errata on the PLoS Genetics website as well. The results in the paper are fine.

edited on May 9 to clear up possible misconception.