Bullies and free loaders

I think one way to conceptualize how people on different ends of the current political spectrum think is that all people tend to have an aversion to bullies and free loaders.  What differs between political ideology is whom you consider to be in these categories.  For libertarian minded conservatives, the government is a bully and recipients of government largesse are free loaders.  For the Marxist left (which no longer seems to exist in the United States),  capitalists are both bullies and free loaders.  It is not too difficult to see the origins and assumptions leading to such view points.  However, even though these ideas represent extremes of the political spectrum, they are also reliant on optimistic (albeit different) assumptions about human nature.

The libertarian believes that all forms of regulation are a direct impingement on their freedoms and a suppression of economic prosperity for all.  In their view, as for example espoused by Ayn Rand, noble entrepreneurs are thwarted in their attempts to be creative and productive by corrupt and irrational regulations.  The fruits of their labour are expropriated by the domineering state and redistributed to the lazy, undeserving hoi polloi.  However, conflict arises in the strict libertarian view when exercising one’s freedom impinges on the freedom of someone else.  Libertarians, as propounded by Milton Friedman, believe that individuals can rationally settle disputes by negotiating or in civil courts.  I think this represents a highly optimistic view of human nature.  What more likely will happen, as articulated brilliantly in Noahpinion, is that those with  more wealth and power will simply bully those with less.  Society will be about navigating the realms of local bullies.

In the Marxist viewpoint, economic value comes from a combination of labour and capital.  However, the system is rigged so that capital is controlled by a few capitalists who exploit the labour for surplus value, (i.e. excess wealth generated by labour beyond what they need to live).  Hence, the capitalists are free loaders that build wealth and more capital on the backs of the workers.  The more capital they accumulate the more the workers are beholden to them.  This will eventually lead to a revolution of some sort and the cycle starts again.  Marx’s solution to break the cycle was to let labour take control of the capital and reap the rewards of their collective labour.  However, again this solution requires an optimistic reading of human nature that a)  people would not take advantage of  a collective society and free ride and b) that people would be happy in such a society where one does not directly reap the rewards of their own efforts.

Ideally, I would like an economic system that is both fair and realistic about human nature.   A system that accepts that regulations are required to prevent bullying but also recognizes that one can capture  regulations to bully and free load.  One that acknowledges that unrestricted welfare can lead to free loading and be a disincentive to be productive but also realizes that not providing a social safety net is a form of bullying because people have no other means of surviving beyond participation in the existing economic system. We have no option to opt out.   I think the American liberal left has designs on doing this but has not been fully successful because of their own incoherence and push back from the right.   Additionally, there is an inherent asymmetry in the current political debate. The Marxist viewpoint has all but disappeared from American political thinking except as a convenient  foil for certain aspirants of higher office.  While there has been some recent revitalization on the left, such as with the Occupy Wall Street movement,  they have not been able to effectively convey how the top one percent are both bullying and free riding.

In praise of monopoly

When I was in graduate school in the eighties, the dream job if you were in theoretical high energy physics or pure math was probably the Institute for Advanced Study in Princeton but for everyone else it was Bell Labs, on which Jon Gertner has a tribute in the New York Times today.  Unfortunately for me, I never had the chance to go.  Also just as I was finishing my PhD, the AT&T monopoly was being broken up and the slow decline of Bell labs had begun.  Gertner points out that while we glorify silicon valley these days as the hot bed of innovation, it stilll doesn’t compare with how Bell Labs changed the world.  The NIH may be the closest thing in the US right now (and it may not last) in terms of size and freedom to pursue risky projects but we’re scattered over a large campus and it can easily be months between talking to colleagues in other buildings even though it would be of great benefit to interact with them more often.  

Talk at MBI

I’m currently about to give a talk at a workshop  on statistical inference in biology at the Mathematical Biosciences Institute at Ohio State.  My talk is a variation of previous ones on using Bayesian methods for parameter estimation and model comparison.  The slides are here.

News briefing

Here is a video of the news briefing on our AAAS symposium yesterday.   Here are some links to news articles (which I got from Kevin Hall) based on this briefing – Times Live,  Herald, Mail Online.

 

AAAS meeting in Vancouver

I’m currently in beautiful Vancouver for the 2012 AAAS meeting.  The convention centre is to the right of the Olympic cauldron with the mountains in the background.  My talk this morning will be on the Myths of Obesity.   The two biggest myths are: 1) the cause of the obesity epidemic is a mystery and 2) the “3500 Calories is a pound” rule used for dieting.  I won’t be talking much about those two myths since Boyd Swinburn and Kevin Hall will be covering them in the talks that precede mine (Abstracts here).   However, in my talk I will cover questions that include: Do thin people have fast metabolism?    Is it easier to gain weight if I am heavy?   How many calories will I lose if I eat one cookie less a day?  How is it that I gain weight even though I’m eating the same amount every day?;   Is food intake tightly controlled?  Come to my talk or check my slides for the answers.

My slides are here.  (It only looks like a lot of slides because of the animations). A list of posts on the topic can be found here.

The econ “Job Market”

Economics blogger Noah Smith has an interesting post on how graduating economics PhDs find jobs in what is known as the “Job Market”.  It’s a very centralized and organized process, much like how medical students match into residency programs.   All the available jobs are posted in one place and the candidates get interviewed at the American Economics Association annual meeting each January.  The academic departments are directly involved in helping students send out applications and obtain recommendation letters.  The only requirement to go on the job market was to have a single paper that he calls the “Job Market Paper”, which doesn’t even have to be published.  What struck me most was Smith’s claim that almost everyone in the top 50 economics departments get decent jobs.  He implies that  many get jobs in universities and government like the Federal Reserve and the rest in the private sector.

This is in complete contrast to how students in science and mathematics find jobs upon graduation.  In the first case, almost all science PhDs now must do one and usually more post doctoral fellowships before finding a tenure track faculty position.  It is quite common to spend six or more years in these temporary positions before securing a permanent job.  During this time, the ones that do find jobs will have had to publish many papers in prestigious journals. As opposed to economics, where assistant professors can be hired on promise of future potential, the science job market is so competitive that a young person must first prove that they are a completely independent researcher that can secure funding.  Additionally, many if not most of the PhDs will not be able to find tenure track faculty positions.  There is then no systematic way for them to find alternative career paths if academia doesn’t work out for them.  They’re on their own tracking down leads in the private sector and some like Douglas Prasher, who was important in the discovery of GFP, may end up driving a bus.  Math is slightly different in that most PhDs take  non-tenure track faculty positions that are time limited.  The super stars can get tenure track jobs directly out of graduate school. It is extremely difficult to find a tenure track job at a research university in math.

I think the main reason that a centralized “Job Market” exists for economics but not for say biology is that the filter for entry into the field is set at the graduate school level.  Because funding for economics is very low, academic departments admit relatively few students each year.  Hence, economics is at a steady state, where the number of available jobs each year  approximately matches the number of graduates.  This is completely different for science, and in particular biology, where many more graduate students are admitted then there are jobs.  Additionally, the number of graduate students admitted is mostly set by how much research funding is available and not how many permanent good jobs will be available when they graduate.  Also, biology is a labour intensive field where it is not uncommon for a single principal investigator to manage a group of twenty or more students and post docs..  As long as federal research funding remains where it is, there will always be a mismatch between the number admitted to graduate school and the number of available faculty jobs.  However, that is not to say that departments couldn’t do more to help students and in particular post docs find jobs when they graduate.  Post docs are generally forgotten entities in academic departments.  They are usually hired directly by a faculty member and there are not a lot of university or department services devoted to them.  I think that departments that take federal funding to hire post docs should have some sort of “post doctoral advisor” that helps post docs find jobs.  In particular, departments should make clear from the outset that the prospect of obtaining an academic position is low and find smoother paths for people to find jobs in the private sector.

 

 

New paper in Biophysical Journal

Bayesian Functional Integral Method for Inferring Continuous Data from Discrete Measurements

Biophysical Journal, Volume 102, Issue 3, 399-406, 8 February 2012

doi:10.1016/j.bpj.2011.12.046

William J. Heuett, Bernard V. Miller, Susan B. Racette, John O. Holloszy, Carson C. Chow, and Vipul Periwal

Abstract: Inference of the insulin secretion rate (ISR) from C-peptide measurements as a quantification of pancreatic β-cell function is clinically important in diseases related to reduced insulin sensitivity and insulin action. ISR derived from C-peptide concentration is an example of nonparametric Bayesian model selection where a proposed ISR time-course is considered to be a “model”. An inferred value of inaccessible continuous variables from discrete observable data is often problematic in biology and medicine, because it is a priori unclear how robust the inference is to the deletion of data points, and a closely related question, how much smoothness or continuity the data actually support. Predictions weighted by the posterior distribution can be cast as functional integrals as used in statistical field theory. Functional integrals are generally difficult to evaluate, especially for nonanalytic constraints such as positivity of the estimated parameters. We propose a computationally tractable method that uses the exact solution of an associated likelihood function as a prior probability distribution for a Markov-chain Monte Carlo evaluation of the posterior for the full model. As a concrete application of our method, we calculate the ISR from actual clinical C-peptide measurements in human subjects with varying degrees of insulin sensitivity. Our method demonstrates the feasibility of functional integral Bayesian model selection as a practical method for such data-driven inference, allowing the data to determine the smoothing timescale and the width of the prior probability distribution on the space of models. In particular, our model comparison method determines the discrete time-step for interpolation of the unobservable continuous variable that is supported by the data. Attempts to go to finer discrete time-steps lead to less likely models.

Cognitive dissonance

The New York Times has a story today describing how the American middle class are becoming more reliant on government aid, much to their chagrin.  However, the reaction of many of the people interviewed  is animosity towards government programs and support for culling them, even though that would hurt themselves economically.

New York Times: One of the oldest criticisms of democracy is that the people will inevitably drain the treasury by demanding more spending than taxes. The theory is that citizens who get more than they pay for will vote for politicians who promise to increase spending.

But Dean P. Lacy, a professor of political science at Dartmouth College, has identified a twist on that theme in American politics over the last generation. Support for Republican candidates, who generally promise to cut government spending, has increased since 1980 in states where the federal government spends more than it collects. The greater the dependence, the greater the support for Republican candidates.

Conversely, states that pay more in taxes than they receive in benefits tend to support Democratic candidates. And Professor Lacy found that the pattern could not be explained by demographics or social issues.

Cognitive dissonance is a term in psychology that describes the uncomfortable feeling when two conflicting thoughts are simultaneously held and the attempts to rationalize the inconsistency. The political dynamics currently playing out in the United States may be a giant manifestation of this phenomenon.  A telling aspect of the article was that many of the people interviewed acknowledged that they could not survive without government assistance but felt that they did not deserve such help and preferred that it be reduced rather than subjecting others to higher taxes to pay for it.   This rather honorable attitude serves as a stark contrast to the premise of the heavily debated new book of Charles Murray, Coming Apart (see New York Times review here) that argues that the economic travails of the white working class is due largely to a lapse in moral values.  What was also striking in the article was that there was no sense that the dire economic situation these people were facing was due to the fact that the economic game was stacked against them.  There was just a silent resignation that this is the way things are.  The American mythos of the self-reliant and self-made individual is a powerful metaphor that is firmly implanted in a large fraction of the population.  People will not always support policies that are in their economic interests.  This facility for self-denial is a large part of what makes us human.  How we obtained it is still an unresolved problem in evolutionary biology.

 

Proof by simulation

The process of science and mathematics involves developing ideas and then proving them true.   However, what is meant by a proof depends on what one is doing.  In science, a proof is empirical.  One starts with a hypothesis and then tests it experimentally or observationally.  In pure math, a proof means that a given statement is consistent with a set of rules and axioms.  There is a huge difference between these two approaches.  Mathematics is completely internal.  It simply strives for self-consistency.  Science is external.  It tries to impose some structure on an outside world.  This is why mathematicians sometimes can’t relate to scientists and especially physicists and vice versa.

Theoretical physicists don’t need to always follow rules.  What they can do is to make things up as they go along.  To make a music analogy – physics is like jazz.  There is a set of guidelines but one is free to improvise.  If in the middle of a calculation one is stuck because they can’t solve a complicated equation, then they can assume something is small or big or slow or fast and replace the equation with a simpler one that can be solved.  One doesn’t need to know if any particular step is justified because all that matters is that in the end, the prediction must match the data.

Math is more like composing western classical music.  There are a strict set of rules that must be followed.  All the notes must fall within the diatonic scale framework.  The rhythm and meter  is tightly regulated.  There are a finite number of possible choices at each point in a musical piece just like a mathematical proof.  However,  there are a countably infinite number of possible musical pieces just as there are an infinite number of possible proofs. That doesn’t mean that rules can’t be broken, just that when they are broken a paradigm shift is required to maintain self-consistency in a new system.  Whole new fields of mathematics and genres of music arise when the rules are violated.

The invention of the computer introduced a third means of proof.  Prior to the computer,  when making an approximation, one could either take the mathematics approach and try to justify the approximation by putting bounds on the error terms analytically or take the physicist approach and compare the end result with actual data.  Now one can numerically solve the more complicated expression and compare it directly to the approximation. I would say that I have spent the bulk of my career doing just that. Although, I don’t think there is anything intrinsically wrong with proving my simulation, I do find it to be unsatisfying at times. Sometimes it is nice to know that something is true by proving it in the mathematical sense and other times it is gratifying to compare predictions directly with experiments. The most important thing is to always be aware of what mode of proof one is employing.  It is not always clear-cut.