Machine ideology

I’ve been mesmerized the past two days by this three-part BBC documentary  All Watched Over by Machines of Loving Grace.  Steve Hsu has a YouTube link for the third episode.  The other two can be found on YouTube.   It is a rather cynical and dystopian view of how elites use machine metaphors to suppress the masses.  The  writer and director Adam Curtis is a genius in evoking a surreal nightmare with his use of images and music.  This is nothing like a Ken Burns documentary.  It is closer to modern video art.

The first episode was about how the ideas of Ayn Rand influenced Alan Greenspan who convinced Bill Clinton to deregulate the markets, which caused the internet bubble, the Asian crisis and the recent great recession.  The machine angle is that computer models were supposed to keep the markets stable.  The theme of the second part was that the concept of the ecosystem, where nature uses feedback loops to attain an equilibrium, has been co-opted by those in power to argue that the world as it is (with then on top) is the natural balance and everyone should just stay in their place and maintain the status quo.  The machine aspect is that these ideas were supported by cybernetics and a largely forgotten field called systems theory, which is basically linear control theory applied to complex systems.   The third part was about how evolution theorist William Hamilton with help from George Price in trying to understand altruism, came up with the selfish gene idea, (promoted by Richard Dawkins), which reduced humans to machines, with a parallel story of how the acts of Western powers (both selfish and altruistic) caused genocide in Africa.  The undercurrent of all three episodes is that machine-inspired ideas have provided elites with a sense of hubris and a rationale to control societies for their own interests.  Even more insidious is that these ideas, which includes the concepts of the network and self-organized systems, have made the general populace believe that we are  creating a society without hierarchy that will naturally reach a stable balance but in reality this is false and thinking so just leaves you defenseless to the whims of the elites.

I think the irony of the show is that developments in science and mathematics actually spawned two distinctly opposite world views in the twentieth century.  One view, as espoused by the series, is  that science, technology and industry can solve our problems and create a better world.  The second view is that the enlightenment goal of unbounded knowledge and rationality is dashed by thermodynamics, quantum mechanics, Godel’s incompleteness theorems, the Halting problem, and deterministic chaos.  In this second world view – disorder increases, physics is probabilistic, there are mathematical truths that can’t be proven, there are problems that can’t be solved by computers, and there is extreme sensitivity to initial conditions. It is  ironic that while the course of modern history and political power has been largely driven by the first world view, much of modern scientific and intellectual thought has been shaped by the second.  For example, the show is rather critical of people like Jay Forrester, a systems theory pioneer, who believed  he could model the world.  However, his work and ideas have had little impact on physics and mathematics where it is dogma that dynamical systems with just three degrees of freedom can exhibit all sorts of behavior and bifurcations.  Right now the study of networks is the rage but the main message is that they are complex, hard to understand and certainly don’t always ensure stable equilibria.   Although Curtis may be correct that the first world view has been the source of some of our major problems, I don’t think we should abandon it completely and take a Hayek attitude that it is impossible to understand complex systems so we shouldn’t even try.  Rather, there can be a middle course where we recognize the power and the limitations of science and technology.

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Rodrik on Globalization

Dani Rodrik of the Harvard Kennedy School of Government was on Econtalk two months ago.  Rodrik did a study recently on why some emerging markets like China and India have greatly benefited from globalization whereas other countries, particularly in Latin America and  Sub-Saharan Africa have not. His argument has many layers but the main point was that the classical view of how closed economies should open up can lead to market failures.  In the latter part of the twentieth century when globalization revved up, the traditional view as preached by the United States and the World Bank was that developing nations with closed economies should drop trade barriers and let their sheltered companies compete on the world market.  This would cause the inefficient industries to shrink or disappear, which would free labour to move into more productive industries where they had a comparative advantage.  However, what happened was that  after these old companies shrank, the newly unemployed workers ended up moving into less productive areas like petty trade, such as street vending, because the supposed new industries never materialized as expected.  The end result was that there was no great increase in economic growth from globalization and in some cases even a decline.

Rodrik’s explanation for why new companies didn’t develop was because they weren’t able to collect what is known as Schumpeterian rents.  Rent is an economic term for a payment that is beyond what the costs of production and normal profits would entail.  Schumpeterian rent is the extra payment an innovator would receive for bringing something new to the market.  For example, patent laws ensure that if you invent something, you are protected from competition for a number of years to recoup your investment.  This encourages innovation and risk taking.  Now, in an advanced country, new companies are generally formed because of some innovation that allows for Schumpeterian rent.  Either by law or some first mover advantage, there is a barrier to entry for competing companies.  Thus we have companies like Amazon.com or Google.  I still don’t know what advantage Facebook had (credibility with adults perhaps?).

However, in developing countries, new industries form because of a cost advantage and not an idea advantage.  So a toy manufacturer in China or a call center in India has an advantage over the United States because labour costs are lower.  However, in an emerging economy, the person who moves first has a disadvantage because by  navigating bureaucratic and other hurdles to get started, they would provide free information to future competitors.  Rodrik uses the example of setting up a call center in Jamaica. If someone were to set one up, just the fact that they succeeded would bring in competitors who could do so with lower cost and risk.  Hence, there is no incentive to start companies.  It is always better to be second.  Rodrik thus proposed that this market failure could be corrected with government policy that subsidizes first movers but not subsequent ones.  This would then encourage innovation.

According to Rodrik, China did not use the classical model.  They did not stop protecting their inefficient state-run companies but instead started special economic zones where new companies would be subsidized to compete on the world economy.  This allowed for an influx of previously unproductive rural workers to become urbanized and more productive.  Employees in state-run companies, although more inefficient compared to those in the new companies were still more productive than their rural comrades.  Thus by protecting new companies, in part by keeping their currency under valued, China was able to sustain rapid growth.  Rodrik has many other very interesting things to say so I recommend listening to the entire podcast.

 

 

Synthetic blood

I gave blood yesterday and couldn’t help noticing that blood donation is a microcosm for why health care is so expensive.  It requires staff to contact and recruit volunteers. A team of nurses to screen the subjects and draw blood, which requires using  multiple single-use items like needles and tubes.  The blood would then have to be tested for pathogens like hepatitis and HIV and then be stored.  While people mostly blame the increase in technology for why health care costs keep increasing, I think the main reason is that quality control must be installed at every step and this takes a lot of effort and people.  If we accepted more risk, health care could be cheaper.  However, I also think that technology could be a means to lowering health care costs.  For example, just think how much money we could save if we had a reliable source of inexpensive synthetic blood.  In fact, I think this may be a perfect application for  stem cells.  Instead of blood donation centers we would have factories of reactors with hematopoietic stem cells producing all blood types 24/7.

Regulation and asymmetric information

I just got back from a trip to Pittsburgh for a successful thesis defense (congratulations to Dr. Justin Dunmyre) and had an education in taxi cab metering.  On my outbound trip to the airport, by sheer coincidence I left seconds behind another cab going to the exact same destination.  The meter on that cab, which I could see through the window, read about 15% higher than my meter.  On my return trip, the fare on the meter was also much higher than a return fare from just three weeks ago. This led me to think about how meters are calibrated and what disincentive there is for drivers to tamper with them.

When I fly into an unfamiliar city, I get into the first cab available.  In most cases, I have no idea how far the hotel is, what is the optimal route, and what the fare should be.  I must trust the driver completely.   There are so many ways that the driver can overcharge me and I really have little defense.  This is why almost all cities regulate taxi cabs.

I think this is also a perfect example of how competition cannot always ensure the best price.  In any situation where transactions are not repeated, the classic results of the prisoner’s dilemma should apply.  That is to say, it is only rational for the cab driver to defraud the passenger.  It is an asymmetric situation where the driver has all the information and the passenger has none.  Now I’m sure that libertarian leaning economists would argue that repeat customers and intracity users will be sufficient to ensure that cabs are fair.  However, it is not too difficult for the driver to identify who is naive and who is experienced and they can behave accordingly especially for drivers that pick up passengers from hotels and airports, which in many cities is a large fraction of the taxi business.

Now, it is not in the interest of the city to have corrupt taxi cab drivers.  It gives a city a bad reputation and that could discourage tourism, conferences and business.  That is why it is in the interest of the population of the city to regulate taxi cabs.   I do believe that there are cases where regulation only serves as a barrier to entry and discourage competition but in the case of taxi cabs, I’m all for it.

Advice to young researchers

If I were ever asked, this is what I would tell  young researchers embarking on their career.  They are in no particular order.  In fact, 8) may be the most important.

1)   Understand your problem as deeply as possible.  You should know everything that there is to know about your topic. Always ask the next question and think hard about how feasible it is to answer it.  Know why it would be hard or easy to do so.

2)   Learn as many tools as possible.  Get into the habit of constantly learning about new methods.  You may not need to implement everything yourself but be aware of what is out there and even more importantly who knows how to use it.

3)   Be known as an expert in something.  You don’t necessarily want to be pigeonholed but it will always serve you well if you are known as the expert in a certain area.

4)   Knowing what you don’t know is as important as knowing what you do know.  This goes with having deep knowledge about your subject.  You should know whether or not the reason you don’t know something is because no one knows or just you don’t know.

5)   Do not slack on scholarship; always do a thorough search of what has been done before.  Never be lazy about checking references.  It is your job to know everything that has been done before.  Also, just because it is not on the web doesn’t mean it doesn’t exist.

6)   Talk to as many people about your ideas as you can.  Getting feedback is extremely important to sharpen your ideas.

7)   Never let the lack of effort be an excuse for not getting something done. Sometimes, research is tedious.  Sometimes one more calculation or simulation will make a huge difference in your result.

8)   Learn to finish.  On the flip side of 7) you also have to know when a project is done.  There will always be unanswered questions and loose ends. Be aware of which are critical to your result and which would represent future projects.  The inability to finish papers is probably the biggest problem young people have.

Stochastic differential equations

One of the things I noticed at the recent Snowbird meeting was an increase in interest in stochastic differential equations (SDEs) or Langevin equations.  They arise wherever noise is involved in a dynamical process.  In some instances, an SDE comes about as the continuum approximation of a discrete stochastic process, like the price of a stock.  In other cases, they arise as a way to reintroduce stochastic effects to mean field differential equations originally obtained by averaging over a large number of stochastic molecules or neurons.  For example, the Hodgkin-Huxley equation describing action potential generation in neurons is a mean field approximation of the stochastic transport of ions (through ion channels) across the cell membrane, which can be modeled as a multi-state Markov process usually simulated with the Gillespie algorithm (for example see here).  This is computationally expensive so adding a noise term to the mean field equations is a more efficient way to account for stochasticity.

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