Replacing the Affordable Care Act -- what's so complicated?

Nobody knew health care could be so complicated? Er, except everybody but Mr Trump.  And yes, it's a huge behemoth of a system, but the devil is in the detail.  It's when you throw in all the special interests, political considerations, back-scratching, etc. that it quickly gets complicated.  But before all that happens, there are only three basic choices when it comes to providing medical insurance, and they are easy to grasp.  Choosing among them, though, has become much more of a political choice than an unloaded purely economic one.

Here's what we had before the Affordable Care Act (ACA): private insurance, either from one's employer or purchased individually.  For this to work, of course, just as any other business, insurance companies must make a profit, and that's harder when customers get sick; purchasers actually using their insurance isn't good for the bottom line.  That's why there's so much talk about people with "pre-existing conditions".  These are people who insurers know will cost them money and that's why people with "pre-existing conditions" were essentially uninsurable before the ACA, except as members of large employee pools comprised primarily of healthy people who had to buy in as a condition of their employment.  And that's why insurance companies used to charge women, older people, smokers, and so on more; they were more likely to cost money.  And that's why insurance companies also had policies such as lifetime caps on benefits.  To stay in business, insurers have to make a profit.  It's their reason for being.  This system can work well for healthy people and insurance companies.

The second option is something like the ACA, where everyone, pre-existing condition or not, can buy insurance -- an appropriate thing to point out on Rare Disease Day 2017.  As with auto  insurance, the only way this is financially viable is if everyone is required to buy in; just as good drivers subsidize unsafe drivers, healthy people subsidize people more likely to use healthy insurance.  Thus, the hated "mandate", the requirement that everyone buy in or be penalized on their taxes.  Many detractors of the ACA believe the mandate can simply be eliminated, that a replacement for the ACA can cover as many people, as cheaply, without one.  But, that's impossible. This is the same kind of privitized system that has worked without major snags for many years in Switzerland, for example. There, insurance is compulsory, and insurance companies must offer a basic plan which they aren't allowed to profit from although people can purchase bells and whistles, which is how the insurers make a profit.

The third option is the public option, often these days described as Medicare for all.  Government-supplied health insurance, paid for by tax dollars.  It's cheaper than the first two options in large part because it's non-profit, and the infrastructure required by private insurers to validate or deny claims doesn't exist.  National health has worked well in many rich countries for decades, keeping costs down and providing access to medical care to all.

And that's it.  There's no other "terrific" "cheaper" alternative anyone has thought of that can replace the ACA. The only options are a system that's totally private; something like the ACA with its mandate; and national health.  Unfortunately, this wasn't very well explained when President Obama was working on the Affordable Care Act, and it's not being explained now.  The Republicans in control of Congress aren't going to give us national health; and while it seems that many of them would be happy going back to what we used to have before the ACA, opinion polls are showing that people are less and less happy with that option.  Will Trumpcare be Obamacare renamed, then?  We'll have to wait and see.  

In any case, whatever system we adopt, we've still got problems.  Although the rising cost of medical care in the United States has slowed some with the ACA, at almost 18% of GDP health care spending here is the most expensive in the world, far exceeding that of any other high-income country, most of which have national health care (e.g., source).  In part it's because of the high cost of medical care, the higher use of expensive technology (e.g., MRI's, mammograms and C sections) and the exorbitant cost of pharmaceuticals.  And this is even with limitations imposed by insurance companies to control costs.  In addition, the cost of individual premiums has soared for people who aren't eligible for government subsidies to help cover the cost of insurance, in part, because fewer healthy people have purchased insurance than companies anticipated.  And deductibles and co-pays have risen sharply.  Insurance companies still have to turn a profit to stay in the health insurance marketplace.

Source

And, all this spending hasn't made us healthier than people in countries that spend even considerably less.

Source

So,  even if Trumpcare is as terrific and as cheap as we've been promised, it's hard to see how it will cut the high cost of medical care, and make us a healthier nation.  That is complicated, especially when private profit, rather than public health, is its fundamental basis.

Hyman Minsky, Charles Darwin, and descent into the cover of minutiae

The financial crisis was basically not predicted by our leading lights in the academic and intellectual economics community.  They had their very technical theories about how markets work, and how people behave economically--the rational, coolly calculating Homo economicus.  They had their 19th century and even earlier theoretical heroes, who are always cited.  There were somewhat differing schools of thought, but in fact they were, so to speak, more like different classrooms in the same building. Even with these differences, but they were alike in one thing: they were basically all wrong!  The wildly unstable speculation that led to the disaster of the 2000's was a policy result of this universal body of trusted advisors, Those Who Knew.

Well, not entirely.  There was a curmudgeonly economist named Hyman Minsky (1919-1996).  We're not economists and have only learned about him second-hand, after the fact, when what he said before the fact was born out by the facts.  A source we recently listened to was the BBC Radio program called Analysis (listen to or download the March 24 program).

Minsky; Levy Economics Institute

While fancy economists were building their mathematical 'models' of economic behavior, which were very intricate and detailed, ordinary people and the bankers who misled them were venturing hither and thither for the quick kills.  Minsky, basically out of the mainstream, was warning in less technical but actually far more relevant and correct ways that stability builds instability. As the Levy Economics Institute described his ideas in brief,

Minsky held that, over a prolonged period of prosperity, investors take on more and more risk, until lending exceeds what borrowers can pay off from their incoming revenues. When overindebted investors are forced to sell even their less-speculative positions to make good on their loans, markets spiral lower and create a severe demand for cash—an event that has come to be known as a "Minsky moment."

In the recent crisis, confidence in quick-profit investments was so great that people became careless and built their hopes and McMansions of sand. When what amounted to a grand, expanding Ponzi scheme finally collapsed, disaster struck for many (except those who could use the legal system to basically buy their way out of going to jail).

Minsky was just independent-thinking enough to be definitely out of what policy and university circles generally tolerate, and had died before the 2008 crash so he never saw his ideas vindicated.  They were subsequently adopted with post hoc enthusiasm, of course, by the very same prophets whose wisdom had led us to what actually happened (that is they didn't lose their university, bank, or think-tank jobs). Minsky is now apparently appearing with some prominence in new editions of economics textbooks (the idea of publishing books is perhaps a sign of total professional shamelessness, but that's another story).

On the radio discussion, the point was made that the Professionals, those Who Know have become ever more enamored of computer modeling, mathematical theory, simulations, and all the paraphernalia of technical 'science'.  In our highly risk-averse, technophilic, bureaucratized world, this passes for wisdom rather than soft-headed mainly verbal arguments (like Minsky's).  If you want to be published, get tenure or reach the next step on the think-tank or Wall Street status ladder, you better be very technical, and do things very narrowly and with elegant mathematics.  That that doesn't work, and it's known that it doesn't work, doesn't seem to matter ("well, it will work this time!").

This is a characteristic of our culture in our scientific age.  Reduction to technicality is what our institutions, reporters, governments, funders, advisors, and the like admire.  And that viewpoint has its tentacles elsewhere, too.

The same in evolution and genetics.
Like 19th century economists, Charles Darwin gave biologists their version of the truth.  It was a very broad theory, based on the traits of organisms.  This was what counted, not the underlying biological mechanism of the traits.  The argument was conceptual, with an implied quantitative basis.  Darwin actually viewed it as a mathematical theory much as Newton's theory of universal gravitation, but the mathematical details were unimportant.

Many scientists want to formalize such theory to give it support and the elegance of mathematics, but in fact, Darwin's own idea about the underlying basis ('gemmules' and 'pangenesis') was basically wrong.  Evolutionary theory proceeded well without any such basis and, indeed, today most biologists don't know or understand the mathematical claimant for the theory (called population genetics).

What the last 50 years have done is to attempt to reduce evolution to molecular and mathematical precision.  In particular, as genomic technologies have themselves evolved as dramatically as anything that ever happened to life, there has been a love-affair, or infatuation, with technology as if it were answering the basic questions about life.  Genetics does, indeed, illuminate many fundamentals about some aspects of life, but as we and many others have written extensively, it does not provide the global or precise kind of prediction that physics-envy would suggest.  Still, despite many facts being ignored or dismissed, such as the often poor predictive power from genotype to trait, contrary to the unstated causal assumption of genes as the fundamental 'instructions' of life, an enormous superstructure based on molecular and computer technology is being built on countless studies of minute details. Again, what we are seeing is reduction to technicality.

Hiding behind minutiae
Both areas shared the same sort of retreat to the depths of minutiae to establish their apparent profundity of understanding, wisdom, and influence.   Over-arching larger-scale understanding, rather unrelated to much of the minutiae, gets no attention: it's not technical and hence not glamorous enough. It sounds deeply important and so both the professions themselves and those who report their activities to the general public, and those who provide the funds for these activities, are impressed, buffaloed, intimidated, or otherwise persuaded.  But the diving into technical minutiae is a kind of bathos, that often does not seem to be much constrained by, or basically just bypasses, what we know and may even be obvious (as in economics).

These are just two areas in which one can draw some parallels.  They are undoubtedly widespread across many areas of our society, in science, semi-science, the arts and so on.  It does seem to be true that every culture has its traits, or themes, or belief systems.  In ours, it's a belief in technology and in particular computing technology.   Technology changes our lives, mainly for the better. But that it can solve many technical problems does not mean it leads to greater understanding.  Mathematics, despite Galileo's claim that it's the language with which God wrote the universe, is fantastically useful and precise when you can write equations whose assumptions are sufficiently accurate for your needs.  It can lead to outcomes that can be tested specifically.

But if the number and sorts of assumptions and structures (e.g., equations) that are constructed yield exact outcomes, those outcomes really are nothing more than the rewording of the assumptions.  That is, the deductions are contained within the assumptions and structures one choose to begin with.  There is no guarantee that the deductions represent the real world, unless the assumptions do.  Indeed, inaccuracies in assumptions and choice of structures can easily lead to unconstrained inaccuracies in the deductions, relative to the actual world.  The appearance of elegance and insight can be illusory even in theory.  (We might note here that the current kerfuffle over attempts to reinstate scientific racism also exemplify this kind of selective invocation of technical details or methods, while ignoring of more general countervailing facts that are well-known or obvious.)

This formal testability of mathematical predictions is often equated to--or confused with--proof of the assumptions on which it is based.  But they are assumptions, and if they are inaccurate your results will be precisely inaccurate.  Even matching predictions under such circumstances can, but need not, imply underlying truth.  This assumed to be causal can be correlated with what's truly causal, for example.

Further, when mathematical models and theories are thought to be precisely true--that is, assumed to be so--results from actual studies will rarely match predictions perfectly.  There will be human measurement and other technical errors, for example.  So how do we deal with these?  We use statistical or other sorts of tests, to judge whether the results match the predictions.  As we've written about before, we must rely on subjectively chosen tests of adequacy, like statistical significance level.  Superficial aspects of truth may pass such tests in a convincing way, but that doesn't mean the deeper, broader truths are being understood.

Worse than assuming that deviation of results from predictions are just technical errors, is the natural tendency to design studies and interpret results, in obliviousness to or willing ignoring of countervailing knowledge or facts. We do this all the time in science, even though we shouldn't.  Economists pretended everyone was a rational, perceptive value-calculating machine, when it was manifestly obvious that we are not.  Evolutionary geneticists assume Nature is a perfect screening machine, when it manifestly is not.

Verbal arguments can be global and true, but are not so easy to turn into specific predictions, hence their lower status than high-level technology. But ultimately science rests on verbal--conceptual--understanding.  Clearly in both economics and genetics (and who knows how many other fields?), we are in love with technology and use it for many reasons, delving deeper than our actual understanding allows.  Often that will generate findings or surprising facts that stimulate broader thinking, but just as often even scientists, enmeshed in the daily routine (rut?) of our careers,  have a hard time telling the difference.

We're human and we need our self-respect, sense of importance, salaries and retirement benefits, ego-stroking, and just plain sense that we are doing something of value and importance to our fellow humans.  We are all vulnerable to overlooking or circumventing deeper truths by hiding in minutiae that masquerade as truth, in order to attain those needs.  It happens all the time.  Usually, it doesn't matter very much.  But when misplaced claims of insight are uttered too charismatically, intercalate into too many societal vested interests, or are taken too seriously, then society can be in for a very rough ride to pay for its credulousness.  None of this is new, but if we are creatures who learn from experience, why don't we, or can't we, learn from our long history?

We are products of our culture.  One law of Nature may be that we cannot over-ride that law.

A lesson on lessening, from economics

By now we've all heard that an Excel spreadsheet error nearly brought down the world economy.  It has been reported by New York Magazine, for example, and the BBC, The Economist, among many other places.  It's a sobering story, a cautionary tale not only about economics but also about science and belief. It's an unwelcome caution, but one we should heed.

Confronting trying economic times, the question became whether governments should spend their way out of the crisis or cut spending to manage the crisis -- should they go the way of Hayek and embrace austerity or Keynes, and increase their spending.  Austerity was the way many countries chose to go -- too many, according to Keynesians, of course -- buoyed by a study done by Harvard economics professors, Carmen Reinhart and  Ken Rogoff, former chief economist of the International Monetary Fund, who delivered their results in a talk called 'Growth in a Time of Debt' at an economics meeting in 2010 (subsequently published here).  They reported that economic growth slows dramatically when a country's debt is more than 90 percent of it's gross domestic product.  Indeed, that there is a "non-linear response" to debt. 

That seemed clear enough, and strong justification for austerity.  In fact, there was a widespread near-panic about the catastrophe that would ensue if budgets were not cut drastically, and quickly, and of course the debate is ongoing as Greece, Spain, and other European countries struggle to right their economies.

But a graduate student at UMASS/Amherst, Thomas Herndon, tried to replicate the Reinhart/Rogoff study, and could not.  He repeatedly wrote to Reinhart and Rogoff to ask for their data, and to his surprise eventually they sent him the original spreadsheet in which they'd made their calculations.  That's when Herndon found that they'd mistakenly neglected to include five major nations in their figures and had selectively included data sets in their calculations in a way that seemed, to Herndon, to yield results that were, at best, disputable, and called their conclusions into question.  Herndon and colleagues wrote up their findings in a paper published on April 15, that has gotten huge play.

Here's the abstract from that paper:

Herndon, Ash and Pollin replicate Reinhart and Rogoff [RR] and find that coding errors, selective exclusion of available data, and unconventional weighting of summary statistics lead to serious errors that inaccurately represent the relationship between public debt and GDP growth among 20 advanced economies in the post-war period. They find that when properly calculated, the average real GDP growth rate for countries carrying a public-debt-to-GDP ratio of over 90 percent is actually 2.2 percent, not -0:1 percent as published in Reinhart and Rogo ff. That is, contrary to RR, average GDP growth at public debt/GDP ratios over 90 percent is not dramatically different than when debt/GDP ratios are lower.

The authors also show how the relationship between public debt and GDP growth varies significantly by time period and country. Overall, the evidence we review contradicts Reinhart and Rogoff 's claim to have identified an important stylized fact, that public debt loads greater than 90 percent of GDP consistently reduce GDP growth.

They conclude the paper saying, "Specfically, RR's findings have served as an intellectual bulwark in support of austerity politics. The fact that RR's findings are wrong should therefore lead us to reassess the austerity agenda itself in both Europe and the United States."

From Herndon et al.

Reinhart and Rogoff have responded (e.g., the Monday 22 episode of BBC Radio 4's More or Less and at length here) thanking Herndon et al. for finding the error, but reiterating their conclusion that high debt and slowed growth go hand-in-hand.  "We do not...believe this regrettable slip affects in any significant way the central message of the paper or that in our subsequent work."  Though we must say that, to our eye, the figure above from the Herndon et al. paper suggests the correlation is weak, at best.

The Economist has published a comparison of the original and the revised figures.  You decide whether you think the differences are significant or not.  Note that for the 'Above 90' value, which is the issue at hand, the RR report showed a negative mean value, whereas the corrected value is strongly positive.  So the average growth rate for countries with debt above 90% should have been 2.2 rather than -0.1.  Whatever your interpretation of the critique and what it means about austerity measures around the globe, we'd bet that it has a lot to do with how you felt about austerity measures before the Herndon paper came out.  And correlates strongly with how you voted.
 

Published April 17, The Economist

This is clearly another nail in the coffin of the self-flattering myth that science is about objectively doing one's best to falsify his/her hypotheses. No one's going to change their mind.  Not politicians.  Not even scientists.

An analogy: The 9/12 Syndrome
On 9/11, the US was infamously attacked in what is still often described as the worst way in our history (well, that's if you don't count the Revolutionary War, the Civil War, or the genocidal wars we waged on the Native Americans).  We were attacked by rabid fundamentalists who somehow thought that killing people flying on business or vacation was a way to correct some political wrongs they imagined that we were doing.

Americans shared their shock and horror at these attacks.  But how was this tragedy explained?  On 9/12, the day after the attacks, the punditry crept out of the woodwork and.....not a single person's ideas were changed!  If you were a gun-toting right-winger, you said "See, I told you we needed to be getting tough with the rest of the world!"  But if you a dove-releasing left-winger, you said "See, I told you we should not have been being the world's bully!"

The events were used, after the fact, to reinforce polar opposite opinions by those who had been waging political battles to advance their views. The reason is that people simply have a hard time seeing somebody's view other than their own and that of their friends.  We in science are human (despite our occasional claims to superiority) and are vulnerable to exactly the same kind of complacency.  Bragging, not apoligizing, is rather too much our way of life.

In the case of recent economics, hugely negative effects have resulted, because politicians bought into convenient ideas, in part citing this influential 'research' in their support.  The word's in quotes because it's treated by the public, politicians, and scientists as if it were the same as 'gospel.  But who knows how many thousands--or millions--of people lost homes or jobs, were driven into crime, disease, divorce or dispair and the like, or even died because of lost access to affordable medical care, because government policy did not come to their rescue--because of a polarized commitment to some preconception?

The lesson is to lessen our claims, not just to adopt things uncritically if they fit our preconceptions.  It's the hardest kind of lesson to learn, in a society that does not reward modesty.  Still, we should do it.

An economy of labels: the evolution of evolutionary economics

"THE anxiety we feel about whether we’ll succeed is evolution’s way of motivating us," or so says Robert H Frank, an economist at Cornell, in the May 14 New York Times.  That's in 'economist' which of course must mean expert in evolution!  After all, we buy groceries and cars and have a checking account, so we are (to a comparable extent and forecasting skill) economists.


Behavioral economics is the area of economics these days that, in frustration over the inadequacies of mathematical predication of economic decision making and consequences, that is, the failure to actually do their job satisfactorily, has turned to psychology, genes and evolution instead (we blogged about this a while back).  Molecular genetics (and, of course, NIH grants) to the rescue!  Why do people make the seemingly irrational economic decisions they do?  Evolution made them do it.


It's a little hard to ferret out the theme of Frank's piece, but essentially it seems to be that we're driven to succeed by worry.  Success apparently being the whole point (material wealth? fame? he presumably doesn't mean having more children than your neighbor).  Frank says people are very bad at predicting how they'll feel about changes in their lives.  Not being promoted, we worry, will make us miserable, but as it turns out, within six months people are generally as happy as, or even happier than they were before they were turned down.  They've got a new and better job, or it didn't matter as much as they'd feared.  Whatever.  Even people who become physically disabled, Frank says, once they've adapted are often as happy as they were beforehand.  

The human brain was formed by relentless competition in the natural world, so it should be no surprise that we adapt quickly to changes in circumstances. Much of life, after all, is graded on the curve. Someone who remained permanently elated about her first promotion, for example, might find it hard to muster the drive to compete for her next one.

By now you know what we have to say about the hyper-Darwinian explanations that hold that evolution is and always has been about 'relentless competition'.  Not to mention facile just-so stories about everything and anything one can assess by survey methods in our society.  Cooperation is much more ubiquitous than competition, and works at all levels of life, and we can not only observe it but retrodict into our ancient past.  Why Frank claims that competition means we adapt quickly to change isn't clear, but in fact adaptability is fundamental, so fundamental that essentially all living creatures have the ability to adapt to change.

Paradoxically, our prediction errors often lead us to choices that are wisest in hindsight. In such cases, evolutionary biology often provides a clearer guide than cognitive psychology for thinking about why people behave as they do.

According to Charles Darwin, the motivational structures within the human brain were forged by natural selection over millions of years. In his framework, the brain has evolved not to make us happy, but to motivate actions that help push our DNA into the next round. Much of the time, in fact, the brain accomplishes that by making us unhappy. Anxiety, hunger, fatigue, loneliness, thirst, anger and fear spur action to meet the competitive challenges we face. 

Turning to Darwin as though his work is the Bible is a common thing for professors (that is, experts) to do, but why?  Like everyone, Darwin was a product of his time, immersed in a culture of hierarchy and competition, which is reflected in much of his writing.  This is most obvious when he is trying to explain human culture and behaviors.

Explaining our drive to succeed or to be happy in Darwinian terms, as Frank tries to do in this piece, conflates contemporary psychological issues and concerns with the evolutionary social scientist's drive to explain how and why behavioral traits arose, most often with just-so stories.  Why are humans driven to succeed? Frank's answer is that "Anxiety is evolution's way of motivating us."  Really?  Evolution wants us to to do better at our jobs and get that promotion?

In fact, of course, evolution doesn't want anything.  It certainly doesn't care whether we're promoted.  Or even whether we're happy.  The only definition of success that counts in the evolutionary long run is surviving to reproduce.  But our contemporary brains are just as able to convince us not to reproduce as to have as many children as we can.  Our contemporary brains can even convince us to become suicide bombers before we give a thought to reproducing -- not very evolutionarily successful behavior.  Or to commit infanticide, or to have abortions, or to become estranged from our kin.  Or heaven forbid, even to be kind to strangers.  Sure, evolution-minded social scientists can always come up with some sort of convoluted 'evolutionary' explanation for these behaviors, but the simplest explanation is that culture trumps all we think we know about evolution when it comes to why we do what we do.

Of course, other denizens of academe will give Darwinian explanations for the destructive effects of angst.  No matter, they apply to different NIH sections for funding and live in different departments.  No matter, too, that the kinds of rationales that are given do not provide any evidence that humans are at all unique in these ways, which means that explanations in terms of human evolution are mis-placed.

There are, perhaps, valid evolutionary explanations of these things.  But they are about cultural rather than biological evolution.  The processes of cultural evolution and cultures' impact on us, given our basic animal physiology and mental states (that, in some ways we cannot really know are the direct, or more likely indirect products of millions of years of evolution and adaptation).  Understanding how these cultural effects work is an important problem.  Of course, undoubtedly professors of economics, being very intelligent, are experts in that, too.  Maybe if funders stopped funding this kind of faux Darwinian speculation, economists would be forced to figure out how their own, actual area of purported expertise  works.

-Anne and Ken

The GWAS of economics?

Why don't economists understand economies?
To try to understand why economics didn't predict or prevent the recent economic crash, BBC Radio4 has done a series on the history of the discipline.  The first of the three programs was about economics as a story of morality, the second about economics as a science, and the third about the psychology of economics.  The central question of the programs is, in a nutshell, why economists can't predict the economy.  Because we write a lot about why geneticists or epidemiologists can't predict disease at MT, this seems like an appropriate topic to expand a bit upon here. The details are all different....but the phenomenon may be similar.

Economics as morality
Economics as a discipline began in Greece, as philosophers like Aristotle thought about the market and how it shaped and reflected morality. Wealth should be used for the Good Life. And indeed many people think of the recent global economic crash as a giant morality tale, with the market forces of greed and evil resulting in harm to millions of innocent victims.  The conclusions people draw about morality, however, seem to vary according to the observer.  To some, the victims weren't all that innocent, or the government should have intervened earlier, or shouldn't have intervened at all, or the bankers should either be stoned or given bonuses, and so forth.  A bit like assessments of success or failure in genetics.

The program discussed the origins of economics as an institution that evolved to build trust among humans, humans being the primates that build trust via "psychological interactions and formal institutions," while, the guest informed us, chimps build trust by coalition management, and bonobos by having sex.

But this seems not only simplistic but a blatantly specious argument because, if economies exist to build trust, why do we need so many laws to regulate economic behavior?  Anti-trust laws, laws against insider trading, laws about how financial institutions work, and on and on, and why is there so much white collar crime?  And didn't the crash happen because there wasn't enough regulation, which is needed because so many economic actors can't be trusted? 

And, if economics is about morality, it's a shifting morality.  Self-interest and selfishness have become acceptable, or even applauded in the last 30 years or so, whereas it used to be shameful to do things for personal profit, and one certainly couldn't boast about it. So clearly much that is real is not hard-wired into human behavior or societal structures.  Of course this is an acute, but largely unheeded lesson for those who yearn for genetic determinism in human behavior, built into our nature by natural selection.

To Adam Smith, how the economy works had to do with what makes humans different from dogs.  We have an inbuilt propensity to barter and exchange, he said, while dogs just fight over bones.  Ours aren't narrow self-interests, but based on our ability to reason. Of course, we know that any human universals--a target of Darwin as well as western economic theorists--are subtle and elusive, if they are even known.

But, don't even lions share?  And ants?  Ok, maybe they don't barter or exchange value for value in human terms, but they do redistribute resources. Some would argue that that's because lions and ants that share are sharing among kin, but even so, this does put a damper on the idea of human exceptionalism.

And still, why did economics get it so wrong about the crash?  Are markets emergent properties that cannot be forecast by or are chaotic relative to, imperfect measurements of their component activities?

Scientific economics
The second program explored whether or not economics is a science, whether it should be, and why as a science it still can't predict economic behavior. At the end of the 19th century, economics books begin to look like mathematics books, and economists began to talk about laws, forces, and mechanisms. If economics is a science, it's a science of observations, like astronomy, not a laboratory science.

To illustrate the law-like nature of economics, an inventor, Bill Phillips, proposed to a Cambridge economist in the 1940s that he could build a machine that could predict the economy.  "I don't understand economics," he said, "but I do understand plumbing."  So, using water flow, he built a model of the Keynesian theory of economics, the Phillips machine.  And indeed it solves Keynesian models, but does it model economies?  Here's a video of the machine that may (or may not) clarify the issues.  What such  devices (and similar kinds of computer simulations) is build in some assumptions and work out the consequences.  But if the assumptions are wrong, or the system is sensitive to conditions at any time, the predictions will follow from the assumptions but won't generate what happens in the real world---which, presumably, is what we care about.

But, just like defenders of the genetic model of disease, many economists insist that some day their models will be more precise, even though we can't estimate with precision yet, nor predict what the net effect of market forces will be.  Economics, even scientific economics, can't yet predict growth or explain or predict the business cycle.  But perhaps that's to do with the human factor.  Again, just like genetics--risk of disease, even if there's a genetic component, seems to have much to do with how we live our lives, which tends to make disease outcomes rather unpredictable.

Homo Economicus
Because economics is so poor at prediction, many economists have decided that this may be because of the human factor, and this is what was discussed in the third program.  The answer must come from understanding humans and what drives their economic behavior.  And yes, to this branch of economics, the answer could be genetic.

So, human behavior is either noble, with implications for economic behavior, or economic behavior is at the mercy of human impulse, and if we can just understand that, we'll understand economics.  Do people have sound economic judgment? Unfortunately, the possibility that we do not has been demonstrated numerous times.

According to one expert, the problem is that economics was established by apes who evolved on the savanna, in small bands of related individuals.  The psychology those apes brought to the task then simply was not up to the complexity of the system they came up with, and that explains why economies run away from us now.

But, assuming that because some human trait began when our ancestors were on the savanna means we can't adapt to current circumstances now that we're no longer on the savanna is just wrong.  We can do calculus, can't we?  We invented rockets and penicillin and nuclear bombs long after our brains evolved to be as able as they are, with presumably no idea of going to the moon.  Successful organisms are nothing if not flexible, adaptable, able to change with changing circumstances, and humans of course are no exception.  And by what reasoning do we go back only to other primates (i.e., other primates alive today)?  The thought processes didn't originate with primates.

John Maynard Keynes believed there was something beastly about our behavior.  He wrote about "animal spirits", referring to the driving force that gets us going in the economy.  Economic models can't explain what makes economies fall into recession, and then what makes them rebound so Keynes said that maybe moods are fundamental to economics.  Populations change their thinking in unpredictable ways, with unpredictable economic results.  The human factor.  This is why Game Theory and even genetics are big in economics today.

The upshot
So, basically, we don't understand what drives economies, nor what drives economic behavior.  Rather akin to how we don't understand disease causation in so many cases, nor know how to predict who will get what.  We do understand how to act, as experts, to continue to ask the public to shed resources on us because of our expertise--that's an undoubted skill.  However, as one economist pointed out in the program, if economists knew anything, planned economies should be more efficient and predictable than the free markets that triumphed over the Soviet Union in the 1980s, but they are not. Statistical approaches to genetic diseases should tell us more than they do. Perhaps, as another guest on the third episode of this series said, we'll eventually understand 9/10ths of the forces that drive markets, but we'll never understand them all.  We'd like to see more geneticists be so accepting of genetic realities.

There is a problem here that is more than the clearly empirical fact of the lack of predictive power in many areas of life, including many areas of genetics.  It is that the 'experts' have a lot of knowledge, but a limited ability to actually predict what we are paid to predict.  In that sense, why should our jobs not be taken away and given to people who actually give us what we'd like: sex, music, other entertainment, new kinds of fast foods, video games, and the like?  Since we still have jobs, clearly experts do provide something that society feels is useful!  Expertise is hard-won and clearly real in many ways.

Yet, experts are the priests of secret knowledge to whom we still turn even knowing that their knowledge, while real, is often not sufficient for accurate prediction.  Even in the age of science, we live on future promise, ignoring past records.  This is very curious!