Is a Theory of Everything Necessary?

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I begin with Wikipedia:

A theory of everything (ToE), final theory, ultimate theory, or master theory is a hypothetical single, all-encompassing, coherent theoretical framework of physics that fully explains and links together all physical aspects of the universe. Finding a ToE is one of the major unsolved problems in physics. Over the past few centuries, two theoretical frameworks have been developed that, as a whole, most closely resemble a ToE. These two theories upon which all modern physics rests are general relativity (GR) and quantum field theory (QFT).

Michael Brooks, in “Has This Physicist Found the Key to Reality?” (The New Statesman, October 21, 2016), puts it this way:

In relativity, time is a mischievous sprite: there is no such thing as a universe-wide “now”. . .

He continues

. . . and movement through space makes once-reliable measures such as length and time intervals stretch and squeeze like putty in Einstein’s hands. Space and time are no longer the plain stage on which our lives play out: they are curved, with a geometry that depends on the mass and energy in any particular region. Worse, this curvature determines our movements. Falling because of gravity is in fact falling because of curves in space and time. Gravity is not so much a force as a geometric state of the universe.

Moreover:

The other troublesome theory is quantum mechanics [the core of QFT], which describes the subatomic world. It, too, is a century old, and it has proved just as disorienting as relativity. As [Carlo] Rovelli puts it, quantum mechanics “reveals to us that, the more we look at the detail of the world, the less constant it is. The world is not made up of tiny pebbles, it is a world of vibrations, a continuous fluctuation, a microscopic swarming of fleeting micro-events.”

But . . .

. . . here is the most disturbing point. Both of these theories are right, in the sense that their predictions have been borne out in countless experiments. And both must be wrong, too. We know that because they contradict one another, and because each fails to take the other into account when trying to explain how the universe works.

All of this is well-known and has been for a long time. I repeat it only to set the stage for my amateur view of the problem.

As is my wont, I turn to baseball for a metaphor. A pitcher who throws a fastball relies in part on gravity to make the pitch hard to hit. Whatever else the ball does because of the release velocity, angle of release, and spin imparted to the ball at the point of release, it also drops a bit from its apparent trajectory because of gravity.

What’s going on inside the ball as it makes it way to home plate? Nothing obvious. The rubber-and-cork core (the “pill”) and the various yarns that aare wound around it remain stationary relative to each other, thanks to the tightness of the cover, the tightness of the winding, and the adhesives that are used on the pill and the top layer of wound yarn. (See this video for a complete explanation of how a baseball is manufactured.)

But that’s only part of the story. The cover and the things inside it are composed of molecules, atoms, and various subatomic particles. The subatomic particles, if not the atoms and molecules, are in constant motion throughout the flight of the ball. Yet that motion is so weak that it has no effect on the motion of the ball as it moves toward the plate. (If there’s a physicist in the house, he will correct me if I’m wrong.)

In sum: The trajectory of the baseball (due in part to gravity) is independent of the quantum mechanical effects simultaneously at work inside the baseball. Perhaps the the universe is like that. Perhaps there’s no need for a theory of everything. In fact, such a theory may be a will-o-the-wisp — the unicorn of physics.

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My Platform

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A voting guide published in my local newspaper asks seven questions of the presidential candidates. I list them below, with the answers that I would give were I a candidate for the presidency of the United States.

Question 1: What is your personal statement?

I am sick and tired of the nanny state, which is centered in Washington DC and extends into almost every city, town, and village in America.

Question 2: What are your top three goals?

Economic and social liberty for all Americans; protection of the lives, liberty, and property of innocent Americans; defense of Americans’ legitimate overseas interests.

Question 3: What will you do to support a vibrant economy across the U.S.?

I will send legislative proposals to Congress that will deregulate the economy; eliminate the death tax and corporate income taxes; reduce the central government to its essential and legitimate functions (mainly national defense), and cut taxes accordingly; and phase out all unconstitutional federal programs (which is most of them), beginning with Social Security, Medicare, and Medicaid. I will revoke all executive-branch policies that are contrary to the program spelled out in the preceding sentence.

Question 4: What, if any, actions will you support to create a pathway to citizenship?

I will ask Congress to deter illegal immigration by eliminating welfare programs that attract it; to provide the manpower and technical means to prevent, detect, and prosecute illegal immigration; and to establish more stringent citizenship requirements, including demonstrated proficiency in English. I will revoke all executive-branch policies that are contrary to the program spelled out in the preceding sentence.

Question 5: What should government do to provide an equitable, quality public education for all children pre-K through grade 12?

The central government should have no role in the funding of education or in the making of policies related to it. I will make one exception, for liberty’s sake, which is to propose an amendment to the Constitution that would require every State (and therefore the subordinate jurisdictions in every State) to allow parents to choose the schools to which they send their children, and to give vouchers to parents who choose private schools. The value of each State’s voucher would be the average cost of educating a child in grades K-12 in that State. (It would be up to each State to decide how to recover the shares owed by local jurisdictions.)

Question 6: What actions would you support the U.S. undertake to protect its interests abroad?

In view of the rising Russian and Chinese threats to Americans’ overseas interests — and the persistent threat posed by terrorist organizations — I will ask Congress to rebuild the nation’s armed forces, at least to the levels attained as a result of President Reagan’s buildup; to provide for the acquisition of superior, all-source intelligence capabilities; to support a robust research and development program for defense and intelligence systems; and to provide the funding needed to fully man our armed forces with well-trained personnel, and to keep the forces in a high state of readiness for sustained combat operations.

Regarding the use of armed forces, I will act immediately and vigorously to defend Americans’ legitimate overseas interests, which include international commerce around the globe, and to protect resources that directly affect international commerce (e.g., oil-rich regions on land and at sea). As necessary, I will seek the authorization of Congress to conduct sustained combat operations for those purposes.

I will not otherwise use or seek the approval of Congress to use the armed forces of the United States, which are maintained at great cost to Americans for the benefit of Americans. Those forces are not maintained for the purpose of defending countries that refuse to spend enough money to defend themselves, nor to “build nations” or engage in humanitarian operations that have no direct bearing on the safety of Americans or their interests. By the same token, America’s armed forces should be used to help defend nations that attempt to defend themselves and whose defeat would destabilize regions of strategic value to Americans’ interests.

Finally, I will not enter into treaties or agreements of any kind with the leaders of nations whose aim is clearly to undermine Americans’ legitimate economic interests. To that end, I will renounce Barack Obama’s agreement with Iran, his endorsement of the Paris agreement regarding so-called anthropogenic global warming, and all other agreements detrimental to the interests of Americans.

I will further ask to Congress to direct by law that the United States withdraw from the United Nations, which serves mainly as a showplace for regimes hostile to Americans’ constitutional ideals and interests. The U.N. will be given two years in which to remove all of its offices and personnel from the United States. I expect the U.N. to become overtly hostile to the United States when this country has withdrawn from it, but those member states who provoke and finance hostile acts on the part of the U.N. will be held to account, and will not be able to hide behind the false front of the United Nations.

Question 7: What kinds of policies will you pursue to promote social and racial justice for all Americans?

I will nominate judges and executive-branch officials who are demonstrably faithful to the Constitution of the United States, as its various portions were understood when they were ratified or modified through Article V amendments. This will mean the reversal of many judicial and executive actions that are contrary to the moral traditions that underlie the greatness of America, and which have been contravened arbitrarily to serve narrow interests and misguided ideologies. I am especially eager to defend life against those who seek to destroy and defile it, and to see that there is truly “equal protection of the law” by restoring freedom of speech and association where they have been suppressed in the name of equal protection.

Social and moral issues such as same-sex marriage should be decided by the States, and preferably by the people themselves, through the peaceful and voluntary evolution and operation of social norms. Such issues are outside the constitutional purview of the central government.

A Lesson in Election-Rigging

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A leading story on yesterday’s NBC evening news broadcast trumpeted an ABC News poll showing Hillary with a 12-point lead over The Donald. It could have been a story about polls in which NBC News participates: The latest NBC News/SM poll gives Clinton an 8-point edge, and the most recent NBC News/Wall Street Journal poll has Clinton up by 10 points. Or it could have been about the latest CBS News poll, which has Clinton leading by 11 points.

Why single out a poll that’s not representative of the world of polling? Why not trumpet the the overall average computed by FiveThirtyEight, a reputable outfit spawned by The New York Times? The answer is that FiveThiryEight‘s consensus forecast gives Clinton only a 6-point edge. (As do I.)

Why do you suppose FiveThirtyEight reports “only” a 6-point edge for Clinton? Because it adjusts for the bias inherent in polls like those conducted by ABC, CBS, and NBC.

And why do you suppose that the three networks conduct and report polls biased in Clinton’s direction, just as they routinely conduct and report polls biased toward Democrats? To ask the question is to answer it.

What better way to rally Clinton voters (and Democrats generally) while discouraging Trump voters (and Republicans generally) than to make a Clinton victory (or any Democrat victory) seem inevitable?

If presidential elections in America are in any sense “rigged,” they’re rigged by the pro-Democrat bias of the mainstream media, which comes through loud and clear on ABC, CBS, and NBC (and others). The bias shows up not only in what stories those networks choose to run and how they report those stories; it also shows up in the polls that they conduct and their reporting on those polls.

A Drought Endeth

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Tonight the Chicago Cubs beat the Los Angeles Dodgers to become champions of the National League for 2016. The Cubs thus ended the longest pennant drought of the 16 old-line franchises in the National and American Leagues, having last made a World Series appearance 71 years ago in 1945. The Cubs last won the World Series 108 years ago in 1908, another ignominious record for an old-line team.

Here are the most recent league championships and World Series wins by the other old-line National League teams: Atlanta (formerly Boston and Milwaukee) Braves — 1999, 1995; Cincinnati Reds — 1990, 1990; Los Angeles (formerly Brooklyn) Dodgers — 1988, 1988; Philadelphia Phillies — 2009, 2008; Pittsburgh Pirates — 1979, 1979; San Francisco (formerly New York) Giants — 2014, 2014; and St. Louis Cardinals — 2013, 2011.

The American League lineup looks like this: Baltimore Orioles (formerly Milwaukee Brewers and St. Louis Browns) — 1983, 1983; Boston Red Sox — 2013, 2013; Chicago White Sox — 2005, 2005; Cleveland Indians — 2016 (previously 1997), 1948; Detroit Tigers — 2012, 1984; Minnesota Twins (formerly Washington Senators) — 1991, 1991; New York Yankees — 2009, 2009; and Oakland (formerly Philadelphia and Kansas City) Athletics — 1990, 1989.

Economic Modeling: A Case of Unrewarded Complexity

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This is the fifth entry in a series of loosely connected posts on economics. Previous entries are here, here, here, and here.

I wrote “About Economic Forecasting” twelve years ago. Here are some highlights:

In the the previous post I disparaged the ability of economists to estimate the employment effects of the minimum wage. I’m skeptical because economists are notoriously bad at constructing models that adequately predict near-term changes in GDP. That task should be easier than sorting out the microeconomic complexities of the labor market.

Take Professor Ray Fair, for example. Prof. Fair teaches macroeconomic theory, econometrics, and macroeconometric models at Yale University. He has been plying his trade since 1968, first at Princeton, then at M.I.T., and (since 1974) at Yale. Those are big-name schools, so I assume that Prof. Fair is a big name in his field.

Well, since 1983, Prof. Fair has been forecasting changes in real GDP over the next four quarters. He has made 80 such forecasts based on a model that he has undoubtedly tweaked over the years. The current model is here. His forecasting track record is here. How has he done? Here’s how:

1. The median absolute error of his forecasts is 30 percent.

2. The mean absolute error of his forecasts is 70 percent.

3. His forecasts are rather systematically biased: too high when real, four-quarter GDP growth is less than 4 percent; too low when real, four-quarter GDP growth is greater than 4 percent.

4. His forecasts have grown generally worse — not better — with time.

FIGURE 1
fair-model-forecasting-errors-vs-time
This and later graphs pertaining to Prof. Fair’s forecasts were derived from The Forecasting Record of the U.S. Model, Table 4: Predicted and Actual Values for Four-Quarter Real Growth, at Prof. Fair’s website. The vertical axis of this graph is truncated for ease of viewing; 8 percent of the errors exceed 200 percent.

You might think that Fair’s record reflects the persistent use of a model that’s too simple to capture the dynamics of a multi-trillion-dollar economy. But you’d be wrong. The model changes quarterly. This page lists changes only since late 2009; there are links to archives of earlier versions, but those are password-protected.

As for simplicity, the model is anything but simple. For example, go to Appendix A: The U.S. Model: July 29, 2016, and you’ll find a six-sector model comprising 188 equations and hundreds of variables.

And what does that get you? A weak predictive model:

FIGURE 2
fair-model-estimated-vs-actual-growth-rate

It fails the most important test; that is, it doesn’t reflect the downward trend in economic growth:

FIGURE 3
fair-model-year-over-year-growth-estimated-and-actual

Could I do better? Well, I’ve done better — without knowing it until now — with the simple model that I devised to estimate the Rahn Curve. It’s described in “The Rahn Curve Revisited.” The following quotations and discussion draw on the October 20, 2016, version of that post:

The theory behind the Rahn Curve is simple — but not simplistic. A relatively small government with powers limited mainly to the protection of citizens and their property is worth more than its cost to taxpayers because it fosters productive economic activity (not to mention liberty). But additional government spending hinders productive activity in many ways, which are discussed in Daniel Mitchell’s paper, “The Impact of Government Spending on Economic Growth.” (I would add to Mitchell’s list the burden of regulatory activity, which grows even when government does not.)

Rahn curve (2)

. . . .

In an earlier post, I ventured an estimate of the Rahn curve that spanned most of the history of the United States. I came up with this relationship (terms modified for simplicity:

G = 0.054 -0.066F

To be precise, it’s the annualized rate of growth over the most recent 10-year span (G), as a function of F (fraction of GDP spent by governments at all levels) in the preceding 10 years. The relationship is lagged because it takes time for government spending (and related regulatory activities) to wreak their counterproductive effects on economic activity. Also, I include transfer payments (e.g., Social Security) in my measure of F because there’s no essential difference between transfer payments and many other kinds of government spending. They all take money from those who produce and give it to those who don’t (e.g., government employees engaged in paper-shuffling, unproductive social-engineering schemes, and counterproductive regulatory activities).

When F is greater than the amount needed for national defense and domestic justice — no more than 0.1 (10 percent of GDP) — it discourages productive, growth-producing, job-creating activity. And because government spending weighs most heavily on taxpayers with above-average incomes, higher rates of F also discourage saving, which finances growth-producing investments in new businesses, business expansion, and capital (i.e., new and more productive business assets, both physical and intellectual).

I’ve taken a closer look at the post-World War II numbers because of the marked decline in the rate of growth since the end of the war:

Real GDP 1947q1-2016q2

Here’s the revised result (with cosmetic changes in terminology):

G = 0.0275 -0.347F + 0.0769A – 0.000327R – 0.135P

Where,

G = real rate of GDP growth in a 10-year span (annualized)

F = fraction of GDP spent by governments at all levels during the preceding 10 years

A = the constant-dollar value of private nonresidential assets (business assets) as a fraction of GDP, averaged over the preceding 10 years

R = average number of Federal Register pages, in thousands, for the preceding 10-year period

P = growth in the CPI-U during the preceding 10 years (annualized).

The r-squared of the equation is 0.73 and the F-value is 2.00E-12. The p-values of the intercept and coefficients are 0.099, 1.75E-07, 1.96E-08, 8.24E-05, and 0.0096. The standard error of the estimate is 0.0051, that is, about half a percentage point. (Except for the p-value on the coefficient, the other statistics are improved from the previous version, which omitted CPI).

Here’s how the equations with and without P stack up against actual changes in 10-year rates of real GDP growth:

rahn-curve-model-actual-vs-estimates-with-and-without-p

The equation with P captures the “bump” in 2000, and is generally (though not always) closer to the mark than the equation without P.

What does the new equation portend for the next 10 years? Based on the values of F, A, R, and P for the most recent 10-year period (2006-2015), the real rate of growth for the next 10 years will be about 1.9 percent. (It was 1.4 percent for the version of the equation without P.) The earlier equation (discussed above) yields an estimate of 2.9 percent. The new equation wins the reality test, as you can tell by the blue line in the second graph above.

In fact the year-over-year rates of real growth for the past four quarters (2015Q3 through 2016Q2) are 2.2 percent, 1.9 percent, 1.6 percent, and 1.3 percent. So an estimate of 1.9 percent for the next 10 years may be optimistic.

I took the data set that I used to estimate the new equation and made a series of out-of-sample estimates of growth over the next 10 years. I began with the data for 1946-1964 to estimate the growth for 1965-1974. I continued by taking the data for 1946-1965 to estimate the growth for 1966-1975, and so on, until I had estimated the growth for every 10-year period from 1965-1974 through 2006-2015. In other words, like Prof. Fair I updated my model to reflect new data, and I estimated the rate of economic growth in the future. How did I do? Here’s a first look:

FIGURE 4
rahn-curve-model-estimation-errors-vs-actual-values

The errors get larger with time, but they are far smaller than the errors in Fair’s model (see figure 1).

Not only that, but there’s a much better fit. Compare the following graph with figure 2:

FIGURE 5
rahn-curve-model-10-year-real-rates-of-growth-actual-and-estimated

Why do the errors in Fair’s model and mine increase with time? Probably of the erratic downward trend in economic growth, which Fair doesn’t capture in his estimates (see figure 3), but which is matched more closely by my estimates:

FIGURE 6
rahn-curve-model-estimated-vs-actual

The moral of the story: It’s futile to build complex models of the economy. They can’t begin to capture the economy’s real complexity, and they’re likely to obscure the important variables — the ones that will determine the future course of economic growth.

A final note: In earlier posts I’ve disparaged economic aggregates, of which GDP is the apotheosis. And yet I’ve built this post around estimates of GDP. Am I contradicting myself?

Not really. There’s a rough consistency in measures of GDP across time, and I’m not pretending that GDP represents anything but an estimate of the monetary value of those products and services to which monetary values can be ascribed.

As a practical matter, then, if you’re a person who wants to know the likely future direction and value of GDP, stick with simple estimation techniques like the one I’ve demonstrated here. Don’t get bogged down in the inconclusive minutiae of a model like Prof. Fair’s.

 

Mathematical Economics

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This is the fourth entry in a series of loosely connected posts on economics. Previous entries are here, here, and here.

Economics is a study of human behavior, not an exercise in mathematical modeling or statistical analysis, though both endeavors may augment an understanding of human behavior. Economics is about four things:

  • wants, as they are perceived by the persons who have those wants
  • how people try to satisfy their wants through mutually beneficial, cooperative action, which includes but is far from limited to market-based exchanges
  • how exogenous forces, including government interventions, enable or thwart the satisfaction of wants
  • the relationships between private action, government interventions, and changes in the composition, rate, and direction of economic activity

In sum, economics is about the behavior of human beings, which is why it’s called a social science. Well, economics used to be called a social science, but it’s been a long time (perhaps fifty years) since I’ve heard or read an economist refer to it as a social science. The term is too reminiscent of “soft and fuzzy” disciplines such as history, social psychology, sociology, political science, and civics or social studies (names for the amalgam of sociology and government that was taught in high schools way back when). No “soft and fuzzy” stuff for physics-envying economists.

However, the behavior of human beings — their thoughts and emotions, how those things affect their actions, and how they interact — is fuzzy, to say the least. Which explains why mathematical economics is largely an exercise in mental masturbation.

In my disdain for mathematical economics, I am in league with Arnold Kling, who is the most insightful economist I have yet encountered in more than fifty years of studying and reading about economics. I especially recommend Kling’s Specialization and Trade: A Reintroduction to Economics. It’s a short book, but chock-full of wisdom and straight thinking about what makes the economy tick. Here’s the blurb from Amazon.com:

Since the end of the second World War, economics professors and classroom textbooks have been telling us that the economy is one big machine that can be effectively regulated by economic experts and tuned by government agencies like the Federal Reserve Board. It turns out they were wrong. Their equations do not hold up. Their policies have not produced the promised results. Their interpretations of economic events — as reported by the media — are often of-the-mark, and unconvincing.

A key alternative to the one big machine mindset is to recognize how the economy is instead an evolutionary system, with constantly-changing patterns of specialization and trade. This book introduces you to this powerful approach for understanding economic performance. By putting specialization at the center of economic analysis, Arnold Kling provides you with new ways to think about issues like sustainability, financial instability, job creation, and inflation. In short, he removes stiff, narrow perspectives and instead provides a full, multi-dimensional perspective on a continually evolving system.

And he does, without using a single graph. He uses only a few simple equations to illustrate the bankruptcy of macroeconomic theory.

Those economists who rely heavily on mathematics like to say (and perhaps even believe) that mathematical expression is more precise than mere words. But, as Kling points out in “An Important Emerging Economic Paradigm,” mathematical economics is a language of “faux precision,” which is useful only when applied to well defined, narrow problems. It can’t address the big issues — such as economic growth — which depend on variables such as the rule of law and social norms which defy mathematical expression and quantification.

I would go a step further and argue that mathematical economics borders on obscurantism. It’s a cult whose followers speak an arcane language not only to communicate among themselves but to obscure the essentially bankrupt nature of their craft from others. Mathematical expression actually hides the assumptions that underlie it. It’s far easier to identify and challenge the assumptions of “literary” economics than it is to identify and challenge the assumptions of mathematical economics.

I daresay that this is true even for persons who are conversant in mathematics. They may be able to manipulate easily the equations of mathematical economics, but they are able to do so without grasping the deeper meanings — the assumptions and complexities — hidden by those equations. In fact, the ease of manipulating the equations gives them a false sense of mastery of the underlying, real concepts.

Much of the economics profession is nevertheless dedicated to the protection and preservation of the essential incompetence of mathematical economists. This is from “An Important Emerging Economic Paradigm”:

One of the best incumbent-protection rackets going today is for mathematical theorists in economics departments. The top departments will not certify someone as being qualified to have an advanced degree without first subjecting the student to the most rigorous mathematical economic theory. The rationale for this is reminiscent of fraternity hazing. “We went through it, so should they.”

Mathematical hazing persists even though there are signs that the prestige of math is on the decline within the profession. The important Clark Medal, awarded to the most accomplished American economist under the age of 40, has not gone to a mathematical theorist since 1989.

These hazing rituals can have real consequences. In medicine, the controversial tradition of long work hours for medical residents has come under scrutiny over the last few years. In economics, mathematical hazing is not causing immediate harm to medical patients. But it probably is working to the long-term detriment of the profession.

The hazing ritual in economics has as least two real and damaging consequences. First, it discourages entry into the economics profession by persons who aren’t high-IQ freaks, and who, like Kling, can discuss economic behavior without resorting to the sterile language of mathematics. Second, it leads to economics that’s irrelevant to the real world — and dead wrong.

Reaching back into my archives, I found a good example of irrelevance and wrongness in Thomas Schelling‘s game-theoretic analysis of segregation. Eleven years ago, Tyler Cowen (Marginal Revolution), who was mentored by Schelling at Harvard, praised Schelling’s Nobel prize by noting, among other things, Schelling’s analysis of the economics of segregation:

Tom showed how communities can end up segregated even when no single individual cares to live in a segregated neighborhood. Under the right conditions, it only need be the case that the person does not want to live as a minority in the neighborhood, and will move to a neighborhood where the family can be in the majority. Try playing this game with white and black chess pieces, I bet you will get to segregation pretty quickly.

Like many game-theoretic tricks, Schelling’s segregation gambit omits much important detail. It’s artificial to treat segregation as a game in which all whites are willing to live with black neighbors as long as they (the whites) aren’t in the minority. Most whites (including most liberals) do not want to live anywhere near any “black rednecks” if they can help it. Living in relatively safe, quiet, and attractive surroundings comes far ahead of whatever value there might be in “diversity.”

“Diversity” for its own sake is nevertheless a “good thing” in the liberal lexicon. The Houston Chronicle noted Schelling’s Nobel by saying that Schelling’s work

helps explain why housing segregation continues to be a problem, even in areas where residents say they have no extreme prejudice to another group.

Segregation isn’t a “problem,” it’s the solution to a potential problem. Segregation today is mainly a social phenomenon, not a legal one. It reflects a rational aversion on the part of whites to having neighbors whose culture breeds crime and other types of undesirable behavior.

As for what people say about their racial attitudes: Believe what they do, not what they say. Most well-to-do liberals — including black one like the Obamas — choose to segregate themselves and their children from black rednecks. That kind of voluntary segregation, aside from demonstrating liberal hypocrisy about black redneck culture, also demonstrates the rationality of choosing to live in safer and more decorous surroundings.

Dave Patterson of the defunct Order from Chaos put it this way:

[G]ame theory has one major flaw inherent in it: The arbitrary assignment of expected outcomes and the assumption that the values of both parties are equally reflected in these external outcomes. By this I mean a matrix is filled out by [a conductor, and] it is up to that conductor’s discretion to assign outcome values to that grid. This means that there is an inherent bias towards the expected outcomes of conductor.

Or: Garbage in, garbage out.

Game theory points to the essential flaw in mathematical economics, which is reductionism: “An attempt or tendency to explain a complex set of facts, entities, phenomena, or structures by another, simpler set.”

Reductionism is invaluable in many settings. To take an example from everyday life, children are warned — in appropriate stern language — not to touch a hot stove or poke a metal object into an electrical outlet. The reasons given are simple ones: “You’ll burn yourself” and “You’ll get a shock and it will hurt you.” It would be futile (in almost all cases) to try to explain to a small child the physical and physiological bases for the warnings. The child wouldn’t understand the explanations, and the barrage of words might cause him to forget the warnings.

The details matter in economics. It’s easy enough to say, for example, that a market equilibrium exists where the relevant supply and demand curves cross (in a graphical representation) or where the supply and demand functions yield equal values of price and quantity (in a mathematical representation). But those are gross abstractions from reality, as any economist knows — or should know. Expressing economic relationships in mathematical terms lends them an unwarranted air of precision.

Further, all mathematical expressions, no matter how complex, can be expressed in plain language, though it may be hard to do so when the words become too many and their relationships too convoluted. But until one tries to do so, one is at the mercy of the mathematical economist whose equation has no counterpart in the real world of economic activity. In other words, an equation represents nothing more than the manipulation of mathematical relationships until it’s brought to earth by plain language and empirical testing. Short of that, it’s as meaningful as Urdu is to a Cockney.

Finally, mathematical economics lends aid and comfort to proponents of economic control. Whether or not they understand the mathematics or the economics, the expression of congenial ideas in mathematical form lends unearned — and dangerous — credibility to the controller’s agenda. The relatively simple multiplier is a case in point. As I explain in “The Keynesian Multiplier: Phony Math,”

the Keynesian investment/government-spending multiplier simply tells us that if ∆Y = $5 trillion, and if b = 0.8, then it is a matter of mathematical necessity that ∆C = $4 trillion and ∆I + ∆G = $1 trillion. In other words, a rise in I + G of $1 trillion doesn’t cause a rise in Y of $5 trillion; rather, Y must rise by $5 trillion for C to rise by $4 trillion and I + G to rise by $1 trillion. If there’s a causal relationship between ∆G and ∆Y, the multiplier doesn’t portray it.

I followed that post with “The True Multiplier“:

Math trickery aside, there is evidence that the Keynesian multiplier is less than 1. Robert J. Barro of Harvard University opens an article in The Wall Street Journal with the statement that “economists have not come up with explanations … for multipliers above one.”

Barro continues:

A much more plausible starting point is a multiplier of zero. In this case, the GDP is given, and a rise in government purchases requires an equal fall in the total of other parts of GDP — consumption, investment and net export. . . .

What do the data show about multipliers? Because it is not easy to separate movements in government purchases from overall business fluctuations, the best evidence comes from large changes in military purchases that are driven by shifts in war and peace. A particularly good experiment is the massive expansion of U.S. defense expenditures during World War II. The usual Keynesian view is that the World War II fiscal expansion provided the stimulus that finally got us out of the Great Depression. Thus, I think that most macroeconomists would regard this case as a fair one for seeing whether a large multiplier ever exists.

I have estimated that World War II raised U.S. defense expenditures by $540 billion (1996 dollars) per year at the peak in 1943-44, amounting to 44% of real GDP. I also estimated that the war raised real GDP by $430 billion per year in 1943-44. Thus, the multiplier was 0.8 (430/540). The other way to put this is that the war lowered components of GDP aside from military purchases. The main declines were in private investment, nonmilitary parts of government purchases, and net exports — personal consumer expenditure changed little. Wartime production siphoned off resources from other economic uses — there was a dampener, rather than a multiplier. . . .

There are reasons to believe that the war-based multiplier of 0.8 substantially overstates the multiplier that applies to peacetime government purchases. For one thing, people would expect the added wartime outlays to be partly temporary (so that consumer demand would not fall a lot). Second, the use of the military draft in wartime has a direct, coercive effect on total employment. Finally, the U.S. economy was already growing rapidly after 1933 (aside from the 1938 recession), and it is probably unfair to ascribe all of the rapid GDP growth from 1941 to 1945 to the added military outlays. [“Government Spending Is No Free Lunch,” The Wall Street Journal (online.WSJ.com), January 22, 2009]

This is from Valerie A. Ramsey of  the University of California-San Diego and the National Bureau of Economic Research:

. . . [I]t appears that a rise in government spending does not stimulate private spending; most estimates suggest that it significantly lowers private spending. These results imply that the government spending multiplier is below unity. Adjusting the implied multiplier for increases in tax rates has only a small effect. The results imply a multiplier on total GDP of around 0.5. [“Government Spending and Private Activity,” January 2012]

In fact,

for the period 1947-2012 I estimated the year-over-year percentage change in GDP (denoted as Y%) as a function of G/GDP (denoted as G/Y):

Y% = 0.09 – 0.17(G/Y)

Solving for Y% = 0 yields G/Y = 0.53; that is, Y% will drop to zero if G/Y rises to 0.53 (or thereabouts). At the present level of G/Y (about 0.4), Y% will hover just above 2 percent, as it has done in recent years. (See the graph immediately above.)

If G/Y had remained at 0.234, its value in 1947:

  • Real growth would have been about 5 percent a year, instead of 3.2 percent (the actual value for 1947-2012).
  • The total value of Y for 1947-2012 would have been higher by $500 trillion (98 percent).
  • The total value of G would have been lower by $61 trillion (34 percent).

The last two points, taken together, imply a cumulative government-spending multiplier (K) for 1947-2012 of about -8. That is, aggregate output in 1947-2012 declined by 8 dollars for every dollar of government spending above the amount represented by G/Y = 0.234.

But -8 is only an average value for 1947-2012. It gets worse. The reduction in Y is cumulative; that is, every extra dollar of G reduces the amount of Y that is available for growth-producing investment, which leads to a further reduction in Y, which leads to a further reduction in growth-producing investment, and on and on. (Think of the phenomenon as negative compounding; take a dollar from your savings account today, and the value of the savings account years from now will be lower than it would have been by a multiple of that dollar: [1 + interest rate] raised to nth power, where n = number of years.) Because of this cumulative effect, the effective value of K in 2012 was about -14.

The multiplier is a seductive and easy-to-grasp mathematical construct. But in the hands of politicians and their economist-enablers, it has been an instrument of economic destruction.

Perhaps “higher” mathematical economics is potentially less destructive because it’s inside game played by economists for the benefit of economists. I devoutly hope that’s true.

Economists as Scientists

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This is the third entry in a series of loosely connected posts on economics. The first entry is here and the second entry is here. (Related posts by me are noted parenthetically throughout this one.)

Science is something that some people “do” some of the time. There are full-time human beings and part-time scientists. And the part-timers are truly scientists only when they think and act in accordance with the scientific method.*

Acting in accordance with the scientific method is a matter of attitude and application. The proper attitude is one of indifference about the correctness of a hypothesis or theory. The proper application rejects a hypothesis if it can’t be tested, and rejects a theory if it’s refuted (falsified) by relevant and reliable observations.

Regarding attitude, I turn to the most famous person who was sometimes a scientist: Albert Einstein. This is from the Wikipedia article about the Bohr-Einstein debate:

The quantum revolution of the mid-1920s occurred under the direction of both Einstein and [Niels] Bohr, and their post-revolutionary debates were about making sense of the change. The shocks for Einstein began in 1925 when Werner Heisenberg introduced matrix equations that removed the Newtonian elements of space and time from any underlying reality. The next shock came in 1926 when Max Born proposed that mechanics were to be understood as a probability without any causal explanation.

Einstein rejected this interpretation. In a 1926 letter to Max Born, Einstein wrote: “I, at any rate, am convinced that He [God] does not throw dice.” [Apparently, Einstein also used the line in Bohr’s presence, and Bohr replied, “Einstein, stop telling God what to do.” — TEA]

At the Fifth Solvay Conference held in October 1927 Heisenberg and Born concluded that the revolution was over and nothing further was needed. It was at that last stage that Einstein’s skepticism turned to dismay. He believed that much had been accomplished, but the reasons for the mechanics still needed to be understood.

Einstein’s refusal to accept the revolution as complete reflected his desire to see developed a model for the underlying causes from which these apparent random statistical methods resulted. He did not reject the idea that positions in space-time could never be completely known but did not want to allow the uncertainty principle to necessitate a seemingly random, non-deterministic mechanism by which the laws of physics operated.

It’s true that quantum mechanics was inchoate in the mid-1920s, and that it took a couple of decades to mature into quantum field theory. But there’s more than a trace of “attitude” in Einstein’s refusal to accept quantum mechanics, to stay abreast of developments in the theory, and to search quixotically for his own theory of everything, which he hoped would obviate the need for a non-deterministic explanation of quantum phenomena.

Improper application of the scientific method is rife. See, for example the Wikipedia article about the replication crisis, John Ioannidis’s article, “Why Most Published Research Findings Are False.” (See also “Ty Cobb and the State of Science” and “Is Science Self-Correcting?“) For a thorough analysis of the roots of the crisis, read Michael Hart’s book, Hubris: The Troubling Science, Economics, and Politics of Climate Change.

A bad attitude and improper application are both found among the so-called scientists who declare that the “science” of global warming is “settled,” and that human-generated CO2 emissions are the primary cause of the apparent rise in global temperatures during the last quarter of the 20th century. The bad attitude is the declaration of “settled science.” In “The Science Is Never Settled” I give many prominent examples of the folly of declaring it to be “settled.”

The improper application of the scientific method with respect to global warming began with the hypothesis that the “culprit” is CO2 emissions generated by the activities of human beings — thus anthropogenic global warming (AGW). There’s no end of evidence to the contrary, some of which is summarized in these posts and many of the links found therein. There’s enough evidence, in my view, to have rejected the CO2 hypothesis many times over. But there’s a great deal of money and peer-approval at stake, so the rush to judgment became a stampede. And attitude rears its ugly head when pro-AGW “scientists” shun the real scientists who are properly skeptical about the CO2 hypothesis, or at least about the degree to which CO2 supposedly influences temperatures. (For a depressingly thorough account of the AGW scam, read Michael Hart’s Hubris: The Troubling Science, Economics, and Politics of Climate Change.)

I turn now to economists, as I have come to know them in more than fifty years of being taught by them, working with them, and reading their works. Scratch an economist and you’re likely to find a moralist or reformer just beneath a thin veneer of rationality. Economists like to believe that they’re objective. But they aren’t; no one is. Everyone brings to the table a large serving of biases that are incubated in temperament, upbringing, education, and culture.

Economists bring to the table a heaping helping of tunnel vision. “Hard scientists” do, too, but their tunnel vision is generally a good thing, because it’s actually aimed at a deeper understanding of the inanimate and subhuman world rather than the advancement of a social or economic agenda. (I make a large exception for “hard scientists” who contribute to global-warming hysteria, as discussed above.)

Some economists, especially behavioralists, view the world through the lens of wealth-and-utility-maximization. Their great crusade is to force everyone to make rational decisions (by their lights), through “nudging.” It almost goes without saying that government should be the nudger-in-chief. (See “The Perpetual Nudger” and the many posts linked to therein.)

Other economists — though far fewer than in the past — have a thing about monopoly and oligopoly (the domination of a market by one or a few sellers). They’re heirs to the trust-busting of the late 1800s and early 1900s, a movement led by non-economists who sought to blame the woes of working-class Americans on the “plutocrats” (Rockefeller, Carnegie, Ford, etc.) who had merely made life better and more affordable for Americans, while also creating jobs for millions of them and reaping rewards for the great financial risks that they took. (See “Monopoly and the General Welfare” and “Monopoly: Private Is Better than Public.”) As it turns out, the biggest and most destructive monopoly of all is the federal government, so beloved and trusted by trust-busters — and too many others. (See “The Rahn Curve Revisited.”)

Nowadays, a lot of economists are preoccupied by income inequality, as if it were something evil and not mainly an artifact of differences in intelligence, ambition, and education, etc. And inequality — the prospect of earning rather grand sums of money — is what drives a lot of economic endeavor, to good of workers and consumers. (See “Mass (Economic) Hysteria: Income Inequality and Related Themes” and the many posts linked to therein.) Remove inequality and what do you get? The Soviet Union and Communist China, in which everyone is equal except party operatives and their families, friends, and favorites.

When the inequality-preoccupied economists are confronted by the facts of life, they usually turn their attention from inequality as a general problem to the (inescapable) fact that an income distribution has a top one-percent and top one-tenth of one-percent — as if there were something especially loathsome about people in those categories. (Paul Krugman shifted his focus to the top one-tenth of one percent when he realized that he’s in the top one percent, so perhaps he knows that’s he’s loathsome and wishes to deny it, to himself.)

Crony capitalism is trotted out as a major cause of very high incomes. But that’s hardly a universal cause, given that a lot of very high incomes are earned by athletes and film stars beside whom most investment bankers and CEOs are making peanuts. Moreover, as I’ve said on several occasions, crony capitalists are bright and driven enough to be in the stratosphere of any income distribution. Further, the fertile soil of crony capitalism is the regulatory power of government that makes it possible.

Many economists became such, it would seem, in order to promote big government and its supposed good works — income redistribution being one of them. Joseph Stiglitz and Paul Krugman are two leading exemplars of what I call the New Deal school of economic thought, which amounts to throwing government and taxpayers’ money at every perceived problem, that is, every economic outcome that is deemed unacceptable by accountants of the soul. (See “Accountants of the Soul.”)

Stiglitz and Krugman — both Nobel laureates in economics — are typical “public intellectuals” whose intelligence breeds in them a kind of arrogance. (See “Intellectuals and Society: A Review.”) It’s the kind of arrogance that I mentioned in the preceding post in this series: a penchant for deciding what’s best for others.

New Deal economists like Stiglitz and Krugman carry it a few steps further. They ascribe to government an impeccable character, an intelligence to match their own, and a monolithic will. They then assume that this infallible and wise automaton can and will do precisely what they would do: Create the best of all possible worlds. (See the many posts in which I discuss the nirvana fallacy.)

New Deal economists, in other words, live their intellectual lives  in a dream-world populated by the likes of Jiminy Cricket (“When You Wish Upon a Star”), Dorothy (“Somewhere Over the Rainbow”), and Mary Jane of a long-forgotten comic book (“First I shut my eyes real tight, then I wish with all my might! Magic words of poof, poof, piffles, make me just as small as [my mouse] Sniffles!”).

I could go on, but you should by now have grasped the point: What too many economists want to do is change human nature, channel it in directions deemed “good” (by the economist), or simply impose their view of “good” on everyone. To do such things, they must rely on government.

It’s true that government can order people about, but it can’t change human nature, which has an uncanny knack for thwarting Utopian schemes. (Obamacare, whose chief architect was economist Jonathan Gruber, is exhibit A this year.) And government (inconveniently for Utopians) really consists of fallible, often unwise, contentious human beings. So government is likely to march off in a direction unsought by Utopian economists.

Nevertheless, it’s hard to thwart the tax collector. The regulator can and does make things so hard for business that if one gets off the ground it can’t create as much prosperity and as many jobs as it would in the absence of regulation. And the redistributor only makes things worse by penalizing success. Tax, regulate, and redistribute should have been the mantra of the New Deal and most presidential “deals” since.

I hold economists of the New Deal stripe partly responsible for the swamp of stagnation into which the nation’s economy has descended. (See “Economic Growth Since World War II.”) Largely responsible, of course, are opportunistic if not economically illiterate politicians who pander to rent-seeking, economically illiterate constituencies. (Yes, I’m thinking of old folks and the various “disadvantaged” groups with which they have struck up an alliance of convenience.)

The distinction between normative economics and positive economics is of no particular use in sorting economists between advocates and scientists. A lot of normative economics masquerades as positive economics. The work of Thomas Piketty and his comrades-in-arms comes to mind, for example. (See “McCloskey on Piketty.”) Almost everything done to quantify and defend the Keynesian multiplier counts as normative economics, inasmuch as the work is intended (wittingly or not) to defend an intellectual scam of 80 years’ standing. (See “The Keynesian Multiplier: Phony Math,” “The True Multiplier,” and “Further Thoughts about the Keynesian Multiplier.”)

Enough said. If you want to see scientific economics in action, read Regulation. Not every article in it exemplifies scientific inquiry, but a good many of them do. It’s replete with articles about microeconomics, in which the authors uses real-world statistics to validate and quantify the many axioms of economics.

A final thought is sparked by Arnold Kling’s post, “Ed Glaeser on Science and Economics.” Kling writes:

I think that the public has a sort of binary classification. If it’s “science,” then an expert knows more than the average Joe. If it’s not a science, then anyone’s opinion is as good as anyone else’s. I strongly favor an in-between category, called a discipline. Think of economics as a discipline, where it is possible for avid students to know more than ordinary individuals, but without the full use of the scientific method.

On this rare occasion I disagree with Kling. The accumulation of knowledge about economic variables, or pseudo-knowledge such as estimates of GDP (see “Macroeconomics and Microeconomics“), either leads to well-tested, verified, and reproducible theories of economic behavior or it leads to conjectures, of which there are so many opposing ones that it’s “take your pick.” If that’s what makes a discipline, give me the binary choice between science and story-telling. Most of economics seems to be story-telling. “Discipline” is just a fancy word for it.

Collecting baseball cards and memorizing the statistics printed on them is a discipline. Most of economics is less useful than collecting baseball cards — and a lot more destructive.

Here’s my hypothesis about economists: There are proportionally as many of them who act like scientists as there are baseball players who have career batting averages of at least .300.
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* Richard Feynman, a physicist and real scientist, had a different view of the scientific method than Karl Popper’s standard taxonomy. I see Feynman’s view as complementary to Popper’s, not at odds with it. What is “constructive skepticism” (Feynman’s term) but a gentler way of saying that a hypothesis or theory might be falsified and that the act of falsification may point to a better hypothesis or theory?