CLICK ON:

• CAMBRIDGE FORECAST GROUP: ORIENTING YOURSELF VIA THE BOOK “THE REAGAN REVOLUTION AND THE DEVELOPING COUNTRIES”

• CAMBRIDGE FORECAST GROUP: RECENT YOUTUBE VIDEO PRESENTATIONS WITH LAWRENCE FEINER INCLUDING THE FALL OF COMMUNISM AND ITS IMPACT ON RACE RELATIONS

I,  Lawrence Feiner,  am one of the authors of the reagan revolution and the developing countries. The other is richard melson

The Refrigerator and the Universe:

Understanding the Laws of Energy

Martin Goldstein (Author)

Inge F. Goldstein (Author)

Readers at all levels, from high school to professional scientists, will find something intriguing in this book…It provides a very readable and informative account of a difficult topic. (Science Books and Films )

The strengths of [this book] are its scope and coverage and much excellent writing…It contains a rich mix of interesting ideas covering important historical events and applications of the laws of energy and entropy. (Harvey S. Leff American Journal of Physics )

The writing is clear, uncluttered, insightful, and makes use of many excellent analogies to explain and clarify difficult but important concepts. (Choice )

Product Description

C. P. Snow once remarked that not knowing the second law of thermodynamics is like never having read Shakespeare. Yet, while many people grasp the first law of energy, “Energy can neither be created nor destroyed,” few recognize the second, “Entropy can only increase.” What is entropy anyway, and why must it increase? Whether we want to know how a device as simple as a refrigerator works or understand the fate of the universe, we must start with the concepts of energy and entropy. In The Refrigerator and the Universe, Martin and Inge Goldstein explain the laws of thermodynamics for science buffs and neophytes alike. They begin with a lively presentation of the historical development of thermodynamics. The authors then show how the laws follow from the atomic theory of matter and give examples of their applicability to such diverse phenomena as the radiation of light from hot bodies, the formation of diamonds from graphite, how the blood carries oxygen, and the history of the earth. The laws of energy, the Goldsteins conclude, have something to say about everything, even if they do not tell us everything about anything.

In The Refrigerator and the Universe, Martin and Inge Goldstein explain the laws of thermodynamics for science buffs and neophytes alike. They begin with a lively presentation of the historical development of thermodynamics. The authors then show how the laws follow from the atomic theory of matter and give examples of their applicability to such diverse phenomena as the radiation of light from hot bodies, the formation of diamonds from graphite, how the blood carries oxygen, and the history of the earth. The laws of energy, the Goldsteins conclude, have something to say about everything, even if they do not tell us everything about anything.

Product Details:

• Hardcover: 433 pages
• Publisher: Harvard University Press
• First Edition September 1993
• Language: English
• ISBN-10: 0674753240
• ISBN-13: 978-0674753242

The book presents the three laws of thermodynamics: the first law (conservation of energy)in chapters 1-4, the second law (dispersal of energy) in chapters 5-9, and the third law (low temperature behavior) in chapter 14. Other chapters apply thermodynamics to light, chemistry, biology, geology, and cosmology. The authors present thermodynamics using both classical and statistical mechanical arguments. References are listed for further study of topics.

Although the book is intended for a general audience, the book will be interesting even to a reader who already has some familiarity with thermodynamics because the book probably treats at least a few applications with which he is unfamiliar. The book also makes a number of refreshing admissions about the limits of thermodynamics; for example, thermodynamics can’t be strictly applied to living organisms (p. 297), and in general relativity, energy need not be conserved (p. 370).

The book requires a knowledge of simple algebra and logarithms; however, a tutorial on these subjects is presented in an appendix.

The Refrigerator and the Universe:

Understanding the Laws of Energy

Martin Goldstein (Author)

Inge F. Goldstein (Author)

Slutsky

“The Summation of Random Causes as a Source of Cyclic Processes”

Slutsky, Eugen. 1927. “The Summation of Random Causes as a Source of Cyclic Processes.” Problems of Economic Conditions 3 (1). Moscow: Conjuncture Institute.

Ten years later, Econometrica translated the original Russian article into English; the 1937 version was revised by Slutsky and incorporated several new results (see note 5).

Slutsky, Eugen. 1937. “The Summation of Random Causes as a Source of Cyclic Processes.” Econometrica 5 (April), p. 110.

The Meaning of Slutsky

The insight of an obscure Soviet statistician–that random processes can form cyclical patterns–has profoundly shaped our understanding of economic booms and recessions.

Business cycles, and their rhythms, have long fascinated and perplexed economists. Why do economic booms alternate with recessions, decade after decade? And why do graphs of long-term data on gross domestic product, employment and other economic indicators form undulating patterns similar to physical phenomena such as ocean waves or sound waves? Over the past 150 years, all sorts of explanations have been put forth for recurrent peaks and valleys in economic activity—economists have hypothesized forces as seemingly far-fetched as sunspot activity and rainfall patterns as the cause of these cyclical patterns in national and world economies.

By the early 20th century, some researchers believed that chance occurrences like wars, crop failures and technological innovations played a role in business cycles. But no one fully appreciated how crucial random (or “stochastic”) processes are to the workings of the economy until Eugen Slutsky, a Soviet statistician and econometrician, did the math. A middle-aged professor working at a Moscow think tank, Slutsky was virtually unknown to economists in Europe and the United States when he published his landmark paper on cyclical phenomena in 1927.¹

In a bold statistical experiment, Slutsky demonstrated that random numbers subjected to statistical calculations similar to those used to reveal trends in economic time-series formed wavelike patterns indistinguishable from business cycles. The implication was that a similar stochastic process—“the summation of random causes,” as Slutsky described it—might be at work in the actual economy, causing prosperity to ebb and flow without the agency of sunspots, meteorological patterns or other cyclical forces.

“That was a hell of an idea,” said Robert Lucas, a University of Chicago economist who pioneered modern business cycle theory, in an interview. “It was just a huge jump from what anyone had done.”

Today, Slutsky is more familiar among economists for his earlier work in consumer theory. Every economics undergraduate learns the Slutsky equation, which analyzes shifts in demand for goods by looking at two components, the income and substitution effects of price changes (see The Mechanics of Demand). But Slutsky’s 1927 paper made an enormous contribution to business cycle theory that forever changed the way economists view economic fluctuations.

Following a peak during the Great Depression, interest in divining the causes of booms and recessions waned after World War II. But with further economic turmoil in the 1970s and 1980s, researchers again became fascinated with business cycles—and with the role of shocks (both random and nonrandom) in propagating them. Slutsky’s enduring insight, combined with advances in economic growth theory, gave rise to modern macroeconomic models that simulate the impact of shocks such as new technologies, energy price hikes, changes in consumer preferences and tax increases or cuts.

Much of the work over the past 25 years on “real business cycles”—the idea that economic oscillations stem from “real” shocks such as innovations or changes in regulations rather than “nonreal” factors such as price trends, interest rates and monetary policy—has been done by investigators associated with the Federal Reserve Bank of Minneapolis. For example, Edward Prescott, a monetary adviser to the Fed, has proposed random shocks to productivity as a key driver of fluctuations from the constant growth trend in U.S. GDP.

However, economists continue to grapple with basic questions about random shocks and their impact on the economy. What are these shocks, exactly, and how do they interact with labor productivity, capital investment, fiscal policy and other factors to cause economic expansions and contractions?

If Slutsky’s energies had remained focused on economics, he might have investigated those questions himself. Instead, he chose to work in statistics and mathematics for the rest of his career—a switch possibly motivated by fears for his life. Joseph Stalin took a dim view of theories that didn’t fit his framework for a centrally planned economy.

When Slutsky died in 1948, he probably had no inkling of the lasting impact his experiment with random numbers would have on macroeconomics. His work sparked a crucial intuition about market economies: Stuff happens. The economy is a dynamic entity that reacts unpredictably to random events (at least in the short term) and resists efforts to smooth out bumps in the road.

Making sense of cycles

Knut Wicksell, in the early 1900s, was perhaps the first economist to suggest that random shocks are complicit in the boom-bust cycles characteristic of market economies. Theorizing that erratic, unforeseen events such as innovations provide much of the impetus behind business cycles, he drew a simple analogy: “If you hit a wooden rocking-horse with a club, the movement of the horse will be very different to that of the club.”² That is, irregular blows to the rocking-horse will make it swing in a more or less regular arc.

In the 1920s, fellow Swede Johan Åkerman elaborated on this idea, comparing random economic shocks to pebbles on a streambed; such irregularities generate regular waves on the stream’s surface.

These images of regularity arising from randomness were at odds with prevailing theories that ascribed business cycles to some underlying, often hidden force. U.S. economist Henry Moore postulated an eight-year meteorological cycle that drove fluctuations in harvests and the production of raw materials. Another American economist, Wesley Mitchell, broke down business cycles into periods of prosperity, crisis, depression and revival in which each phase created the conditions for the next. This metronomic view of the economy was taught at many U.S. universities in the 1920s and 1930s.

Though Wicksell and Åkerman raised the possibility that stochastic processes were involved in business cycles, nobody had demonstrated the mechanism by which random events could cause recurrent, fairly regular oscillations in economic activity. That task fell to Slutsky, a researcher at the Moscow Conjuncture Institute, a government-run organization devoted to the study of business conditions in the young Soviet Union.

http://www.minneapolisfed.org/publications_papers/pub_display.cfm?id=4348

The method is similar to that used to track stock prices and currency exchange rates, where computers calculate moving averages (rather than sums) to smooth the jagged profile of hourly or daily observations, letting analysts discern broad trends over time.

The key to Slutsky’s model is that the moving summation process forges connections among the numbers derived from the original series of completely random lottery numbers. In statistical terms, they become serially correlated; the value of each sum is associated with, but not identical to, the values of previous sums because they share nine out of 10 elements. This means that the effect of a single random number persists in the chain of moving summations. If that digit comes up several times in close succession—as can occur in any random drawing—it can skew the moving sums either high or low. Over time, this process causes the values of the sums to oscillate—the statistical equivalent of a seesaw.

When Slutsky and assistants finished their tedious calculations, the result was breathtaking—to a statistician at least. Their series of sums of random numbers created a nonrandom pattern. When graphed, it described a wave-like curve similar to cycles in time-series of aggregate output, employment and other economic variables. One section of Slutsky’s moving-summation plot closely matched an index of British business cycles from 1855 to 1877 (see In Sum: Slutsky’s Experiment above, bottom panel), “an initial graphic demonstration of the possible effects of the summation of unconnected causes,” he wrote.⁵

Other models in the paper that use more complex moving summations generated similar cyclical patterns—all derived from numbers pulled purely at random. (Slutsky would have seen similar patterns if he had summed dice rolls; charts of such series bear an uncanny resemblance to fluctuations in stock prices.)

Slutsky’s discovery—that the moving summation or average of a random series may generate oscillations when no such movements exist in the original data—is called the Slutsky-Yule effect. (Yule, in a 1927 paper, arrived independently at the same finding.) Slutsky later proved that when the number of summations approaches infinity, the randomly generated undulations form sine waves—smooth arcs like ocean swells, or the pulse of alternating electric current.
Slutsky had shown in dramatic fashion that stochastic processes could create patterns virtually identical to the putative effects of weather patterns, self-perpetuating boom-bust phases and other factors on the economy. The obvious question was whether—as Slutsky’s paper seemed to imply—series of random events actually contributed to business cycles in the real world.

In 1927, Slutsky was a 47-year-old math and statistics whiz who had traveled a long road to attain his position among the country’s intellectual elite. In his youth, he had taken part in student protests against Czarist policies; expelled twice from the University of Kiev and barred from attending university anywhere within the Russian Empire, he was forced to leave the country to continue his education. Studying engineering in Germany, Slutsky developed instead a keen interest in economics that blossomed when he was allowed to re-enroll at the University of Kiev in 1905. “I already had plans for working on the application of mathematics to economics,” he wrote decades later in an autobiographical sketch.³

As he studied political economy, he became enthralled with the new statistical techniques of British mathematician Karl Pearson and wrote a well-received book on mathematical statistics. The book was published in 1912, a year after Slutsky finally got his bachelor’s degree at the ripe age of 31. Slutsky’s famous paper on consumer demand followed in 1915, after he had secured a teaching post at the Kiev Commercial Institute.

By the mid-1920s, Slutsky was working on probability theory and a variety of economic topics steeped in mathematics and statistics. Slutsky hints in his autobiography that his decision to focus on the technical side of economics was motivated at least in part by political events in the wake of the Bolshevik Revolution—the fall of capitalism and its replacement by a state-controlled economy.

In 1926, Slutsky landed a plum post at the Conjuncture Institute in Moscow, headed by renowned Soviet economist Nikolai Kondratiev. There he threw himself into an intense study of apparent cycles in economic time-series. He was familiar with the theories of Moore, Mitchell and his boss Kondratiev, who posited “long-wave” business cycles of 50 to 60 years in market economies.

But he saw another potential driver of business cycles, one hinted at by British mathematician George Udny Yule’s recent work on “nonsense correlations” in economic data. Could the laws of probability account for the recurrent spikes and dips seen in time-series? To find out, Slutsky performed statistical experiments on random numbers from a government lottery.

He wrote excitedly to his wife Yulia in Kiev that he was “lucky to arrive at a rather considerable finding, to discover the secret of … those wavy movements that are observed in social phenomena.”⁴

Luck of the draw

Slutsky’s method was unorthodox at the time—indeed, it was revolutionary. Instead of coming up with a business cycle theory and then using it to try to explain observed historical data, he manipulated an artificial set of data to see what patterns emerged. His approach was inductive and agnostic, noted Lucas. It didn’t start from an initial belief or hypothesis, but rather arrived at a theory after examining mathematical facts. “There’s no particular view of business cycles advanced in the paper,” said Lucas. “What he shows is that if you construct a simulated time-series … you can generate patterns that look just like the patterns we see in economic time-series.”

The statistical model that Slutsky created was a forerunner of modern computer simulations of random systems, the “Monte Carlo” methods used today in fields ranging from economics and finance to engineering and meteorology.

Assisted by Institute staffers armed with sharp pencils, Slutsky took random lottery numbers and added them sequentially. He created a new series of numbers consisting of the sum of a given random digit plus the nine that preceded it, then the sum of the next digit plus the previous nine, then the sum of the next digit plus … and so on. The process is akin to repeatedly rolling dice and adding up the values of the previous rolls; in Slutsky’s case, he calculated a 10-item moving sum of random digits, producing a new numerical series analogous to the dice totals (see In Sum: Slutsky’s Experiment below,)

In Sum: Slutsky’s Experiment

Slutsky’s simplest model began with a long string of random numbers. A 10-item moving sum of these produced a second series (34,35,37 and so on in this example). A graph of this series formed wave patterns similar to time-series economic data.

Slutsky showed that output from his random-number model bore a striking resemblance to actual British business cycles.

Deconstructing Slutsky

“The Summation of Random Causes as a Source of Cyclic Processes” was written in Russian; the paper wasn’t widely available to Western economists until 10 years later, when a longer English version was published in the journal Econometrica. But the few business cycle researchers in Europe and the United States who gained access to Slutsky’s 1927 paper immediately recognized its significance. A random element had to be accounted for in analyzing economic time-series.

One interpretation of Slutsky’s work is that the remarkable synchrony he found between simulated cycles and the ups and downs of the British economy is an artifact of statistics, an illusion with no bearing on the movements of the real economy. But Western economists were intrigued by an alternative reading of his paper: The similarity of randomly generated cycles to actual business cycles is no accident; random events such as inventions, storms and conflicts somehow shape the rhythms of the real-life economy. (Slutsky himself isn’t clear on the matter, although the paper’s title and the letter to his wife suggest that he favored this view.)

Simon Kuznets, a Russian émigré to the United States who studied under Mitchell at Columbia University, reviewed Slutsky’s findings and conducted statistical experiments on time-series to test his ideas. In a 1929 paper, Kuznets theorized that a careful analysis of business cycles would reveal the signatures of different types of random shocks acting upon the economy: a string of small shocks versus one or two big shocks, for example. He also noted the ramifications of this line of reasoning for the business cycle theories of Mitchell and his contemporaries: “If cycles arise from random events … then we obviously do not need the hypothesis of an independent regularly recurring cause which is deemed necessary by some theorists of business cycles.”⁶

Norwegian economist Ragnar Frisch also seized upon Slutsky’s findings in his 1933 analysis of the forces driving business cycles. Hitching Slutsky’s work to Wicksell’s rocking-horse analogy, Frisch (co-winner of the first Nobel Prize in economics) developed a dynamic macroeconomic model that incorporated random shocks.

In his model, delays in capital investment needed to satisfy increased consumer demand cause recurrent oscillations in economic output—the swings of the rocking-horse. But the rocking-horse would come to rest after two or three cycles without some external force acting upon it. Frisch wondered what would happen if the horse were hit with a club—“a stream of erratic shocks that constantly upsets the continuous evolution, and by so doing introduces into the system the energy necessary to maintain the swings.”⁷ He mimicked such shocks with a “stochastic difference equation”—a mathematical apparatus still used today to simulate the impact of chance events on economies.

While Western economists were pondering the meaning of Slutsky, the man himself had abandoned economics to apply his statistical acumen to hydrology and meteorology. In 1928, Stalin had released a five-year master plan for controlling every aspect of the Soviet economy. When Kondratiev dared to criticize the plan, the Conjuncture Institute was shut down, and its former director imprisoned and later executed. Slutsky realized that continuing to work in economics—even on abstruse theoretical topics—was too dangerous under Stalin’s rule, said John Chipman, an economics professor at the University of Minnesota who has studied Slutsky’s career.

“He saw what happened to Kondratiev,” Chipman said in an interview. “I think it’s incontrovertible that Slutsky switched fields in order to preserve his life.” Tellingly, in his 1938 autobiography, written as part of a job application, Slutsky skips over his two-year tenure at the Conjuncture Institute.

In the 1930s and during World War II, working in government research posts, Slutsky studied weather patterns instead of business cycles. In his last years, he performed important but laborious duty in statistics, preparing tables of probabilities for various distribution functions. When he died at age 67 of lung cancer, his obituary was written by the great Soviet mathematician Andrey Kolmogorov.

Business cycles revisited

After World War II, economists largely lost interest in business cycles. In an era of rising global prosperity, the emphasis was on measuring economic growth and fine-tuning it by applying Keynesian stabilization policy. In the United States, the Cowles Commission for Research in Economics developed complex macroeconomic models designed to identify optimum levels of government spending and taxation to achieve economic growth and full employment. In the 1960s, economic advisers to the Kennedy administration shaped tax and spending policies in an ill-fated effort to eliminate recessions altogether.

“There was a period when people thought that business cycles no longer exist, and we don’t have to worry about them in the future,” said Chipman, who was a researcher for the Cowles Commission in the 1950s.

But in the 1970s and 1980s, a new generation of economists—motivated in part by the “stagflation” of the 1970s, which showed that high inflation could coexist with high unemployment—looked at business cycles with fresh eyes. Lucas, Prescott and other investigators rediscovered the meaning of Slutsky and, blending his ideas with advances in modeling how economies grow, developed their own conceptions of rhythmic forces at work in the economy.

By the 1970s, many theorists had come to view the economy as a dynamic system that achieves a balance between the output of firms and household demand, even as its aggregate output fluctuates from quarter to quarter and year to year. In a 1977 article, Lucas defined the U.S. business cycle as “movements about trend in gross national product.”⁸ Over time, the economy grows at a fairly steady rate, but GDP oscillates around that trend, like a sailboat tacking back and forth in order to reach its destination. A series of positive deviations from trend rising to a peak constitutes an economic expansion, while a string of negative deviations leading to a trough indicates a recession. Like gusts buffeting the sailboat, these economic fluctuations are irregular and unpredictable.

Random shocks in dynamic models

A few years later Prescott and Finn Kydland, an economist at Carnegie Mellon University, proposed random events as the major motive behind these oscillations. In a seminal 1982 paper, Prescott (then at the University of Minnesota) and Kydland estimated that random shocks to productivity accounted for 70 percent of the fluctuations in U.S. economic growth after World War II. Their research was inspired in part by the findings of Slutsky and Frisch half a century earlier; in a later paper, Kydland and Prescott describe the work of both men.

Kydland and Prescott envisioned the effects of random shocks to productivity—new technology, energy price spikes, regulatory changes—accumulating over time. The reverberations of economic shocks linger for months or years, reinforcing the effects of new shocks and causing deviations from the long-term growth trend. Thus, the economy steers a serpentine course through booms and recessions.

To simulate these movements, Kydland and Prescott created a macroeconomic model in which households and firms optimally respond to changes in productivity, choosing to work and invest more or less when random shocks either increase or diminish the value of their labor and capital. The economists found that their model with random shocks, mirroring Slutsky’s results, produced estimates of fluctuations in GDP and other variables that corresponded with those in actual economic time-series—in this case, data on U.S. economic performance between 1950 and 1979.

By incorporating random shocks into a dynamic model in which individual agents act on their preferences, Kydland and Prescott, with Lucas and others, ushered in a revolution in macroeconomic theory. Today, dynamic stochastic general equilibrium (DSGE) models are standard tools for investigating business cycles and other macroeconomic phenomena.

At the Minneapolis Fed, scholars have built upon Kydland and Prescott’s foundational work in real business cycles by modeling the impact of other types of nonmonetary shocks. For example, Ellen McGrattan, a University of Minnesota professor and monetary adviser to the Minneapolis Fed, has examined the impact of fiscal shocks such as tax changes on economic activity.⁹

Look out—shocks ahead

Precisely how random events exert their influence on the economy is still not fully understood. Researchers beg to differ on many aspects of the mechanics of business cycles, airing their opposing views in professional journals. Real business cycle theories, for example, have come under fire from economists who believe that shocks to consumer demand, or to the money supply, offer a better explanation for economic fluctuations.

One prominent example is Lawrence Summers, now director of the White House’s National Economic Council, who in 1986 criticized Prescott for, among other things, assuming that random shocks to productivity are at the root of economic fluctuations. “He provides no discussion of the source or nature of these shocks, nor does he cite any microeconomic evidence for their importance,” Summers wrote.¹⁰ Prescott offered a spirited reply.¹¹

Summers’ critique highlights a lack of consensus on the nature of the shocks constantly peppering the economy. Economists continue to posit all kinds of shocks as propagators of business cycles, but for the most part, their modeling efforts have focused not on the shocks, but on how the economy reacts to those shocks. To return to Wicksell’s analogy, more attention has been paid to the rocking-horse than to the club.

The deep recession of the past two years has raised anew questions about the interaction of chance events with government action (or inaction) in causing severe economic downturns. (Researcher interest in business cycle theory, it seems, follows a nonrandom, countercyclical pattern.) Some economists contend that misguided industrial regulation or monetary policy can exacerbate contractions already under way due to random shocks.

For all the disagreements, Slutsky’s original insight about the snowballing effect of random causes remains at the core of ongoing research on business cycles. A gifted statistician and frustrated economist, Slutsky revealed hidden rhythms in simulated time-series—rhythms that appear to pulse through the economy as well, often with far-reaching consequences. “The economics keep changing,” observed Lucas, “but the basic idea that we’re going to model the economy as a system of equations subject to external shocks—that’s stayed with us.”

Endnotes

1 Slutsky, Eugen. 1927. “The Summation of Random Causes as a Source of Cyclic Processes.” Problems of Economic Conditions 3 (1). Moscow: Conjuncture Institute. Ten years later, Econometrica translated the original Russian article into English; the 1937 version was revised by Slutsky and incorporated several new results (see note 5).

2 Frisch, Ragnar. 1933. “Propagation Problems and Impulse Problems in Dynamic Economics.” In Economic Essays in Honor of Gustav Cassell. London: Allen and Unwin. Reprinted in Robert Gordon and Lawrence Klein, eds. 1965. Readings in Business Cycles. Homewood, Ill.: Richard D. Irwin, p. 178.

3 Slutsky, Eugen. “Autobiography.” In Sheynin, Oscar, trans., Probability and Statistics: Russian Papers of the Soviet Period. Berlin, 2004, p. 91.

4 Chetverikov, N.S. 1959. “The Life and Scientific Work of Slutsky.” In Sheynin, Oscar, trans., Probability and Statistics: Russian Papers of the Soviet Period. Berlin, 2004, p. 100. Online (see note 3).

5 Slutsky, Eugen. 1937. “The Summation of Random Causes as a Source of Cyclic Processes.” Econometrica 5 (April), p. 110.

6 Kuznets, Simon. 1929. “Random Events and Cyclical Oscillations.” Journal of the American Statistical Association 24 (September), p. 274.

7 Frisch, ibid.

8 Lucas, Robert E. Jr. 1977. “Understanding Business Cycles.” In Karl Brunner and Allan H. Meltzer, eds., Stabilization of the Domestic and International Economy. Carnegie Rochester Series on Public Policy. Amsterdam: North Holland, pp. 7–29. Reprinted in Lucas, Robert E. Jr., Studies in Business Cycle Theory. Cambridge, Mass.: MIT Press, 1981, p. 217.

9 McGrattan, Ellen R. 1994. “The Macroeconomic Effects of Distortionary Taxation.” Journal of Monetary Economics 33, pp. 573–601; also McGrattan, Ellen R. 2009. “Capital Taxation during the Great Depression.” Working Paper 670, Federal Reserve Bank of Minneapolis.

10 Summers, Lawrence H. 1986. “Some Skeptical Observations on Real Business Cycle Theory.” Federal Reserve Bank of Minneapolis Quarterly Review 10 (Fall), p. 24.

11 Prescott, Edward C. 1986. “Response to a Skeptic.” Federal Reserve Bank of Minneapolis Quarterly Review 10 (Fall), p. 28.

Phil Davies – Senior Writer
Joe Mahon – Staff Writer

Minneapolis Fed

Slutsky, Eugen. 1927. “The Summation of Random Causes as a Source of Cyclic Processes.” Problems of Economic Conditions 3 (1). Moscow: Conjuncture Institute. Ten years later, Econometrica translated the original Russian article into English; the 1937 version was revised by Slutsky and incorporated several new results (see note 5).

MDPI Open Access Entropy E-Mail Alert (8 new articles)‏

MDPI E-Mail Alert

MDPI AG
Postfach, CH – 4005 Basel, Switzerland
Office: Kandererstrasse 25, 4057 Basel
Tel. +41 61 683 77 34
Fax +41 61 302 89 18
E-mail: support@mdpi.com
http://www.mdpi.com/

Sat 5/07/11

Dear User,

We are pleased to send you a list of new publications from www.mdpi.com.

8 new articles found.

Entropy

Peter Crompton
The Decoherence of the Electron Spin and Meta-Stability of ¹³C Nuclear Spins in Diamond
Entropy 2011, 13(5), 949-965; doi:10.3390/e13050949
http://www.mdpi.com/1099-4300/13/5/949/
Published online: 5 May 2011

Shan Gao
Is Gravity an Entropic Force?
Entropy 2011, 13(5), 936-948; doi:10.3390/e13050936
http://www.mdpi.com/1099-4300/13/5/936/
Published online: 28 April 2011

Michael Paul Gough
Holographic Dark Information Energy
Entropy 2011, 13(4), 924-935; doi:10.3390/e13040924
http://www.mdpi.com/1099-4300/13/4/924/
Published online: 21 April 2011

Theo Kurtén
A Comment on Nadytko et al., “Amines in the Earth’s Atmosphere: A Density Functional Theory Study of the Thermochemistry of Pre-Nucleation Clusters”. Entropy 2011, 13, 554–569
Entropy 2011, 13(4), 915-923; doi:10.3390/e13040915
http://www.mdpi.com/1099-4300/13/4/915/
Published online: 19 April 2011

Daniele Cerra and Mihai Datcu
Algorithmic Relative Complexity
Entropy 2011, 13(4), 902-914; doi:10.3390/e13040902
http://www.mdpi.com/1099-4300/13/4/902/
Published online: 19 April 2011

Yun Zheng and Chee Keong Kwoh
A Feature Subset Selection Method Based On High-Dimensional Mutual Information
Entropy 2011, 13(4), 860-901; doi:10.3390/e13040860
http://www.mdpi.com/1099-4300/13/4/860/
Published online: 19 April 2011

Yudong Zhang and Lenan Wu
Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach
Entropy 2011, 13(4), 841-859; doi:10.3390/e13040841
http://www.mdpi.com/1099-4300/13/4/841/
Published online: 13 April 2011

Yang Chen and Kazuyuki Aihara
Some Convex Functions Based Measures of Independence and Their Application to Strange Attractor Reconstruction
Entropy 2011, 13(4), 820-840; doi:10.3390/e13040820
http://www.mdpi.com/1099-4300/13/4/820/
Published online: 8 April 2011

This message is automatically generated monthly.

MDPI AG
Postfach, CH – 4005 Basel, Switzerland
Office: Kandererstrasse 25, 4057 Basel
Tel. +41 61 683 77 34
Fax +41 61 302 89 18
E-mail: support@mdpi.com
http://www.mdpi.com/

BRUEGELPUBLICATIONALERT

Bruegel (bruegel@bruegel.org)

Tue 4/05/11

A G2 for science?

Policy brief by Reinhilde Veugelers

This policy brief analyses the effects of increasing globalisation of science. The emerging economic powerhouses, particularly China, are building up their own scientific capabilities rapidly and in a targeted way. This is provoking concern within advanced economies that they might be losing their advantage in scientific domains. Strategies for knowledge-based growth, such as the European Union’s 2020 strategy, must take these global trends into account if they are to deliver long-term international competitiveness.

Click here to download the policy brief

BRUEGELPUBLICATIONALERT

Bruegel (bruegel@bruegel.org)

Tue 4/05/11

Christiaan Huygens and Johann de Witt, two seventeenth century Dutchmen, were respectively instrumental in the emergence of modern physics and modern finance.

Johan de Witt

Johan de Witt, heer van Zuid- en Noord-Linschoten, Snelrewaard, Hekendorp and IJsselveere ^[1] (Dordrecht, 24 September 1625 – The Hague, 20 August 1672) was a key figure in Dutch politics at a time when the Republic of the United Provinces was the dominant power in Europe, dominating trade routes and thus the wealthiest nation in the world. In the mid 17th century he controlled the Netherlands political system in close cooperation with his uncle Cornelis de Graeff.^[2]

Mathematician

Besides being a statesman Johan de Witt, also was an accomplished mathematician. In 1659 he wrote “Elementa Curvarum Linearum” as an appendix to his translation of René Descartes‘ “La Géométrie”.

In 1671 his “Waardije van Lyf-renten naer Proportie van Los-renten” was published (‘The Worth of Life Annuities Compared to Redemption Bonds’). This work combined the interests of the statesman and the mathematician. Ever since the Middle Ages, a Life Annuity was a way to “buy” someone a regular income from a reliable source. The state, for instance, could provide a widow with a regular income until her death, in exchange for a ‘lump sum’ up front. There were also Redemption Bonds that were more like a regular state loan. De Witt showed – by using probability mathematics – that for the same amount of money a bond of 4% would result in the same profit as a Life Annuity of 6% (1 in 17). But the ‘Staten’ at the time were paying over 7% (1 in 14).

The publication about Life Annuities is seen as the first mathematical approach of chance and probability.

The drop in income for the widows contributed no doubt to the “bad press” for the brothers De Witt. Significantly, after the violent deaths of the brothers the ‘Staten’ issued new Life Annuities in 1673 for the old rate of 1 in 14.

In 1671 De Witt conceived of a life annuity as a weighted average of annuities certain where the weights were mortality probabilities (that sum to one), thereby producing the expected value of the present value of a life annuity. Edmond Halley’s (of comet fame) representation of the life annuity dates to 1693, when he re-expressed a life annuity as the discounted value of each annual payment multiplied by the probability of surviving long enough to receive the payment and summed until there are no survivors. De Witt’s approach was especially insightful and ahead of its time.

In modern terminology, De Witt treats a life annuity as a random variable and its expected value is what we call the value of a life annuity. Also in modern terminology, De Witt’s approach allows one to readily understand other properties of this random variable such as its standard deviation, skewness, kurtosis, or any other characteristic of interest.

In addition, in his Elementa curvarum linearum, De Witt derived the basic properties of quadratic forms, an important step in the field of linear algebra.

References

1. www.herenvanholland.nl Johan de Witt at Heren van Holland (nl)

2. Andries Bickers Biographie at the DBNL

3. www.herenvanholland.nl Anna de Witt at Heren van Holland (nl)

4. Rowen, Herbert H. John de Witt, Statesman of the True Freedom (Camebridge University Press 1986, New edition 2002), page 220

5. Troost, 43

6. Kok, J. (1794) Vaderlandsch woordenboek; oorspronkelijk verzameld door Jacobus Kok. Deel 32, p. 352; Veeghens, D. (1884) Historische studien: Uitg. door J.D. Veegens. Eerste Deel, p. 48; the first name of Verhoeff was Hendrik according to Fruin, R. (1901) Robert Fruin’s verspreide geschriften, p. 374, fn. 2

Literature

• Herbert H. Rowen, John de Witt, grand pensionary of Holland, 1625-1672. Princeton, N.J.: Princeton University Press, 1978, which is summarized in

• Herbert H. Rowen (1985). Johan de Witt. Staatsman van de ‘Ware Vrijheid’. Leiden.

The Analytical Society was a group of individuals in early-19th century Britain whose aim was to promote the use of Leibnizian or analytical calculus as opposed to Newtonian calculus. The latter system came into being in the 18th century as an invention of Sir Isaac Newton, and was in use throughout Great Britain for political rather than practical reasons. The Newton system of fluxions and fluents proved cumbersome to use, and less flexible and usable than Leibnizian calculus, which was used by the rest of Europe.

The Society was founded in 1812 over a Sunday morning breakfast. Its membership originally consisted of a group of Cambridge students led by Robert Woodhouse. Woodhouse, a professor at the university, had published a series of papers that promoted Leibnizian calculus as early as 1803. These papers proved difficult to understand and thus failed to promote the idea. Other charter members included Charles Babbage, Sir John Herschel and George Peacock. It soon attracted many new members, predominantly students.

The first solid action by the Society did not take place until 1816, when a French textbook on analytical calculus was translated and distributed. This was followed in 1817 by the introduction, by Peacock, of Leibnizian symbols in that year’s examinations in the local senate-house.

Both the exam and the textbook met with little criticism until 1819, when both were criticised by a D.M. Peacock. However, the reforms were encouraged by younger members of Cambridge University. George Peacock successfully encouraged a colleague, Richard Gwatkin of St John’s College at Cambridge University, to adopt the new notation in his exams.

Use of Leibnizian notation began to spread after this. In 1820, the notation was used by William Whewell, a previously neutral but influential Cambridge University faculty member, in his examinations. In 1821, Peacock again used Leibnizian notation in his examinations, and the notation became well established.

The Society followed its success by publishing two volumes of examples showing the new method. One was by George Peacock on differential and integral calculus; the other was by Herschel on the calculus of finite differences. They were joined in this by Whewell, who in 1819 published a book, An Elementary Treatise on Mechanics, which used the new notation and which became a standard textbook on the subject.

Sir John Ainz, a pupil of Peacock’s, published a notable paper in 1826 which showed how to apply Leibnizian calculus on various physical problems.

These activities did not go unnoticed at other universities in Great Britain, and soon they followed Cambridge’s example. By 1830, Leibnizian calculus had superseded Newtonian calculus. It soon underwent constructive use, for instance in devising and expressing James Clerk Maxwell‘s equations.

In 1832, the Society, which had been renamed the Cambridge Philosophical Society in 1819, incorporated officially. The members included Peacock and mathematician Oliver Heaviside. This society still exists today.

The Philosophical Breakfast Club:

Four Remarkable Friends Who Transformed Science and Changed the World

Laura J. Snyder

Laura J. Snyder (Author)

ABOUT THIS BOOK

The Philosophical Breakfast Club recounts the life and work of four men who met as students at Cambridge University: Charles Babbage, John Herschel, William Whewell, and Richard Jones. Recognizing that they shared a love of science (as well as good food and drink) they began to meet on Sunday mornings to talk about the state of science in Britain and the world at large. Inspired by the great 17th century scientific reformer and political figure Francis Bacon—another former student of Cambridge—the Philosophical Breakfast Club plotted to bring about a new scientific revolution. And to a remarkable extent, they succeeded, even in ways they never intended.

Historian of science and philosopher Laura J. Snyder exposes the political passions, religious impulses, friendships, rivalries, and love of knowledge—and power—that drove these extraordinary men. Whewell (who not only invented the word “scientist,” but also founded the fields of crystallography, mathematical economics, and the science of tides), Babbage (a mathematical genius who invented the modern computer), Herschel (who mapped the skies of the Southern Hemisphere and contributed to the invention of photography), and Jones (a curate who shaped the science of economics) were at the vanguard of the modernization of science.

This absorbing narrative of people, science and ideas chronicles the intellectual revolution inaugurated by these men, one that continues to mold our understanding of the world around us and of our place within it. Drawing upon the voluminous correspondence between the four men over the fifty years of their work, Laura J. Snyder shows how friendship worked to spur the men on to greater accomplishments, and how it enabled them to transform science and help create the modern world.

From Publishers Weekly

• · Hardcover: 448 pages
• · Publisher: Broadway (February 22, 2011)
• · Language: English
• · ISBN-10: 0767930487
• · ISBN-13: 978-0767930482

A Victorian science expert at St. John’s University, Snyder offers a four-in-one biography of 19th-century scientists William Whewell, a polymath whose expertise ranged from geology to moral philosophy; Charles Babbage, credited with inventing the first computer; John Herschel, a noted astronomer and mathematician; and Richard Jones, who created the academic discipline of economics. When academic science was still a backward field, the four Cambridge students founded the Philosophical Breakfast Club, devoted to scientific discussion. Snyder provides insights into their personal lives, their myriad professional accomplishments, and their influence on science and economics. She underscores the importance of their accomplishments by placing them into modern context, for example, pointing out that Jone’s empirically based economics, which placed economics in a larger social and political context, is in vogue again. Snyder also describes Whewel’s important integration of religion and Darwinism. Each of the four figures is a worthy subject in his own right, and by combining their stories Snyder provides the right balance of biography and science. It also allows Snyder to discuss a wide range of scientific developments that are sufficiently modern to appeal to today’s readers.

From Booklist

When Coleridge complained in 1833 that a man digging for fossils or experimenting with electricity did not deserve the title natural philosopher, physicist William Whewell responded by coining a new word: scientist. Behind this coinage, Snyder discerns a cultural revolution, one that Whewell had helped to launch in a series of Cambridge breakfast meetings with three classmates: Charles Babbage, John Herschel, and Richard Jones. Together these four mapped out a plan for perfecting the scientific method and harnessing it for social benefit. Snyder chronicles the subsequent collaboration of these breakfast visionaries: Whewell mapped ocean tides; Babbage designed the first computer; Herschel pioneered photographic technology; Jones translated economics into rigorous mathematics.

Collectively, this band forged an identity for the scientist and thus cleared cultural space for Darwin and James Clerk Maxwell.

Snyder, however, also recognizes the irony in the professional narrowing inherent in this new identity, since the daring four who established it claimed horizons too broad to fit within its limits. A striking account of how a few bold individuals catalyzed profound social change. –Bryce Christensen

The Philosophical Breakfast Club:

Four Remarkable Friends Who Transformed Science and Changed the World

This scholarly but very accessible history of science in the early nineteenth century centers on four young Cambridge undergraduates, William Whewell, Charles Babbage, John Herschel, and Richard Jones, who meet for breakfast on Sundays in 1812 to discuss their passion for “natural philosophy” (science) and their equally strong passion to reform how science is done. They are strong admirers of Francis Bacon, who emphasized an inductive methodology whereby data is gathered and observations made that lead to theories being developed that can then be further tested. This contrasted with the standard science methodology of the time, which was deductive and depended more on logic than observation, hence the common term “natural philosophy”. The young men also want science to emphasize work that will help mankind. Such idealism has been common in young people throughout history, but these four men do not give up their dreams, and they each play important roles in a transformation of science that significantly shaped our modern world.
People interested in science hear of Babbage, the father of the present-day computer, and the Herschel family of astronomers. Whewell is a less familiar name, but he is revered enough to have his statue facing that of Francis Bacon at Trinity College in Cambridge, an honor that would no doubt please him immensely. I never heard of Jones, although his treatise on economics criticizing Ricardo and calling for the use of statistics was very influential.
The book discusses the lives of these men and their activism in the name of modernizing science within a broader discussion of the major developments in science in the first half of the nineteenth century. It may be astonishing to a modern reader, but in the period when they lived, little thought seemed to have been given to combining theory and experience by using individual observations to develop general formulae or predictions, even in practical matters such as timing of tides. The chapter on forming the British Association for the Advancement of Science in reaction to the Royal Society is a fascinating glimpse of academic and professional politics of the nineteenth century.

Some things never change. A chapter is devoted to the ever-ongoing disputes about the relation of science to religion, which caused quite a rift between Babbage and Whewell. There are also sections on specific scientific fields, such as Babbage’s quest to build the first computer and the work of various members of the group on astronomy, tides, the mapping of the earth, the development of photography, and even cryptology.

Babbage’s project has interest far beyond its visionary anticipation of today’s computers. Babbage saw his Difference Engine as an analogy to the way God might interact with the world, and Darwin attended a demonstration of the Engine soon after finishing his voyage on the Beagle that introduced him to the notion of God as a divine programmer.

There is some entertaining discussion of the astronomical work of the time, such as the discovery of Neptune, and I especially enjoyed the chapter on economics and was amused by their belief that economics would be a good subject to address as their first major example of how Baconian induction could be applied to science.

This first attempt to put economics into a mathematical form proved to be somewhat more difficult than anticipated!

Like many of the best books of its type, The Philosophical Breakfast Club is a mixture of broad themes, such as the reform of science that the quartet so passionately pursued, and fascinating smaller details, such as the fact that Whewell originated the term “scientist” (after the poet Coleridge objected to continued use of the term “natural philosopher”), as well as the terms “uniformitarianism” for Lyell’s geological theory,” Eocene”, “Miocene”, and “Pliocene” for historical epochs, and “ion”, “cathode”, and “anode”.

If one is interested in history, science, or how scientific methodology developed, The Philosophical Breakfast Club is well worth the time.

Laura J. Snyder’s “The Philosophical Breakfast Club” focuses on the work of four remarkable men who changed the course of history. They were William Whewell, John Herschel, Richard Jones, and Charles Babbage. Before they became widely known, these individuals were friends who, while having breakfast together on Sundays at Cambridge, discussed ways of elevating and modernizing scientific inquiry. They were admirers of the seventeenth century reformer, Francis Bacon, who asserted that keen observation, rational thinking, and precise measurements would lead to significant and practical discoveries. Whewell, Herschel, Jones, and Babbage were destined to gain fame as brilliant innovators in such fields as astronomy, mathematics, economics, botany, and chemistry.

Babbage is best remembered for his ingenious invention that is considered to be an early version of our modern computers. Herschel, like his renowned father, William, was an astronomer who swept the skies with his powerful telescope. Jones focused on political economy, a controversial discipline in the nineteenth century. Adam Smith, Thomas Malthus, and David Ricardo had promulgated various theories and Jones took issue with a number of their conclusions. Whewell was a mathematician and an academic who wrote quite a few influential works.

Snyder’s impressive research and fascinating anecdotes bring the atmosphere of this amazing era to brilliant life. She points out that “natural philosophers” used to rely on little more than personal observation and guesswork. Whewell coined a new term, “scientist,” to designate an individual who combines intellect and verifiable facts to reach conclusions that can be replicated and verified by others. The author humanizes her subjects by describing their triumphs and accomplishments as well as their failures and tragic losses. They had their share of pettiness and neuroses, but they could also be generous, loyal, and altruistic. It is eye-opening to learn how much these four men managed to accomplish throughout their lives.

In addition to her depiction of Whewell, Herschel, Babbage, Jones, and their colleagues, Snyder provides a valuable picture of the political and social climate of England from the 1820′s until the 1870′s. For the most part, women stood on the sidelines, not for lack of ability but for lack of opportunity. Snyder provides useful background information about how the Industrial Revolution brought about a demographic shift from farms to cities. Unemployment and poor living conditions led to labor unrest and even outbreaks of violence. One controversy that raged (it still does today) is whether the benefits of technological innovations outweigh their disadvantages.

This is a challenging and occasionally dense book in which Snyder goes into the minutiae of complex mathematical and astronomical concepts. Those who are not well-versed in these areas may not understand all of Ms. Snyder’s explanations. However, readers who can tolerate the occasionally abstruse technical writing will be richly rewarded. This is a well-documented and thought-provoking work of non-fiction that shows the many ways in which today’s men and women of science stand on the shoulders of giants.

The Philosophical Breakfast Club:

Four Remarkable Friends Who Transformed Science and Changed the World

William Whewell’s destiny changed between noon and 2 p.m. in late 1808 or early 1809. The headmaster and parish curate knew William was destined for academic greatness and it was on lunch hour that he spoke to William’s father. William’s father was reluctant to give up his apprenticing son in the family business of carpentry, to study math and science. In the end, however, the offer was to good to pass up; William would be given a scholarship and then further help would come from all of the town.

All of Lancaster would contribute as they could to their rising star, William Whewell. Amongst the very well off students, William stood out: “a tall, ungainly youth, with grey worsted stockings and country-made shoes.”

This book is the very meticulously researched story of four men who together brought about the scientific method of advancing science. William Whewell, Charles Babbage, John Herschel and Richard Jones. Each of these men is fascinating, brilliant and accomplished (not to mention good looking- Whewell found, to his surprise, he was something of a ladies’ man) John Hercshel, only son of the famous astronomer, initially fought the idea of following in his father’s footsteps.

Prior to their breakfast club there was in 1812, the Analytical Society attended by Babbage, Herschel, Whewell and many others. They met weekly to discuss mathematical papers.

Clubs, during this period in British history, were commonplace. There were reading clubs, country clubs, coffee-drinking clubs, dining clubs, card playing clubs… In fact, there were reported to be as many as twenty thousand men meeting in various clubs in London alone during the mid-eighteenth century. So the Philosophical Breakfast club was not unique for being a club. This Philosophical Breakfast Club was in one regard, just one more club. The astounding thing was it was made up of four amazing men, men who did not look at their lives as something to overcome but simply loved science, loved learning and could not be stopped.

The Breakfast Club met to eat (obviously breakfast), gossip, laugh and drink, (“more ale than coffee was drunk”). They met on Sunday mornings right after chapel. Breakfast clubs came to be all the rage and professors disliked them for their apparent frittering away of the day in what they considered idle discussion.

Reconstructing the Cognitive World
The Next Step
Michael Wheeler

Table of Contents and Sample Chapters

In Reconstructing the Cognitive World, Michael Wheeler argues that we should turn away from the generically Cartesian philosophical foundations of much contemporary cognitive science research and proposes instead a Heideggerian approach.

Wheeler begins with an interpretation of Descartes. He defines Cartesian psychology as a conceptual framework of explanatory principles and shows how each of these principles is part of the deep assumptions of orthodox cognitive science (both classical and connectionist).

Wheeler then turns to Heidegger’s radically non-Cartesian account of everyday cognition, which, he argues, can be used to articulate the philosophical foundations of a genuinely non-Cartesian cognitive science.

Finding that Heidegger’s critique of Cartesian thinking falls short, even when supported by Hubert Dreyfus’s influential critique of orthodox artificial intelligence, Wheeler suggests a new Heideggerian approach. He points to recent research in “embodied-embedded” cognitive science and proposes a Heideggerian framework to identify, amplify, and clarify the underlying philosophical foundations of this new work.

He focuses much of his investigation on recent work in artificial intelligence-oriented robotics, discussing, among other topics, the nature and status of representational explanation, and whether (and to what extent) cognition is computation rather than a noncomputational phenomenon best described in the language of dynamical systems theory.

Wheeler’s argument draws on analytic philosophy, continental philosophy, and empirical work to “reconstruct” the philosophical foundations of cognitive science in a time of a fundamental shift away from a generically Cartesian approach. His analysis demonstrates that Heideggerian continental philosophy and naturalistic cognitive science need not be mutually exclusive and shows further that a Heideggerian framework can act as the “conceptual glue” for new work in cognitive science.

About the Author

Michael Wheeler is Reader in Philosophy at the University of Stirling. He is the author of Reconstructing the Cognitive World: The Next Step (MIT Press, 2005).

Publisher:

The MIT Press

March 2007
6 x 9, 356 pp.

ISBN-10:
0-262-73182-7
ISBN-13:
978-0-262-73182-9

Reconstructing the Cognitive World
The Next Step
Michael Wheeler

The Royal Society 2010

Selected Abstracts

· Beyond ‘dangerous’ climate change: emission scenarios for a new world

The Copenhagen Accord reiterates the international community’s commitment to ‘hold the increase in global temperature below 2 degrees Celsius’. Yet its preferred focus on global emission peak dates and longer-term reduction targets, without recourse to cumulative emission budgets, belies seriously the scale and scope of mitigation necessary to meet such a commitment. Moreover, the pivotal importance of emissions from non-Annex 1 nations in shaping available space for Annex 1 emission pathways received, and continues to receive, little attention. Building on previous studies, this paper uses a cumulative emissions framing, broken down to Annex 1 and non-Annex 1 nations, to understand the implications of rapid emission growth in nations such as China and India, for mitigation rates elsewhere. The analysis suggests that despite high-level statements to the contrary, there is now little to no chance of maintaining the global mean surface temperature at or below 2^{°C. Moreover, the impacts associated with 2°C have been revised upwards, sufficiently so that 2°C now more appropriately represents the threshold between ‘dangerous’ and ‘extremely dangerous’ climate change. Ultimately, the science of climate change allied with the emission scenarios for Annex 1 and non-Annex 1 nations suggests a radically different framing of the mitigation and adaptation challenge from that accompanying many other analyses, particularly those directly informing policy.}

^{· emission scenarios}

^{· Annex 1}

^{· non-Annex 1}

^{· cumulative emissions}

^{· climate policy}

^{· emission pathways}

^{· 2011 The Royal Society}

^{This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.}

^{Full Text}

^{· Abstract 2 of 11Articles}

^{Cumulative carbon emissions, emissions floors and short-term rates of warming: implications for policy}

^{A number of recent studies have found a strong link between peak human-induced global warming and cumulative carbon emissions from the start of the industrial revolution, while the link to emissions over shorter periods or in the years 2020 or 2050 is generally weaker. However, cumulative targets appear to conflict with the concept of a ‘floor’ in emissions caused by sectors such as food production. Here, we show that the introduction of emissions floors does not reduce the importance of cumulative emissions, but may make some warming targets unachievable. For pathways that give a most likely warming up to about 4°C, cumulative emissions from pre-industrial times to year 2200 correlate strongly with most likely resultant peak warming regardless of the shape of emissions floors used, providing a more natural long-term policy horizon than 2050 or 2100. The maximum rate of CO_{2-induced warming, which will affect the feasibility and cost of adapting to climate change, is not determined by cumulative emissions but is tightly aligned with peak rates of emissions. Hence, cumulative carbon emissions to 2200 and peak emission rates could provide a clear and simple framework for CO2 mitigation policy.}}

^{_{· cumulative emissions}}

^{_{· emissions floors}}

^{_{· rate of warming}}

^{_{· climate change}}

^{_{· 2011 The Royal Society}}

^{_{Full Text}}

^{_{· Abstract 3 of 11Articles}}

^{_{When could global warming reach 4°C?}}

^{_{The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) assessed a range of scenarios of future greenhouse-gas emissions without policies to specifically reduce emissions, and concluded that these would lead to an increase in global mean temperatures of between 1.6°C and 6.9°C by the end of the twenty-first century, relative to pre-industrial. While much political attention is focused on the potential for global warming of 2°C relative to pre-industrial, the AR4 projections clearly suggest that much greater levels of warming are possible by the end of the twenty-first century in the absence of mitigation. The centre of the range of AR4-projected global warming was approximately 4°C. The higher end of the projected warming was associated with the higher emissions scenarios and models, which included stronger carbon-cycle feedbacks. The highest emissions scenario considered in the AR4 (scenario A1FI) was not examined with complex general circulation models (GCMs) in the AR4, and similarly the uncertainties in climate–carbon-cycle feedbacks were not included in the main set of GCMs. Consequently, the projections of warming for A1FI and/or with different strengths of carbon-cycle feedbacks are often not included in a wider discussion of the AR4 conclusions. While it is still too early to say whether any particular scenario is being tracked by current emissions, A1FI is considered to be as plausible as other non-mitigation scenarios and cannot be ruled out. (A1FI is a part of the A1 family of scenarios, with ‘FI’ standing for ‘fossil intensive’. This is sometimes erroneously written as A1F1, with number 1 instead of letter I.) This paper presents simulations of climate change with an ensemble of GCMs driven by the A1FI scenario, and also assesses the implications of carbon-cycle feedbacks for the climate-change projections. Using these GCM projections along with simple climate-model projections, including uncertainties in carbon-cycle feedbacks, and also comparing against other model projections from the IPCC, our best estimate is that the A1FI emissions scenario would lead to a warming of 4°C relative to pre-industrial during the 2070s. If carbon-cycle feedbacks are stronger, which appears less likely but still credible, then 4°C warming could be reached by the early 2060s in projections that are consistent with the IPCC’s ‘likely range’.}}

^{_{· climate modelling}}

^{_{· climate-change projections}}

^{_{· 4°C}}

^{_{· global warming}}

^{_{· dangerous climate change}}

^{_{· 2011 The Royal Society}}

^{_{This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.}}

^{_{Full Text}}

^{_{· Abstract 4 of 11Articles}}

_{^{Regional temperature and precipitation changes under high-end (≥4°C) global warming}}

_{^{Climate models vary widely in their projections of both global mean temperature rise and regional climate changes, but are there any systematic differences in regional changes associated with different levels of global climate sensitivity? This paper examines model projections of climate change over the twenty-first century from the Intergovernmental Panel on Climate Change Fourth Assessment Report which used the A2 scenario from the IPCC Special Report on Emissions Scenarios, assessing whether different regional responses can be seen in models categorized as ‘high-end’ (those projecting 4°C or more by the end of the twenty-first century relative to the preindustrial). It also identifies regions where the largest climate changes are projected under high-end warming. The mean spatial patterns of change, normalized against the global rate of warming, are generally similar in high-end and ‘non-high-end’ simulations. The exception is the higher latitudes, where land areas warm relatively faster in boreal summer in high-end models, but sea ice areas show varying differences in boreal winter. Many continental interiors warm approximately twice as fast as the global average, with this being particularly accentuated in boreal summer, and the winter-time Arctic Ocean temperatures rise more than three times faster than the global average. Large temperature increases and precipitation decreases are projected in some of the regions that currently experience water resource pressures, including Mediterranean fringe regions, indicating enhanced pressure on water resources in these areas.}}

_{^{· regional climate change}}

_{^{· precipitation}}

_{^{· temperature}}

_{^{· global climate models}}

_{^{· 2011 The Royal Society}}

_{^{This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.}}

_{^{Full Text}}

_{^{· Abstract 5 of 11Articles}}

_{^{Water availability in +2°C and +4°C worlds}}

_{^{While the parties to the UNFCCC agreed in the December 2009 Copenhagen Accord that a 2°C global warming over pre-industrial levels should be avoided, current commitments on greenhouse gas emissions reductions from these same parties will lead to a 50 : 50 chance of warming greater than 3.5°C. Here, we evaluate the differences in impacts and adaptation issues for water resources in worlds corresponding to the policy objective (+2°C) and possible reality (+4°C). We simulate the differences in impacts on surface run-off and water resource availability using a global hydrological model driven by ensembles of climate models with global temperature increases of 2°C and 4°C. We combine these with UN-based population growth scenarios to explore the relative importance of population change and climate change for water availability. We find that the projected changes in global surface run-off from the ensemble show an increase in spatial coherence and magnitude for a +4°C world compared with a +2°C one. In a +2°C world, population growth in most large river basins tends to override climate change as a driver of water stress, while in a +4°C world, climate change becomes more dominant, even compensating for population effects where climate change increases run-off. However, in some basins where climate change has positive effects, the seasonality of surface run-off becomes increasingly amplified in a +4°C climate.}}

_{^{· climate change impacts}}

_{^{· global water resources}}

_{^{· water resources stresses}}

_{^{· macro-scale hydrological model}}

_{^{· ensembles}}

_{^{· uncertainty}}

_{^{· 2011 The Royal Society}}

_{^{This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.}}

_{^{Full Text}}

_{^{· Abstract 6 of 11Articles}}

_{^{Agriculture and food systems in sub-Saharan Africa in a 4°C+ world}}

^{_{Agricultural development in sub-Saharan Africa faces daunting challenges, which climate change and increasing climate variability will compound in vulnerable areas. The impacts of a changing climate on agricultural production in a world that warms by 4°C or more are likely to be severe in places. The livelihoods of many croppers and livestock keepers in Africa are associated with diversity of options. The changes in crop and livestock production that are likely to result in a 4°C+ world will diminish the options available to most smallholders. In such a world, current crop and livestock varieties and agricultural practices will often be inadequate, and food security will be more difficult to achieve because of commodity price increases and local production shortfalls. While adaptation strategies exist, considerable institutional and policy support will be needed to implement them successfully on the scale required. Even in the 2}°C+ world that appears inevitable, planning for and implementing successful adaptation strategies are critical if agricultural growth in the region is to occur, food security be achieved and household livelihoods be enhanced. As part of this effort, better understanding of the critical thresholds in global and African food systems requires urgent research.}

_{^{· food security}}

_{^{· adaptation}}

_{^{· climate change}}

_{^{· livelihoods}}

_{^{· 2011 The Royal Society}}

_{^{This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.}}

_{^{Full Text}}

_{^{· Abstract 7 of 11Articles}}

_{^{Changes in the potential distribution of humid tropical forests on a warmer planet}}

^{_{The future of tropical forests has become one of the iconic issues in climate-change science. A number of studies that have explored this subject have tended to focus on the output from one or a few climate models, which work at low spatial resolution, whereas society and conservation-relevant assessment of potential impacts requires a finer scale. This study focuses on the role of climate on the current and future distribution of humid tropical forests (HTFs). We first characterize their contemporary climatological niche using annual rainfall and maximum climatological water stress, which also adequately describe the current distribution of other biomes within the tropics. As a first-order approximation of the potential extent of HTFs in future climate regimes defined by global warming of 2°C and 4°C, we investigate changes in the niche through a combination of climate-change anomaly patterns and higher resolution (5} km) maps of current climatology. The climate anomalies are derived using data from 17 coupled Atmosphere–Ocean General Circulation Models (AOGCMs) used in the Fourth Assessment of the Intergovernmental Panel for Climate Change. Our results confirm some risk of forest retreat, especially in eastern Amazonia, Central America and parts of Africa, but also indicate a potential for expansion in other regions, for example around the Congo Basin. The finer spatial scale enabled the depiction of potential resilient and vulnerable zones with practically useful detail. We further refine these estimates by considering the impact of new environmental regimes on plant water demand using the UK Met Office land-surface scheme (of the HadCM3 AOGCM). The CO_{2-related reduction in plant water demand lowers the risk of die-back and can lead to possible niche expansion in many regions. The analysis presented here focuses primarily on hydrological determinants of HTF extent. We conclude by discussing the role of other factors, notably the physiological effects of higher temperature.}}

^{_{· tropical forests}}

^{_{· climate change}}

^{_{· climate patterns}}

^{_{· water stress}}

^{_{· maximum climatological water deficit}}

^{_{· carbon dioxide}}

^{_{· This journal is © 2011 The Royal Society}}

^{_{Full Text}}

^{_{· Abstract 8 of 11Articles}}

^{_{Sea-level rise and its possible impacts given a ‘beyond 4°C world’ in the twenty-first century}}

^{_{The range of future climate-induced sea-level rise remains highly uncertain with continued concern that large increases in the twenty-first century cannot be ruled out. The biggest source of uncertainty is the response of the large ice sheets of Greenland and west Antarctica. Based on our analysis, a pragmatic estimate of sea-level rise by 2100, for a temperature rise of 4°C or more over the same time frame, is between 0.5 m and 2 m—the probability of rises at the high end is judged to be very low, but of unquantifiable probability. However, if realized, an indicative analysis shows that the impact potential is severe, with the real risk of the forced displacement of up to 187 million people over the century (up to 2.4% of global population). This is potentially avoidable by widespread upgrade of protection, albeit rather costly with up to 0.02 per cent of global domestic product needed, and much higher in certain nations. The likelihood of protection being successfully implemented varies between regions, and is lowest in small islands, Africa and parts of Asia, and hence these regions are the most likely to see coastal abandonment. To respond to these challenges, a multi-track approach is required, which would also be appropriate if a temperature rise of less than 4°C was expected. Firstly, we should monitor sea level to detect any significant accelerations in the rate of rise in a timely manner. Secondly, we need to improve our understanding of the climate-induced processes that could contribute to rapid sea-level rise, especially the role of the two major ice sheets, to produce better models that quantify the likely future rise more precisely. Finally, responses need to be carefully considered via a combination of climate mitigation to reduce the rise and adaptation for the residual rise in sea level. In particular, long-term strategic adaptation plans for the full range of possible sea-level rise (and other change) need to be widely developed.}}

^{_{· sea-level rise}}

^{_{· impacts}}

^{_{· adaptation}}

^{_{· protection}}

^{_{· retreat}}

^{_{· 2011 The Royal Society}}

^{_{Full Text}}

^{_{· Abstract 9 of 11Articles}}

_{^{Climate-induced population displacements in a 4°C+ world}}

^{_{Massive population displacements are now regularly presented as one of the most dramatic possible consequences of climate change. Current forecasts and projections show that regions that would be affected by such population movements are low-lying islands, coastal and deltaic regions, as well as sub-Saharan Africa. Such estimates, however, are usually based on a 2°C temperature rise. In the event of a 4°C+ warming, not only is it likely that climate-induced population movements will be more considerable, but also their patterns could be significantly different, as people might react differently to temperature changes that would represent a threat to their very survival. This paper puts forward the hypothesis that a greater temperature change would affect not only the magnitude of the associated population movements, but also—and above all—the characteristics of these movements, and therefore the policy responses that can address them. The paper outlines the policy evolutions that climate-induced displacements in a 4°}C+ world would require.}

_{^{· migration}}

_{^{· displacement}}

_{^{· climate change}}

_{^{· mobility}}

_{^{· adaptation}}

_{^{· 2011 The Royal Society}}

_{^{Full Text}}

_{^{· Abstract 10 of 11Articles}}

_{^{Rethinking adaptation for a 4°C world}}

^{_{With weakening prospects of prompt mitigation, it is increasingly likely that the world will experience 4°C and more of global warming. In such a world, adaptation decisions that have long lead times or that have implications playing out over many decades become more uncertain and complex. Adapting to global warming of 4°C cannot be seen as a mere extrapolation of adaptation to 2°}C; it will be a more substantial, continuous and transformative process. However, a variety of psychological, social and institutional barriers to adaptation are exacerbated by uncertainty and long timeframes, with the danger of immobilizing decision-makers. In this paper, we show how complexity and uncertainty can be reduced by a systematic approach to categorizing the interactions between decision lifetime, the type of uncertainty in the relevant drivers of change and the nature of adaptation response options. We synthesize a number of issues previously raised in the literature to link the categories of interactions to a variety of risk-management strategies and tactics. Such application could help to break down some barriers to adaptation and both simplify and better target adaptation decision-making. The approach needs to be tested and adopted rapidly.}

_{^{· adaptation}}

_{^{· uncertainty}}

_{^{· decision-making}}

_{^{· risk management}}

_{^{· complexity}}

_{^{· climate change}}

_{^{· 2011 The Royal Society}}

_{^{This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.}}

_{^{Full Text}}

_{^{· Abstract 11 of 11Articles}}

_{^{The role of interactions in a world implementing adaptation and mitigation solutions to climate change}}

_{^{The papers in this volume discuss projections of climate change impacts upon humans and ecosystems under a global mean temperature rise of 4°C above preindustrial levels. Like most studies, they are mainly single-sector or single-region-based assessments. Even the multi-sector or multi-region approaches generally consider impacts in sectors and regions independently, ignoring interactions. Extreme weather and adaptation processes are often poorly represented and losses of ecosystem services induced by climate change or human adaptation are generally omitted. This paper addresses this gap by reviewing some potential interactions in a 4°C world, and also makes a comparison with a 2°C world. In a 4°C world, major shifts in agricultural land use and increased drought are projected, and an increased human population might increasingly be concentrated in areas remaining wet enough for economic prosperity. Ecosystem services that enable prosperity would be declining, with carbon cycle feedbacks and fire causing forest losses. There is an urgent need for integrated assessments considering the synergy of impacts and limits to adaptation in multiple sectors and regions in a 4°C world. By contrast, a 2°C world is projected to experience about one-half of the climate change impacts, with concomitantly smaller challenges for adaptation. Ecosystem services, including the carbon sink provided by the Earth’s forests, would be expected to be largely preserved, with much less potential for interaction processes to increase challenges to adaptation. However, demands for land and water for biofuel cropping could reduce the availability of these resources for agricultural and natural systems. Hence, a whole system approach to mitigation and adaptation, considering interactions, potential human and species migration, allocation of land and water resources and ecosystem services, will be important in either a 2°C or a 4°C world.}}

_{^{· climate change}}

_{^{· integrated assessment modelling}}

_{^{· adaptation}}

_{^{· extreme weather events}}

_{^{· ecosystem services}}

_{^{· biodiversity}}

_{^{· 2011 The Royal Society}}

_{^{This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.}}

_{^{http://rsta.royalsocietypublishing.org/gca?gca=roypta%3B369%2F1934%2F20&gca=roypta%3B369%2F1934%2F45&gca=roypta%3B369%2F1934%2F67&gca=roypta%3B369%2F1934%2F85&gca=roypta%3B369%2F1934%2F99&gca=roypta%3B369%2F1934%2F117&gca=roypta%3B369%2F1934%2F137&gca=roypta%3B369%2F1934%2F161&gca=roypta%3B369%2F1934%2F182&gca=roypta%3B369%2F1934%2F196&gca=roypta%3B369%2F1934%2F217&submit=Get+All+Checked+Abstracts}}

Wolfgang Ernst Pauli and His Phrase

“It Is Not Even Wrong”

Every college freshman who takes the basic chemistry course comes across the Pauli Exclusion Principle, enunciated by the Austrian physicist Wolfgang Pauli.

Wolfgang Ernst Pauli (April 25, 1900 – December 15, 1958) was an Austrian theoretical physicist and one of the pioneers of quantum physics. In 1945, after being nominated by Albert Einstein, he received the Nobel Prize in Physics for his “decisive contribution through his discovery of a new law of Nature, the exclusion principle or Pauli principle,” involving spin theory, underpinning the structure of matter and the whole of chemistry.

His most severe criticism, which he reserved for theories or theses so presented as to be untestable or unevaluatable and, thus, not properly belonging within the realm of science, even though posing as such.

They were worse than wrong because they could not be proven wrong.

Famously, he once said of such an unclear paper:

Das ist nicht nur nicht richtig, es ist nicht einmal falsch!

“Not only is it not right, it’s not even wrong!”

An argument that appears to be scientific is said to be not even wrong if it cannot be falsified (i.e., tested) by experiment or cannot be used to make predictions about the natural world. The phrase was coined by theoretical physicist Wolfgang Pauli, who was known for his colorful objections to incorrect or sloppy thinking.^[1]

Rudolf Peierls writes that “a friend showed [Pauli] the paper of a young physicist which he suspected was not of great value but on which he wanted Pauli’s views. Pauli remarked sadly, ‘It is not even wrong.’ “^[2]

Basis

Statements that are “not even wrong” may be well-formed, but lack reference to anything physical (as in “Souls are immortal”, because the noun “soul” is not well-defined in terms of experimental results), or may simply be gobbledygook which appears meaningless.

The phrase implies that even a wrong argument would have been better than the argument proposed, because an argument can only be found wrong after at least meeting the criteria for being considered academically (proper assumptions, falsifiable, makes predictions). Arguments that are not even wrong do not meet these criteria.

The phrase “not even wrong” is often used to describe pseudoscience or bad science and is considered derogatory.^[3]

Further meanings

“Not even wrong” has also come to mean science that is well-meaning and based on current scientific knowledge, but can neither be used for prediction nor falsified. Such conjectures are non-scientific, even when they are spoken in scientific language. The phrase has been applied to aspects of the super string theory of physics on the grounds that, although mathematically elegant, it provides (as of now) neither predictions nor tests.^[4]

Not Even Wrong is also the title of a book by Paul Collins in which he discusses the history of beliefs about autism and searches for an appropriate educational setting for his autistic son.

Notes

1. Shermer M (2006). “Wronger Than Wrong”. Scientific American. http://www.sciam.com/article.cfm?id=wronger-than-wrong.

2. Peierls, R. (1960). “Wolfgang Ernst Pauli, 1900-1958″. Biographical Memoirs of Fellows of the Royal Society 5: 186. doi:10.1098/rsbm.1960.0014.

3. Oliver Burkeman (September 19, 2005). “Not even wrong”. The Guardian. http://www.guardian.co.uk/g2/story/0,3604,1573072,00.html.

4. Woit, Peter, Not Even Wrong: The Failure of String Theory and the Search for Unity in Physical Law, Basic Books, 2007, ISBN 978-0465092765

Gerald Holton Discusses This Pauli Story:

Gerald Holton is Mallinckrodt Research Professor of Physics and Research Professor of the History of Science at Harvard University.

He discusses this Pauli story in his various recent Harvard books:

Among his recent Harvard University Press books are:

The Scientific Imagination

(Harvard University Press, 1998)

The Advancement of Science, and its Burdens

(Harvard University Press, 1998)

· The Scientific Imagination

(Harvard Univ. Press, 1998)

· Thematic Origins of Scientific Thought: Kepler to Einstein

(Harvard Univ. Press, 1973; rev. ed., 1988)

· Science and Anti-Science

(Harvard Univ. Press, 1993)

· Einstein, History, and Other Passions

(Harvard University Press, 2000)

This Pauli phrase again shows that science can never be understood within science but must be analyzed outside science which brings you into the realm of philosophy.Science is one step only.

Wolfgang Ernst Pauli:

Born 25 April 1900 (1900-04-25) Vienna, Austria-Hungary

Died 15 December 1958 (1958-12-15) (aged 58) Zürich, Switzerland

Citizenship Switzerland

Nationality Austria

Fields Physics

Institutions University of Göttingen
University of Copenhagen
University of Hamburg
ETH Zürich
Princeton University

Alma mater Ludwig-Maximilians University

Doctoral advisor Arnold Sommerfeld

Other academic advisors Max Born

Doctoral students Nicholas Kemmer Felix Villars

Other notable students Markus Fierz Sigurd Zienau

Known for Pauli exclusion principle
Pauli-Villars regularization
Pauli matrices
Pauli effect
Pauli equation
Pauli group
Coining ‘not even wrong’

Influences Ernst Mach Carl Jung

Influenced Ralph Kronig

Notable awards Lorentz Medal (1931)
Nobel Prize in Physics (1945)
Matteucci Medal (1956)
Max Planck Medal (1958)

Notes
His godfather was Ernst Mach.