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“It takes you 500,000 microseconds just to click a mouse. But if you’re a Wall Street algorithm and you’re five microseconds behind, you’re a loser.” ~ Kevin Slavin.
TED video lecture – Kevin Slavin (link) argues that we’re living in a world designed for – and increasingly controlled by – algorithms. In this riveting talk from TEDGlobal, he shows how these complex computer programs determine: espionage tactics, stock prices, movie scripts, and architecture. And he warns that we are writing code we can’t understand, with implications we can’t control. Kevin Slavin navigates in the “algoworld“, the expanding space in our lives that’s determined and run by algorithms (link at TED).
Fig. – A Symbolical Head (phrenological chart) illustrating the natural language of the faculties. At the Society pages / Economic Sociology web page.
You have much probably noticed by now how Scoop.it is emerging as a powerful platform for those collecting interesting research papers. There are several good examples, but let me stress one entitled “Bounded Rationality and Beyond” (scoop.it web page) curated by Alessandro Cerboni (blog). On a difficult research theme, Alessandro is doing a great job collecting nice essays and wonderful articles, whenever he founds them. One of those articles I really appreciated was John Conlisk‘s “Why Bounded Rationality?“, delivering into the field several important clues, for those who (like me) work in the area. What follows, is an excerpt from the article as well as part of his introductory section. The full (PDF) paper could be retrieved here:
In this survey, four reasons are given for incorporating bounded rationality in economic models. First, there is abundant empirical evidence that it is important. Second, models of bounded rationality have proved themselves in a wide range of impressive work. Third, the standard justifications for assuming unbounded rationality are unconvincing; their logic cuts both ways. Fourth, deliberation about an economic decision is a costly activity, and good economics requires that we entertain all costs. These four reasons, or categories of reasons, are developed in the following four sections. Deliberation cost will be a recurring theme.
Why bounded rationality? In four words (one for each section above): evidence, success, methodology, and scarcity. In more words: Psychology and economics provide wide-ranging evidence that bounded rationality is important (Section I). Economists who include bounds on rationality in their models have excellent success in describing economic behavior beyond the coverage of standard theory (Section II). The traditional appeals to economic methodology cut both ways; the conditions of a particular context may favor either bounded or unbounded rationality (Section III). Models of bounded rationality adhere to a fundamental tenet of economics, respect for scarcity. Human cognition, as a scarce resource, should be treated as such (Section IV). The survey stresses throughout that an appropriate rationality assumption is not something to decide once for all contexts. In principle, we might suppose there is an encompassing single theory which takes various forms of bounded and unbounded rationality as special. cases. As with other model ingredients, however, we in practice want to work directly with the most convenient special case which does justice to the context. The evidence and models surveyed suggest that a sensible rationality assumption will vary by context, depending on such conditions as deliberation cost, complexity, incentives, experience, and market discipline. Beyond the four reasons given, there is one more reason for studying bounded rationality. It is simply a fascinating thing to do. We can mix some Puck with our Hamlet.
Book cover from Economics Nobel prize (2008), Paul Krugman (link): Paul R. Krugman, “The Self-Organizing Economy“, Cambridge, Massachusetts, and Oxford: Blackwell Publishers, 1996. What follows is the entire excerpt review on his book, by Cosma Shalizi, a friend and colleague, done in March 1996 with minor corrections in 1997 and 2006:
[…] Paul Krugman cannot be accused of lacking good opening lines:
Is an economic slump like a hurricane, or is it more like an earthquake? Is a growing city like an embryo, or is it more like a meteorite?
Obviously some explaining needs to be done here.
The term “self-organization” seems to have entered the language in a 1947 paper in the Journal of General Psychology on cybernetic mechanisms of adaptation, though related ideas are considerably older. It has, of course, become a buzzword, indeed an indispensable element in modern techno-cant – I was once on a mailing list where some people proposed to write a “self-organizing” novel – but a lot of real science carries on under the label, in some remarkably different fields:
[W]hat links the study of embryos and hurricanes, of magnetic materials and collections of neurons, is that they are all self-organizing systems: systems that, even when they start from an almost homogeneous or almost random state, spontaneously form large-scale patterns. One day the air over a particular patch of tropical ocean is no different in behavior from the air over any other patch; maybe the pressure is a bit lower, but the difference is nothing dramatic. Over the course of the next few days, however, that slight dip in pressure becomes magnified through a process of self-reinforcement: rising air pulls water up to an altitude at which it condenses, releasing heat that reduces the pressure further and makes more air rise, until that particular piece of the atmosphere has become a huge, spinning vortex. Early in the process of growth an embryo is a collection of nearly identical cells, but (or at least so many biologists believe) these cells communicate with each other through subtle chemical signals that reinforce and inhibit each other, leading to the “decision” of some cells to become parts of a wing, others parts of a leg. [pp. 3–4]
This is vague, for which I am grateful, since sharpening the notion of self-organization into something quantifiable is the subject of my thesis. But let us draw a veil over this and press on.
There’s a sense in which economists have been studying little but the “spontaneous formation of large-scale patterns” ever since Adam Smith and the creation of self-regulating markets, but Krugman is not interested in convincing his profession that it has been speaking prose all its life; it would, in his words, “be entitled to change the channel.” Rather he wants to look at the way economies organize themselves in space and over time, which they do in a most conspicuous way. (There is, however, a brief discussion of technological lock-in in chapter six.) Krugman picks out three examples in particular: the way cities differentiate themselves into specialized districts; the power-law distribution of city sizes; and business cycles.
That cities – even Los Angeles – are not homogenous lumps, with everything mixed together, seems to have been true from earliest times. Some of this is simply because different locations have different advantages – dockyards go by the water, fortresses on heights, and brothels and bars near fortresses. Some of it is due to zoning boards, planning commissions, real-estate magnates and red-lining, to say nothing of the police and cross-burning – whether such forces contribute to urban self-organization is a delicate question, and in any case Krugman is silent about them. Still, this leaves a lot of differentiation which seems to be due to nothing but self-reinforcement, which (unlike zoning boards and red-lining) has actually been studied in economic theory, especially by economic geographers, for some time. (Krugman is at pains to point out that self-organization was not unknown to economists before the arrival of missionaries from Santa Fe.) He begins his discussion of this with so-called “edge cities,” and then passes on to a marvelously elegant illustration from Thomas Schelling’s marvelously elegant book, Micromotives and Macrobehavior, which suggests how the former (requiring that at least 37% of your neighbors be like yourself) can lead to the latter (massive segregation). He then dives into a discussion of “urban morphogenesis” directly inspired by Turing’s classic work on embryological morphogenesis. He assumes his city is one-dimensional and either infinite or circular, but claims no more value for it than that of qualitative illustration; I can think of physicists who should be so modest. Self-organization happens here because the initial random noise contains components which correspond to many different patterns in nucleo, and the dynamics are such that some of these get magnified faster than others. The pattern which grows fastest ultimately dominates the system, which is called “pattern selection” in traditional reaction-diffusion systems. [Note containing the words “Fourier decomposition.”]
A power law distribution means that the number of objects whose size is at least S is proportional to S^a, for some (hopefully negative) constant a. It happens that the size of cities obeys a power law known as Zipf’s law, which is usually stated thus: the size of a city is inversely proportional to its rank order so that, for example, the 100th largest city is a tenth the size of the 10th largest city. This rule is almost exactly true of the sizes of American cities (it corresponds to a value of a of -2), and has been for at least a century. Physicists have liked power laws ever since Galileo, and recently we’ve developed a taste for power law distributions; Prof. Per Bak and his disciples sometimes seem to want to claim everything obeying a power law distribution for “self-organized criticality.” Krugman explains the power law distribution as an outcome of growth processes where the expected rate of growth is independent of the size already attained. Such processes do, in fact, generate power laws, and have been known to for some time – Herbert Simon proposed one, “in a completely impenetrable exposition,” in 1955. Alas, none of them are as regular as the empirical data!
At this point Krugman tells us that what we have just seen are order from instability (“When a system is so constituted that a flat or disordered structure is unstable, order spontaneously emerges.” – p. 99) and order from random growth (“[O]bjects are formed by a growth process in which the expected rate of growth is approximately independent of scale, but the actual rate of growth is random.” – p. 100). He declines, thankfully, to call these universal laws of self-organization, but he does suggest they are “principles,” or rather common ways of self-organizing.
Temporal self-organization in the economy is more familiarly known as business cycles, or yet more familiarly as booms and crashes (err, depressions; err, recessions; err, slumps; err, slow-downs). Since Keynes (at least) the idea that these are in some way self-reinforcing has been common, and Krugman resurrects a body of Keynesian theory from the ’50s, “non-linear business cycle theory.” This is of the order-from-instability type, and so predicts a characteristic size to business cycles, which, on a comparison of 1933 with the recent unpleasantness, or even the early 1980s, is less than plausible. Krugman also presents an order-from-random-growth theory – really percolation theory in wolf’s clothing – which avoids that problem at the cost of “[making] less contact with what seems to happen during a boom or slump,” and predicting a power law distribution for business fluctuations, which is not observed. Charitably, Krugman chooses to “regard them both more as illustrations as how one might approach self-organization in time than as finished statements of how one actually ought to do it.” Some such theory will be necessary if we are not to continue treating shifts in aggregate demand as external shocks administered, perhaps, by the vengeful specter of Karl Marx.
If this were a formal and comprehensive treatise, I would be disappointed by the passing over of evolutionary and institutional economics; the cavalier starting of models from tabulæ rasæ rather than already-differentiated settings; the fact that most of the models are what in physics would be called “phenomenological,” i.e. they don’t try to connect to the underlying mechanisms, such as markets; the absence of even qualitative comparisons with empirical data; and the indifference to exogenous forces, natural , social or political, which in practice are very important in all of the topics Krugman discusses. But this is not a formal and comprehensive work; that will have to wait for a good many years. It is a self-described “discourse” of exactly a hundred pages, a very brief introduction and an appeal for further development, for the work which will make the treatise possible. As befits such a book, Krugman’s writing is clear, informal and concise (but then, it usually is). His attitude towards the mathematics is that it should be used and not seen; accordingly we get the hypotheses once as stories (“I do not want to dignify them by calling them models”); a second time with a bit of calculus; and a third time in an appendix for those who want the nitty-gritty. (Even there he draws the line at Fourier decomposition and linear stability analysis; phase space and attractors are explained verbally, and well, in the body of the text.) The economics should be accessible to anyone who’s taken Econ. 1, and probably many (such as your humble narrator) who haven’t. The book will be of particular interest to those crossing the bridge from economics to self-organization and dynamics in either direction, but most educated readers should find it informative and engaging. […] Cosma S. Shalizi, 1996.
Video – A 1964 film based on the novel Zorba the Greek by Nikos Kazantzakis. The film was directed by Michael Cacoyannis and the title character was played by Anthony Quinn. The supporting cast included Alan Bates as a visiting Englishman as well as Irene Papas. The theme, “Sirtaki” by Mikis Theodorakis, has become famous and popular as a song and as a dance. The movie was shot on location on the Greek island of Crete. Specific places featured include the town of Chania, the Apokoronas region and the Akrotiri peninsula. The famous scene, in which Quinn’s character dances the Sirtaki, was shot on the beach of the village of Stavros. (from YouTube)
Basil (Alan Bates), a young English writer, meets a free-spirited Greek peasant named Zorba (Anthony Quinn) while waiting to travel to the island of Crete. While Zorba pursues a relationship with aging French courtesan Madame Hortense, Basil attempts to court a young widow. Along the way, he learns valuable life lessons from the earthy Zorba, who has an unquenchable joie de vivre (link):
[…] Basil: I don’t want any trouble. Alexis Zorba: Life is trouble. Only death is not. To be alive is to undo your belt and look for trouble. […] Zorba: Damn it boss, I like you too much not to say it. You’ve got everthing except one thing: madness! A man needs a little madness, or else… Basil: Or else? Zorba: …he never dares cut the rope and be free. […] Basil: Teach me to dance, will you? Zorba: Dance? Did you say… dance?! … Come on my boy… together… Let’s go… hop … Again… hop … […] Zorba: Boss, I have so much to tell you, … I never had loved a man like you … […] Zorba: Hey boss, did you ever see a more splendiferous crash?! … Oh, … You can laugh too!… hmmm… Hey!… You laugh! […]
Video lecture by Ken Long (at the Statistics Problem Solvers blog) on Nassim Taleb‘s 4th Quadrant problems [1,2], i.e. a region where statistics not only don’t work but in which statistics are downright dangerous, because they lead you to make predictions as well as control systems that are unprepared for the kinds of systems shocks awaiting you.
“Statistical and applied probabilistic knowledge is the core of knowledge; statistics is what tells you if something is true, false, or merely anecdotal; it is the “logic of science”; it is the instrument of risk-taking; it is the applied tools of epistemology; you can’t be a modern intellectual and not think probabilistically—but… let’s not be suckers. The problem is much more complicated than it seems to the casual, mechanistic user who picked it up in graduate school. Statistics can fool you. In fact it is fooling your government right now. It can even bankrupt the system (let’s face it: use of probabilistic methods for the estimation of risks did just blow up the banking system).”, Nassim Taleb, in .
 Nassim Nicholas Taleb, “The Fourth Quadrant: A Map of the Limits of Statistics“, An Edge Original Essay, Set., 2008. (link)
 Nassim Nicholas Taleb,”Convexity, Robustness, and Model Error inside the Fourth Quadrant“, Oxford Lecture (Draft version), Oxford, July 2010. [PDF paper]
Figure – High-Frequency Financial trading world-wide map showing optimal hotspots, from (fig.2, pp.5) in A.D. Wissner-Gross and C.E. Freer,”Relativistic Statistical Arbitrage“, Physical Review E 82, 056104, 2010. (ABS.:) Recent advances in high-frequency financial trading have made light propagation delays between geographically separated exchanges relevant. Here we show that there exist optimal locations from which to coordinate the statistical arbitrage of pairs of spacelike separated securities, and calculate a representative map of such locations on Earth. Furthermore, trading local securities along chains of such intermediate locations results in a novel econophysical effect, in which the relativistic propagation of tradable information is effectively slowed or stopped by arbitrage.
All those tiny blue circles above, come together into a real financial treasure map! In case you wonder, below is part of my own financial treasure DNA map (of course, blurred and noised on purpose. Just don’t ask me what type of noise this is. Hint: it’s not salt & pepper!). Meanwhile, check this out… and guess what? We have got company… :)
[…] Golden Networking’s High-Frequency Trading Happy Hour, December 7th, 2010, will bring the high-frequency trading community together to listen to Adam Afshar, President and CEO, Hyde Park Global Investments, Milind Sharma, CEO, QuantZ Capital Management, and Peter van Kleef, CEO, Lakeview Arbitrage, on “How to Get High-Frequency Trading Right First Time” […] Mr. Afshar is Hyde Park Global’s President and Chief Executive Officer. He has over two decades of financial industry experience including 12 years at Bear Stearns where he was a Managing Director, overseeing long/short multi asset portfolios for both onshore and offshore clients. Hyde Park Global Investments is a 100% robotic investment and trading firm based on Artificial Intelligence (AI). The system is built primarily on Genetic Algorithms (GA) and other Evolutionary models to identify mispricings, arbitrage and patterns in electronic financial markets. Additionally, Hyde Park Global Investments has developed programs applying natural language processing and sentiment analytics to trade equities based on machine readable news. Hyde Park Global employs no analysts, portfolio managers or traders, ONLY scientists and engineers. Mr. Afshar has a BA in Economics from Wofford College and received his MBA from the University of Chicago, Booth School of Business. […] Mr. Sharma is Chief Executive Officer, QuantZ Capital Management. He ran the LTMN desk in Global Arbitrage & Trading at RBC where he served as Portfolio Manager for Quant EMN, Short Term & Event Driven portfolios [up to $700mm gross]. In his capacity as Director & Senior Proprietary Trader at Deutsche, he managed Quant EMN portfolios of significant size & contributed to the broader prop mandate in Cap Structure Arb & with LBOs. Prior to that he was co-founder of Quant Strategies (previously R&P) at BlackRock (MLIM), where his investment role spanned a dozen quantitatively managed funds & separate accounts with approx $30B in AUM pegged to the models. Prior to MLIM, he was Manager of the Risk Analytics and Research Group at Ernst & Young LLP where he was co-architect of Raven (one of the earliest derivatives pricing/ validation engines) & co-created the 1st model for pricing cross-currency puttable Bermudan swaptions. […] in How to Get High-Frequency Trading Right First Time, NY, Dec.2, 2010 + www.hfthappyhour.com .
On Bilateral Monopolies: […] Mary has the world’s only apple, worth fifty cents to her. John is the world’s only customer for the apple, worth a dollar to him. Mary has a monopoly on selling apples, John has a monopoly (technically, a monopsony, a buying monopoly) on buying apples. Economists describe such a situation as bilateral monopoly. What happens? Mary announces that her price is ninety cents, and if John will not pay it, she will eat the apple herself. If John believes her, he pays. Ninety cents for an apple he values at a dollar is not much of a deal but better than no apple. If, however, John announces that his maximum price is sixty cents and Mary believes him, the same logic holds. Mary accepts his price, and he gets most of the benefit from the trade. This is not a fixed-sum game. If John buys the apple from Mary, the sum of their gains is fifty cents, with the division determined by the price. If they fail to reach an agreement, the summed gain is zero. Each is using the threat of the zero outcome to try to force a fifty cent outcome as favorable to himself as possible. How successful each is depends in part on how convincingly he can commit himself, how well he can persuade the other that if he doesn’t get his way the deal will fall through. Every parent is familiar with a different example of the same game. A small child wants to get her way and will throw a tantrum if she doesn’t. The tantrum itself does her no good, since if she throws it you will refuse to do what she wants and send her to bed without dessert. But since the tantrum imposes substantial costs on you as well as on her, especially if it happens in the middle of your dinner party, it may be a sufficiently effective threat to get her at least part of what she wants. Prospective parents resolve never to give in to such threats and think they will succeed. They are wrong. You may have thought out the logic of bilateral monopoly better than your child, but she has hundreds of millions of years of evolution on her side, during which offspring who succeeded in making parents do what they want, and thus getting a larger share of parental resources devoted to them, were more likely to survive to pass on their genes to the next generation of offspring. Her commitment strategy is hardwired into her; if you call her bluff, you will frequently find that it is not a bluff. If you win more than half the games and only rarely end up with a bargaining breakdown and a tantrum, consider yourself lucky.
Herman Kahn, a writer who specialized in thinking and writing about unfashionable topics such as thermonuclear war, came up with yet another variant of the game: the Doomsday Machine. The idea was for the United States to bury lots of very dirty thermonuclear weapons under the Rocky Mountains, enough so that if they went off, their fallout would kill everyone on earth. The bombs would be attached to a fancy Geiger counter rigged to set them off if it sensed the fallout from a Russian nuclear attack. Once the Russians know we have a Doomsday Machine we are safe from attack and can safely scrap the rest of our nuclear arsenal. The idea provided the central plot device for the movie Doctor Strangelove. The Russians build a Doomsday Machine but imprudently postpone the announcement they are waiting for the premier’s birthday until just after an American Air Force officer has launched a unilateral nuclear attack on his own initiative. The mad scientist villain was presumably intended as a parody of Kahn. Kahn described a Doomsday Machine not because he thought we should build one but because he thought we already had. So had the Russians. Our nuclear arsenal and theirs were Doomsday Machines with human triggers. Once the Russians have attacked, retaliating does us no good just as, once you have finally told your daughter that she is going to bed, throwing a tantrum does her no good. But our military, knowing that the enemy has just killed most of their friends and relations, will retaliate anyway, and the knowledge that they will retaliate is a good reason for the Russians not to attack, just as the knowledge that your daughter will throw a tantrum is a good reason to let her stay up until the party is over. Fortunately, the real-world Doomsday Machines worked, with the result that neither was ever used.
For a final example, consider someone who is big, strong, and likes to get his own way. He adopts a policy of beating up anyone who does things he doesn’t like, such as paying attention to a girl he is dating or expressing insufficient deference to his views on baseball. He commits himself to that policy by persuading himself that only sissies let themselves get pushed around and that not doing what he wants counts as pushing him around. Beating someone up is costly; he might get hurt and he might end up in jail. But as long as everyone knows he is committed to that strategy, other people don’t cross him and he doesn’t have to beat them up. Think of the bully as a Doomsday Machine on an individual level. His strategy works as long as only one person is playing it. One day he sits down at a bar and starts discussing baseball with a stranger also big, strong, and committed to the same strategy. The stranger fails to show adequate deference to his opinions. When it is over, one of the two is lying dead on the floor, and the other is standing there with a broken beer bottle in his hand and a dazed expression on his face, wondering what happens next. The Doomsday Machine just went off. With only one bully the strategy is profitable: Other people do what you want and you never have to carry through on your commitment. With lots of bullies it is unprofitable: You frequently get into fights and soon end up either dead or in jail. As long as the number of bullies is low enough so that the gain of usually getting what you want is larger than the cost of occasionally having to pay for it, the strategy is profitable and the number of people adopting it increases. Equilibrium is reached when gain and loss just balance, making each of the alternative strategies, bully or pushover, equally attractive. The analysis becomes more complicated if we add additional strategies, but the logic of the situation remains the same.
This particular example of bilateral monopoly is relevant to one of the central disputes over criminal law in general and the death penalty in particular: Do penalties deter? One reason to think they might not is that the sort of crime I have just described, a barroom brawl ending in a killing more generally, a crime of passion seems to be an irrational act, one the perpetrator regrets as soon as it happens. How then can it be deterred by punishment? The economist’s answer is that the brawl was not chosen rationally but the strategy that led to it was. The higher the penalty for such acts, the less profitable the bully strategy. The result will be fewer bullies, fewer barroom brawls, and fewer “irrational” killings. How much deterrence that implies is an empirical question, but thinking through the logic of bilateral monopoly shows us why crimes of passion are not necessarily undeterrable. […]
Note – Further reading should include David D. Friedman’s “Price Theory and Hidden Order“. Also, a more extensive treatment could be found on “Game Theory and the Law“, by Douglas G. Baird, Robert H. Gertner and Randal C. Picker, Cambridge, Mass: Harvard University Press, 1994.
[via Foreign Policy / Contrafactos Twitter] (…) Want to get a sense of just how bad things are? Take a spin on Google Earth. The above image, pulled yesterday from Vesseltracker.com’s Google Earth file, shows container ships languishing off the Singapore coast. Welcome to the largest parking lot on Earth. International Economy explains (…):
“The world’s busiest port for container traffic, Singapore saw its year-over-year volume drop by 19.6 percent in January 2009, followed by a 19.8 percent drop in February. As of mid-March 2009, 11.3 percent of the world’s shipping capacity, sat idle, a record.”
Extremely bad news. Meanwhile, someone have made a huge mistake…
Two “The Economist” covers. The first one was manually created and posted by Richard Dawkins himself, … – yes – the evolutionary Biologist. The second one is real as well recent (as of April 4th) – UNDER ATTACK, a 14-page special report on the rise and fall of the wealthy. Do note the Blackberry on top of the dead guy in the front and the skyline of London’s Canary Warf financial district in the background (via HS Dent blog). The Laissez-faire Economy lead to all this (more).
After reading at Yahoo! Finance (Oct. 24), “The Twilight of Free-Market Ideology” by Charles Wheelan (lecturer at Univ. of Chicago and author of Naked Economics) I decided to create a poll. Here are some passages from his article (as well as my very similar opinion over here):
[…] When I heard Alan Greenspan’s testimony before Congress last Thursday, I had one immediate thought: This is the beginning of the end for the free-market ideologues (…) According to press reports of the testimony, Greenspan told Congress that he “had put too much faith in the self-correcting power of free markets.” That’s no small statement. In fact, it struck me that if 1989 was the year when no reasonable person could still believe in communism (or any of its government-intensive relatives), then 2008 will go down in history as the year in which the free-market zealots saw their “wall” come crumbling down. […]
So, what’s your opinion?
October, 29. Just 3 days ahead of us, almost 80 years ago. Up till now, October 10, 2008 – Black Friday – was the worse. On that day the biggest DJIA point drop in history was found. A collection of these photos (below) are available at “A Photo Essay on the Great Depression“. Plus, to compare what happened in 1929 versus today I vividly recommend you this New York Times Journal graphic comparing severity versus time for several historical crashes.
Dorothea Lange‘s “Migrant Mother,” destitute in a pea picker’s camp, because of the failure of the early pea crop. These people had just sold their tent in order to buy food. Most of the 2,500 people in this camp were destitute. By the end of the decade there were still 4 million migrants on the road. (Source)
The trading floor of the New York Stock Exchange just after the crash of 1929. On Black Tuesday, October 29, the market collapsed. In a single day, sixteen million shares were traded -a record- and thirty billion dollars vanished into thin air. Westinghouse lost two thirds of its September value. DuPont dropped seventy points. The “Era of Get Rich Quick” was over. Jack Dempsey, America’s first millionaire athlete, lost $3 million. Cynical New York hotel clerks asked incoming guests, “You want a room for sleeping or jumping?” (Source). Finally (photo below): Bud Fields and his family. Alabama. 1935 or 1936. Photographer: Walker Evans. (Source)
August 1, 2007
August 10, 2007 (the beginning)
December 20, 2007
January 15, 2008
May 15, 2008
July 10, 2008
September 15, 2008 (post-Lehman)
October 10, 2008 (biggest DJIA point drop in history)
With a short empirical investigation, Reginald Smith (MIT – Sloan School of Management) have come to some interesting complex networks (nodes in here are financial stocks) over time, since the beginning of the financial crisis in August 10, 2007, till today. His rather simple econophysics study (draft PDF link) somehow demonstrates that the losses in certain markets, in this case the US equity markets, follow a cascade or “epidemic” flow like model along the correlations of various stocks. His networks shows the correlation (similar rise and fall movements) among the stocks in the S&P 500 and NASDAQ-100 using the latest stocks in the index (as of 10/10/2008). The abbreviations are the ticker symbols. Network edges here connect stocks (nodes) based on their correlations. More then 500 tickers were used. After correlations among any two stocks were calculated (J.C. Gower, Biometrika, 1966), a distance metric is computed. Finally these distances are used to create a minimal spanning tree. For the graphics and animations Reginald have used the python-graph module, pydot and Graphviz. Extra details and a F.A.Q. is here as well as some movies. If the stock share price return had a return (minus dividends) greater than or equal to -10% the nodes are green, less than -10% but greater than -25% yellow, and less than or equal to -25% red.
In what relates red nodes over time, I now wonder what would be the probability distribution of vertex connectivity change (is it scale-free?!), the characteristic path length L as well as the clustering coefficient C. It would be quite funny to know.
It’s not everyday we see a 40 year ideology collapsing through a dramatic act of contrition. It has happened just a few hours ago (check video above), yesterday in the “financial crisis” congressional hearing in Washington (23. Oct. 2008). Moreover, what seems remarkable, is that the recognition comes from one of his most universally respected founding fathers and defenders.
Alan Greenspan was the longest serving chairman in the Federal Reserve board history (1987-2006), and during this 18-year period of time he were perhaps the leading proponent of de-regulation along with libertarian capitalism, vividly expressed on his “The Age of Turbulence – Adventures in a New World” 2007 book, advocating above all issues, Adam Smith’s “Invisible Hand” that markets can regulate themselves. As it’s known, for his whole adult life, the former Fed chairman has been a devotee of the philosophy of Ayn Rand, who celebrated free-market capitalism as the world’s most moral economic order and advocated a strict laissez-faire approach to government regulation of the marketplace. Ironically, he was a regulator that did not believed in any regulation at all.
It is now quite a remarkable historic moment seeing former Federal Reserve chairman, a lifelong champion of free markets, publicly questioning the philosophy that guided him throughout his years as the world’s most powerful economic policymaker. A philosophy followed and strongly defended by him (along with many others like Margaret Thatcher and Ronald Reagan), at least in the last 40 years, as he himself acknowledged yesterday. Asked by the congressional committee chairman, whether his free-market convictions pushed him to make wrong decisions, especially his failure to rein in unsafe mortgage lending practices, Greenspan replied that indeed he had found a flaw in his ideology, one that left him very distressed. “In other words, you found that your view of the world, your ideology was not right?” he was asked:
“Absolutely, precisely“, replied Greenspan. “That’s precisely the reason I was shocked, because I have been going for 40 years or more with very considerable evidence it was working exceptionally well“. Albeit he was surely one of the most influential voices for de-regulation: “There is nothing in Federal regulation that makes it superior to Market regulation”, said Greenspan back in 1994, in one among many of his past radical free-market statements.
I presume we now all wonder, where was Greenspan, when back in 2003 one of the most prestigiously recognized and legendary financial investors such as Warren Buffet, called credit default obligations and derivatives “weapons of financial mass destruction“? Or where was he when Princeton Professor of Economics, Paul Krugman – the recent Nobel laureate – said back in 2006 that “If anyone is to blame on the current situation (sub prime) is Mr Greenspan who poopooed warnings about an emerging bubble and did nothing to crack down on irresponsible lending“. Or what did he, Greenspan itself, said just a few days after ENRON collapsed?
People working in complex systems – and surely financial markets are one of them (yes, for the past 4-5 years including these present turbulent times I am working hard in this area as well) – for long know that any systemic structure could collapse if only positive-feedbacks are injected into them, creating an auto-catalytic snow-ball effect, leading among other things to a power-law like Black Swan. Indeed power-laws are a striking powerful signature. This is specially true when we address self-organization (read it in the present context as self-regulation). In order to be a truly self-regulated system, financial markets should also be embedded with negative-feedbacks as well, as I have addressed in a post about finance and complex systems one month ago. In fact, in order to emerge as a truly self-organized system, self-interest, should constitute just one among many of the ingredients over the entire financial system, and not the isolated unique ingredient. Self-interest promotes amplification and positive feedback, which is – as I recognize – necessary. However, left alone, promotes instead dramatic snowballing drifts over chaotic regimes, due to it’s intrinsic amplification. What’s amazing (at least for me), is that Alan Greenspan just recognized that in a tiny few seconds along his current discourse (check video above), pointing it to the precise key-word:
[…] I made a mistake in presuming that the self-interest of organizations, specifically banks and others, was such that they were best capable of protecting their own shareholders. […] So the problem, here is something which looked to be as a very solid edifice, and indeed a critical pillar to market competition and free-markets, did breakdown and I think that, as I said, shock me. I still do not fully understand why it happened, and obviously to the extend, that I figure out where it happened and why, … aaaaaa, … I will change my views. If the facts change I will change. […]
As a result, “the whole intellectual edifice” of risk management collapsed, Greenspan said. In what regards his unexpected words yesterday at the congressional hearing, at least, I frankly praise him for his huge intellectual courage and present honesty. In the end, it seems that during the past 18 years, former FED chair was nothing else then a simple-man driven by his own blind faith on markets, from which he apparently comes out now. Unfortunately, only now at a very high price. Meanwhile as we know, severe consequences are here to stay, as was already evident when Greenspan addressed the House Financial Services Committee on 2003 (video below). Let’s hope that all these will not be forgotten in 3 decades from now (though, I doubt it – after all, nothing really serious came out from the entitled 3-man dream-team Bush-Sarkozy-Barroso “new global finance order” summit at Camp David last weekend, as expected):
“You have told the American people that you support a trade policy which is selling them out.” – Rep. Bernard Sanders to Federal Reserve Chairman Alan Greenspan on 7/16/03. Rep. Bernard Sanders (Independent-Vermont), now a US Senator, dresses down Federal Reserve Chairman Alan Greenspan in front of the House Financial Services Committee on 7/16/03.
Now, above in the video, do you remember the words: Structured Investment Vehicle (S.I.V. or Conduits)? No? Okay, let’s now pass to a very brief and relatively more technical approach to it (as you will see it, reality transcends fiction) – CNBC via Youtube:
Care for more?
[…] From the ice-age to the dole-age
There is but one concern
I have just discovered: Some girls are bigger than others
Some girls are bigger than others
Some girls mothers are bigger than
Other girls mothers […]
The Smiths – “Some Girls Are Bigger Than Others“, (Queen is dead) 1986. Song written by Morrissey and Johnny Marr.
________________ § ________________
Key: “S:” = Show Synset (semantic) relations, “W:” = Show Word (lexical) relations / S: (n) dole (a share of money or food or clothing that has been charitably given) / S: (n) dole, pogy, pogey (money received from the state)
________________ § ________________
(via William Gibson‘s blog)
SUBJECT: REQUEST FOR URGENT BUSINESS RELATIONSHIP
posted 12:29 PM
I NEED TO ASK YOU TO SUPPORT AN URGENT SECRET BUSINESS RELATIONSHIP WITH A TRANSFER OF FUNDS OF GREAT MAGNITUDE.
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I AM WORKING WITH MR. PHIL GRAM, LOBBYIST FOR UBS, WHO WILL BE MY REPLACEMENT AS MINISTRY OF THE TREASURY IN JANUARY. AS A SENATOR, YOU MAY KNOW HIM AS THE LEADER OF THE AMERICAN BANKING DEREGULATION MOVEMENT IN THE 1990S. THIS TRANSACTIN IS 100% SAFE.
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YOURS FAITHFULLY MINISTER OF TREASURY PAULSON