From: Sender: (Yaneer Bar-Yam) To: complex-science Date: Wed, 06 Aug 2008 00:02:06 -0400 Message-ID: X-Original-Return-Path: Received: from imo-d20.mx.aol.com ([205.188.139.136] verified) by necsi.org (CommuniGate Pro SMTP 4.0.6) with ESMTP id 22237025 for complex-science@necsi.org; Thu, 17 Jul 2008 13:21:45 -0400 Received: from MMBTUPR@aol.com by imo-d20.mx.aol.com (mail_out_v38_r9.4.) id m.c3f.29b7740b (42807) for ; Thu, 17 Jul 2008 13:21:39 -0400 (EDT) X-Original-Message-ID: X-Original-Date: Thu, 17 Jul 2008 13:21:39 EDT Subject: =?ISO-8859-1?Q?Re:=20[POSSIBLE=20SPAM]=A0=20The=20'Brookhavenato?= =?ISO-8859-1?Q?r':=20Self-organizing=20systmes=A0=20with=20critical=20pr?= =?ISO-8859-1?Q?operties?= X-Original-To: complex-science@necsi.org MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="part1_c3f.29b7740b.35b0d9a3_boundary" X-Mailer: Thunderbird - Mac OS X sub 308 X-Spam-Flag:NO --part1_c3f.29b7740b.35b0d9a3_boundary Content-Type: text/plain; charset="US-ASCII" Content-Transfer-Encoding: 7bit from Lewis L. Smith The sender is a semiretired energy economist who came into economics from finance and into complexity from economics and the securities markets. Since 1994, he has been a pro bonum researcher in the application of complexity to economics, with six papers and one collaboration to his credit. Long before there was a controversy over power laws, there was one over the Gaussian or "normal" Distribution [ the famous "bell shaped curve" ] which was derived from the heights of French soldiers in 18th century. For roughly two hundred years, many analysts thought that it was the universal descriptor of random events and of the random aspects of nonrandom events, such as are the heights of French soldiers, exclusive of their end points. Indeed for some four decades in securities markets, it was devoutly believed that each major market was driven by its own random process and that its outputs, at least in terms of period-to-period log returns, could be described by the normal curve. [ If you didn't believe this, you were in danger of be "excommunicated" from the academic division of the finance community or at the very least, of being denied tenure. ] In the last decade a vast literature has arisen disputing this "article of faith" without settling on an alternative for securities markets, or even settling the question of whether one should use one, two or three distributions to characterize a given market. [ Say separate distributions for each tail and one other for the bulk of the events ! See especially the writings of Didier Sornette and Benoit Mandelbrot, among others. Also my paper for the June 2008 Oxford Conference on Business and Economics, "Do random series exist ?". ] My own awakening came when I went to work for an electric utility and discovered that the expected lives of wooden poles are described by the Poisson Distribution, and reinforced later on, when I headed a study of sites for wind farms in which wind speed at a given height and location was described by the Wiebull Distribution. And now, in the course or revisiting econometrics for assorted reasons, I realize that as a result of the current proliferation of estimators, econometricians are stumbling upon all kinds of weird distributions, some of which don't even have "official" names as yet, situations in which are old friends, the chi-square and the t distributions are not much help. In particular, people who analyze stock markets and bioelectric series often have problems in classifying a given run of data as conforming to a log-normal distribution or a power law one. So the fact that a distribution has turned up which seems to be the product of the two, doesn't surprise me in the least. So those who would like to find power laws in biology should be forewarned > [1] It aint going to be easy ! [2] The presence of power laws may NOT be in one-to-one correspondence with some phenomenon of interest, and such presence may not be restricted to a coherent group of phenomena, so the presence or absence of a power law distribution in the data cannot always be taken as "the signature" of a particular phenomenon, of a class of phenomena or of a particular type of dynamic behavior. Cordially. ### ************** Get the scoop on last night's hottest shows and the live music scene in your area - Check out TourTracker.com! (http://www.tourtracker.com?NCID=aolmus00050000000112) --part1_c3f.29b7740b.35b0d9a3_boundary Content-Type: text/html; charset="US-ASCII" Content-Transfer-Encoding: quoted-printable        &n= bsp;       from  Lewis L. Smith

The sender is a semiretired energy economist who came into economics from fi= nance and into complexity from economics and the securities markets. Since 1= 994, he has been a pro bonum researcher in the application o= f complexity to economics, with six papers and one collaboration to his cred= it.

Long before there was a controversy over power laws, there was one over the=20= Gaussian or "normal" Distribution [ the famous "bell shaped curve" ] which w= as derived from the heights of French soldiers in 18th century. For roughly=20= two hundred years, many analysts thought that it was the universal descripto= r of random events and of the random aspects of nonrandom events, such as ar= e the heights of French soldiers, exclusive of their end points. Indeed for=20= some four decades in securities markets, it was devoutly believed that each=20= major market was driven by its own random process and that its outputs, at l= east in terms of period-to-period log returns, could be described by the nor= mal curve.

[ If you didn't believe this, you were in danger of be "excommunicated" from= the academic division of the finance community or at the very least, of bei= ng denied tenure. ]

In the last decade a vast literature has arisen disputing this "article of f= aith" without settling on an alternative for securities markets, or even set= tling the question of whether one should use one, two or three distributions= to characterize a given market. 

[ Say separate distributions for each tail and one other for the bulk of the= events !  See especially the writings of Didier Sornette and Benoit M= andelbrot, among others. Also my paper for the June 2008 Oxford Conference o= n Business and Economics, "Do random series exist ?". ]

My own awakening came when I went to work for an electric utility and discov= ered that the expected lives of wooden poles are described by the Poisson Di= stribution, and reinforced later on, when I headed a study of sites for wind= farms in which wind speed at a given height and location was described by t= he Wiebull Distribution.  And now, in the course or revisiting econome= trics for assorted reasons, I realize that as a result of the current prolif= eration of estimators, econometricians are stumbling upon all kinds of weird= distributions, some of which don't even have "official" names as yet, situa= tions in which are old friends, the chi-square and the t distributions are n= ot much help. 

In particular, people who analyze stock markets and bioelectric series often= have problems in classifying a given run of data as conforming to a log-nor= mal distribution or a power law one. So the fact that a distribution has tur= ned up which seems to be the product of the two, doesn't surprise me in the=20= least.

So those who would like to find power laws in biology should be forewarned&n= bsp; >

[1]     It aint going to be easy !

[2]     The presence of power laws may NOT be in one-to-= one correspondence with some phenomenon of interest,  and such presenc= e may not be restricted to a coherent group of phenomena, so the presence or= absence of a power law distribution in the data cannot always be taken as "= the signature" of a particular phenomenon, of a class of phenomena or of a p= articular type of dynamic behavior.

Cordially.  ###


**************
Get the scoop= on last night's hottest shows and the live music scene in your area - Check= out TourTracker.com!
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