Podcast
Spirituality, Science and Technology
Hosted by Unknown Author · EN
Viruses of the mind and mindfulness; ramblings about Mathematics, Science, Spirituality, Software and Artificial Intelligence
4episodes
Episodes
Newest firstAll episodes
The Paradox of Choice in Open Source Software
Oct 22, 2008Tap to summarizeProfessor Barry Schwartz, the author of The Paradox of Choice: How More is Less, in a talk at Google, showed, surprisingly to many of us, that our sacred assumptions, assumptions used in the following syllogism, can be false. These assumptions and syllogism are the:more freedom => more welfaremore choice => more freedomHence more choice => more welfareThis was discussed in more detail in his book.In essence, more choice can lead to worse decision making due to the increase complexity and resulting regret. In fact, people are often paralyzed and would not make any decision at all, if faced with too many choices. If they do make a decision in such situation, the decision is often made based on non-rational reasons and superstitions, and the result is far from optimal.Schwartz gave many examples of this situation. Examples from daily life are the explosion of choices in a supermarket: 175 salad dressings, 250 kinds of cereal, 360 types of shampoo, gel and mousse.Schwartz used studies to support his findings, and also makes use of behavioral economics as in Kahneman Tversky's prospect theory.Of course some choice is better than no choice, but when the number of choices in increased, more is less. The discussion of its implications are very interesting, it includes why people who have everything are less happy. Beyond subsistence, increasing abundance does not increase happiness.The above situation is applicable to the software industry.I will look at an example of open source software. Proprietary software has its own advantages and disadvantages, which are not discussed here.Consider Schwartz's example of making a stereo system from components: there are approximately 6,512,000 ways to do it.Now consider a software for a Python based application framework.We have frameworks like Turbo Gears, Django, Pylon and more.Each of them uses other open source components (the following are just a few of the components, not a complete list):Databases: SQLite, MySQL, Postgress, etcORM: SQLObject, SQLAlchemy, etcJavascript libraries: Prototype, Scriptaculous, Mochikit, Yui, Dojo, jQuery, etcTemplating systems: Kid, Mako, etcWidget libraries: TocsaWidgets, Tk, etcUtilities: WSGI, JSON, XML, Genshi, virtualenv, paster, Cheetah, CherryPy, etcThe combinations are many, and confusing. Nobody can make a reasonable good combination without knowing most of the components.And worse yet, the end-user who just want to use the software to write programs for his domain, will have a hard time installing the software with all their dependencies.Another example is Open Object, which seems to be very good, but the installation is very difficult even for an IT person.Many years ago, I had the same experience with building software using Java components.I think leadership means giving people enough choices, but not too many. The leader must decide (even if the decision is not optimal) upon one combination of components which can produce quality software and ease of use, not leave the decisions to the user. The whole should then be packaged and branded.I am not advocating proprietary software, but there are things we can learn from it.Open source software need leaders.Ultimately, open source software leader will make users happier.Related:Paradox of Choice Mind MapThe Paradox of Choice 75 Iced Teas 40 Toothpastes 230 Soups 175 ...Doing Better but Feeling Worse: The Paradox of Choice Barry ...Spirituality, Science & Technology: http://10outof10.blogspot.com
Transcribe →Gartner warns Microsoft Windows is Collapsing
Apr 11, 2008Tap to summarizeGartner analysts warn that Windows is collapsing.Earlier, Microsoft announced that Windows XP is dead, see "Thank You Microsoft for Prematurely Killing Windows XP" which was worrying people who still do not want to upgrade to Vista. The benefits of Vista are not enough to make an upgrade. In fact Vista is slower than Windows XP ("Testing Shows XP Still Outperforms Vista").I think Windows XP and Windows NT, along with DOS, were the popular and widely used Microsoft operating systems for a relatively long period of time. Windows XP is said to be the most popular operating system.The trouble with Vista is it is becoming too complex and yet do not offer much more than Windows XP. As Gartner says, ” Among Microsoft's problems, the pair [Gartner analysts] said, is Windows' rapidly-expanding code base, which makes it virtually impossible to quickly craft a new version with meaningful changes. That was proved by Vista, they said, when Microsoft -- frustrated by lack of progress during the five-year development effort on the new operating -- hit the "reset" button and dropped back to the more stable code of Windows Server 2003 as the foundation of Vista.Related: Vista is Slower, But XP Is Still DyingSpirituality, Science & Technology: http://10outof10.blogspot.com
Transcribe →New Findings on Loving Kindness Meditation
Mar 27, 2008Tap to summarizeBenefits of meditation have often been reported, see e.g. Meditation increases grey matter in right hemisphere of the brain.Benefits range from concentration, stress reduction, to increases in the brain's grey matter.These results have been associated mostly with Vipassana or mindfulness meditation.For Metta Bhavana (Loving Kindness Meditation) no such study has been made until recently, a group of neuro-scientists wrote a paper " Regulation of the Neural Circuitry of Emotion by Compassion Meditation: Effects of Meditative Expertise"They used functional magnetic resonance imaging (fMRI) techniques to show increase activity in insula due to meditation training.The result was also reported in the Scientific American article "Meditate on This: You Can Learn to Be More Compassionate" It indicates that it might be possible for compassion and loving-kindness to be learned. Buddhists have always believe that we can develop our mental faculties, including compassion, like we build our muscles.Metta Bhavana is one of the cornerstones of Buddhist meditation, in the Theravada and the Mahayana traditions. It complements Samatha and Vipassana meditation. Samatha aims at tranquility and leads to Jhanas, Vipassana leads to Insight and Purification. Metta Bhavana tenderizes the heart and develops good-will, it can be practiced separately or together with the other types of meditation. Many schools teach all three types of meditation.Some Guided Metta meditation tapes:Loving-kindness Meditation - Ven. Pannyavaro: loving1.mp3 714 KB Instruction, loving2.mp3 482 KB A Guided Meditation Guided Meditations with Malcolm Huxter: [31,293 KB] Guided Loving-kindness MeditationGil Fronsdal Guided Meta MeditationMeta Meditation by Thubten Chodron in rm formatRelated: When we wish happiness for all.....Spirituality, Science & Technology: http://10outof10.blogspot.com
Transcribe →An Introduction to Hurst Exponent
Mar 24, 2008Tap to summarizeAs an example of an application of Mathematics to finance, we will look at Hurst exponents, omitting the technical details.Given a time series, the Hurst exponent (H) of the mathematical object is a single number between 0 and 1. What can a single number tell us about the series?It can be interpreted in many ways, one of them is that it measures the jaggedness or smoothness of the series.It helps us classify time series. For example, one of the basic questions, is whether a time series is purely random (a random walk or Brownian movement) or not.Many people have suggested that financial data such as stock prices are random, Hurst exponent helps explain that it is not.H = 1/2 is a random walk with no memory of past states, H between 1/2 and 1 is a persistent time series, where the series has long term memory, and H between 0 and 1/2 is an anti-persistent time series (the persistence works in a negative way). A mean reverting series for example is anti-persistent, but the converse is not always true. In common parlance, "what goes up must come down, and vice versa" applies to reverting series.Geometrically, anti-persistent series are more jagged than a random walk. Persistent series are smoother than a random walk.In the extreme case of H very close to 0, the series is almost like a space filling curve.If H is close to 1, the series is a single line (can be broken and curved).From this, we can understand that H is also measuring the fractal dimension D. The relation can be shown to beD = 2 - H H is also related to the color of noise, white noise has H=1/2, brown noise H=0, and pink noise has H between 0 and 1/2 (incorrect, see comments below) .H was first introduced by H.E. Hurst, a hydrologist who studied data of river overflows of the river Nile. He wanted to know whether the overflows occurs randomly or not. In so doing he had a wonderful idea, defining H by rescaling (or normalization).Let the original series be x1, x2............ xn We divide the series into p buckets, each of size m, with n = m.p For each bucket, we calculate the mean and the standard deviation S.For each bucket, define a new series y1, y2, ...yj.....ym, where yj is the cumulative sum of all (x - mean) from index 1 to j in the bucket.Define R for the bucket as the largest minus the smallest yj. R will always be positive.Then divide R by S: R/S. R/S is defined for each bucket 1,2, ...i,....p. Take the average of R/S for all buckets, called this the R/S for the bucket-size.Now vary the bucket-size and calculate the corresponding R/S.Hypothesize that (R/S) obeys a power law in the bucket size b, so that(R/S)b = c. bH with c a constant.The Hurst exponent is the power in the power law relation above. When H = 1/2, we have Einstein's formula for Brownian movement.To calculate or estimate H, take the log of the power law equation, and H becomes the slope of the line. The precise algorithm in C++ for estimating H can be found in here.Some of the attraction of the Hurst exponent, is that it is related to many fields of Mathematics, fractals, chaos, wavelets, spectral analysis, statistics, etc. Accordingly there are many ways of estimating H, unfortunately they don't always give the same answer.Hurst used the definition to investigate time series data from river discharges, tree rings, rainfalls, and others. He found they have H between 0.6 and 0.8 indicating persistent series with long term memory.Edgar Peters introduced H into the financial world with his Fractal Market Hypothesis, see e.g. his book Fractal Market Analysis, applying chaos theory to investments and economics.Peters applied H to stock and bond returns, forex exchange, and their volatilities.Here is an example of a 5-day return of GBPUSD exchange rates:I estimated H using Karagianis et.al. software called SELFIS The result is H = 0.645:The data uses 5-day returns, as it happens, longer day returns have more persistence than short term returns.Despite its application to finance, Hurst exponent is not, perhaps to the disappointment of many, a forecasting tool, it shows theoretical predictability, but it is not a prediction method.Spirituality, Science & Technology: http://10outof10.blogspot.com
Transcribe →