welcome to 2008, the year of free....

The recent article of note in The Economist by Chris Anderson explains the merits of giving it away...

...What is getting too cheap to meter is processing power, storage, bandwidth and all the other enabling technologies of the digital revolution. Thanks to the exponential doublings of Moore’s Law and its equivalents for hard drives and communications, the cost of a given unit of computation, storage or transmission is inexorably dropping towards zero.
...a Caltech professor named Carver Mead.... the late 1970s,... was reflecting on the amazing learning curve that the combination of quantum mechanics and information science had started on the surface of silicon wafers. Like Moore before him, he could see that the 18-month doublings in performance would continue to stretch out as far as anyone could see. But he went one step further to consider what that implied about computers. He realised that we should start “wasting” them.
Waste is a dirty word, and no more so than in the 1970s and 1980s. An entire generation of computer professionals had come to power doing just the opposite. In the glass-walled computer facilities of the mainframe era, they exercised their power in deciding whose programs should be allowed to run on the expensive computing machines.
Among Mead’s disciples was Alan Kay, working at Xerox’s Palo Alto Research Centre. Rather than conserve transistors for core processing functions, he developed a computer that would frivolously throw them at such silly things as drawing icons, windows, pointers and even animations on the screen. The point of this profligate eye candy? It was ease of use for regular folks, a previously neglected market. Kay’s work became the inspiration for the Apple Macintosh, which changed the world by opening computing to the rest of us.
Today the same is happening in everything from bandwidth to storage. The learning curves of technology cut prices at a rate never before seen. The cost of storing or transmitting a kilobyte of data really is now too cheap to meter. Soon the same will be true for a megabyte, and then soon after that a terabyte. And the internet touches nearly as much of our economy as electricity did when Lewis Strauss issued his prediction.

visualising words

visuwords, a very useful dictionary or thesaurus

making forecast when you don't know everything?

It’s tough to make predictions, especially about future. Yogi Berra
Th recent issue of The Economist reviews the book by Mr Frydman (Here is the first chapter of the book).

“THE forecaster is like an entrepreneur,.. he uses quantitative methods, but he also studies history, and relies on intuition and judgment. He is not a scientist.” ...this fact has been lost on contemporary economists, who continue to pursue the perfect economic forecast despite abundant evidence that it does not, and cannot, exist. They dismiss their repeated failures in much the same way that self-styled reformers in ...Poland once insisted that socialism was great, but just needed to be carried out better.
In the economics profession the leading inheritors of this communistic mindset... are the proponents of rational-expectations theory, which assumes that the economy and the individuals within it act with perfect foresight. Yet.. equally critical of the more fashionable school of behavioural economics, or at least those of its practitioners who claim that although people are irrational, their irrationality can be modelled so precisely that the future can be forecast with great precision.
In a new book, “Imperfect Knowledge Economics”....Mr Frydman sets out an alternative approach to prediction, in which the forecaster recognises that his model will inevitably be less than perfect. ..

There is nothing new in economics about the idea that people must make decisions based on imperfect knowledge. Frank Knight...,“Knightian uncertainty”..., ... noted that most business decisions involve a step into an unknown that is to some degree unmeasurable. ...Keynes observed that “human decisions affecting the future, whether personal or political or economic, cannot depend on strict mathematical expectation, since the basis for making such calculations does not exist.”
While reflecting these insights, imperfect-knowledge economics still sees a role for economic theory in forecasting. ....argue that, to be useful, economic forecasting models should be based on qualitative regularities in the way that market participants respond to new information—that is, patterns of behaviour that are observable and somewhat predictable. Though not perfect, these will often give a better clue to the future than no model at all, or models based on rational expectations or behavioural economics.
....bulls and bears, for example. Their analysis of the fundamentals leads them to opposite conclusions about where prices are going. But there is evidence that the way they revise their forecasts in the light of price movements may share common features, such as a tendency to become more risk averse the further the price of an asset moves away from what is generally believed to be its long-term fundamental value. This may work eventually to return the asset price to its fundamental value, though it may also cause it to deviate significantly from this value for long periods of time. This approach will not generate the “sharp predictions” beloved of most contemporary economists—which are doomed by imperfect knowledge to be wrong. But it will provide a broad sense of the state of play, which an enterprising forecaster can usefully combine with experience, intuition and so forth when making a decision.
...Frydman... examine the persistent failure of economists to predict movements in the currency markets. According to Kenneth Rogoff, ..., “it is stunning how hard it is to explain movements in exchange rates.” All the models based on rational expectations now say that, on fundamentals, the euro is overvalued against the dollar, he reckons. But does that mean the dollar will soon rise? Mr Rogoff says he has no idea.
In rational-expectations theory, a range of variables including inflation, interest rates and growth should have a predictable impact on currency movements, but in practice this theory has proved less useful for forecasting than tossing a coin. Among rational economists, the debate is over “whether the glass is 5% full or 95% empty,” he says. Only over longer periods—say two to four years—is there any evidence of exchange-rate predictability, which is far too long to be useful to traders or policymakers.
By contrast, the model developed by Messrs Frydman and Goldman, which assumes imperfect knowledge and learning, does significantly better than tossing a coin, although it is by no means right all the time. Mr Rogoff describes this work as innovative. Now, ... it must demonstrate that it can consistently maintain explanatory power in the future and over a range of markets, he believes.
Maths lesson

Messrs Frydman and Goldberg are now turning their attention to the troubled subprime-mortgage markets, and the performance of the rating agencies. The rating agencies,...have generally been better at rating corporate bonds than rating asset-backed collateralised-debt obligations. Why? One reason is that the rating agencies used both a mathematical model and the judgment of their in-house specialists when forecasting the default probabilities of corporate bonds; on subprime-related securities, they could only use mathematical models, not least because the instruments were so new. “They had no experience, no intuition, no entrepreneur,” he says. That is “empirical proof that relying on models alone is not wise.”

the oil map

Moebius Transformations Explained

A short film depicting the beauty of Moebius Transformations in mathematics. The movie shows how moving to a higher dimension can make the transformations easier to understand.The full version is available at http://www.ima.umn.edu/~arnold/moebius/