While reading Butterfly Economics, I visited Amazon and ordered another book of Ormerod. The most recent one, with the most interesting title a book on popular economics ever could have: Why most things Fail, evolution, extinction & economics.
After reading the preface and introduction I'm looking forward to reading the whole book.

The first great gain concerning the book is that it refered to Mr. Ormerod's homepage and there I found his paper on Random Matrix Theory and the Failure of Macro-economic Forecasts which contains his arguments against economic predictions in detail. The article describes the problem very differently as I put it in the previous post, and it seems to be more reconciliable with my own views expressed there. Basically as it is expressed in the abstract Ormerod sais that the genuine information content in economic growth data is low, and so forecasting failure arises from inherent properties of the data. Introducing more confusing terms while putting the problem into a radically different perspective. Stating that the data or the subject of forecasting is what causes the problem and not the methods of forecasting. The amiguity, vagueness is due to the ill-defined macro-economic concepts like the GDP itself. So my argument in the previous post is somewhat irrelevant. However it can be restated and for this time we can formulate it as an Ormerodian argument.

Economists in the first place give credentials to data rows, then by established methods create forecasts that at best inherit the orginal (rather low) credentials of the data. Hence economic forecasts result in statments with very little credibility and usually fail.

I think that introducing the concept of information content does not add to the argument, and may even be misleading at least for a philosophically minded reader like myself. The argument can be formulated without reference to the (vague) concept of information, as I tried to show above.