Author: Axel Schumacher
Date: 15:59:18 03/02/05
Hi all, I have two question regarding the storage requirements for information; I hope somebody can help me with answering them. Please excuse if my questions are stupid. 1. For each data-point (e.g. let's say the position of a pawn on the chessboard) one requires 1 bit (either 0 or 1). Right? However, the information does not include where the pawn is located. So, how much data has to be stored to describe e.g. the position of a pawn? 2. How much calculation power is need to calculate a certain amount of data? I know, this this may sound a little bit abstract and, of course, it depends on the time-factor. But let's you have 1 terabyte of data in a data-spreadsheet. What calculation-power (e.g. amount of average desktop computers) is needed to make simple algebraic calculations with such a data-table? I hope sombody can help me with this. I'm writing a paper in which I make an analogy from biostatistic calculations with chess and calculations in chess (e.g. from a typical chess program). The reason for this is to examplify how biological data can be stored and how it can be interpreted. In this special case we are dealing with 3.6 x 10^14 raw data points deriving from chemical modifications in the human genome (so called epigenetics). For example, is a specific DNA base in the genome methylayted or not we have the state 0 or 1 again (plus this data has to be referenced). These information-units could interact in an infinite number of ways, so that it seems that it impossible to make sense out of them. However, IMHO, the analogy with the game of chess exemplifies that it still should be feasible to approach the problem of complex genetic information. In chess, a small number of rules can generate a huge number of board configurations (states), which are analogous to the configurations of molecules obeying physiological laws. Chess is known to have also an infinite number of possible combinations in its play but in theory the number is finite, since specific positions are impossible, as not all (epi)genetic factors can be found in all functional working combinations. E.g. it is said that in chess ‘merely’ ~10^43 to 10^50 states (positions) are needed to to describe the state (or the game) of the system. Out of these subsets of possible states, patterns can be established and calculated. So it is not necessary, to know every possible state. It is obvious that pure reductionism, the theory that all complex systems can be completely understood in terms of their components, may not be a fully fruitful approach. Yet, recent development in the field of complexity (e.g. statistical mechanics) has come up with alternative statistical approaches. It considers the average behaviour of a large number of components rather than the behaviour of any individual component, drawing heavily on the laws of probability, and aims to predict and explain the measurable properties of macroscopic systems on the basis of the properties and behaviour of their microscopic constituents. Chess programs don't rely on brute force alone anymore. Maybe such 'pattern recognition' or reduction of legal states can help in making sense out of complex data. Your opinion? Answers to the qustions? :-) Axel
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