There are two important computational models, the unit cost RAM (random access machine) and the Turing machine. The essential difference is that the Turing machine uses the binary representations of numbers and the computational time is measured precisely down to the number of (unit cost) bit operations. I believe that the RAM model, in which each elementary arithmetic operation takes a unit time and each integer number takes a unit space, is the standard model for the polyhedral computation. This model, despite its simplicity, often illuminates the critical parts of an algorithm and thus reflects the actual computation well. Of course, ignoring the number of bits of a largest number arising in the computation is dangerous, if one does not control the exponential growth of bit lengths of the numbers (in terms of the input bit length). This warning should be always kept in mind to design a good implementation. Furthermore, there are certain cases in which we need to use the Turing complexity. For example, all known ``polynomial'' algorithms for the linear programming (see Section 4) are Turing polynomial but not RAM polynomial. We may avoid this problem by pretending that there were a RAM polynomial algorithm for LP. After all, we (those interested in geometric computation) are interested in an analysis which reflects the reality and the simplex method for LP is practically a RAM polynomial (or equivalently, strongly polynomial) method. We refer to the recent book [Yap00] for further discussions.