A biological ecosystem is an oft-used and reasonably good analogy because its dynamics depend on a complex mix of complementary and competitive relationships among all organisms. Like the software industry, pairs of organisms often share complementary and competitive relationships at the same time. For example, a parasite depends on its host but also detracts from it.
If the prevailing interest rate is r per unit time, then revenue (or cost) incurred at a future time t is discounted by .
From a security perspective, this is potentially a way to spread viruses. This can be prevented by attaching digital signatures from the original (trusted) supplier and always verifying those signatures before executing code obtained from any party (see section 5.4.6).
The NET Framework is an example of a platform that supports side-by-side installation of multiple versions of a component.
To be sensible, this definition of pricing nondiscrimination requires referencing "customer" to some common unit of use, like individual user or an e-commerce transaction. It wouldn't, for example, make sense to compare the gross margins of a sale to a large organization against an individual user.
The difference is that in the software field the whole point of versioning is for customers to select from among a set of product variants based on their willingness to pay. Typically, only one software version is sold at a time, although there may be older versions still in use.
This supplier strategy suggests that the complaints of airline customers about coach class are justified; in part, coach class must be unpleasant enough that less price-sensitive customers would not be tempted to fly coach.
This is a modern view of general-purpose computers. However, in the early days of computing, competing architectures were advocated that strongly separated programs from the data those programs manipulated. Once such "Harvard architecture" is still widely used in special-purpose computers, such as those for digital signal processing.
The information theory presented by Shannon and Weaver (1949) quantifies this statement. It asserts that the numerical "amount" of information is determined by its entropy. The entropy of unexpected outcomes is greater than the entropy of expected outcomes. For example, if you expect that a weighted coin when tossed will come up tails 99 percent of the time, then an actual outcome of a tail conveys little information, whereas a surprising outcome of a head conveys much more information.