A buzzword or catchphrase expresses a broad, fuzzy and general concept which is very popular. A term, principle or concept is a buzzword if it is very popular although it is unclear what it means. There is typically no strict agreement on exactly what the term means. A buzzword has usually a wide range of meanings, within many different contexts it means quite different things. Nearly all systems for instance can be considered as complex, organized, distributed or adaptive and the real question is to what extent or degree. This is an advantage of buzzwords (one can cover a lot of different phenomena and systems with only one word), but also a potential pitfall (since one may mean a certain system, while another means a completely different system). Therefore every buzzword should be treated with care. The popularity of such a word is sometimes based on positive feedback: often it is a general word or phrase that is used because it is important and popular, and it is both important and popular because others use it frequently. It takes on added significance through repetition or usage.
Buzzwords are typically terms that resist a clear, narrow and precise definition, a trait they have in common which abstract words as 'life' and 'intelligence'. There are either too few or too many formal treatments, and compact formal definitions which are suitable, meaningful and useful are rare. Because buzzwords are hard to define, they are also hard to measure. Many terms like complexity and self-organization are indeed notoriously difficult to define and to measure, although everyone seems to know roughly what they mean in general ("I know it when I see it"): something is complex if it is difficult to describe, and something organizes itself if no external organizer does it. Yet the attempt to pin them down formally or mathematically leads to great difficulties. Even if a formal definition is possible, without generality it is difficult to apply them to practical situations, because one or more of the underlying assumptions will often fail to be satisfied. The attempt to clarify buzzwords and the attempt to define them clearly can lead to a kind of "schism" in a community. The word schism means a division or a split.
Thus buzzwords have their assets and drawbacks, on the one hand they are powerful tools to get funding, to write successful project proposals, to get things published, to capture attention, to impress people, etc., because if one pretends to solve problems associated with them, one pretends automatically to solve a wide range of problems due to their inherent wide range of meanings. On the other hand they should be treated with care (because they might lead to mathematical quagmire, confusion, misunderstandings, illusions, contradictions, schisms in communities, etc.)
Examples for popular buzzwords in the field of distributed and complex systems are:
- Autonomic Computing
- Cloud Computing
- Ubiquitous Computing
- Web 2.0
- Web Services
- Service-Oriented Architecture
- Loose Coupling
Buzzwords are often created by negations which describe the opposite of simple things. Something is complex if it is not simple, something is self-organizing if it is not organized by someone else, something is asynchronous if it is not synchronous, etc:
- Complexity is the opposite of simplicity
- Non-Linearity is the opposite of linearity
- "Asynchrony" is the opposite synchrony
- Non-Equilibrium is the opposite of equilibrium
The phrases are of course vague, ambiguous and broad, they are like calling Zoology the 'study of nonelephant animals.' It is maybe true, but it does not say what kind of "nonelephant animals" exactly. A popular phrase in early theories of self-organization was for example "systems far from equilibrium". This phrase is not very helpful, because it does not say how far, or in which direction at all. It only replaces one buzzword with another. There are many nonlinear, non-equilibrium, asynchronous and complex systems, but only a few linear, equilibrium, synchronous and simple systems: simplicity has a unified form, but complexity has many varieties.