History [of complexity definitions]

There have been terms for complexity in everyday language since antiquity. But the idea of treating complexity as a coherent scientific concept potentially amenable to explicit definition is quite new: indeed this became popular only in the late 1980s—in part as a result of my own efforts. That what one would usually call complexity can be present in mathematical systems was for example already noted in the 1890s by Henri Poincaré in connection with the three-body problem (see page 972). And in the 1920s the issue of quantifying the complexity of simple mathematical formulas had come up in work on assessing statistical models (compare page 1083). By the 1940s general comments about biological, social and occasionally other systems being characterized by high complexity were common, particularly in connection with the cybernetics movement. Most often complexity seems to have been thought of as associated with the presence of large numbers of components with different types or behavior, and typically also with the presence of extensive interconnections or interdependencies. But occasionally—especially in some areas of social science—complexity was instead thought of as being characterized by somehow going beyond what human minds can handle. In the 1950s there was some discussion in pure mathematics of notions of complexity associated variously with sizes of axioms for logical theories, and with numbers of ways to satisfy such axioms. The development of information theory in the late 1940s—followed by the discovery of the structure of DNA in 1953—led to the idea that perhaps complexity might be related to information content. And when the notion of algorithmic information content as the length of a shortest program (see page 1067) emerged in the 1960s it was suggested that this might be an appropriate definition for complexity. Several other definitions used in specific fields in the 1960s and 1970s were also based on sizes of descriptions: examples were optimal orders of models in systems theory, lengths of logic expressions for circuit and program design, and numbers of factors in Krohn–Rhodes decompositions of semigroups. Beginning in the 1970s computational complexity theory took a somewhat different direction, defining what it called complexity in terms of resources needed to perform computational tasks. Starting in the 1980s with the rise of complex systems research (see page 862) it was considered important by many physicists to find a definition that would provide some kind of numerical measure of complexity. It was noted that both very ordered and very disordered systems normally seem to be of low complexity, and much was made of the observation that systems on the border between these extremes—particularly class 4 cellular automata—seem to have higher complexity. In addition, the presence of some kind of hierarchy was often taken to indicate higher complexity, as was evidence of computational capabilities. It was also usually assumed that living systems should have the highest complexity—perhaps as a result of their long evolutionary history. And this made informal definitions of complexity often include all sorts of detailed features of life (see page 1178). One attempt at an abstract definition was what Charles Bennett called logical depth: the number of computational steps needed to reproduce something from its shortest description. Many simpler definitions of complexity were proposed in the 1980s. Quite a few were based just on changing p_{i} Log[p_{i}] in the definition of entropy to a quantity vanishing for both ordered and disordered p_{i}. Many others were based on looking at correlations and mutual information measures—and using the fact that in a system with many interdependent and potentially hierarchical parts this should go on changing as one looks at more and more. Some were based purely on fractal dimensions or dimensions associated with strange attractors. Following my 1984 study of minimal sizes of finite automata capable of reproducing states in cellular automaton evolution (see page 276) a whole series of definitions were developed based on minimal sizes of descriptions in terms of deterministic and probabilistic finite automata (see page 1084). In general it is possible to imagine setting up all sorts of definitions for quantities that one chooses to call complexity. But what is most relevant for my purposes in this book is instead to find ways to capture everyday notions of complexity—and then to see how systems can produce these. (Note that since the 1980s there has been interest in finding measures of complexity that instead for example allow maintainability and robustness of software and management systems to be assessed. Sometimes these have been based on observations of humans trying to understand or verify systems, but more often they have just been based for example on simple properties of networks that define the flow of control or data—or in some cases on the length of documentation needed.) (The kind of complexity discussed here has nothing directly to do with complex numbers such as √-1 introduced into mathematics since the 1600s.)