article : Evolution, scientific acceptance and extension to other disciplines

he theory of evolution makes statements about three different, though related, issues: (1) the fact of evolution—that is, that organisms are related by common descent; (2) evolutionary history—the details of when lineages split from one another and of the changes that occurred in each lineage; and (3) the mechanisms or processes by which evolutionary change occurs.

The first issue is the most fundamental and the one established with utmost certainty. Darwin gathered much evidence in its support, but evidence has accumulated continuously ever since, derived from all biological disciplines. The evolutionary origin of organisms is today a scientific conclusion established with the kind of certainty attributable to such scientific concepts as the roundness of Earth, the motions of the planets, and the molecular composition of matter. This degree of certainty beyond reasonable doubt is what is implied when biologists say that evolution is a “fact”; the evolutionary origin of organisms is accepted by virtually every biologist.

But the theory of evolution goes far beyond the general affirmation that organisms evolve. The second and third issues—seeking to ascertain evolutionary relationships between particular organisms and the events of evolutionary history, as well as to explain how and why evolution takes place—are matters of active scientific investigation. Some conclusions are well established. One, for example, is that the chimpanzee and the gorilla are more closely related to humans than is any of those three species to the baboon or other monkeys. Another conclusion is that natural selection, the process postulated by Darwin, explains the configuration of such adaptive features as the human eye and the wings of birds. Many matters are less certain, others are conjectural, and still others—such as the characteristics of the first living things and when they came about—remain completely unknown.

Since Darwin, the theory of evolution has gradually extended its influence to other biological disciplines, from physiology to ecology and from biochemistry to systematics. All biological knowledge now includes the phenomenon of evolution. In the words of Theodosius Dobzhansky, “Nothing in biology makes sense except in the light of evolution.”

The term evolution and the general concept of change through time also have penetrated into scientific language well beyond biology and even into common language. Astrophysicists speak of the evolution of the solar system or of the universe; geologists, of the evolution of Earth's interior; psychologists, of the evolution of the mind; anthropologists, of the evolution of cultures; art historians, of the evolution of architectural styles; and couturiers, of the evolution of fashion. These and other disciplines use the word with only the slightest commonality of meaning—the notion of gradual, and perhaps directional, change over the course of time.

Toward the end of the 20th century, specific concepts and processes borrowed from biological evolution and living systems were incorporated into computational research, beginning with the work of the American mathematician John Holland and others. One outcome of this endeavour was the development of methods for automatically generating computer-based systems that are proficient at given tasks. These systems have a wide variety of potential uses, such as solving practical computational problems, providing machines with the ability to learn from experience, and modeling processes in fields as diverse as ecology, immunology, economics, and even biological evolution itself.

To generate computer programs that represent proficient solutions to a problem under study, the computer scientist creates a set of step-by-step procedures, called a genetic algorithm or, more broadly, an evolutionary algorithm, that incorporates analogies of genetic processes—for instance, heredity, mutation, and recombination—as well as of evolutionary processes such as natural selection in the presence of specified environments. The algorithm is designed typically to simulate the biological evolution of a population of individual computer programs through successive generations to improve their “fitness” for carrying out a designated task. Each program in an initial population receives a fitness score that measures how well it performs in a specific “environment”—for example, how efficiently it sorts a list of numbers or allocates the floor space in a new factory design. Only those with the highest scores are selected to “reproduce,” to contribute “hereditary” material—i.e., computer code—to the following generation of programs. The rules of reproduction may involve such elements as recombination (strings of code from the best programs are shuffled and combined into the programs of the next generation) and mutation (bits of code in a few of the new programs are changed at random). The evolutionary algorithm then evaluates each program in the new generation for fitness, winnows out the poorer performers, and allows reproduction to take place once again, with the cycle repeating itself as often as desired. Evolutionary algorithms are simplistic compared with biological evolution, but they have provided robust and powerful mechanisms for finding solutions to all sorts of problems in economics, industrial production, and the distribution of goods and services. (See also artificial intelligence: Evolutionary computing.)

Darwin's notion of natural selection also has been extended to areas of human discourse outside the scientific setting, particularly in the fields of sociopolitical theory and economics. The extension can be only metaphoric, because in Darwin's intended meaning natural selection applies only to hereditary variations in entities endowed with biological reproduction—that is, to living organisms. That natural selection is a natural process in the living world has been taken by some as a justification for ruthless competition and for “survival of the fittest” in the struggle for economic advantage or for political hegemony. Social Darwinism was an influential social philosophy in some circles through the late 19th and early 20th centuries, when it was used as a rationalization for racism, colonialism, and social stratification. At the other end of the political spectrum, Marxist theorists have resorted to evolution by natural selection as an explanation for humankind's political history.

Darwinism understood as a process that favours the strong and successful and eliminates the weak and failing has been used to justify alternative and, in some respects, quite diametric economic theories (see economics). These theories share in common the premise that the valuation of all market products depends on a Darwinian process. Specific market commodities are evaluated in terms of the degree to which they conform to specific valuations emanating from the consumers. On the one hand, some of these economic theories are consistent with theories of evolutionary psychology that see preferences as determined largely genetically; as such, they hold that the reactions of markets can be predicted in terms of largely fixed human attributes. The dominant neo-Keynesian (see economics: Keynesian economics) and monetarist schools of economics make predictions of the macroscopic behaviour of economies (see macroeconomics) based the interrelationship of a few variables; money supply, rate of inflation, and rate of unemployment jointly determine the rate of economic growth. On the other hand, some minority economists, such as the 20th-century Austrian-born British theorist F.A. Hayek and his followers, predicate the Darwinian process on individual preferences that are mostly underdetermined and change in erratic or unpredictable ways. According to them, old ways of producing goods and services are continuously replaced by new inventions and behaviours. These theorists affirm that what drives the economy is the ingenuity of individuals and corporations and their ability to bring new and better products to the market.

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