Self-organization as a widespread phenomenon first came to the attention of researchers during the mid-twentieth century. The interest in self-organization comes from many different fields of study. The biologist Ludwig von Bertalanffy drew attention to the role of internal interactions and processes in creating organization within biological systems. His 'general systems theory' drew heavily on analogies to highlight the existence of common processes in superficially different systems. Meanwhile, W. Ross Ashby and Norbert Wiener explored self-organization from the perspective of communications and feedback in the control of systems. Ashby introduced the term self-organizing in 1947. Wiener coined the term cybernetics to refer to the interplay of control systems and information. In the 1950s, systems ecologist H. T. Odum collaborated with engineer Richard Pinkerton to develop the principle of maximum power, which states that systems self-organize to maximize energy transformation.
During the 1970s and 1980s, increasing computing power made it possible to use simulation to explore the consequences of complex networks of interactions. By the last two decades of the twentieth century, the nature and implications of biological self-organization were increasingly being explored as a part of the complexity theory. The new field of Artificial life (Alife), initiated by pioneers such as Chris Langton, Pauline Hogeweg, and Bruce Hesper, has produced a series of seminal models that demonstrate self-organization in a variety of ecological and evolutionary contexts. Around the same time, H. T. Odum introduced the systems concept of 'emergy' to represent the total energy used in developing a process.
By the 1990s, researchers were looking for broad-based theories of self-organization. John Holland stressed the role of adaptation in self-organization. He suggested that seven basic elements are involved in the emergence of order in complex adaptive systems. These include four properties - aggregation, nonlinearity, flows, and diversity - and three mechanisms - tagging, internal models, and building blocks. In contrast, Stuart Kauffman's work on autocatalytic sets within Boolean networks emphasizes ways in which self-organization may structure biological systems independent of selection. Likewise, embryologist Brian Goodwin suggested that to understand macroevo-lution, we require a theory of morphogenesis which takes account of physical, spatial, and temporal dynamics in addition to selection. The work of James Kay provided an interpretation of life from a thermodynamic perspective, arguing that self-organizing systems maximize the dissipation of gradients in nature. In particular, Kay argues that over time, ecosystems evolve to dissipate energy more efficiently by becoming increasingly complex and diverse.
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