Generative sciences Guide, Meaning , Facts, Information and Description

Generative sciences is a generic term used to refer to the interdisciplinary and multidisciplinary sciences that explores the natural world and its complex behaviours as a generative process.

These sciences include the Cognitive and Psychological sciences, Cybernetics, Cellular automata, Generative linguistics, Natural language processing, Social network analysis, Process Physics, Connectionism, Evolutionary biology, Self Organizing dynamical systems, Neural network theory, Communication networks, Cognitive musicology, Information Theory, General Systems Theory, Genetic algorithm, Artificial life, Chaos theory, Complexity theory, Epistemology, Generative Philosophy, Genetic sciences, Philosophy of science, Cybernetic sciences, Bioinformatics, and Catastrophe theory.

Elemental Perspective

Generative sciences explores the natural phenomena at several levels including physical, biological and social processes as emergent processes. Generative sciences explores complex natural processes as generating through continious interactions between elemental entities on parsimonious and simple universal rules and parameters.

Scientific and Philosophical Origins

The origins of Genarative sciences goes to the monadistic philosophy of Leibniz. It was further developed by the neural model of Walter Pitts and Warren McCulloch. The development of computers or Turing Machines laid a technical source for the growth of the generative sciences. However the cornerstones of the generative sciences came from the works on Cellular automata theory by John Von Neumann, which was based on the Walter Pitts and Warren McCulloch model of the Neuron. The Cellular automatas were mathematical representations of simple entities interacting on common rules and parameters to manifest complex behaviors.

Generative sciences were further unified by the arch theories of Cybernetics by Norbert Weiner and information theory by Claude E. Shannon and Warren Weaver in 1948. The mathematician Claude E Shannon gave the theory of BIT as a unit of Information to make a basic decision, in his paper A mathematical theory of communication (1948). On this was further build the idea of uniting the physical, biological and social sciences into a wholistic discipline of Generative Philosophy under the rubric of General systems Theory, by Bertalanffy, Anatol Rapoport, Ralph Gerard, and Kenneth Boulding. This was further advanced by the works of Stuart Kauffman in the field of self-organization. It also has advanced through the works of Heinz von Foerster, Ernst von Glasersfeld, Gregory Bateson and Humberto Maturana in what came to be called as Constructivist epistemology or radical constructivism.

The most influential advancement in the generative sciences came from the development of the Cognitive sciences through the theory of Generative grammar by the American Linguist Noam Chomsky (1957). At the same time the theory of Perceptron were advanced by Marvin Minsky and Seymour Papert at the MIT. It was also in the early 1950s that Clark and Watson gave the double helix model of the DNA, at the same time Psychologists at the MIT including Kurt Lewin, Jacob Ludwig Moreno and Fritz Heider laid the foundations for group dynamics research which later developed into social network analysis.

Prospective directions

Generative scientists are working towards further developments and new frontiers. Latest and emerging directions in the generative sciences include the computer simulations of complex social process, artificial life and Boids. The modeling of strategic decision making in cognitive organization psychology and the emergence of communication patterns in Cognitive organization theory. The research on anaphora in natural language processing is an important step towards the advancement of Artificial intelligence which is also influencing Process physics and semantic network modeling of physics and physical properties. Dynamical cognitive evolutionary psychology and Dynamical psychology is the latest direction in the systematic unification of the psychological sciences. This is further expanded through the mathematical theories of the Cognitive grammar of music.

Selected Bibliography

• W. Weaver and C. E. Shannon, (1948) The Mathematical Theory of Communication, Urbana, Illinois: University of Illinois Press.

• Chomsky N (1957) Syntactic Structures. The Hague: Mouton.

• Warren McCulloch and Walter Pitts,(1943) A Logical Calculus of Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics 5:115-133.

• Lewin, K. (1951) Field theory in social science; selected theoretical papers. D. Cartwright (Ed.). New York: Harper & Row.

• Weiner N (1948) Cybernetics; John Wiley, New York, 1948.

• von Neumann, Jon (1966) The Theory of Self-Reproducing Automata, edited and completed by Arthur W. Burks (Urbana, IL: University of Illinois Press).

• Rapoport, A. (1953). Spread of information through a population with sociostructural bias: I. Assumption of transitivity. Bulletin of Mathematical Biophysics, 15, 523-533.

• James L. McClelland and David E. Rumelhart. (1987) Explorations in Parallel Distributed Processing Handbook. MIT Press, Cambridge, MA, USA, 1987.

• Gleick J (1987); Chaos: Making a New Science; Copyright 1987, Viking, N.Y.

• Jackendoff, Ray, and Fred Lerdahl (1981). "Generative music and its relation to psychology." Journal of Music Theory 25(1): 45-90

• Allen, T.J. (1970). Communication networks in R&D laboratories. R&D Management, 1(1), 14-21.

• Skvoretz, J. 2002. Complexity Theory and Models for Social Networks. Complexity 8: 47-55

• Seidman, Stephen B. (1985). Structural consequences of individual position in nondyadic social networks, Journal of Mathematical Psychology, 29: 367-386

• Thietart, R. A., & Forgues, B. (1995). Chaos theory and organization. Organization Science, 6, 19-31.

• Holland, John H., "Genetic Algorithms", Scientific American, July 1992, pp. 66-72

• Albert-Laszlo Barabasi and Eric Bonabeau, "Scale-Free Networks", Scientific American, May 2003, pp 60-69

• T. Winograd, Understanding Natural Language, Academic Press, New York, 1972.

• M. Minsky, The Society of Mind, Simon and Schuster, New York, 1986.

• Reginald T. Cahill, Christopher M. Klinger and Kirsty Kitto (2000) Process Physics: Modelling Reality as Self-Organising Information; Published in The Physicist, 37(6), 191-195.

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