Generative sciences

From Wikipedia, the free encyclopedia

The generative sciences (or generative science) are the interdisciplinary and multidisciplinary sciences that explore the natural world and its complex behaviours as a generative process. Generative science shows how deterministic and finite rules and parameters in the natural phenomena interact with each other to generate indeterministic and infinite behaviour.

These sciences include psychology and cognitive science, cellular automata, generative linguistics, natural language processing, social network analysis, process physics, connectionism, evolutionary biology, self-organization, neural network theory, communication networks, cognitive musicology, information theory, systems theory, genetic algorithms, artificial life, chaos theory, complexity theory, epistemology, systems thinking, genetics, philosophy of science, cybernetics, bioinformatics, and catastrophe theory.

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[edit] Elemental perspective

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

[edit] Scientific and philosophical origins

The generative sciences originate from the monadistic philosophy of Leibniz. This 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 work on cellular automaton theory by John Von Neumann, which was based on the Walter Pitts and Warren McCulloch model of the neuron. Cellular automata were mathematical representations of simple entities interacting under common rules and parameters to manifest complex behaviors.

The generative sciences were further unified by the cybernetics theories of Norbert Wiener and the information theory of Claude E. Shannon and Warren Weaver in 1948. The mathematician Shannon gave the theory of the bit as a unit of information to make a basic decision, in his paper A mathematical theory of communication (1948). On this was further built the idea of uniting the physical, biological and social sciences into a holistic 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 constructivist epistemology or radical constructivism.

The most influential advance 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 the perceptron was advanced by Marvin Minsky and Seymour Papert at MIT. It was also in the early 1950s that Crick and Watson gave the double helix model of the DNA, at the same time as 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.

In 1996 Joshua Epstein and Robert Axtell wrote the seminal work Sugarscape. In their work they expressed the idea of Generative science which would explore and simulate the world through generative processes.

[edit] 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.

[edit] Prominent generative scientists

[edit] 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.
  • Epstein J.M. and Axtell R. (1996) Growing Artificial Societies - Social Science from the Bottom. Cambridge MA, MIT Press.
  • Epstein J.M. (1999) Agent Based Models and Generative Social Science. Complexity, IV (5)
  • Kaneko K. (1998) Life as Complex System: Viewpoint from Intra-Inter Dynamics. Complexity, 6, pp.53-63.
  • Robert Axtell, Robert Axelrod, Joshua Epstein, and Michael D. Cohen, (1996) Aligning Simulation Models: A Case Study and Results; Computational and Mathematical Organization Theory, 1, pp. 123-141 (http://www-personal.umich.edu/~axe/research/Aligning_Sim.pdf)
  • McTntyre L. (1998) Complexity: A Philosopher's Reflection. Complexity, 6, pp.26-32.

[edit] See also

[edit] External links

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