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Interaction between a few simple rules and parameters can produce endless, seemingly unpredictable complexity.

Generative science is an interdisciplinary and multidisciplinary area of philosophy and science that explores the natural world and its complex behaviours. It explores ways "to generate apparently unanticipated and infinite behaviour based on deterministic and finite rules and parameters reproducing or resembling the behavior of natural and social phenomena".[1] By modelling such interactions, it can suggest that properties exist in the system that had not been noticed in the real world situation.[2] An example field of study is how unintended consequences arise in social processes.

Elemental perspective

Generative sciences often explore natural phenomena at several levels of organization.[3][4] Self-organizing natural systems are a central subject, studied both theoretically and by simulation experiments. The study of complex systems in general has been grouped under the heading of "general systems theory", particularly by Ludwig von Bertalanffy, Anatol Rapoport, Ralph Gerard, and Kenneth Boulding.

Scientific and philosophical origins

The development of computers laid a technical source for the growth of the generative sciences. For example:

  • Cellular automata are mathematical representations of simple entities interacting under common rules and parameters to manifest complex behaviors.
    • Conway's Game of Life is a zero-player game based on cellular automata, meaning that the only input is in setting the initial conditions, and the game is to see how the system evolves.
    • In 1996 Joshua M. Epstein and Robert Axtell wrote the book Growing Artificial Societies which proposes a set of automaton rules and a system called Sugarscape which models a population dependant on resources (called sugar).
  • Artificial neural networks attempt to solve problems in the same way that the human brain would, although they are still several orders of magnitude less complex than the human brain and closer to the computing power of a worm. Advances in the understanding of the human brain often stimulate new patterns in neural networks.

One of the most influential advances in the generative sciences as related to cognitive science came from Noam Chomsky's (1957) development of generative grammar, which separated language generation from semantic content, and thereby revealed important questions about human language. It was also in the early 1950s that psychologists at the MIT including Kurt Lewin, Jacob Levy Moreno and Fritz Heider laid the foundations for group dynamics research which later developed into social network analysis.

Determinism

Turbulence in the tip vortex from an airplane wing. Studies of the critical point beyond which a system creates turbulence were important for Chaos theory, analyzed for example by the Soviet physicist Lev Landau who developed the Landau-Hopf theory of turbulence. David Ruelle and Floris Takens later predicted, against Landau, that fluid turbulence could develop through a strange attractor, a main concept of chaos theory.

In the Weltanschauung of generative sciences including cognitive sciences and evolutionary psychology, free will does not exist.[5][6][7] However, an illusion of free will is experienced, due to the perception of the generation of infinite or computationally complex behavior from the interaction of a finite set of rules and parameters. Thus, the unpredictability of the emerging behavior from deterministic processes leads to a perception or illusion of free will, even though free will as an ontological entity does not exist.[5][6][7] Therefore, even if the behavior could be computed ahead of time, no way of doing so will be simpler than just observing the outcome of the brain's own computations.

The Lorenz attractor displays chaotic behavior. These two plots demonstrate sensitive dependence on initial conditions within the region of phase space occupied by the attractor.

As an illustration, the strategy board-games chess and Go have rigorous rules in which no information is hidden from either player and no random events (such as dice-rolling) happen within the game. Yet, chess and especially Go with its extremely simple deterministic rules, can still have an extremely large number of unpredictable moves. By this analogy, it is suggested, the experience of free will emerges from the interaction of finite rules and deterministic parameters that generate nearly infinite and practically unpredictable behaviour. In theory, if all these events were accounted for, and there were a known way to evaluate these events, the seemingly unpredictable behaviour would become predictable.[5][6][7] Another hands-on example of generative processes is John Horton Conway's playable Game of Life.[8] Cellular automata and the generative science explain and model emergent processes of physical universe, neural cognitive processes and social behavior on this philosophy of determinism.[9][5][6][7]

Implications

Generative sciences model the development of behavior and outcomes on the basis of the interaction of underlying rules and parameters. This enables the explanation of the development and manifestation of actions, behaviors and outcomes that are seemingly unrelated, contradictory or diverse. This helps to explain the development of unforeseen outcomes in physical and biological processes. Generative science also helps to explain the development of complex societies, historical processes and unexpected events,[10] unexpected changes and development in ecological and evolutionary process [11] and also help in the theoretical explanation of human psychological development [12] and cognitive processes.[13] Nobel Prize–winning physicist Gerard 't Hooft shows in his work that all of existence is essentially a generative output of a deterministic complex quantum cellular automata.[14][15]

Prospective directions

Computer simulation of the branching architecture of the dendrites of pyramidal neurons.[16]

Computer simulations of complex social process include artificial life and behaviour simulations such as Boids. Cognitive organization theory models strategic decision making and communication within organizations.

The natural phenomenon of herd behaviour as in a flock of birds can be modelled artificially using simple rules in individual units, with swarm intelligence rather than any centralized control.

See also

References

  1. ^ "Computing Nature – A Network of Networks of Concurrent Information Processes", Computing nature: Turing centenary perspective, Springer, 2013, p. 7, ISBN 978-3-642-37225-4 {{citation}}: Unknown parameter |authors= ignored (help); Unknown parameter |editors= ignored (|editor= suggested) (help)
  2. ^ "Unintended consequences of collocation: using agent-based modeling to untangle effects of communication delay and in-group favor", Computational & Mathematical Organization Theory, 14 (2): 57–83, 2008, doi:10.1007/s10588-008-9024-4 {{citation}}: Unknown parameter |authors= ignored (help)
  3. ^ Farre, G. L. (1997). "The Energetic Structure of Observation: A Philosophical Disquisition". American Behavioral Scientist. 40 (6): 717–728. doi:10.1177/0002764297040006004.
  4. ^ J. Schmidhuber. (1997) A computer scientist's view of life, the universe, and everything. Foundations of Computer Science: Potential – Theory – Cognition, Lecture Notes in Computer Science, pages 201–208, Springer
  5. ^ a b c d Epstein, Joshua M.; Axtell, Robert L. (1996). Growing Artificial Societies: Social Science From the Bottom Up. Cambridge MA: MIT/Brookings Institution. p. 224. ISBN 978-0-262-55025-3.
  6. ^ a b c d "Society of Self: The emergence of collective properties in self-structure", Psychological Review, 107 (1): 39–61, 2000, PMID 10687402 {{citation}}: Unknown parameter |authors= ignored (help)
  7. ^ a b c d Epstein J.M. (1999) Agent Based Models and Generative Social Science. Complexity, IV (5)
  8. ^ John Conway's Game of Life
  9. ^ Kenrick, DT; Li, NP; Butner, J (2003). "Dynamical evolutionary psychology: individual decision rules and emergent social norms". Psychological Review. 110 (1): 3–28. doi:10.1037/0033-295X.110.1.3. PMID 12529056.
  10. ^ Burke, Timothy (2005) Matchmaker Matchmaker, Make Me a Match: Artificial Societies vs. Virtual Worlds. Paper presented at the Digital Games Research Association (DIGRA) Conference. Vancouver, Canada, July 2005.
  11. ^ DeAngelis, DL; Mooij, WM (2005). "Individual-based modeling of ecological and evolutionary processes". Annual Review of Ecology, Evolution, and Systematics. 36: 147–168. doi:10.1146/annurev.ecolsys.36.102003.152644.
  12. ^ Van Geert, P. (2003). Dynamic systems approaches and modeling of developmental processes. In J. Valsiner and K. J. Conolly (Eds.), Handbook of developmental Psychology. London: Sage. Pp. 640-672
  13. ^ Smith, L. B.; Thelen, E. (2003). "Development as a dynamic system". TRENDS in Cognitive Science. 7: 343–348. doi:10.1016/s1364-6613(03)00156-6.
  14. ^ Hooft, G 't (2009) Entangled quantum states in a local deterministic theory", 2nd Vienna Symposium on the Foundations of Modern Physics (June 2009), ITP-UU-09/77, SPIN-09/30; arXiv:0908.3408v1 [quant-ph]. http://arxiv.org/pdf/0908.3408.pdf
  15. ^ Hooft, G (2003). "Can Quantum Mechanics Be Reconciled with Cellular Automata?". International Journal of theoretical Physics. 42 (2): 349–354. doi:10.1023/A:1024407719002.
  16. ^ "PLoS Computational Biology Issue Image | Vol. 6(8) August 2010". PLoS Computational Biology. 6 (8): ev06.ei08. 2010. doi:10.1371/image.pcbi.v06.i08.{{cite journal}}: CS1 maint: unflagged free DOI (link)

Further reading

  1. Warren McCulloch and Walter Pitts,(1943) A Logical Calculus of Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics 5:115-133.
  2. von Neumann, Jon (1966) The Theory of Self-Reproducing Automata, edited and completed by Arthur W. Burks (Urbana, IL: University of Illinois Press).
  3. James L. McClelland and David E. Rumelhart. (1987) Explorations in Parallel Distributed Processing Handbook. MIT Press, Cambridge, MA, USA, 1987.
  4. Gleick, James (1987); Chaos: Making a New Science; Copyright 1987, Viking, N.Y.
  5. Jackendoff, Ray, and Fred Lerdahl (1981). "Generative music and its relation to psychology." Journal of Music Theory 25(1): 45-90
  6. Skvoretz, J. 2002. Complexity Theory and Models for Social Networks. Complexity 8: 47-55
  7. Seidman, Stephen B. (1985). Structural consequences of individual position in nondyadic social networks, Journal of Mathematical Psychology, 29: 367-386
  8. Thietart, R. A., & Forgues, B. (1995). Chaos theory and organization. Organization Science, 6, 19-31.
  9. Holland, John H., "Genetic Algorithms", Scientific American, July 1992, pp. 66–72
  10. Albert-Laszlo Barabasi and Eric Bonabeau, "Scale-Free Networks", Scientific American, May 2003, pp 60–69
  11. T. Winograd, Understanding Natural Language, Academic Press, New York, 1972.
  12. M. Minsky, The Society of Mind, Simon and Schuster, New York, 1986.
  13. Epstein J.M. and Axtell R. (1996) Growing Artificial Societies - Social Science from the Bottom. Cambridge MA, MIT Press.
  14. Epstein J.M. (1999) Agent Based Models and Generative Social Science. Complexity, IV (5)
  15. Kaneko K. (1998) Life as Complex System: Viewpoint from Intra-Inter Dynamics. Complexity, 6, pp. 53–63.
  16. 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)
  17. McTntyre L. (1998) Complexity: A Philosopher's Reflection. Complexity, 6, pp. 26–32.
  18. Grossing, G and Zeilinger, A (1988) Quantum cellular automata, Complex Systems (2) pp. 197–208 http://www.complex-systems.com/pdf/02-2-4.pdf
  19. Epstein J. M.(2007) Generative Social Science: Studies in Agent-Based Computational Modeling, Princeton University Press ISBN 9781400842872 http://press.princeton.edu/chapters/s8277.pdf
  20. J. Schmidhuber. (1997) A computer scientist's view of life, the universe, and everything. Foundations of Computer Science: Potential – Theory – Cognition, Lecture Notes in Computer Science, pages 201–208, Springer
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