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

Generative science is an interdisciplinary and multidisciplinary science that explores 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 seemingly unanticipated and infinite behaviour. Yet, these innumerable unforeseen generative patterns and unexpected generative behaviours are fundamentally deterministic.

Elemental perspective

In John Conway's cellular automata called Game of Life, A single Gosper's Glider Gun creating "gliders"

Generative sciences explore natural phenomena at several levels[1] including physical, biological and social processes as generative processes.[2][3][4][5][6][7] It explores complex natural processes through continuous interactions among elemental entities based upon parsimonious and simple universal rules and parameters.[2][3][4][5][6][7]

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 cybernetic 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).[8] 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.

One of the most influential advances 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 Levy Moreno and Fritz Heider laid the foundations for group dynamics research which later developed into social network analysis.

In 1996 Joshua M. 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.

Michael Leyton, professor of Cognitive Psychology at Rutgers University, has written a book on "generative geometry".[9]

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.[6][10][11][12][13] 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.[6][10][11][12][13] 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.[6]

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 (such as cards' face-values) 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.[10][11][12][13] Another hands-on example of generative processes is John Horton Conway's playable Game of Life.[14] 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.[10][11][12][13]

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. It also explains the generation of unintended consequences in social processes.[15] Generative science also helps to explain the development of complex societies, historical processes and unexpected events,[16] unexpected changes and development in ecological and evolutionary process [17] and also help in the theoretical explanation of human psychological development [18] and cognitive processes.[19] 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.[20][21]

Prospective directions

Computer simulation of the branching architecture of the dendrites of pyramidal neurons.[22]
Historical map of research paradigms and associated scientists in sociology and complexity science.

Generative scientists are working towards further developments and new frontiers. Latest and emerging directions in the generative sciences include 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 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.

simple herd behavior generates the emergence of swarm intelligence and crowd behaviours

See also

References

  1. ^ Farre, G. L. (1997). "The Energetic Structure of Observation: A Philosophical Disquisition". American Behavioral Scientist. 40 (6): 717–728. doi:10.1177/0002764297040006004.
  2. ^ a b 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
  3. ^ a b Konrad Zuse, 1969. Rechnender Raum. Braunschweig: Friedrich Vieweg & Sohn. ftp://ftp.idsia.ch/pub/juergen/zuserechnenderraum.pdf
  4. ^ a b 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 E. Fredkin and T. Toffoli. Conservative logic. International Journal of Theoretical Physics, 21:219–253, 1982.
  6. ^ a b c d e Wolfram, Stephen, A New Kind of Science. Wolfram Media, Inc., May 14, 2002. ISBN 1-57955-008-8
  7. ^ a b Gruene-Yanoff, Till (2006) Agent-Based Simulation, Generative Science, And Its Explanatory Claims. in Models and Simulations. Paris.
  8. ^ Shannon, C. E. (2001). "A mathematical theory of communication". ACM SIGMOBILE Mobile Computing and Communications Review. 5: 3. doi:10.1145/584091.584093.
  9. ^ Leyton, Michael (2001). A generative theory of shape. Berlin New York: Springer. ISBN 3540427171.
  10. ^ a b c d 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.
  11. ^ 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.
  12. ^ a b c d Nowak A., Vallacher R.R., Tesser A., Borkowski W., (2000) "Society of Self: The emergence of collective properties in self-structure," Psychological Review 107.
  13. ^ a b c d Epstein J.M. (1999) Agent Based Models and Generative Social Science. Complexity, IV (5)
  14. ^ John Conway's Game of Life
  15. ^ Ning Nan, Erik W. Johnston, Judith S. Olson (2008) 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
  16. ^ 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.
  17. ^ 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.
  18. ^ 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
  19. ^ 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.
  20. ^ 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
  21. ^ 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.
  22. ^ "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. W. Weaver and C. E. Shannon, (1948) The Mathematical Theory of Communication, Urbana, Illinois: University of Illinois Press.
  2. Chomsky N (1957) Syntactic Structures. The Hague: Mouton.
  3. Warren McCulloch and Walter Pitts,(1943) A Logical Calculus of Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics 5:115-133.
  4. Lewin, K. (1951) Field theory in social science; selected theoretical papers. D. Cartwright (Ed.). New York: Harper & Row.
  5. Wiener N (1948) Cybernetics; John Wiley, New York, 1948.
  6. von Neumann, Jon (1966) The Theory of Self-Reproducing Automata, edited and completed by Arthur W. Burks (Urbana, IL: University of Illinois Press).
  7. Rapoport, A. (1953). Spread of information through a population with sociostructural bias: I. Assumption of transitivity. Bulletin of Mathematical Biophysics, 15, 523-533.
  8. James L. McClelland and David E. Rumelhart. (1987) Explorations in Parallel Distributed Processing Handbook. MIT Press, Cambridge, MA, USA, 1987.
  9. Gleick, James (1987); Chaos: Making a New Science; Copyright 1987, Viking, N.Y.
  10. Jackendoff, Ray, and Fred Lerdahl (1981). "Generative music and its relation to psychology." Journal of Music Theory 25(1): 45-90
  11. Allen, T.J. (1970). Communication networks in R&D laboratories. R&D Management, 1(1), 14-21.
  12. Skvoretz, J. 2002. Complexity Theory and Models for Social Networks. Complexity 8: 47-55
  13. Seidman, Stephen B. (1985). Structural consequences of individual position in nondyadic social networks, Journal of Mathematical Psychology, 29: 367-386
  14. Thietart, R. A., & Forgues, B. (1995). Chaos theory and organization. Organization Science, 6, 19-31.
  15. Holland, John H., "Genetic Algorithms", Scientific American, July 1992, pp. 66–72
  16. Albert-Laszlo Barabasi and Eric Bonabeau, "Scale-Free Networks", Scientific American, May 2003, pp 60–69
  17. T. Winograd, Understanding Natural Language, Academic Press, New York, 1972.
  18. M. Minsky, The Society of Mind, Simon and Schuster, New York, 1986.
  19. Epstein J.M. and Axtell R. (1996) Growing Artificial Societies - Social Science from the Bottom. Cambridge MA, MIT Press.
  20. Epstein J.M. (1999) Agent Based Models and Generative Social Science. Complexity, IV (5)
  21. Kaneko K. (1998) Life as Complex System: Viewpoint from Intra-Inter Dynamics. Complexity, 6, pp. 53–63.
  22. 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)
  23. McTntyre L. (1998) Complexity: A Philosopher's Reflection. Complexity, 6, pp. 26–32.
  24. Hooft, G 't (2003) Can Quantum Mechanics Be Reconciled with Cellular Automata? International Journal of theoretical Physics Volume 42, Number 2 p 349-354, DOI: 10.1023/A:1024407719002 http://www.staff.science.uu.nl/~hooft101/gthpub/digit01.pdf
  25. 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
  26. 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
  27. 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
  28. Konrad Zuse, 1969. Rechnender Raum. Braunschweig: Friedrich Vieweg & Sohn. ftp://ftp.idsia.ch/pub/juergen/zuserechnenderraum.pdf
  29. 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
  30. Wolfram, Stephen, A New Kind of Science. Wolfram Media, Inc., May 14, 2002. ISBN 1-57955-008-8
  31. E. Fredkin and T. Toffoli. Conservative logic. International Journal of Theoretical Physics, 21:219–253, 1982.
  32. Gruene-Yanoff, Till (2006) Agent-Based Simulation, Generative Science, And Its Explanatory Claims. in Models and Simulations; Paris.
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